1. Cancer Biology
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NLRP12 suppresses hepatocellular carcinoma via downregulation of cJun N-terminal kinase activation in the hepatocyte

  1. SM Nashir Udden
  2. Youn-Tae Kwak
  3. Victoria Godfrey
  4. Md Abdul Wadud Khan
  5. Shahanshah Khan
  6. Nicolas Loof
  7. Lan Peng
  8. Hao Zhu
  9. Hasan Zaki  Is a corresponding author
  1. UT Southwestern Medical Center, United States
  2. MD Anderson Cancer Center, United States
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Cite this article as: eLife 2019;8:e40396 doi: 10.7554/eLife.40396

Abstract

Hepatocellular carcinoma (HCC) is a deadly human cancer associated with chronic inflammation. The cytosolic pathogen sensor NLRP12 has emerged as a negative regulator of inflammation, but its role in HCC is unknown. Here we investigated the role of NLRP12 in HCC using mouse models of HCC induced by carcinogen diethylnitrosamine (DEN). Nlrp12-/- mice were highly susceptible to DEN-induced HCC with increased inflammation, hepatocyte proliferation, and tumor burden. Consistently, Nlrp12-/- tumors showed higher expression of proto-oncogenes cJun and cMyc and downregulation of tumor suppressor p21. Interestingly, antibiotics treatment dramatically diminished tumorigenesis in Nlrp12-/- mouse livers. Signaling analyses demonstrated higher JNK activation in Nlrp12-/- HCC and cultured hepatocytes during stimulation with microbial pattern molecules. JNK inhibition or NLRP12 overexpression reduced proliferative and inflammatory responses of Nlrp12-/- hepatocytes. In summary, NLRP12 negatively regulates HCC pathogenesis via downregulation of JNK-dependent inflammation and proliferation of hepatocytes.

https://doi.org/10.7554/eLife.40396.001

Introduction

Hepatocellular carcinoma (HCC) is the 5th most common malignancy and the 3rd most common cause of cancer-related death worldwide (Farazi and DePinho, 2006). Major risk factors for HCC include hepatitis B and C virus (HBV and HCV) infections, obesity, alcohol abuse, and drug toxicity (El-Serag and Rudolph, 2007; Farazi and DePinho, 2006). The common physiological process prior to HCC development is inflammation, which triggers DNA damage and mutagenesis, hepatic cell death, and compensatory proliferation (Maeda et al., 2005; Sakurai et al., 2008). Consistently, inflammatory signaling pathways including NF-κB, MAPK, STAT3 and AKT are hyperactivated in HCC and considered critical players in HCC pathogenesis (Calvisi et al., 2006; He and Karin, 2011; Hui et al., 2008; Maeda et al., 2005; Pikarsky et al., 2004; Wang et al., 2016).

A major class of stimuli for these pathways are pathogen-associated molecular patterns (PAMPs) which are sensed by pattern recognition receptors (PRRs). The best-characterized PRRs are toll-like receptors (TLRs) that sense a wide array of PAMPs on the cell surface or endosomal compartment (Akira et al., 2006). Because of its close anatomical connection with the intestine, the liver is constantly exposed to gut microbiota-derived PAMPs (Son et al., 2010). The translocation of microbes and their PAMPs is enhanced during chronic liver disorders (Campillo et al., 1999; Cirera et al., 2001; Fukui et al., 1991; Pascual et al., 2003; Rutenburg et al., 1957; Yoneyama et al., 2002) and likely an important contributor of HCC development. Indeed, sensing lipopolysaccharide (LPS) by liver parenchymal cell-specific TLR4 contributes to liver fibrosis and HCC (Dapito et al., 2012; Machida et al., 2009; Paik et al., 2003; Seki et al., 2007). However, the regulatory mechanisms in the gut-liver inflammatory and carcinogenic axis are poorly explored but critical to understanding and potentially treating HCC.

