The role of the MAD2-TLR4-MyD88 axis in paclitaxel resistance in ovarian cancer

Mark Bates; Cathy D Spillane; Michael F Gallagher; Amanda McCann; Cara Martin; Gordon Blackshields; Helen Keegan; Luke Gubbins; Robert Brooks; Doug Brooks; Stavros Selemidis; Sharon O’Toole; John J O’Leary

doi:10.1371/journal.pone.0243715

. 2020 Dec 28;15(12):e0243715. doi: 10.1371/journal.pone.0243715

The role of the MAD2-TLR4-MyD88 axis in paclitaxel resistance in ovarian cancer

Mark Bates ^1,^2,^3,^4,^*, Cathy D Spillane ^1,^2,³, Michael F Gallagher ^1,^2,³, Amanda McCann ⁵, Cara Martin ^1,^2,^3,⁶, Gordon Blackshields ^2,^3,⁶, Helen Keegan ^1,^2,^3,⁶, Luke Gubbins ⁵, Robert Brooks ⁷, Doug Brooks ⁷, Stavros Selemidis ⁸, Sharon O’Toole ^1,^2,^3,^4,^‡, John J O’Leary ^1,^2,^3,^6,^‡

Editor: David Wai Chan⁹

¹Department of Histopathology, Trinity College Dublin, Dublin, Ireland

²Emer Casey Molecular Pathology Research Laboratory, Coombe Women & Infants University Hospital, Dublin, Ireland

³Trinity St James’s Cancer Institute, Dublin, Ireland

⁴Department of Obstetrics and Gynaecology, Trinity College Dublin, Dublin, Ireland

⁵College of Health Sciences, University College Dublin, Belfield, Dublin, Ireland

⁶Department of Pathology, Coombe Women & Infants University Hospital, Dublin, Ireland

⁷School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, Australia

⁸School of Health and Biomedical Sciences, Royal Melbourne Institute of Technology, Bundoora, Australia

⁹The University of Hong Kong, HONG KONG

Competing Interests: The authors have declared that no competing interests exist.

‡ These authors are joint senior authors on this work.

^✉

* E-mail: batesm1@tcd.ie

Roles

Mark Bates: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Cathy D Spillane: Conceptualization, Data curation, Formal analysis, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing

Michael F Gallagher: Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

Amanda McCann: Conceptualization, Supervision, Writing – original draft, Writing – review & editing

Cara Martin: Conceptualization, Supervision, Writing – original draft, Writing – review & editing

Gordon Blackshields: Data curation, Formal analysis, Software

Helen Keegan: Formal analysis, Writing – original draft, Writing – review & editing

Luke Gubbins: Methodology

Robert Brooks: Writing – review & editing

Doug Brooks: Writing – original draft, Writing – review & editing

Stavros Selemidis: Writing – review & editing

Sharon O’Toole: Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

John J O’Leary: Conceptualization, Funding acquisition, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

David Wai Chan: Editor

PMCID: PMC7769460 PMID: 33370338

Abstract

Despite the use of front-line anticancer drugs such as paclitaxel for ovarian cancer treatment, mortality rates have remained almost unchanged for the past three decades and the majority of patients will develop recurrent chemoresistant disease which remains largely untreatable. Overcoming chemoresistance or preventing its onset in the first instance remains one of the major challenges for ovarian cancer research. In this study, we demonstrate a key link between senescence and inflammation and how this complex network involving the biomarkers MAD2, TLR4 and MyD88 drives paclitaxel resistance in ovarian cancer. This was investigated using siRNA knockdown of MAD2, TLR4 and MyD88 in two ovarian cancer cell lines, A2780 and SKOV-3 cells and overexpression of MyD88 in A2780 cells. Interestingly, siRNA knockdown of MAD2 led to a significant increase in TLR4 gene expression, this was coupled with the development of a highly paclitaxel-resistant cell phenotype. Additionally, siRNA knockdown of MAD2 or TLR4 in the serous ovarian cell model OVCAR-3 resulted in a significant increase in TLR4 or MAD2 expression respectively. Microarray analysis of SKOV-3 cells following knockdown of TLR4 or MAD2 highlighted a number of significantly altered biological processes including EMT, complement, coagulation, proliferation and survival, ECM remodelling, olfactory receptor signalling, ErbB signalling, DNA packaging, Insulin-like growth factor signalling, ion transport and alteration of components of the cytoskeleton. Cross comparison of the microarray data sets identified 7 overlapping genes including MMP13, ACTBL2, AMTN, PLXDC2, LYZL1, CCBE1 and CKS2. These results demonstrate an important link between these biomarkers, which to our knowledge has never before been shown in ovarian cancer. In the future, we hope that triaging patients into alterative treatment groups based on the expression of these three biomarkers or therapeutic targeting of the mechanisms they are involved in will lead to improvements in patient outcome and prevent the development of chemoresistance.

Introduction

Ovarian cancer is a major cause of cancer death in women worldwide with less than 40% of women surviving beyond 5 years post-diagnosis [1]. This is due mainly to the development of recurrent chemoresistant disease which cannot as of yet be effectively treated in patients once it develops [2]. To improve patient outcomes, we must be able to either effectively destroy chemoresistant tumours once they resurface or prevent them from developing in the first instance. One way in which their development could be prevented is through the use of prognostic biomarkers which can identify patients who will likely develop chemoresistance prior to the commencement of treatment. These patients once identified could be selected out from the main patient population and given more appropriate treatments to prevent the onset of chemoresistance. In recent years our group and others have extensively investigated three new prognostic biomarkers, known as toll-like receptor 4 (TLR4), myeloid differentiation factor 88 (MyD88) and mitotic arrest deficient 2 (MAD2) for the most common and lethal form of ovarian cancer; high grade serous ovarian cancer (HGSOC). All three markers have been shown to be involved in the development of chemoresistance to paclitaxel [3–10], one of the first-line chemotherapies used to treat ovarian cancer and their expression levels have been shown to correlate with poor clinical outcome in patients [6, 7, 11–16]. TLR4 is an innate immune receptor responsible for the recognition of lipopolysaccharide (LPS) on gram-negative bacteria. Upon ligand engagement, TLR4 activates inflammatory cytokine production through its downstream adaptor molecule MyD88. Activation of this signalling pathway is thought to drive tumour-associated inflammation, resistance to apoptosis and promote the induction of a stem-like phenotype [3, 17–19]. Paclitaxel, due to its homology to LPS [20] and its ability to bind TLR4 and activate downstream signalling, is thought to promote the development of this aggressive phenotype [17, 18, 21, 22]. Elevated expression levels of TLR4 or it’s adaptor protein MyD88 have been associated with reduced survival outcome in HGSOC patients [6, 8, 15, 23–25], while therapeutic targeting of TLR4 has been shown to restore paclitaxel sensitivity in ovarian cancer cell models [6, 16]. Although, it must be acknowledged that a recent largescale study found no prognostic association between TLR4 expression and HGSOC [24], despite previous contra-indications in a smaller study by the same group, particularly when TLR4 was combined with MyD88 [23]. Interestingly however the TLR4 downstream adaptor molecule MyD88 was found to be prognostic in this largescale cohort in agreement with a number of other studies including our own [6, 8, 15, 23–25]. Given these findings and the fact that paclitaxel is a known ligand for TLR4 [20], further interrogation of how this pathway contributes to paclitaxel chemoresistance is warranted.

MAD2 is a key component of the spindle assembly checkpoint (SAC) responsible for correct segregation of chromosomes during cell division. Suppression of MAD2 leads to mitotic catastrophe as cells divide without proper chromosomal segregation. This leads to anaphase bridge formation and generation of a DNA damage response which mimics normal telomere shortening resulting in the induction of cellular senescence [5, 26]. Cellular senescence allows tumour cells to resist paclitaxel which only targets actively dividing cells while also promoting tumour growth through the release of a milieu of over 40 different cytokines/chemokines and other factors as part of what is known as the senescence associated secretory phenotype (SASP) [27]. As all three biomarkers have been shown, individually, to have a significant impact on patient prognosis and the modulation of paclitaxel chemoresponsiveness and also given the fact that many cytokines secreted during senescence are also known downstream targets of TLR4-MyD88 signalling we hypothesised that there may be crosstalk between these three important biomarkers in ovarian cancer. The aim of this study, therefore, was to assess whether there was any molecular link between MAD2, TLR4 and MyD88 in ovarian cancer and to further explore the mechanisms each of these biomarkers utilise, in order to render ovarian cancer cells resistant to paclitaxel therapy.

