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• W2889210641 abstract "Article29 August 2018Open Access Source DataTransparent process Function of HNRNPC in breast cancer cells by controlling the dsRNA-induced interferon response Yusheng Wu Yusheng Wu Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Wenwei Zhao Wenwei Zhao MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Yang Liu Yang Liu MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China Search for more papers by this author Xiangtian Tan Xiangtian Tan MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Xin Li Xin Li MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Qin Zou Qin Zou MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China Search for more papers by this author Zhengtao Xiao Zhengtao Xiao Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Hui Xu Hui Xu Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Yuting Wang Yuting Wang MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China Search for more papers by this author Xuerui Yang Corresponding Author Xuerui Yang [email protected] orcid.org/0000-0002-7731-2147 Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Yusheng Wu Yusheng Wu Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Wenwei Zhao Wenwei Zhao MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Yang Liu Yang Liu MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China Search for more papers by this author Xiangtian Tan Xiangtian Tan MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Xin Li Xin Li MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Qin Zou Qin Zou MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China Search for more papers by this author Zhengtao Xiao Zhengtao Xiao Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Hui Xu Hui Xu Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Yuting Wang Yuting Wang MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China Search for more papers by this author Xuerui Yang Corresponding Author Xuerui Yang [email protected] orcid.org/0000-0002-7731-2147 Tsinghua-Peking Joint Center for Life Sciences, Beijing, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China School of Life Sciences, Tsinghua University, Beijing, China Search for more papers by this author Author Information Yusheng Wu1,2,3,4,‡, Wenwei Zhao2,3,4,‡, Yang Liu2,3,4,5,‡, Xiangtian Tan2,3,4, Xin Li2,3,4, Qin Zou2,3,4,5, Zhengtao Xiao1,2,3,4, Hui Xu1,2,3,4, Yuting Wang2,3,4,5 and Xuerui Yang *,1,2,3,4 1Tsinghua-Peking Joint Center for Life Sciences, Beijing, China 2MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China 3Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China 4School of Life Sciences, Tsinghua University, Beijing, China 5Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, China ‡These authors contributed equally to this work *Corresponding author. Tel: +86 10 62783943; E-mail: [email protected] The EMBO Journal (2018)37:e99017https://doi.org/10.15252/embj.201899017 See also: SL Sarbanes et al (December 2018) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Elevated expression of RNA binding protein HNRNPC has been reported in cancer cells, while the essentialness and functions of HNRNPC in tumors were not clear. We showed that repression of HNRNPC in the breast cancer cells MCF7 and T47D inhibited cell proliferation and tumor growth. Our computational inference of the key pathways and extensive experimental investigations revealed that the cascade of interferon responses mediated by RIG-I was responsible for such tumor-inhibitory effect. Interestingly, repression of HNRNPC resulted in accumulation of endogenous double-stranded RNA (dsRNA), the binding ligand of RIG-I. These up-regulated dsRNA species were highly enriched by Alu sequences and mostly originated from pre-mRNA introns that harbor the known HNRNPC binding sites. Such source of dsRNA is different than the recently well-characterized endogenous retroviruses that encode dsRNA. In summary, essentialness of HNRNPC in the breast cancer cells was attributed to its function in controlling the endogenous dsRNA and the down-stream interferon response. This is