In addition to TLRs, several cytosolic PRRs sense and respond to PAMPs or danger-associated molecular patterns (DAMPs) and activate downstream cell signaling pathways. The NOD-like receptors (NLRs) are a family of cytosolic PRRs which are associated with diverse diseases related to infections, inflammation and cancer (Saxena and Yeretssian, 2014; Zhong et al., 2013). The NLR family member NLRP12 has recently emerged as a critical regulator of inflammation and cancer. Previous studies showed that deficiency of NLRP12 leads to enhanced incidence and faster progression of colorectal tumorigenesis (Allen et al., 2012; Chen et al., 2017; Zaki et al., 2011). NLRP12 negatively regulates NF-κB and ERK in macrophages, dendritic cells, and T cells (Lukens et al., 2015; Zaki et al., 2014; Zaki et al., 2011), and increased colorectal tumorigenesis in Nlrp12-/- mice is associated with higher activation of the NF-κB and ERK signaling pathways (Allen et al., 2012; Zaki et al., 2011). In the liver, NLRP12 is highly expressed and dampens inflammatory responses secondary to Salmonella Typhimurium infection (Zaki et al., 2014). These observations suggest that NLRP12 may regulate inflammatory disorders of the liver such as HCC.

Here, we investigated the role of NLRP12 in HCC using mouse models. The expression of NLRP12 was seen negatively correlated with human and mouse HCC. Nlrp12-/- mice developed significantly higher tumor burden in the liver following administration of mutagens. HCC susceptibility in Nlrp12-/- mice was eliminated with antibiotics treatment. Our in vivo and in vitro data demonstrated that NLRP12 suppresses PAMP-mediated proliferation and inflammatory gene expression in hepatocytes via attenuation of JNK signaling. This study underscores a novel cancer suppressive pathway in the liver involving NLRP12.

Results

The loss of NLRP12 is associated with increased HCC susceptibility

To understand an association of NLRP12 with human HCC, we analyzed publicly available cancer genomics databases. According to The Cancer Genome Atlas (TCGA) database, about 2% of HCC patients carry mutations in NLRP12 (Figure 1A). Analysis of RNA-seq data in the TCGA database using the UALCAN web-portal (Chandrashekar et al., 2017) revealed that the expression of NLRP12 is significantly (p=0.0004) reduced in human HCC (Figure 1B). To mechanistically characterize the role of NLRP12 in HCC, we used a mouse model in which HCC was induced with the administration of a single dose of diethylnitrosamine (DEN) (Figure 1—figure supplement 1A). DEN is a procarcinogen that induces DNA damage and cell death in the liver, leading to the development of HCC (Bakiri and Wagner, 2013; Rajewsky et al., 1966). 10 months post a single DEN injection into WT and Nlrp12-/- mice, we collected whole livers and measured the number and size of tumors. Consistent to reduced NLRP12 in human HCC, the expression of Nlrp12 was significantly reduced in DEN-induced HCC compared to healthy livers of WT mice (Figure 1C). As we counted the number of visible tumors, we observed significantly higher number of tumors in Nlrp12-/- mouse livers compared to that of WT mice (Figure 1D and E). Tumor sizes and tumor/body weight ratios of Nlrp12-/- mice were significantly larger compared to those of WT mice (Figure 1E). The areas of adenoma in Nlrp12-/- livers were significantly larger than that of WT (Figure 1F and G). HCC is associated with liver damage leading to the elevation of serum levels of ALT and AST. As expected, ALT and AST levels were significantly higher in Nlrp12-/- mice at 10 months after DEN administration (Figure 1H). We confirmed the role of NLRP12 in HCC development in a second model involving carbon tetrachloride (CCl4) along with DEN (Figure 1—figure supplement 1B). CCl4 is a toxic chemical which causes hepatic necrosis, compensatory hepatocyte proliferation, and ultimately drives fibrosis (Sarma et al., 1986). Similar to that seen in the DEN model, DEN plus CCl4-treated Nlrp12-/- mice developed a greater tumor burden with significantly larger tumors than WT mice (Figure 1I and J). Notably, control (DEN-untreated) Nlrp12-/- mice did not develop any tumors and did not exhibit elevated ALT and AST levels (Figure 1—figure supplement 1C–E). These results suggest that NLRP12 plays a protective role against carcinogen-induced HCC in mice.