Results

Identifying the molecular link between MAD2 and TLR4-MyD88 signalling

In order to discern a possible relationship between MAD2 and TLR4-MyD88 signalling, transfection experiments were performed initially in both A2780 (MyD88 null) and SKOV-3 (MyD88 positive) ovarian cancer cells (Fig 1). Firstly, TLR4 was knocked down in both cell models using siRNA. Secondly MyD88 was knocked down in SKOV-3 cells while A2780 cells were transfected with a MyD88 overexpression plasmid. Following each transfection experiment MAD2 expression levels were assessed. Knockdown of TLR4 in both cell models did not alter MAD2 expression levels nor did knockdown or overexpression of MyD88 in SKOV-3 or A2780 cell lines respectively. Thus, indicating that TLR4-MyD88 signalling and MAD2 were independent or at the very least that MAD2 expression was not influenced by changes in TLR4 or MyD88 expression in these cell models. In parallel with this work, in-silico analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) v10 software [28] in order to identify any potential interaction between the TLR4-MyD88 signalling pathway and MAD2. In support of the transfection experiments in-silico analysis identified no direct relationship between MAD2 and TLR4 or MyD88. TLR4, MyD88 and their interactants segregated into entirely different clusters than MAD2 and its interactants (Fig 1C).

Fig 1 — MyD88, TLR4 and MAD2 gene expression levels in (A) A2780 and (B) SKOV-3 cells, 72 hours following transfection with siRNA targeting TLR4 or MyD88. Neither knockdown of TLR4 in both SKOV-3 and A2780 cells or knockdown of MyD88 in SKOV-3 cells had any significant impact on the expression of MAD2. (C) Screenshots from the STRING website, which was queried for relationships between TLR4, MyD88 and MAD2. Coloured lines between the proteins indicate the various types of interaction evidence. *In-silico* analysis predicted that there was no direct interaction between MAD2 and MyD88 or TLR4. (D) Western blot analysis and (E) densitometric analysis of MAD2 protein expression levels in SKOV-3 cells following transfection with siRNA targeting MyD88 or TLR4. (F) MyD88, TLR4 and MAD2 gene expression levels, (G) western blot analysis and (H) densitometric analysis of A2780 cells transfected with a MyD88 overexpression plasmid for 72 hours. The results demonstrate that overexpression of MyD88 had no significant impact on MAD2 gene or protein expression. Results are expressed as mean +/-SD, at least n = 3; NS—Not significant, *p<0.05, **p<0.01, ***p<0.01 (Student’s t-test). Densitometry results are expressed in arbitrary units (A.U) normalised to GAPDH. **Note**:- Blots are cropped from original images available in the S1 Raw Images.

These initial transfection experiments and the in-silico analysis supported the idea that TLR4-MyD88 signalling and MAD2 acted as independent biomarkers in ovarian cancer. However, to conclusively demonstrate this, in the reverse setting, TLR4 and MyD88 expression was analysed following knockdown of MAD2 in both A2780 and SKOV-3 cells (Fig 2). Most interestingly when MAD2 levels were suppressed using siRNA, both A2780 and SKOV-3 cells exhibited a significant 3-fold increase in TLR4 gene expression, demonstrating a previously never-before shown link between TLR4 and MAD2 in ovarian cancer (Fig 2A and 2D). However, surprisingly a similar increase in TLR4 protein expression post knockdown of MAD2 was not observed at the selected timepoint in either cell line (Fig 2B and 2E).

Fig 2 — MyD88, TLR4 and MAD2 gene expression levels in (A) A2780 and (D) SKOV-3 cells, 72 hours following transfection with siRNA targeting MAD2. Interestingly TLR4 gene expression, but not that of its adaptor molecule MyD88, was increased 3-fold following siRNA knockdown of MAD2 for 72 hours in both cell lines. (B) Western blot and (C) densitometry analysis of protein lysates harvested from A2780 cells following knockdown of MAD2 revealed however that suppression of MAD2 had no impact on TLR4 or MyD88 protein expression. Similarly, western blot (E) and densitometric analysis (F) in SKOV-3 cells found no significant increase in TLR4 protein expression post knockdown of MAD2. Results are expressed as mean +/-SD, n = 3; *p<0.05, **p<0.01, ***p<0.001 (Student’s t-test). Densitometry results are expressed in arbitrary units (A.U) normalised to GAPDH. **Note**:- Blots are cropped from original images available in the S1 Raw Images.

To further explore the link between TLR4 and MAD2 we next analysed the expression of MAD2, TLR4 and MyD88 in 5 additional ovarian cancer cell lines; OVCAR-3, PEO1, OAW42, KURAMOCHI and 59M cells (Fig 3A). Of these only OVCAR-3 and PEO1 expressed TLR4, MyD88 and MAD2. OAW42, KURAMOCHI and 59M were TLR4 negative. OVCAR-3 cells due to their TLR4 positivity and as a representative model of serous ovarian cancer were subsequently transfected with siRNA targeting TLR4 or MAD2 and then TLR4, MAD2 and MyD88 expression levels were assessed. Interestingly knockdown of TLR4 or MAD2 in the OVCAR-3 cell model caused a significant 2.4 and 2.9 fold increase in MAD2 or TLR4 expression respectively further highlighting an important link between these two biomarkers (Fig 3B).

Fig 3 — (A) MyD88, TLR4 and MAD2 gene expression in A2780, OVCAR-3, PEO1, OAW42, KURAMOCHI and 59M cells relative to SKOV-3 cells. (B) MyD88, TLR4 and MAD2 gene expression in OVCAR-3 cells following siRNA knockdown of TLR4 or MAD2. Gene expression levels were normalised to the endogenous control GAPDH and calibrated to that of untreated cells to establish the relative change in gene expression.

Suppression of MAD2 induces cellular senescence and paclitaxel resistance

Following knockdown of MAD2, SKOV-3 cells exhibited an increase in cell and nuclear size and alteration of cell shape (Fig 4A). Subsequently, the chemoresponse of SKOV-3 cells to paclitaxel following knockdown of MAD2 was assessed (Fig 4C). When SKOV-3 cells were treated with a 20nM or 1μM dose of paclitaxel following knockdown of MAD2, they exhibited a reduction in cell viability of 36.2% and 36.1% compared with untransfected cells which were not treated with paclitaxel. In contrast, untransfected cells or cells transfected with the scrambled negative control which were treated with 20nM of paclitaxel exhibited a decrease in cell viability of 55.4% and 56.3% respectively. While untransfected cells or cells transfected with the scrambled negative control siRNA which were treated with 1μM of paclitaxel exhibited a decrease in cell viability of 66.2% and 66.4% respectively. Additionally, despite there being no visual signs of cytotoxicity, untreated cells transfected with MAD2 siRNA also exhibited a 19% significant reduction in cell viability compared to untransfected cells which were untreated potentially indicating a reduction in cell proliferation. Furthermore, in transfected cells treated with either dose of paclitaxel minimal if any visual signs of cytotoxicity were observed. In fact, the addition of paclitaxel appeared to accelerate the timeframe for the emergence of the enlarged cell phenotype. Additionally, the difference in cell viability between cells transfected with MAD2 siRNA which were untreated and transfected cells treated with either dose of paclitaxel was not statistically significant. This result was further supported by a trypan blue exclusion assay which detected a significant decrease in the number of dead cells in the supernatants of paclitaxel treated cells following knockdown of MAD2 compared to controls (Fig 4E). The results indicated that SKOV-3 cells transfected with siRNA targeting MAD2 were rendered resistant to paclitaxel and potentially undergoing cellular senescence. In order to investigate this further, SKOV-3 cells were stained with the senescence β-galactosidase staining kit. The number of cells which were β-galactosidase positive were counted and then compared against background levels in negative control and untreated cells (Fig 4B). A three-fold increase in the percentage of β-galactosidase positive cells was observed following knockdown of MAD2 compared to the untransfected and scramble negative controls (p<0.001), which was sufficient to indicate the induction of cellular senescence (Fig 4D). We also assessed the cytotoxicity of SKOV-3 cells to carboplatin following knockdown of either TLR4 or MAD2. Cells were transfected for 24 hours and then treated with a 200μM dose of carboplatin or left untreated, however, neither knockdown of TLR4 or MAD2 altered the response of SKOV-3 cells to carboplatin (Fig 4F).

Following these interesting results, High Mobility Group Box 1 (HMGB1) gene and protein expression was assessed post knockdown of MAD2 (Fig 5). HMGB1 was investigated as it had previously been shown to directly upregulate TLR4 expression [29] and due to the fact that its secretion during the early stages of senescence is known to be key to the formation of a SASP [30, 31]. However, no difference in HMGB1 expression was detected in either cell line and further evidence needs to be gathered to support the hypothesis that HMGB1 acts as the link between MAD2 mediated senescence and TLR4 upregulation.

Microarray analysis

As the knockdown of TLR4 had been shown to enhance the sensitivity of SKOV-3 cells to paclitaxel and the knockdown of MAD2 had been shown to render these cells paclitaxel resistant it was decided to perform microarray analysis post knockdown of TLR4 or MAD2 in this cell model. This was done in order to further discern any links between these biomarkers and gain greater insight into how they modulate the cellular response to paclitaxel. Following knockdown of TLR4 a total of 166 protein coding targets were found to be significantly upregulated and 286 targets found to be significantly (S1 Data). The differentially expressed genes identified following knockdown of TLR4 were subsequently analysed using the online gene ontology database DAVID, in order to identify important biological processes in which these genes participate [32]. A number of important biological processes were highlighted including cell death, cell adhesion, steroid biosynthesis and metabolism, complement and coagulation cascades and ErbB signalling among others (Fig 6 and S1 Table).