a novel extension from the previous understandings about HNRNPC in binding with introns and regulating RNA splicing. Synopsis Repression of RNA binding protein HNRNPC in breast cancer cells MCF7 and T47D resulted in accumulation of endogenous dsRNA species mostly from Alu introns, which triggered interferon response and tumor growth arrest. HNRNPC is highly expressed in breast cancer tumors and repression of HNRNPC arrests proliferation of MCF7 and T47D cells. Interferon response mediated by RNA sensor RIG-I is responsible for anti-proliferation effect of HNRNPC repression. Repression of HNRNPC induces immunostimulatory endogenous dsRNA mostly from introns with Alu. Production of Alu dsRNA can be traced back to RNA quality control machinery such as nonsense-mediated decay. Introduction Aberrant up-regulation of heterogeneous nuclear ribonucleoprotein C (HNRNPC) has been observed in multiple tumors or tumor cell lines, including glioblastoma (Park et al, 2012), hepatocellular carcinoma (Sun et al, 2007), melanoma (Mulnix et al, 2014), and lung cancer (Pino et al, 2003). As an RNA binding protein (RBP), HNRNPC is well known for its regulatory roles in RNA splicing (Konig et al, 2010; Zarnack et al, 2013), sequence-unspecific RNA exportation (McCloskey et al, 2012), RNA expression (Christian et al, 2008; Park et al, 2012), stability (Shetty, 2005; Velusamy et al, 2008), 3′ end processing (Gruber et al, 2016), and translation (Kim et al, 2003; Meng et al, 2008; Spahn et al, 2008; Lee et al, 2010). Indeed, among these HNRNPC-involved regulatory events are processing of multiple cancer-related genes, including BRCA (Anantha et al, 2013), uPAR (Shetty, 2005), MALAT1 (Yang et al, 2013), PDCD4 (Park et al, 2012), cMyc (Kim et al, 2003). However, the essentialness of HNRNPC in tumors and the exact molecular processes that are responsible for the potential physiological function of HNRNPC in the tumor cells are still not clear. In the present study, we showed that even a partial repression of HNRNPC could result in arrestment of cell proliferation and tumorigenesis of the breast cancer cell lines MCF7 and T47D, suggesting an indispensable role of HNRNPC in these cells. Our further survey of the transcriptome profiles after HNRNPC knock-down in these cells revealed an unanticipated dramatic elevation of the interferon-stimulated genes (ISGs), including the type I interferon IFNβ itself. IFNβ can be expressed in most of the cell types upon invasion of microbes and sensing of the microbial components by the pattern recognition receptors (PRRs; Nagarajan, 2011; Schneider et al, 2014). In addition, the type I interferon production can also be activated by the PRRs sensing the endogenous nucleic acids under stress conditions such as radiation, autoimmune disease, and cancer (West et al, 2015; Roers et al, 2016). Specifically, upon activation by their ligands, the cytoplasmic DNA sensors (ALR and cGAS) and RNA sensors (RIG-I and MDA5) initiate the signaling cascade of interferon response (Honda et al, 2006; Goubau et al, 2013; Wu & Chen, 2014; McNab et al, 2015), leading to transcription of the ISGs (Platanias, 2005; Schneider et al, 2014). These interferon responses have been shown to be involved in regulating tumor development due to its well-characterized pro-apoptotic and anti-proliferative effects in various types of cancer cells, including myeloma cell lines (Chen et al, 2001), lymphoma (Yang et al, 2013), liver cancer cells (Maeda et al, 2014; Murata et al, 2006; Sangfelt et al, 1997), and sarcoma cell lines (Sanceau et al, 2000). In fact, recent studies have shown that the IFN response triggered by endogenous dsRNA plays a central role in executing the therapeutic effects of anti-tumor drugs such as DNA methyltransferase inhibitors and CDK4/6 inhibitors in multiple types of cancer (Chiappinelli et al, 2015; Roulois et al, 2015; Goel et al, 2017). In the present study, we showed that the tumor-inhibitory effect of HNRNPC knock-down was mediated through the cascade of interferon response, which was specifically initiated via retinoic acid-inducible gene I (RIG-I, gene name DDX58), but not the other