Figure 1 with 1 supplement see all
NLRP12 negatively regulates hepatocellular carcinoma.

(A) Analysis of genetic association of NLRP12 in HCC using the TCGA database through the cBioportal online platform. (B) Analysis of NLRP12 expression in human normal liver and HCC RNA-seq data in the TCGA database through the UALCAN web-portal. Data are presented in a box plot where whiskers represent maximum and minimum variables. (C) WT mice were injected with DEN (25 mg/kg i.p.) at the age of 14 days or left untreated. Livers from DEN-treated (n = 10) and untreated mice (n = 6) were analyzed for the expression of Nlrp12 by real-time qPCR. Data represent means ± SEM. (D–H) WT mice (n = 20) and Nlrp12-/- (n = 20) were injected with DEN (25 mg/kg i.p.) at the age of 14 days and sacrificed at 10 months after DEN injection. (D) Representative images of liver tumors are shown. (E) The number of tumors per liver, tumor sizes, and liver to body weight ratios were measured. Data represent means ± SEM (n = 20). Statistical difference was determined by two-tailed unpaired t-test. (F) Livers from mice described in D were stained for H and E. Representative H and E-stained sections are shown. (G) H and E-stained liver sections were histopathologically examined for adenoma development. Data represent means ± SEM (n = 15). Statistical difference was determined by two-tailed unpaired t-test. (H) Serum ALT and AST levels in tumor-bearing mice. Data represent means ± SEM (n = 15). Statistical difference was determined by two-tailed unpaired t-test. (I–J) WT (n = 6) and Nlrp12-/- (n = 6) mice were injected with DEN (25 mg/kg i.p.) at the age of 14 days followed by eight weekly injections of CCl4 (0.5 ml/kg i.p., dissolved in corn oil) starting at 10 weeks of age and euthanized at the age of 6 months. (I) Representative images of liver tumors are shown. (J) The numbers, sizes, and liver to body weight ratio were measured. Data represent means ± SEM. Statistical difference was determined by two-tailed unpaired t-test.

https://doi.org/10.7554/eLife.40396.002

Increased tumorigenesis in Nlrp12-/- mice is associated with higher inflammation

Development of HCC is a multistep process involving hepatic steatosis, fibrosis, and cirrhosis (Capece et al., 2013; El-Serag and Rudolph, 2007). To characterize the role of NLRP12 in HCC histopathologically, we examined the H and E-stained liver sections of WT and Nlrp12-/- mice collected at 10 months after DEN injection. There were significantly higher inflammatory infiltrates, steatosis, and fibrosis in Nlrp12-/- livers relative to WT (Figure 2A and B). Similarly, DEN plus CCl4-treated Nlrp12-/- livers exhibited worsened histopathology (Figure 2—figure supplement 1A–C). Proinflammatory mediators such as IL-1α, IL-6, TNFα, and Cox2 have been implicated in HCC (Bromberg and Wang, 2009; He et al., 2013; Luedde and Schwabe, 2011; Park et al., 2010; Sakurai et al., 2008). To understand the nature of inflammatory responses in the context of NLRP12-deficiency, we measured different cytokines, chemokines, and inflammatory mediators in the tumor-bearing livers from DEN or DEN plus CCl4-treated mice. Consistent with pathological features, there was significantly higher expression of cytokines IL-6 (Il6) and TNFα (Tnfa), chemokines KC (Cxcl1), MIP2 (Cxcl2), and MCP1 (Ccl2), and tumor-promoting molecule COX2 (Cox2) in Nlrp12-/- HCC (Figure 2C and Figure 2—figure supplement 1D). Higher protein levels of IL-6, TNFα, and KC in Nlrp12-/- HCC tissue were confirmed by ELISA (Figure 2—figure supplement 1E). However, no differences were observed in the levels of T cell-dependent cytokines IL-4, IFNγ, and IL-17 (Figure 2—figure supplement 1F).