Fig 6 — Microarray analysis revealed that TLR4 controls genes related to EMT, survival and proliferation, steroid and lipid metabolism, olfactory receptor signalling, adhesion, coagulation and complement cascades, and ErbB signalling. A hierarchial clustering heatmap was generated using Morpheus from the broad institute. KEGG pathway maps were generated using DAVID, red stars indicate genes significantly altered in the TLR4 knockdown microarray data set.

Following knockdown of MAD2,126 protein coding genes were found to be upregulated and 95 protein coding genes were found to be downregulated (S2 Data). Microarray analysis highlighted several features of senescence, which were deregulated following knockdown of MAD2. These included an effect on DNA packaging, lipase activity, ion transporter activity, Insulin-like growth factor binding protein (IGFBP) activity, arachidonic acid metabolism, regulation of cell motility and migration, ossification and bone metabolism, the sensory perception of smell and the response to hormones and various chemical and extracellular stimuli (S2 Table). The complete microarray data sets for both knockdown experiments are available at ArrayExpress (Accession #370077). Cross-comparison of the microarray data sets highlighted 12 common genes which were deregulated in both data sets. This can be observed along with a map of the entire network of differentially expressed genes highlighted in both data sets in (Fig 7).

Fig 7 — Microarray analysis revealed that suppression of MAD2 and the induction of senescence affects various genes involved in DNA packaging, lipid metabolism, ion transport, Insulin-like growth factor signalling, ECM remodelling and olfactory receptor signalling and components of the actin cytoskeleton. Cross comparison of the two microarray data sets identified 7 key genes which are differentially regulated in both data sets. String network analysis plots of each of the altered genes identified in both data sets and how they relate to each other and TLR4, MyD88 and MAD2 which are highlighted by red circles.

Discussion

Our group and others have previously demonstrated that TLR4, MyD88 and MAD2 play key roles in paclitaxel resistance in ovarian cancer and are associated with poor patient outcome [3, 4, 6–9, 11–13, 15, 16, 24, 25, 33–36]. Although the recent and impressively sized study by Block et al. [24] demonstrated that TLR4 was not prognostic, several other studies including our own have previously demonstrated that TLR4 is linked to poor patient outcome and this trend can even be observed in the wider cancer space, with similar patterns observed in breast [10, 37], oesophageal [38] and other cancer types [39]. Block and colleagues [24] did find however that the TLR4 downstream adaptor protein MyD88 was prognostic, which is the signalling arm of the TLR4 pathway used to modulate the response of SKOV-3 cells to paclitaxel. This study sought to further explore how the TLR4-MyD88 signalling pathway and MAD2 mediated senescence contribute to the cellular response to paclitaxel and discern any molecular link between these three biomarkers as well as identifying new markers for potential future therapeutic exploitation. TLR4 itself is potentially targetable, a previous in-vitro study found that targeting TLR4-MyD88 signalling using the small molecule Atractylenolide-I could resensitise cells to paclitaxel [16]. Other TLR4 antagonists such as TAK-242 and Eritoran have also been examined in clinical trials for sepsis/inflammation as reviewed in [40] and may be suitable for ovarian cancer patients overexpressing TLR4 or MyD88. MyD88 is also targetable and such treatments may potentially eradicate paclitaxel resistant cancer stem cells (CSCs) by inducing differentiation [41, 42]. A wide array of inhibitors are also available for the TLR4-MyD88 pathway downstream transcription factor NFκB [43]. In this study suppression of TLR4 or alteration of MyD88 expression in either SKOV-3 or A2780 cells had no resulting impact on MAD2 expression. Intererestingly however in the OVCAR-3 serous ovarian model a significant increase in MAD2 expression was observed. One observation with this experiment was that the replicate with the highest knockdown of TLR4 had the highest upregulation of MAD2. Furthermore with both the SKOV-3 and A2780 cells lines MAD2 expression was partially increased ~1.5 fold post knockdown of TLR4, however this was not statistically significant. The observed differences may indicate that TLR4 needs to be supressed below a certain threshold to have a substantial impact on MAD2 expression. Increasing the efficacy of the TLR4 knockdown, through the use of shRNA vectors and cell selection or the use of newer technologies such as CRISPR may yield more definitive evidence for this.

Furthermore, suppression of MAD2 expression using siRNA in A2780, OVCAR-3 and SKOV-3 cell lines led to a significant increase in TLR4 gene expression levels demonstrating a key link between TLR4 and MAD2. Although it must be acknowledged that although TLR4 gene expression was upregulated post knockdown of MAD2, there was no recipient increase at the protein level in SKOV-3 or A2780 cells. This is a curious result, although TLR4 protein expression following knockdown of MAD2 was only examined at a single timepoint, 72 hours post transfection in both cell lines. However, there are two potential biological mechanisms which could explain why the recipient increase in TLR4 expression at the protein level was not observed. Firstly, the activation of the unfolded protein response as is known to occur during cellular senescence may have blocked or limited the amount of protein translation taking place [44]. Secondly, TLR4 shedding can occur as a result of oxidative stress [45] and during cellular senescence reactive oxygen species (ROS) levels are known to be dramatically increased [27]. Indeed, TLR4 shedding may even represent a feedback mechanism to blunt hyper-reactive TLR4-ligand signalling which may occur due to HMGB1 release which also occurs during senescence [31, 46]. HMGB1 was examined as part of this study however its expression was not found to be altered post knockdown, which may be as a result of its function as a secreted cytokine. TLR4 is also known to be upregulated by various other cytokines which may be secreted as part of the SASP [47–49].

A number of interesting target genes were highlighted during the microarray analysis in SKOV-3 cells following knockdown of TLR4 associated with metastasis, angiogenesis, EMT/differentiation and circulating tumour cell (CTC) biology including CD44, HER2, PI3K, MMP13, members of the claudin, cadherin, integrin and laminin family and various olfactory receptors (ORs). Although these markers require further validation, many of them have previously been shown to be prognostic for ovarian and other types of cancer and are potentially targetable [41, 42, 50–54]. TLR4 also appears to be suppressing both coagulation and complement which again may contribute to metastasis and therapeutic resistance [55–62]. Targeting of another marker identified on the arrays in the steroid biosynthesis group, farnesyl transferase has also previously been shown to enhance paclitaxel sensitivity in ovarian models [63] and has previously been targeted in clinical trials [64]. Some of the other steroid pathway targets have been shown to control vesicular trafficking of HER2 and related family members [65]. The microarray analysis indicates that the TLR4-MyD88 signalling pathway is likely driving paclitaxel resistance by promoting the induction of an EMT/stem like phenotype. Ligation of TLR4 by paclitaxel also likely provides a further survival advantage to these cells by upregulating pro-survival signalling molecules including pAKT, BCL-2, BCL-XL and XIAP as reported previously [7, 8, 37]. Given the crosstalk identified between MAD2 and TLR4 in this study it is likely that senescence is also helping to drive/amplify this phenomenon. Clearly new therapeutic strategies which actively target these mechanisms need to be introduced in order to enhance the efficacy of current ovarian cancer therapy.

Knockdown of MAD2 in the SKOV-3 cell model revealed a number of altered senescence associated genes and processes [27, 66–72]. Among those affected were genes involved in OR activity and the response to a number of different chemical and extracellular stimuli, IGFBP activity and ossification, cell motility, lipase & phospholipase activity and arachidonic acid metabolism, DNA packaging and ion transporter activity. Senescent cells likely act as a protective barrier against paclitaxel and perhaps other chemotherapeutic agents, shielding non-senescent populations of cancer cells such as CSCs from drug-induced cell death while simultaneously promoting their growth through the milieu of cytokines they release as part of the SASP [26, 27]. Previous reports have demonstrated that senescent cells are capable of promoting the growth of tumours and induce the progression of pre-malignant lesions into malignant tumours in in-vivo xenografts [73, 74]. They may also promote tumour growth through active suppression of immune cell populations [75]. Thus, senescence and an activated TLR4 signalling pathway likely promotes tumour growth through the generation of inflammatory niche which selects for invasive paclitaxel resistant CSC populations leading to shorter survival time in patients. Cross-comparison of microarray data sets highlighted several genes involved in cell adhesion, proliferation, differentiation, migration and extracellular matrix (ECM) degradation [76–86]. Targeting some of the markers highlighted in the arrays or treating patients with exogenous MAD2 may help to reverse the senescence phenotype and restore paclitaxel sensitivity. We also previously identified that the MAD2 regulatory microRNA miR-433 was dysregulated and associated with poor prognosis in ovarian cancer patients and may act as an upstream inducer of MAD2 mediated senescence [11]. Therefore, these patients may benefit from miR-433 antagomir therapy. A variety of other anti-senescence therapies are available which may help boost paclitaxel efficacy when MAD2 or miR-433 are used as triage markers [26, 87]. The results also demonstrate that these senescent cell populations appear to be selectively resistant to paclitaxel but are sensitive to carboplatin, therefore patients could potentially be selected out for single arm therapy with carboplatin.