PRRs. Interestingly, we found that HNRNPC knock-down resulted in increase in the endogenous double-stranded RNA (dsRNA), which is the binding ligand of RIG-I. We then developed a dsRNA pull-down-based experiment to enrich the dsRNA species from the total RNA. Sequencing of these dsRNA libraries and the following bioinformatics analysis systematically identified the dsRNA regions and quantified their abundances. Accumulation of some dsRNA regions was indeed observed after HNRNPC knock-down. These elevated dsRNA species were mostly found in introns, corroborating known transcriptome-wide HNRNPC binding regions (Konig et al, 2010; Zarnack et al, 2013). This differs from previous observation of elevated dsRNA derived from normally hypermethylated endogenous retroviruses (ERVs) that are activated by anti-tumor inhibitors to trigger the IFN response as a therapeutic approach (Chiappinelli et al, 2015; Roulois et al, 2015; Goel et al, 2017). Heterogeneous nuclear ribonucleoprotein C is well known for its function in regulating RNA splicing by binding with introns, especially the introns containing Alu (Konig et al, 2010; Zarnack et al, 2013). Indeed, almost all the up-regulated dsRNA regions contain Alu or Alu fragments. Therefore, our discovery of dsRNA accumulation upon HNRNPC repression is a novel extension of the previously characterized functions of HNRNPC in binding with pre-mRNA introns and regulating RNA splicing. Results Repression of HNRNPC arrested the proliferation and tumorigenesis of MCF7 and T47D Elevated expression of HNRNPC has been observed in multiple types of tumors and tumor cells (Pino et al, 2003; Sun et al, 2007; Park et al, 2012; Mulnix et al, 2014). The present study was focused on the potential function of HNRNPC in breast cancer cells. The RNA-seq data of breast cancer in The Cancer Genome Atlas (TCGA) also showed a significant increase in HNRNPC expression in 111 breast cancer tumors compared to their adjacent normal tissues (Appendix Fig S1). Gene expression knock-down of HNRNPC with siRNAs (Appendix Fig S2A–D) greatly reduced the proliferation rates of two breast cancer cell lines, MCF7 and T47D (Fig 1A and B, and Appendix Fig S3A and B). This was confirmed by CRISPR/Cas9-mediated partial knock-down of HNRNPC (Appendix Fig S4A and B), which suppressed cell proliferation as well (P < 0.05 for MCF7 and 0.01 for T47D, Fig 1C and D). The anchorage-independent growth assay further showed that shRNA-mediated silencing of HNRNPC (Appendix Fig S4C and D) strongly repressed colony formation of MCF7 and T47D (Fig 1E and F), indicating the reduced malignant transformation potential. However, such proliferation-inhibitory effect of HNRNPC repression is absent in the primary breast epithelial cell MCF10A or in the triple-negative breast cancer cells BT549 and MDA-MB-231 (Appendix Fig S5A–F). Figure 1. Knock-down of HNRNPC inhibited growth and tumorigenesis of breast cancer cells Growth curves of the MCF7 (left) and T47D cells (right) upon gene silencing with siRNAs. siNC: non-targeting siRNA as a negative control, siHN-1: siRNA sequence 1 for HNRNPC, siHN-2: siRNA sequence 2 for HNRNPC, siLMNA: siRNA for LMNA as another negative control. Each sample has three replicates. Data represent mean ± SD. Relative protein levels of HNRNPC upon siRNA-mediated silencing, quantified from Western blots of three replicates. The error bars represent ± SD. Growth curves of the Tet-on CRISPR-Cas9-MCF7 (left) and Tet-on CRISPR-Cas9-T47D cells (right). Expression of Cas9 was induced with 5 μM doxycycline after transfection of the sgRNAs. sgNC: non-targeting sgRNA as a negative control, sgHN-1: sgRNA sequence 1 for HNRNPC, sgHN-2: sgRNA sequence 2 for HNRNPC, sgLMNA: sgRNA for LMNA as another negative control. Each sample has three replicates. Data represent mean ± SD. Relative protein levels of HNRNPC upon CRISPR-mediated gene knock-down, quantified from Western blots of three replicates. The error bars represent ± SD. Anchorage-independent cell growth assays of the HNRNPC-deficient MCF7 (right) and T47D (left) cells. Relative protein levels of HNRNPC upon shRNA-mediated silencing, quantified from Western