Figure 2 with 1 supplement see all
NLRP12-deficiency leads to increased inflammation in the liver.

WT and Nlrp12-/- mice were injected with DEN (25 mg/kg i.p.) at the age of 14 days and euthanized 10 months later. (A–B) H and E-stained liver sections were examined histopathologically and scored for inflammation, steatosis, and fibrosis. Representative images of inflammation, steatosis, and fibrosis are shown. Data represent means ± SEM (n = 15). Statistical difference was determined by two-tailed unpaired t-test. (C) Real-time qPCR analysis of cytokines and chemokines in the HCC tissues. Data represent means ± SEM (n = 15). Statistical difference was determined by two-tailed unpaired t-test. (D) Tumor tissues were analyzed for the quantification of T cells (CD45+, TCRb+, CD11b-), dendritic cells (CD45+, CD11b+, CD11c+high, Gr1-), Kupffer cells (CD45+, CD11b+, F4/80+, Gr1-), and neutrophils (CD45+, CD11b+, Gr1+, CD11c-) by flow cytometry. (E) Relative abundance of different immune cell types was analyzed by FlowJo software. Data represent means ± SEM (n = 5). Statistical difference was determined by two-tailed unpaired t-test. (F) Formalin-fixed and paraffin-embedded HCC sections were stained with F4/80 antibody and the number of F4/80+ cells (brown) per high power field (20X) was counted. The picture shows representative immunostaining of F4/80 (brown). Data represent means ± SEM (n = 25). Statistical difference was determined by two-tailed unpaired t-test. (G) The expression of Emr1 (F4/80) in the HCC tissues was measured by real-time PCR. Data represent means ± SEM (n = 13–14). Statistical difference was determined by two-tailed unpaired t-test.

https://doi.org/10.7554/eLife.40396.006

We next characterized different immune cell populations infiltrated into tumor tissues by flow cytometry. No differences were found in the number of neutrophils (Gr1-positive cells) and T-cells (TCRβ-positive) (Figure 2D and E). However, there were more F4/80-positive macrophages (Kupffer cells) and CD11c-positive dendritic cells in Nlrp12-/- HCCs (Figure 2D and E). Immunostaining and real-time qPCR analysis for F4/80 confirmed increased number of Kupffer cells in Nlrp12-/- HCC as compared to WT (Figure 2F and G). F4/80 (Emr1) mRNA levels were unchanged between healthy WT and Nlrp12-/- livers (Figure 2—figure supplement 1G), suggesting that increased expression of macrophage chemoattractant proteins MIP2 and MCP1 in Nlrp12-/- HCC tissue resulted in increased infiltration of macrophages/Kupffer cells. These data indicate that NLRP12-deficiency promotes inflammatory responses during DEN-induced liver injury and tumorigenesis.

NLRP12 regulates proliferation and cell death in the liver

DEN-induced liver injury is thought to promote hepatocarcinogenesis by inducing compensatory proliferation after widespread apoptosis (Maeda et al., 2005; Sakurai et al., 2008). To understand whether sensing PAMPs by NLRP12 regulates proliferative responses during HCC, we immunostained healthy and HCC livers for Ki67. The number of Ki67-positive cells were significantly higher in livers containing HCC compared to healthy livers of either WT or Nlrp12-/- mice (Figure 3A). However, NLRP12-deficiency significantly increased hepatocyte proliferation in tumor tissues (Figure 3A). Higher expression of Ki67 in Nlrp12-/- tumors was corroborated by real-time qPCR (Figure 3B). We further assessed the role of NLRP12 in proliferation by BrdU incorporation assay. Similar to Ki67 staining, there was significantly higher BrdU-positive cells in Nlrp12-/- HCC tissues (Figure 3—figure supplement 1A).