Conclusions

The molecular link between TLR4-MyD88 signalling and MAD2 identified in this study has potentially important implications for the development of new treatment strategies for ovarian cancer patients. Individually these markers highlight paclitaxel resistance mechanisms within a patient’s tumour. Depending on the expression of these markers, one or multiple mechanisms may need to be targeted. The complexity of downstream signalling pathways identified by microarray analysis also further highlight the fact that a single biomarker alone may be insufficient to capture the multiple pathophysiological processes occurring within a patient’s tumour which contribute to chemoresistance. Assessing multiple biomarkers such as TLR4, MyD88 and MAD2 which give greater insight into the pathological makeup of a patient’s tumour may help to direct therapies and more suitable treatment combinations in order to improve overall outcome.

Methods

Cell culture

A2780, OAW42, OVCAR-3, PEO1 and KURAMOCHI cells were cultured in RPMI 1640 medium (Sigma Aldrich, St Louis, USA), 59M cells were cultured in DMEM (Sigma Aldrich) and SKOV-3 cells were cultured in McCoys modified 5A medium (Sigma Aldrich) respectively. All media was supplemented with 10% foetal bovine serum (FBS) (Sigma Aldrich) and 2% penicillin/streptomycin (5000IU, Sigma Aldrich) and cells were maintained in a humidified atmosphere at 37°C and 5% CO₂.

Small interfering RNA transfection

siRNA targeting TLR4 (TLR4 siRNA, s14194), MyD88 (MyD88 siRNA, s9136) and silencer select negative control #1 siRNA (siNeg, 4390843) were purchased from (Thermo Fisher Scientific, Waltham, USA) and on target plus SMARTpool MAD2L1 siRNA (MAD2 siRNA, L-003271-00-0005) and on target plus SMARTpool non-targeting siRNA (Scr siRNA, D-001810-01-05) were purchased from (Dharmacon, Lafayette, USA). SKOV-3 cells were transfected into either 24 or 6 well plates at seeding densities of 25,000 or 125,000 cells per well respectively. Cells were transfected with Lipofectamine RNAiMAX (13778–075, Thermo Fisher Scientific), Opti-MEM^® I reduced serum medium (31985–047, Thermo Fisher Scientific) and siRNA at a final concentration of 1nM per well. A2780 cells were transfected into 6 well plates at a seeding density of 400,000 cells per well and were transfected with siRNA targeting TLR4 or MAD2 at final concentrations of 10nM or 30nM per well respectively. OVCAR-3 cells were transfected into 24 well plates at a seeding density of 25,000 cells per well. OVCAR-3 cells were transfected with 30nM of siRNA targeting TLR4 or MAD2. All transfections were carried out using media not containing antibiotics.

MyD88 transfections

For the MyD88 transfection experiments A2780 cells were transfected with a MyD88 overexpression plasmid (MyD88 OE) or an empty vector negative control plasmid (eV Control), both purchased from IMAgenes, or were left untreated for 72 hours. For each transfection experiment, plasmid DNA and lipofectamine was first diluted in Opti-MEM^® I reduced serum medium. A2780 cells were transfected into 6 well plates at a seeding density of 400,000 cells per well. All transfections were carried out using media not containing antibiotics. The final plasmid DNA concentration per well was 1ng/ul.

RNA extraction and TaqMan RT-PCR

Total RNA was isolated as per the manufacturer’s instructions using the mirVana^™ miRNA Isolation Kit (AM1560, Thermo Fisher Scientific). RNA concentration was determined using a nanodrop 2000c spectrophotometer (Thermo Fisher Scientific). Reverse transcription was carried out using the High Capacity cDNA Reverse Transcription Kit (4368813, Thermo Fisher Scientific) on the Gene Amp PCR System 9600 (Perkin Elmer, Waltham, USA). TaqMan RT-PCR was then performed using the 7900HT Real-Time PCR System (Thermo Fisher Scientific). Primers and probes for TLR4 (Hs00152939_m1), MAD2 (H203063324_g1), MyD88 (Hs00182082_m1), HMGB1 (Hs01037385_s1) and the endogenous controls, glyceraldehyde 3-phosphate dehydrogenase (GAPDH, 4333764T) or Beta-2 Microglobulin (B2M, 4333766F) were obtained from (Thermo Fisher Scientific). These are supplied as commercial pre-designed primer and probe mixes (20X). Gene expression levels following transfection were calculated using the ΔΔCT method relative to the endogenous control [88]. A significant change in gene expression was considered to be present if at least a 2-fold change (above 200% expression or below 50% expression) in gene expression was observed, with a p value of ≤0.05 compared to untreated cells and/or negative control cells.

Western blot analysis

Following transfection for 72 hours protein was extracted from SKOV-3 cells using RIPA lysis buffer (Sc-24948, Santa Cruz Biotechnology, Santa Cruz, USA) modified with phenylmethanesulfonyl fluoride (PMSF) (200mM), a protease inhibitor cocktail, and sodium orthovanadate (Na₃VO₄) (100mM). Cell suspensions were later sonicated to ensure complete lysis using the soniprep 150 (MSE Labs, East Sussex, UK). Protein concentration was then determined using the Pierce^™ BCA Protein Assay Kit (23225, Thermo Fisher Scientific). 30μg of protein samples were then resolved by SDS-PAGE on 4–12% Bis-Tris NuPage gels (NP0321, Thermo Fisher Scientific) using the XCell SureLock^® Mini-Cell SDS PAGE rig (Thermo Fisher Scientific). Resolved proteins were then transferred to 0.2μM Hybond PVDF membranes (10600021, Amersham, Amersham, UK) using the XCell II^™ Blot Module (Thermo Fisher Scientific). Following transfer membranes were blocked using 5% w/v milk protein and probed using antibodies directed against TLR4 (1:100, Ab47093, Abcam), MAD2 (1: 1000, 610679, BD Biosciences), MyD88 (1:1000, D80F5, Cell Signalling Technology), P53 (1:500, sc-126, Santa Cruz Biotechnology), HMGB1 (1:50, sc-56698, Santa Cruz Biotechnology) or GAPDH (1: 10,000, Ab9485, Abcam). After washing, the membrane was incubated with either a horseradish peroxidase (HRP) linked anti-rabbit secondary antibody (#7074, 1:1000, Cell Signalling Technology) or an anti-mouse HRP-linked secondary antibody (#7076, 1:1000, Cell Signalling Technology). Following incubation with the primary and secondary antibodies, a detection reagent luminol (SC-2048, Santa Cruz Biotechnology) was applied to blots and chemiluminescence images were then developed using a LAS-4000 luminescent image analyser (Fujifilm, Minato, Japan). Molecular weight was confirmed using a MagicMark^™ XP Western Protein Standard (LC5602, Thermo Fisher Scientific) and SeeBlue^™ Plus2 Pre-stained Protein Standard (LC5925, Thermo Fisher Scientific). Restore^™ PLUS Western Blot Stripping Buffer (46430, Thermo Fisher Scientific) was used to remove bound primary and secondary antibodies from membranes so they could be reprobed with additional antibodies. Densitometry was then carried out using Quantity One software (Bio-Rad Laboratories, Hercules, USA). Abundance of protein in arbitrary units (A.U.) was normalised to GAPDH. The mean density ratio of triplicate bands for each condition was then determined.

Senescence β-galactosidase staining kit

The induction of senescence in cells is usually accompanied by an increase in β-galactosidase activity [89]. In order to demonstrate this, cells were stained with the senescence β-galactosidase staining kit (#9860, Cell Signalling Technology) following transfection for 72 hours and 120 hours. Images were then taken at 10X magnification using an Olympus CKX41 microscope and an Olympus E600 camera (Olympus, Shinjuku, Japan). The percentage of β-galactosidase positive cells within each image was then calculated for each condition for (n = 3) technical and (n = 3) biological replicates.

Drug treatment and assessment of cell viability

Carboplatin (C2538), Paclitaxel (T402) and DMSO (D2650) were purchased from Sigma Aldrich. Carboplatin was diluted in sterile nuclease free water to a concentration of 10mg/ml and stored at room temperature based on recommendations by the manufacturer. Paclitaxel was diluted in DMSO to a concentration of 50g/l (58.6mM) based on recommendations by the manufacturer aliquoted and stored at -20°C while DMSO was kept at room temperature. Aliquots of Carboplatin, Paclitaxel and DMSO were freshly diluted with media for each experiment to the desired working concentrations. Following transfection for 24 hours, SKOV-3 cells were either left untreated, treated with 0.0017% DMSO (vehicle control) or 20nM or 1μM of paclitaxel or 200μM of Carboplatin for 48 hours. Forty-eight hours post-treatment, cell viability was assessed using the cell cycle kit 8 (CCK-8) assay. Absorbance values were read at 450nm using the Sunrise^™ microplate reader (Tecan Trading AG, Männedorf, Switzerland). Cell viability for each condition/drug treatment was calculated as a % of non-transfected cells which were left untreated. Images were taken at 4X magnification using an axiovert 35 inverted microscope (Zeiss, Germany) and at 6X zoom using a Canon Powershot A620 digital camera.