blots of three replicates. The error bars represent ± SD. Images, weights, and growth records of the xenograft tumor models in female NSG mice established from the MCF7 cells with lentivirus-mediated stable gene knock-down. Each group has six mice. The error bars represent ± SEM. Images, weights, and growth records of the MCF7-derived xenograft tumors, which were subjected to periodic siRNA injection, starting from 2 weeks after transplantation of the cells. Each group has four mice. The error bars represent ± SEM. Download figure Download PowerPoint Consistently, xenograft transplantation mouse models demonstrated that shRNA-mediated long-term knock-down of HNRNPC (Appendix Fig S4E) greatly repressed the in vivo tumorigenesis of MCF7 (Fig 1G). Furthermore, periodic (half-weekly) injection of the HNRNPC siRNA packed with a polymer-based delivery reagent, into the MCF7 cell-derived xenograft tumors, also repressed tumor growth in vivo (P < 0.01, Fig 1H and Appendix Fig S4F). Taken together, these results suggest that HNRNPC is indispensable for the proliferation and tumorigenesis of the cells MCF7 and T47D. Repression of HNRNPC activated the type I interferon response To further elucidate the molecular mechanism for the potent tumor-inhibitory function of HNRNPC repression in the breast cancer cells, we profiled the genome-wide gene expression levels with RNA-seq upon HNRNPC knock-down in MCF7 and T47D cells. Silencing of HNRNPC with siRNAs in MCF7 and T47D cells resulted in up-regulation of a number of genes (Fig 2A and B), which are highly and exclusively enriched by the genes involved in the interferon response-related processes, according to the gene ontology and KEGG pathway enrichment analyses (Fig 2C and D). Indeed, many of the up-regulated genes belong to the category of ISGs (Fig 2A and B). These results from RNA-seq were validated with qPCR experiments, which confirmed the elevated mRNA expressions of IFNβ (IFNB1) and multiple ISGs after HNRNPC knock-down (Fig 2E). Finally, this was further backed by the inducible CRISPR/Cas9-mediated silencing of HNRNPC, which also led to up-regulation of the ISGs (Fig 2F). Figure 2. Repression of HNRNPC induced the interferon response and expression of ISGs in breast cancer cells A, B. Volcano plots showing the differential expression of the genes after HNRNPC knock-down in the MCF7 (A) and T47D (B) cells. The ISGs with significant differential expression were marked as red dots. The cutoffs (dashed line) were set at the log2 fold change > 1.5 or < −1.5 and the P < 0.001. C, D. Enrichment of the GO and KEGG functional annotations in the up-regulated gene sets upon HNRNPC knock-down in MCF7 (C) and T47D (D) cells. The P-values (−log10) of such enrichments were provided on the y-axis. E, F. qPCR measurements of the expressions of ISGs upon knock-down of HNRNPC in MCF7 and T47D cells using siRNA (E) or CRISPR-Cas9 (F). Each sample has three replicates. Data represent mean ± SD. G. 11 TFs involved in the interferon response and signaling, which were identified by MARINa as master TFs upon HNRNPC knock-down. In each row, all genes were sorted (from left to right) by their differential expressions in siHNRNPC vs. control cells. The predicted target genes that are positive or negative regulated by the TF are marked as red or blue bars. All the TFs were sorted by the P-values (FDR-corrected) from the MARINa analysis. Ranks of these 11 TFs among all the master TFs identified by MARINa were provided to the right. Download figure Download PowerPoint Next, we examined the differential activities of the transcription factors (TFs) that may have been driving the global gene expression profile shift in response to HNRNPC repression. Here, we used the MARINa (MAster Regulator INference algorithm), which were designed to identify the master TFs across two conditions by assessing the differential expression of the TF target gene sets (Lim et al, 2009). The MARINa analysis identified almost all the core TFs in the interferon response cascade (Fig 2G), including IRF3/7, the transcriptional activator of the IFNβ gene, and two components of the ISGF3 complex (STAT1 and