Figure 3 with 1 supplement see all
Increased HCC in Nlrp12-/- mice is associated with increased cell death and proliferation in the livers.

WT and Nlrp12-/- were injected with DEN (25 mg/kg i.p.) or PBS (healthy control) at the age of 14 days and sacrificed at 10 months after DEN administration. (A) Liver tissue sections from healthy controls and DEN-treated mice were immunostained with Ki67 antibody and the number of Ki67-positive cells was counted under 20X objective. Data were collected from at least 10 fields per liver section and three mice/group. Data represent means ± SEM (n = 50). Statistical difference was determined by two-tailed unpaired t-test. (B) The expression of Ki67 in tumor tissues was measured by real-time qPCR. Data represent means ± SEM (n = 15; each sample represents individual mouse). Statistical difference was determined by two-tailed unpaired t-test. (C) Apoptosis in the healthy and HCC livers were measured by TUNEL assay. The number of TUNEL-positive cells (green) under 20X objective was counted and plotted as individual values. Data were collected from at least 10 fields per liver section and three mice/group. Data represent means ± SEM. Statistical difference was determined by two-tailed unpaired t-test. (D) Liver sections from DEN-treated mice (n = 3) were immunostained for cleaved caspase-3 (brown). Cleaved caspase-3 positive cells were counted under 20X objective. Data represent means ± SEM (n = 40). Statistical difference was determined by two-tailed unpaired t-test. (E) Liver tumor lysates were immunoblotted with anti-cleaved caspase-3, cytochrome c, and β-actin. The band intensities of caspase-3 and cytochrome c were measured. Data represent means ± SEM (n = 5; each sample represents individual mouse). Statistical difference was determined by two-tailed unpaired t-test.

https://doi.org/10.7554/eLife.40396.010

To determine whether higher proliferation in Nlrp12-/- HCC is linked to cell death, we measured apoptosis by TUNEL assay in HCC livers collected at 10 months after DEN treatment. The number of TUNEL-positive cells in Nlrp12-/- HCC livers was significantly higher than WT, although no such difference was observed in healthy untreated WT and Nlrp12-/- mouse livers (Figure 3C). Increased cell death in Nlrp12-/- HCC was further demonstrated by immunohistochemical detection of cleaved capsase-3; Nlrp12-/- HCC livers had a significantly higher number of caspase-3-positive cells compared to livers from DEN-treated WT mice (Figure 3D). Furthermore, Western blot analysis showed higher caspase-3 cleavage and cytochrome C release in Nlrp12-/- HCC (Figure 3E).

Inflammatory mediators are known to contribute to hepatocyte cell death (Kamata et al., 2005; Sakurai et al., 2008). We wondered whether inflammation could promote apoptosis in Nlrp12-/- hepatocytes in a cell extrinsic fashion. Hence, we investigated the role of NLRP12 in hepatocyte cell death by culturing primary hepatocytes from WT and Nlrp12-/- mice and treating them with LPS. While unstimulated WT and Nlrp12-/- hepatocytes showed a similar level of TUNEL-positive cells, there was significantly higher apoptosis in LPS-treated Nlrp12-/- hepatocytes, suggesting that higher inflammatory responses in Nlrp12-/- hepatocytes during LPS stimulation contributed to increased cell death (Figure 3—figure supplement 1B and C). Altogether, these data imply that NLRP12 does not directly interfere with apoptosis, but indirectly modulates hepatic cell death during DEN-induced HCC via regulation of inflammatory responses.