Trypan blue dye exclusion assay

SKOV-3 cells were transfected into 6 well plates with siRNA targeting MAD2 a nontargeting scrambled negative control siRNA or were left untreated for 72 hours. After 72 hours, cells were left untreated, treated with DMSO or were treated with 20nM paclitaxel and incubated for a further 48 hours. After the 48-hour drug incubation time, supernatants from each well were collected and wells rinsed with PBS to remove any residual dying cells. Collected supernatant and washings were pelleted by centrifugation and resuspended in a small volume of PBS. Cell suspensions were then mixed at 1:1 ratio with trypan blue (T8154, Sigma Aldrich) and the number of dead cells were counted using a haemocytometer.

Microarray analysis

Prior to analysing RNA samples using Affymetrix microarrays, the quality of RNA samples was assessed using the Agilent 2100 Bioanalyzer. Samples were run on chips from the RNA 6000 Nano kit (5067–1511, Agilent Technologies, Santa Clara, USA) and an RNA Integrity Number (RIN) was obtained. 250ng of each RNA sample was then converted into sense strand cDNA using the GeneChip^® WT PLUS Reagent Kit (902280, Affymetrix). Each cDNA sample was then hybridised to Affymetrix GeneChip^® human gene 2.0 ST arrays (902113, Affymetrix). Arrays were washed using the Affymetrix GeneChip^® fluidics station 450 and scanned using the Affymetrix GeneChip^® Scanner 3000. Gene array data was analysed using Bioconductor software libraries available at (www.bioconductor.org) [90] and the RMA method [91]. Differential expression analysis across all the arrays was carried out using RankProd [92]. DAVID, a free bioinformatics resource was used to characterise differentially expressed genes in order to identify molecular function and biological process-related genes through gene ontology [32]. Microarray analysis was performed using three biological replicates of SKOV-3 cells transfected with either the scrambled or negative control siRNA or siRNA targeting TLR4 or MAD2. A 1.5 fold change in gene expression and a p value of <0.05 was set as the threshold for a significantly upregulated/downregulated gene, this threshold is in line with other published works [93]. Heatmaps were generated using Morpheus (https://software.broadinstitute.org/morpheus) or Graphpad Prism v8.4. KEGG pathway maps were generated using DAVID. The Affymetrix microarray data sets generated as part of this study are available in an ArrayExpress repository, accession #370077.

In-silico analysis

In-silico analysis was performed in order to identify any potential interaction between the TLR4-MyD88 pathway and MAD2 using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) v10 software which is freely available at (http://string-db.org/). This free online bioinformatics resource identifies protein-protein interactions through both direct (physical) as well as indirect (functional) associations [28].

Statistical analysis

A student’s t-test was performed on all qPCR, densitometry and cell viability data to assess the statistical significance of gene silencing experiments and differences in cell viability between drug-treated versus untreated and vehicle control groups. A statistically significant difference was considered to be present at p≤0.05. Statistical analysis was performed using Microsoft Excel 2016.

Supporting information

S1 Data. List of differentially expressed genes following siRNA knockdown of TLR4 in SKOV-3 cells.

(XLSX)

Click here for additional data file.^{(3.5MB, xlsx)}

S2 Data. List of differentially expressed genes following siRNA knockdown of MAD2 in SKOV-3 cells.

(XLSX)

Click here for additional data file.^{(2.9MB, xlsx)}

S1 Raw Images. Uncropped western blots from A2780 and SKOV-3 cells.

(PDF)

Click here for additional data file.^{(1.8MB, pdf)}

S1 File. IC50 data for A2780 and SKOV-3 cells.

(DOCX)

Click here for additional data file.^{(225.7KB, docx)}

S1 Table. Significantly over-represented biological processes identified by the DAVID database following knockdown of TLR4 in SKOV-3 cells.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S2 Table. Features of senescence highlighted by microarray analysis following knockdown of MAD2 in SKOV-3 cells for 72 hours.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

Data Availability

All relevant data is within the paper and its Supporting information files. The Affymetrix microarray data sets generated as part of this study are available in the ArrayExpress repository using accession number E-MTAB-8440 (direct link: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8440).

Funding Statement

NO - This research was supported by a research grant from the Royal City of Dublin Hospital Trust, the Emer Casey Foundation, SOCK and the Irish Ladies Golf Union. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0243715.r001

Decision Letter 0

David Wai Chan

27 Nov 2019

PONE-D-19-29743

The MAD2-TLR4-MyD88 Axis

in Paclitaxel Resistance in Ovarian Cancer

PLOS ONE

Dear Dr. Mark Bates,

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Additional Editor Comments (if provided):

This study reports a novel MAD2-TLR4-MyD88 signaling axis involved in paclitaxel resistance in ovarian cancers. This finding is interesting. However, there are some key findings needed for further strengthen. It's particularly concerned of two cell models A2780 and SKOV3 in this study as they are not HGSOC cell lines. For others, please check for the comments of the reviewers.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: The quality of the figures is very poor and the labeling are unreadable, preventing a proper reading and reviewing of the manuscript.

It is just impossible to read the figures in either a printed copy or on computer screen. In a general looking at the graphs and images, the changes are not very small or subtle and thus the conclusions are likely not so significant.

Reviewer #2: This manuscript describes a very well executed series of experiments that have been approached in a comprehensive manner. Both the writing and organization of the presentation are excellent. The authors have been very thorough in how they have addressed their hypothesis. It has been a pleasure reading and reviewing this submission and I strongly support its publication. Especially worthy of inspection by the scientific public are the findings from the micro array analyses suggesting that multiple molecular processes may mitigate sensitivity and contribute to resistance so that singular biomarker dependence may not be sufficient or informative.

I would like the manuscript to address one additional consideration about senescence. It is well-appreciated that borderline tumors of the ovary, granulosa cell tumors and others have very slow growing characteristic which can be equated to some degree with "senescence". These intrinsically slow growing tumors are very non-responsive to chemotherapy. This non-responsiveness arises from a proliferation that is too slow to be halted by chemotherapy. To what extent then can the manipulations described here be the result of the induction of senescence and not specific to the molecular link between TLR4-MyD88 signalling and MAD2? Please address this possibility.

Minor: note that somehow "aswell" rather than "as well" appears multiple times in the manuscript.

Reviewer #3: This study presents data on the MAD2-TLR4-MyD88 axis in paclitaxel resistance in ovarian cancer. The data include, molecular biology experiments showing loss of function and gain of function assays of the three genes and gene expression profiling in loss of function of TLR4 and MAD2 in SKOV3 cells.

Comments to authors.

Results

In general, more attention to details throughout, jargon and abbreviations like - no. of scorers, etc would improve readability of an otherwise well written manuscript. In spite of the document being well written, there are some concerning aspects to the data and rationale for using the approach and reagents.

1. Only 1 siRNA was used to target each gene, hence the variation in knockdown efficiency.

2. SKOV3 and A2780 cells were used as a model of paclitaxel resistance or generic ovarian cancer cells. It is well recognized now that these lines do not represent best models for HGSOC. Evaluating cell lines as tumour models by comparison of genomic profiles (Silvia Domcke et al, Nat. Communications 2013), Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours (Tan A. Ince et al., Nat Communications, 2015).

3. Supplementary Figure 1., Section 3, Legend does not correlate with Figure 1C , should be Fig 1D or F?

4. In general the Figure legends are too detailed with general wester blot and other methodologies which should be included in the Methods section.

5. Figure 1E, in general, MAD2 knockdown is not significant, why was this then used in microarray experiments?

6. Figure 3, Section - Suppression of MAD2 induces cellular senescence and paclitaxel resistance.” Authors wrote, 5th line, that MAD2 KD cells exhibited 30% increase in cell viability compare to controls. It appears to me that 30% DECREASED viability as a result of loss of function MAD2, but more viable than treatment alone, indicating that loss of MAD2 improves survival when cells are treated with paclitaxel, which is counter to the hypothesis.

7. Bgal assay is a good representation of the disconnect between proliferation and viability.

8. Curious that cisplatin was mentioned in discussion and no true attention to the platinum based treatments which are truly first line therapy in EOC.

9. The microarray data should be submitted to a public database such as GEO or EMBL.

**********

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Reviewer #1: No

Reviewer #2: Yes: Edward John Pavlik

Reviewer #3: No

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PLoS One. 2020 Dec 28;15(12):e0243715. doi: 10.1371/journal.pone.0243715.r002

Author response to Decision Letter 0

27 Oct 2020

Please see the Response to Reviewers Document

Response to Reviewers

The manuscript has been amended to follow PLOS One style/formatting requirements.

1) The title has been now changed and now uses sentence case with capitilasation only for the first letter of the first word and the names of the proteins MAD2, MyD88 and TLR4

2) Headings now match the font size outlined in the guide and use sentence case

3) The postal code has been removed from the affiliation for authors based in Dublin

4) The supplementary file has been reduced and split up to enhance readability and file naming conventions have been changed to reflect PLOS One policies

2. Please ensure that your Methods section contains a description of Paclitaxel source.

This has now been added to the manuscript in the Methods section PG16 of the manuscript.