IRF9), which is the down-stream TF effector of the interferon signaling pathway and responsible for the transcriptional up-regulation of the ISGs. Taken together, these results indicate that suppression of HNRNPC in MCF7 and T47D resulted in both the first wave of interferon response, i.e., production of IFNβ potentially by transcription factors IRF3/7, and the second wave of interferon signaling pathway that activates the ISGF3 complex, leading to up-regulated expression of the ISGs. Indeed, high levels of IFNβ in the cell media were observed after HNRNPC knock-down (Fig 3A), and treatment of MCF7 and T47D cells with IFNβ induced up-regulation of the ISGs and inhibition of cell proliferation in a dose-dependent manner (Fig EV1A and B). Figure 3. HNRNPC repression up-regulated the ISGs and suppressed MCF7 and T47D proliferation by activating the IFNβ production Concentrations of IFNβ, measured by ELISA, in the culturing media of MCF7 and T47D cells 48 hours after siRNA transfections. siNC: non-targeting siRNA as a negative control, siHN-1: siRNA sequence 1 for HNRNPC, siHN-2: siRNA sequence 2 for HNRNPC, siLMNA: siRNA for LMNA as another negative control. Each sample has three replicates. Data represent mean ± SD. The normal MCF7 (left) and T47D (right) cells were cultured in the media collected from the corresponding cells 48 h after siRNA-mediated gene knock-down. Expressions of the ISGs in these normal cells were measured with qPCR. Each sample has three replicates. Data represent mean ± SD. Different doses of the IFNβ antibody were added to the media of the siRNA-transfected MCF7 (left) or T47D (right) cells right before the media were transferred to the wild-type cells. The wild-type MCF7 or T47D cells were then cultured in these media for 48 hours, and the expressions of ISGs were measured by qPCR. Each sample has three replicates. Data represent mean ± SD. Growth curves of the MCF7 (left) and T47D (right) cells cultured in the media collected from the siRNA-transfected cells. Each sample has three replicates. Data represent mean ± SD. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. IFNβ treatment up-regulated the expressions of ISGs and inhibited proliferation of breast cancer cells The expression levels of ISGs were measured in MCF7 (left) and T47D (right) cells treated with different concentrations of IFNβ. Data represent mean ± SD. Growth curves of MCF7 (left) and T47D (right) cells treated with different concentrations of IFNβ. Data represent mean ± SD. Download figure Download PowerPoint Interestingly, such a strong interferon response was absent in MCF10A, BT549, and MDA-MB-231 (Appendix Fig S6A–C), of which the proliferation rates were unaltered upon HNRNPC knock-down (Appendix Fig S5). This leads to the hypothesis that the production and secretion of IFNβ are responsible for the up-regulated ISGs in MCF7 and T47D, and the cascade of interferon production and signaling is responsible for the tumor-inhibitory effect of HNRNPC repression. IFNβ and the IFN signaling pathway mediated the arrestment of proliferation in response to HNRNPC knock-down Considering the high level of IFNβ in the media after HNRNPC knock-down in MCF7 and T47D (Fig 3A), we transferred the media, 48 hours after the siRNA transfection of HNRNPC, to the wild-type cells without HNRNPC repression. These cells also gained elevated expression levels of the ISGs (Fig 3B), whereas neutralization of IFNβ by adding its antibody into the media resulted in significantly reduced responses of the ISGs in these cells (Fig 3C). The cell proliferation was also repressed in such media (P < 0.01, Fig 3D), which is similar to the effect of HNRNPC knock-down itself, even though the HNRNPC expression level remained unchanged (Fig 3B). Therefore, it is highly suspected that the cell proliferation-repressive effect of HNRNPC knock-down was indeed mediated by the secreted IFNβ and the subsequent activation of the type I interferon signaling pathway. The well-studied type I interferon signaling pathway starts with binding of the extracellular IFNβ with the cell surface receptors IFNAR1/2, in which IFNAR2 is essential (Schreiber & Piehler, 