“Carboplatin (C2538), Paclitaxel (T402) and DMSO (D2650) were purchased from Sigma Aldrich. Carboplatin was diluted in sterile nuclease free water to a concentration of 10mg/ml and stored at room temperature based on recommendations by the manufacturer. Paclitaxel was diluted in DMSO to a concentration of 50g/l (58.6mM) based on recommendations by the manufacturer aliquoted and stored at -200C while DMSO was kept at room temperature. Aliquots of Carboplatin, Paclitaxel and DMSO were freshly diluted with media for each experiment to the desired working concentrations”

All uncropped western blots are available in the supplementary material and this is indicated in the figure legend and a note has now also been added to the methods section to further highlight this and it will be noted in the new cover letter.

4.This study reports a novel MAD2-TLR4-MyD88 signalling axis involved in paclitaxel resistance in ovarian cancers. This finding is interesting. However, there are some key findings needed for further strengthen. It's particularly concerned of two cell models A2780 and SKOV3 in this study as they are not HGSOC cell lines. For others, please check for the comments of the reviewers.

We now include additional MAD2 & TLR4 knockdown results from the serous ovarian cancer cell line OVCAR-3 within the paper. This cell line was chosen as it expresses TLR4, MyD88 and MAD2, other serous lines such as Kuramochi and OAW42 which were also available at our lab do not express TLR4 as indicated by an expression profile comparison which is also now included in the manuscript. Please see the new Figure 3 for these results. Interestingly knockdown of TLR4 in OVCAR-3 cells resulted in a significant increase in MAD2 expression while knockdown of MAD2 in this model resulted in an increase in TLR4 expression similar to the other cell models further highlitghing the relationship between these two biomarkers. The effect of the TLR4 knockdown on MAD2 expression was suprising, although perhaps a partial increase in MAD2 expression was observed post knockdown of TLR4 with both the A2780 and SKOV-3 cells. However this fell below the 2-fold cut-off for consideration and neither were significant. Although significant knockdowns of TLR4 in both cell lines were observed perhaps a certain threshold of TLR4 downregulation is required, notedly with the OVCAR-3, the biological replicate with the highest level of TLR4 knockdown also had the greatest increase in MAD2 expression.

It is interesting also that ovarian cancer cell lines seem to show stark differences in TLR4 expression, but this is also reflected in ovarian cancer patients. We and others have previously shown that high grade serous ovarian cancer patients which have high TLR4 expression have poorer outcomes. It is possible that within the tumour microenviroment ovarian cancer cells acquire TLR4 through interactions with stromal or immune cells populations something which would not be reflected in cell line models. Additionally the microarray data sets indicate that TLR4 may also highlight aggressive cancer cells undergoing EMT something which might be further amplified in the hypoxic tumour niche as hypoxia is known to promote the emergence of stem like phenotypes and can also induce the downregulation of MAD2 as we have previously shown (Prencipe 2010, PMID20676051) another factor which may not be observed in conventional 2D cell models.

Fig 3. Cell line cross comparison and OVCAR-3 TLR4 and MAD2 siRNA knockdown. (A) MyD88, TLR4 and MAD2 gene expression in A2780, OVCAR-3, PEO1, OAW42, KURAMOCHI and 59M cells relative to SKOV-3 cells. (B) MyD88, TLR4 and MAD2 gene expression in OVCAR-3 cells following siRNA knockdown of TLR4 or MAD2. Gene expression levels were normalised to the endogenous control GAPDH and calibrated to that of untreated cells to establish the relative change in gene expression .

Preliminary data (n=1) below from PEO1 cells an additional serous ovarian cell line indicate that this effect may not be universal however, although this particular cell line interestingly has a P16 Null phenotype (Furlong 2009, PMID: 22069160) and may not be capable of undergoing cellular senescence, this is something we hope to explore further in future studies.

Furthermore, knockdown of TLR4 has been previously shown to enhance sensitivity to paclitaxel in the OVCAR-3 cell line (Szajnik 2009, PMID 19826413) similar to what we have observed in the SKOV-3 cell line. Additionally, we have also demonstrated the senescence effect following knockdown of MAD2 in MCF-7 breast cancer cells (Prencipe 2009, PMID: 2788249) demonstrating that this is likely not restricted to specific subtypes of ovarian cancer nor is it even restricted to ovarian cancer but it is probably a ubiquitous phenomenon which likely occurs in various cancers. A link between MAD2 and senescence has also been demonstrated in prostate cancer (To-Ho 2007, PMID 17621272). Also, paclitaxel most importantly is a known ligand for TLR4 (Byrd-Leifer 2001, PMID: 11500829) and the effect of TLR4 on paclitaxel resistance similarly is not restricted to ovarian cancer either, please see Rajput 2013, PMID 23720768 & Kashani 2020 PMID: s12026-019-09113-8).

5. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

________________________________________

6. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

7. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

All data is available in the manuscript, supporting material or otherwise where indicated in the data availability statement

8. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

9. Reviewer #1: The quality of the figures is very poor and the labeling are unreadable, preventing a proper reading and reviewing of the manuscript. It is just impossible to read the figures in either a printed copy or on computer screen. In a general looking at the graphs and images, the changes are not very small or subtle and thus the conclusions are likely not so significant.

Apologies there appears to have been some compression issue with images and this has been amended in the revised version of the manuscript.

10.Reviewer #2: This manuscript describes a very well executed series of experiments that have been approached in a comprehensive manner. Both the writing and organization of the presentation are excellent. The authors have been very thorough in how they have addressed their hypothesis. It has been a pleasure reading and reviewing this submission and I strongly support its publication. Especially worthy of inspection by the scientific public are the findings from the micro array analyses suggesting that multiple molecular processes may mitigate sensitivity and contribute to resistance so that singular biomarker dependence may not be sufficient or informative. I would like the manuscript to address one additional consideration about senescence. It is well-appreciated that borderline tumors of the ovary, granulosa cell tumors and others have very slow growing characteristic which can be equated to some degree with "senescence". These intrinsically slow growing tumors are very non-responsive to chemotherapy. This non-responsiveness arises from a proliferation that is too slow to be halted by chemotherapy. To what extent then can the manipulations described here be the result of the induction of senescence and not specific to the molecular link between TLR4-MyD88 signalling and MAD2? Please address this possibility.

I would like to thank reviewer #2, for their comment I think they make a very interesting point. It is perhaps a difficult question to answer, it seems as though there is a synergy between MAD2 and the TLR4 signalling pathway. Whether senescence can occur without the TLR4 pathway it would be interesting to determine, something we may be able to answer through further exploration with TLR4 negative cell lines. MyD88 itself does not appear to be required as the senescence effect as a result of MAD2 suppression has been shown by our group to occur in both SKOV-3 cells (MyD88 positive) and A2780 (MyD88 Negative, See Furlong 2009, PMID: 22069160). Certainly, you can have the TLR4 pathway mechanism of paclitaxel resistance on its own which is driving apoptotic resistance and pro-tumorigenic inflammatory niche formation. Whether MAD2 suppression can lead to paclitaxel resistance in the absence of senescence I suppose we can’t answer currently, although for the purpose of this article we are attributing the effect of MAD2 suppression on paclitaxel resistance to senescence. It would be interesting though to interrogate this further in cell models which have functional defects which don’t allow them to undergo senescence.

A big debate around senescence also is whether the process is reversible “in vivo”, can these cells recover after a hibernation phase and re-emerge once the chemotherapy subsides. The 2nd thing with this is whether all cells undergo senescence in that type of microenvironment, even in this model not all off the cells display an increase in size and display enhanced B-gal activity, although this could reflect transfection efficiency.

We do describe in a recent a review in Cancer Letters (PMID: 31593803) that perhaps these senescent cells act as a physiological barrier and soak up the paclitaxel essentially and facilitate the emergence/re-emergence of non-senescent tumour cell populations such as cancer stem cells, which are known to display many of the features described in the TLR4 microarray data set presented in the paper. Thus, we really need to target these senescent cell populations.

11.Minor: note that somehow "aswell" rather than "as well" appears multiple times in the manuscript.

This error has now been amended.

12.Reviewer #3: This study presents data on the MAD2-TLR4-MyD88 axis in paclitaxel resistance in ovarian cancer. The data include molecular biology experiments showing loss of function and gain of function assays of the three genes and gene expression profiling in loss of function of TLR4 and MAD2 in SKOV3 cells. In general, more attention to details throughout, jargon and abbreviations like - no. of scorers, etc would improve readability of an otherwise well written manuscript. In spite of the document being well written, there are some concerning aspects to the data and rationale for using the approach and reagents.

The manuscript has now been amended to address this

13. Only 1 siRNA was used to target each gene, hence the variation in knockdown efficiency.

The TLR4 siRNA is a silencer select siRNA from Thermofisher. This siRNA has been chemically modified with a Locked Nucleic Acid (LNA) a method which has been shown to reduce many undesired, sequence-related off-target effects (PMID: 15653644). Furthermore, Thermofisher measure potential off-target activity using microarray analysis and bioinformatically screen their siRNAs to maximise accuracy and specificity. This is a highly validated commercial siRNA from a well-established supplier.

Furthermore, this siRNA has been validated in a number of publications including high impact journals such as Nature Communications. See below

PMID: 24614850

PMID: 28832545

We also previously published an article in PLOS one on the effect of this particular siRNA on paclitaxel sensitivity PMID: 24977712. The work presented in this study is essentially a follow-on study exploring this mechanism further.

We do agree that siRNA pools, shRNA or even other technologies that didn’t exist when this study began such as CRISPR might lead to better targeting of this gene and reduce any potential off-target effects even further. However, regardless of the mechanism of interrogation, we still feel the results presented here are valid and are based on a confirmed significant reduction of TLR4 protein/gene expression.

This study sought however to confirm two things, first did the knockdown of TLR4 and the resulting impact on paclitaxel sensitivity influence MAD2 expression and secondly, we wanted to further interrogate this pathway using microarray technology, again with a particular focus on the SKOV-3 cell line.

As for the MAD2 knockdown, this was performed using siRNA pools specifically a SMARTpool siRNA purchased from Dharmacon and for this experiment, it is a non-issue. Additionally, in either knockdown, a scrambled or negative control siRNA was used to demonstrate any off-target effects of activating the RNAi machinery.

14. SKOV3 and A2780 cells were used as a model of paclitaxel resistance or generic ovarian cancer cells. It is well recognized now that these lines do not represent best models for HGSOC. Evaluating cell lines as tumour models by comparison of genomic profiles (Silvia Domcke et al, Nat. Communications 2013), Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours (Tan A. Ince et al., Nat Communications, 2015).

In 2013, partway through this project, the Domcke et al. (2013) study questioned the appropriateness of many cell models for ovarian cancer research. The article highlighted how many ovarian cancer cell lines based on genome sequencing and mutational analysis may not be truly representative of high grade serous ovarian cancer. Although this research article does highlight a very important issue in ovarian cancer research, it must also be acknowledged that cell models are simply models of disease and are only representative of a single ovarian cancer patient.

The A2780 and SKOV-3 cells models, were the most frequently utilised cell models for ovarian cancer research and are still widely utilised today. The SKOV-3 cell model is among one of the cell models used in the national cancer institute’s NCI-60 panel used to test new cytotoxic drugs by the FDA. The two cell models A2780 and SKOV-3 were also chosen mainly for the fact that they represented positive and negative models of MyD88 respectively, with the A2780 model being MyD88 null and paclitaxel sensitive and the SKOV-3 cells being MyD88 positive and paclitaxel resistant. Both also expressed MAD2 and knockdown of MAD2 in the A2780 cells had been shown to induce paclitaxel resistance (PMID: 22069160, PMID: 21063845). They also both express TLR4, however, only knockdown of TLR4 in SKOV-3 cells but not A2780 cells had been shown to restore paclitaxel sensitivity [PMID: 19826413, PMID: 24527095]. Therefore, we felt these cells were still appropriate models to take forward for evaluation. While we appreciate HGS is the most common subtype, we do think there is merit in this approach even for other ovarian cancer subtypes.

In the Domcke et al study, sequencing data performed on cell lines was compared to TCGA data. While the TCGA has given an enormous amount of data to scientists, there are some concerns over the interpretation of this data which have preventing the translation of the 4 molecular subtypes of HGSOC identified from the TCGA data into the clinic. Criticisms of the TGCA data are published here [PMID:18698038, PMID:21720365] which include technical limitations such as inconsistency between platforms, sample batch effects [PMID:20838408], reagent batch effects and inconsistent macro-dissection resulting in variable stroma to tumour content in the gene expression data [PMID:18698038, PMID:21720365]. Our own work in which A2780 were injected into mouse models, tumours formed which histologically resembled high grade serous ovarian tumours as confirmed by a pathologist.

Furthermore, we have assessed MAD2, TLR4 and MyD88 to be prognostic in patients from high grade serous ovarian tumours. But this paper goes beyond the use of single biomarkers and looks at more complex pathways at play many of which are known to contribute to ovarian cancer pathogenesis.

We have also previously demonstrated the senescence effect in breast cancer cell models, this may, in fact, be a universal phenomenon, The effect of TLR4 suppression on paclitaxel sensitivity has also been demonstrated previously in breast and other tumours.

We have also now included data on the OVCAR-3 serous ovarian cell line, which can be observed above.

Results Section – PG5/6 of the manuscript – “To further explore the link between TLR4 and MAD2 we next analysed the expression of MAD2, TLR4 and MyD88 in 5 additional ovarian cancer cell lines; OVCAR-3, PEO1, OAW42, KURAMOCHI and 59M cells (Fig 3A). Of these only OVCAR-3 and PEO1 expressed TLR4, MyD88 and MAD2. OAW42, KURAMOCHI and 59M were TLR4 negative. OVCAR-3 cells due to their TLR4 positivity and as a representative model of serous ovarian cancer were subsequently transfected with siRNA targeting TLR4 or MAD2 and then TLR4, MAD2 and MyD88 expression levels were assessed. Interestingly knockdown of TLR4 or MAD2 in the OVCAR-3 cell model caused a significant 2.4 and 2.9 fold increase in MAD2 or TLR4 expression respectively further highlighting an important link between these two biomarkers (Fig 3B).”

Discussion PG10 of the manuscript - “Intererestingly however in the OVCAR-3 serous ovarian model a significant increase in MAD2 expression was observed. One observation with this experiment was that the replicate with the highest knockdown of TLR4 had the highest upregulation of MAD2”, “Furthermore, suppression of MAD2 expression using siRNA in A2780, OVCAR-3 and SKOV-3 cell lines led to a significant increase in TLR4 gene expression levels demonstrating a key link between TLR4 and MAD2”

15. Supplementary Figure 1., Section 3, Legend does not correlate with Figure 1C , should be Fig 1D or F?

This error has been amended

16. In general the Figure legends are too detailed with general wester blot and other methodologies which should be included in the Methods section.

The amount of detail in the figure legends has been reduced in both the main text and supplementary data.

17. Figure 1E, in general, MAD2 knockdown is not significant, why was this then used in microarray experiments?

Apologies there seems to have been some compression issue with the images that were uploaded. Figure 1E however is examining MAD2 gene expression following knockdown of either TLR4 or MyD88 (Both of which were significant), this did not affect MAD2 gene expression. This experiment was important for assessing the relationship between these 3 biomarkers. The TLR4 knockdown was taken forward for microarray analysis as this induced enhanced paclitaxel sensitivity in the SKOV-3 cell model but not knockdown of MyD88.

The MAD2 knockdown is depicted in Figure 2 and was highly significant in both SKOV-3 and A2780 cells, the SKOV-3 cell was subsequently chosen for microarrays being a MyD88 positive cell line, which had been shown to display both mechanisms of paclitaxel resistance/sensitivity independently.

18. Figure 3, Section - Suppression of MAD2 induces cellular senescence and paclitaxel resistance.” Authors wrote, 5th line, that MAD2 KD cells exhibited 30% increase in cell viability compare to controls. It appears to me that 30% DECREASED viability as a result of loss of function MAD2, but more viable than treatment alone, indicating that loss of MAD2 improves survival when cells are treated with paclitaxel, which is counter to the hypothesis.

I believe the reviewer is referring to below text

“When SKOV-3 cells were treated with a lethal 1µM dose of paclitaxel following knockdown of MAD2, they exhibited a 30% increase in cell viability compared to untreated cells and scrambled negative control cells treated with the same dose of paclitaxel demonstrating that SKOV-3 cells were rendered paclitaxel-resistant (Fig 3B)”

MAD2 knockdown cells do indeed exhibit a decrease in viability according to the CCK-8 assay by about 30% compared to untreated/untransfected cells which did not receive any drug. Although very little cell loss was observed post knockdown of MAD2, any decrease here in “viability” as determined by the cell viability assay in these cells is likely rather a representation of decreased cell proliferation rates due to the induction of senescence.

However, compared to untransfected cells or cells transfected with the negative control siRNA viability is enhanced by 30% in the MAD2 knockdown cells definitively demonstrating that these cells are paclitaxel-resistant which is in line with findings in our previous works. It is not counter to our hypothesis, the fact that loss of MAD2 improves the survival of cells treated with paclitaxel is exactly our hypothesis and exactly what we have shown, and this is also mirrored in patients with high grade serous ovarian cancer i.e. patients with low expression of MAD2 have worse outcomes (Furlong 2009, PMID: 22069160).

The wording of this section (Pg 6 of the manuscript) has been amended to enhance readability.