2015; Lopez de Padilla & Niewold, 2016). In response to this binding signal, the JAK-STAT pathway is activated, leading to transcriptional up-regulation of the ISGs (Ivashkiv & Donlin, 2014; Schneider et al, 2014). Therefore, to show that the up-regulated ISGs upon HNRNPC knock-down depend on the interferon signaling cascade, we used an IFNβ antibody, an IFNAR2 blocking antibody, and the JAK-STAT inhibitor ruxolitinib to block the interferon signaling pathway at different levels. Indeed, disruptions of the interferon signaling pathway in the HNRNPC-repressed cells, by neutralization of the extracellular IFNβ, blockage of the membrane receptor IFNAR2, or inhibition of the JAK-STAT cascade, all attenuated up-regulations of the ISGs, including IFNB1, IFI27, IFI44, and IFI44L, in a dose-dependent manner (Fig 4A–C). In addition, more importantly, neutralization of IFNβ and inhibition of JAK-STAT both rescued the cell growth repression resulted from HNRNPC knock-down, in an antibody or inhibitor dose-dependent manner (Fig 4D–F). In fact, the high dosages almost completely offset the growth arrestment effect of HNRNPC repression. Figure 4. Up-regulated ISG expression and suppressed MCF7 and T47D proliferation upon HNRNPC repression mediated via the interferon beta signaling pathway A–C. In the siRNA-transfected MCF7 (left) or T47D cells (right), the interferon signaling pathway was blocked at different stages by means of IFNβ neutralization (A), IFNAR2 neutralization (B), or JAK-STAT inhibition (C). The expressions of the ISGs were measured by qPCR. Each sample has three replicates. Data represent mean ± SD. D, E. Growth curves of the MCF7 (D) or T47D cells (E) upon HNRNPC knock-down but with the interferon response blocked with the IFNβ antibody. Each sample has three replicates. Data represent mean ± SD. F. Growth curves of the MCF7 (left) or T47D (right) cells upon HNRNPC knock-down and JAK-STAT inhibition with ruxolitinib (5 μM). siNC: non-targeting siRNA as a negative control, siLMNA: siRNA for LMNA as another negative control, siHN-1: siRNA sequence 1 for HNRNPC, siHN-2: siRNA sequence 2 for HNRNPC. Each sample has three replicates. Data represent mean ± SD. Download figure Download PowerPoint In summary, the results above have demonstrated that the anti-proliferation effects of HNRNPC repression in MCF7 and T47D cells can indeed be attributed to the production of IFNβ and the resulted activation of the type I interferon signaling pathway. Next, we sought to elucidate how repression of the RBP HNRNPC could lead to activation of such potent interferon responses. The dsRNA sensor RIG-I mediated the interferon response and tumorigenesis arrestment upon repression of HNRNPC Under various stress conditions, abnormal accumulations of endogenous double- or single-stranded DNA or RNA may be sensed by cytoplasmic PRRS and trigger the interferon response (West et al, 2015; Roers et al, 2016), with effects on cell proliferation and the innate and adaptive immune systems (Chiappinelli et al, 2015; Roulois et al, 2015; Goel et al, 2017). Given that HNRNPC has been well known for its involvement in multiple RNA-related processes such as pre-mRNA splicing (Anantha et al, 2013), mRNA stabilization (Shetty, 2005), and RNA exportation (McCloskey et al, 2012), we looked into the RNA sensors for their potential involvements in mediating the interferon response upon repression of the normal HNRNPC function in MCF7 and T47D. RIG-I (gene name DDX58), melanoma differentiation-associated protein 5 (MDA5, gene name IFIH1), and Toll-like receptor 3 (TLR3) are three major dsRNA sensors in non-immune cells (Kawai & Akira, 2008). TLR3 is located on the cell and endosome membrane, and its mRNA expression level was barely detectable in MCF7 and T47D cells with either qPCR or RNA-seq. To test the involvements of the other two RNA sensors in the interferon responses upon HNRNPC repre" @default.
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• W2889210641 title "Function of HNRNPC in breast cancer cells by controlling the dsRNA‐induced interferon response" @default.
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