“Suppression of MAD2 induces cellular senescence and paclitaxel resistance

Following knockdown of MAD2, SKOV-3 cells exhibited an increase in cell and nuclear size and alteration of cell shape (Fig 4A). Subsequently, the chemoresponse of SKOV-3 cells to paclitaxel following knockdown of MAD2 was assessed (Fig 4C). When SKOV-3 cells were treated with a 20nM or 1µM dose of paclitaxel following knockdown of MAD2, they exhibited a reduction in cell viability of 36.2% and 36.1% compared with untransfected cells which were not treated with paclitaxel. In contrast, untransfected cells or cells transfected with the scrambled negative control which were treated with 20nM of paclitaxel exhibited a decrease in cell viability of 55.4% and 56.3% respectively. While untransfected cells or cells transfected with the scrambled negative control siRNA which were treated with 1µM of paclitaxel exhibited a decrease in cell viability of 66.2% and 66.4% respectively. Additionally, despite there being no visual signs of cytotoxicity, untreated cells transfected with MAD2 siRNA also exhibited a 19% significant reduction in cell viability compared to untransfected cells which were untreated potentially indicating a reduction in cell proliferation. Furthermore, in transfected cells treated with either dose of paclitaxel minimal if any visual signs of cytotoxicity were observed. In fact, the addition of paclitaxel appeared to accelerate the timeframe for the emergence of the enlarged cell phenotype. Additionally, the difference in cell viability between cells transfected with MAD2 siRNA which were untreated and transfected cells treated with either dose of paclitaxel was not statistically significant. This result was further supported by a trypan blue exclusion assay which detected a significant decrease in the number of dead cells in the supernatants of paclitaxel treated cells following knockdown of MAD2 compared to controls (Fig 4E). The results indicated that SKOV-3 cells transfected with siRNA targeting MAD2 were rendered resistant to paclitaxel and potentially undergoing cellular senescence.”

CCK8 Assay- Now Figure 4C, PG22 in the manuscript and also now includes and additional dose 20uM to match what was examined with the Trypan blue exclusion assay.

We also now include a previous trypan blue exclusion assay we performed on the supernatants of cells transfected and treated with paclitaxel which also reflects this.

Trypan Blue Exclusion Assay Fig 4E, PG22

Details of the trypan blue exclusion assay experiment are now also included in the methods section pg18

“Trypan Blue dye exclusion assay

SKOV-3 cells were transfected into 6 well plates with siRNA targeting MAD2, a non-targeting scrambled negative control siRNA or were left untreated for 72 hours. After 72 hours, cells were left untreated, treated with DMSO or were treated with 20nM paclitaxel and incubated for a further 48 hours. After the 48-hour drug incubation time, Supernatants from each well were collected and wells rinsed with PBS to remove any residual dying cells. Collected supernatant and washings were pelleted by centrifugation and resuspended in a small volume of PBS. Cell suspensions were then mixed at 1:1 ratio with trypan blue (T8154, Sigma Aldrich) and the number of dead cells were counted using a haemocytometer”

The fact that knockdown of MAD2 increases TLR4 expression is also curious given that we previously published that knockdown of TLR4 enhances the sensitivity of SKOV-3 cells to paclitaxel (d’Adhemar 2014, PMID 24977712)

Interestingly the addition of paclitaxel actually also seemed to accelerate the emergence of the senescent phenotype observed at 120 hours. In essence paclitaxel not only doesn’t kill these MAD2 knockdown cells, it actually amplifies the senescence phenotype (Therapy induced senescence is a frequently described phenomenon). As stated below the Bgal assay also further demonstrates the disconnect between proliferation and viability.

19. Bgal assay is a good representation of the disconnect between proliferation and viability.

We agree with the reviewer on this, a limitation of cell viability assays is that they are representative of cell numbers rather than mechanistic/visual effects

20. Curious that cisplatin was mentioned in discussion and no true attention to the platinum-based treatments which are truly first line therapy in EOC.

We now include new results on this. Neither the knockdown of TLR4 or MAD2 had any impact on carboplatin sensitivity in the SKOV-3 model. The effect of knocking down TLR4 in SKOV-3 cells on carboplatin sensitivity is line with a previous report (Szajnik 2009, PMID 19826413) Previous work by our group (Unpublished data) had shown a similar effect with A2780 cells with cisplatin. This demonstrates that the effects with TLR4 and MAD2 seem to be specific to paclitaxel and this is now also discussed.

SKOV-3 cells were treated with a 200uM dose of paclitaxel now Figure 4F

CCK-8 assay results for SKOV-3 cells treated with 200µM carboplatin for 48 hours following a 24-hour transfection. Results are expressed as mean +/-SD, n=3. *p<0.05, **p<0.01, ***p<0.001 (Student’s t-test).

This was based on IC50 results with the SKOV-3 cells, see below which is also now included in supplementary data.

SKOV-3 carboplatin dose-response curve. SKOV-3 cells were treated with various doses of carboplatin for 48 hours. Cell viability was assessed using the cell counting kit 8 (CCK-8). The IC50 value for SKOV-3 at this timepoint was 139.1µM.

Result Section PG7 of the manuscript- “We also assessed the cytotoxicity of SKOV-3 cells to carboplatin following knockdown of either TLR4 or MAD2. Cells were transfected for 24 hours and then treated with a 200µM dose of carboplatin or left untreated, however, neither knockdown of TLR4 or MAD2 altered the response of SKOV-3 cells to carboplatin (Fig 4F).

Discussion Section PG12 of the manuscript - “The results also demonstrate that these senescent cell populations appear to be selectively resistant to paclitaxel but are sensitive to carboplatin, therefore patients could potentially be selected out for single arm therapy with carboplatin”

21. The microarray data should be submitted to a public database such as GEO or EMBL.

The Affymetrix microarray data sets generated as part of this study are available in an ArrayExpress repository, accession #370077 as indicated in the data availability statement.

________________________________________

22. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public.

Yes, we are happy for the peer review history to be published.

Attachment

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Click here for additional data file.^{(1.5MB, docx)}

PLoS One. doi: 10.1371/journal.pone.0243715.r003

Decision Letter 1

David Wai Chan

26 Nov 2020

The role of the MAD2-TLR4-MyD88 axis

in paclitaxel resistance in ovarian cancer

PONE-D-19-29743R1

Dear Dr. Bates,

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Reviewer #1: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

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6. Review Comments to the Author

Reviewer #1: The presentation is improved, and the authors appear to make good efforts to change/improve the manuscript.

However, the mechanistic links between the markers are not investigated, and the changes/impacts of each gene modulation are small or moderate. The study provides little new understanding on paclitaxel resistance.

Such manuscript may be publishable but is a low quality study.

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PLoS One. doi: 10.1371/journal.pone.0243715.r004

Acceptance letter

David Wai Chan

14 Dec 2020

PONE-D-19-29743R1

The role of the MAD2-TLR4-MyD88 axis in paclitaxel resistance in ovarian cancer

Dear Dr. Bates:

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Associated Data

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

Supplementary Materials

S1 Data. List of differentially expressed genes following siRNA knockdown of TLR4 in SKOV-3 cells.

(XLSX)

Click here for additional data file.^{(3.5MB, xlsx)}

S2 Data. List of differentially expressed genes following siRNA knockdown of MAD2 in SKOV-3 cells.

(XLSX)

Click here for additional data file.^{(2.9MB, xlsx)}

S1 Raw Images. Uncropped western blots from A2780 and SKOV-3 cells.

(PDF)

Click here for additional data file.^{(1.8MB, pdf)}

S1 File. IC50 data for A2780 and SKOV-3 cells.

(DOCX)

Click here for additional data file.^{(225.7KB, docx)}

S1 Table. Significantly over-represented biological processes identified by the DAVID database following knockdown of TLR4 in SKOV-3 cells.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S2 Table. Features of senescence highlighted by microarray analysis following knockdown of MAD2 in SKOV-3 cells for 72 hours.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

Attachment

Submitted filename: Response to Reviewers Comments.docx

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Data Availability Statement

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PERMALINK

The role of the MAD2-TLR4-MyD88 axis in paclitaxel resistance in ovarian cancer

Mark Bates

Cathy D Spillane

Michael F Gallagher

Amanda McCann

Cara Martin

Gordon Blackshields

Helen Keegan

Luke Gubbins

Robert Brooks

Doug Brooks

Stavros Selemidis

Sharon O’Toole

John J O’Leary

Roles

Abstract

Introduction

Results

Identifying the molecular link between MAD2 and TLR4-MyD88 signalling

Fig 1. Alteration of TLR4 and MyD88 expression does not alter MAD2 expression in A2780 or SKOV-3 cells.

Fig 2. MAD2 is an inhibitor of TLR4 gene expression.

Fig 3. Cell line cross comparison and OVCAR-3 TLR4 and MAD2 siRNA knockdown.

Suppression of MAD2 induces cellular senescence and paclitaxel resistance

Fig 4. Suppression of MAD2 induces cellular senescence and paclitaxel resistance in SKOV-3 cells.

Fig 5. SiRNA knockdown of MAD2 does not alter HMGB1 gene or protein expression in A2780 or SKOV-3 cells.

Microarray analysis

Fig 6. Altered genes and biological processes following knockdown of TLR4.

Fig 7. Altered genes and biological processes following knockdown of MAD2, string network analysis and cross comparison of the MAD2 and TLR4 knockdown data sets.

Discussion

Conclusions

Methods

Cell culture

Small interfering RNA transfection

MyD88 transfections

RNA extraction and TaqMan RT-PCR

Western blot analysis

Senescence β-galactosidase staining kit

Drug treatment and assessment of cell viability

Trypan blue dye exclusion assay

Microarray analysis

In-silico analysis

Statistical analysis

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

David Wai Chan

Roles

Author response to Decision Letter 0

Decision Letter 1

David Wai Chan

Roles

Acceptance letter

David Wai Chan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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