NLRP12 dampens oncogenic signals in the liver

Next, we focused on elucidating the molecular mechanisms of increased proliferation in Nlrp12-/- HCC. Alpha-fetoprotein (AFP), a marker of HCC, was significantly higher in Nlrp12-/- tumor tissue (Figure 4A). We measured the expression of proliferation-associated genes in WT and Nlrp12-/- HCC by real-time qPCR. There was increased expression of pro-proliferative and tumor promoting genes such as Ccnb1, Ccnd1, Survivin, and Myc (Figure 4A), while the expression of Cdkn1a, an inhibitor of cell cycle progression, was significantly reduced in Nlrp12-/- HCC (Figure 4A). The protein level of Ccnd1 (Cyclin d1) was also significantly higher in Nlrp12-/- tumors (Figure 4B and C). The expression of Cyclin d1 is regulated by several transcription factors including cMyc and cJun, which are major HCC relevant oncogenes (Dang, 1999; Eferl et al., 2003; Lin et al., 2010; Schwabe et al., 2003). Consistently, there was higher protein level of cMyc and activated cJun (P-cJun) in Nlrp12-/- HCC (Figure 4B and C).

Figure 4 with 1 supplement see all
NLRP12-deficiency leads to increased expression of proliferative genes and activation of the JNK pathway.

WT (n = 15) and Nlrp12-/- (n = 15) mice were injected with DEN (25 mg/kg i.p.) at the age of 14 days and euthanized at 10 months later. (A) Liver tumor tissues were analyzed for the expression of the indicated genes by real-time qPCR. Data represent means ± SEM (n = 15; each sample represents individual mouse). Statistical difference was determined by two-tailed unpaired t-test. (B) Liver tumor lysates were immunoblotted for Cyclin d1, cMyc, and P-cJun. β-actin was used as a loading control. (C) Band intensities of Cyclind1, cMyc, and P-cJun were measured. Data represent means ± SEM (n = 5, each sample represents individual mouse). Statistical difference was determined by two-tailed unpaired t-test. (D) Liver tumor lysates were analyzed for the activation of JNK, ERK, p38, p65, and STAT3 by Western blotting. β-actin was used as a loading control. Each lane represents individual mouse. (E) Band intensities of P-JNK, P-ERK, P-p65, and P-STAT3 shown in D were measured. Data represent means ± SEM (n = 5). Statistical difference was determined by two-tailed unpaired t-test. (F) The levels in p65, P-p65, P-ERK, P-JNK, P-p38, and P-STAT3 in tumor lysates (0.5 mg/ml) from different mice were measured by ELISA. Data represent means ± SEM (n = 6). Statistical difference was determined by two-tailed unpaired t-test. (G) Hepatocytes were isolated from liver tumors and analyzed for the activation of JNK, ERK, p38, p65, and STAT3 by Western blotting. Each lane represents individual mouse sample. (H) Densitometric analysis of P-JNK, and P-p65 immunoreactive bands are shown. Data represent means ± SEM (n = 6). Statistical difference was determined by two-tailed unpaired t-test. (I) RNA isolated from the tumor hepatocytes was analyzed for the expression of chemokines and proliferative genes. Data represent means ± SEM (n = 6, each sample represents individual mouse). Statistical difference was determined by two-tailed unpaired t-test. (J) Hepatocytes, Kupffer cells, and hepatic stellate cells were isolated from liver tumors and stimulated with LPS (1 ug/ml). Activation of JNK was measured by Western blotting.

https://doi.org/10.7554/eLife.40396.014

Multiple pathways including NF-κB, ERK, JNK, p38, and STAT3 regulate the expression of proliferative genes and transcription factors involved in cancer (Grivennikov et al., 2010; He and Karin, 2011). To explore the pathways through which NLRP12 regulates oncogene expression, we measured the activation of NF-κB, ERK, p38, JNK, and STAT3, in HCC tissues from WT and Nlrp12-/- mice by Western blotting and ELISA. While all these pathways were activated in both WT and Nlrp12-/- HCC, the JNK pathway was consistently highly activated in Nlrp12-/- tumors as compared to those of WT (Figure 4D–F). JNK plays critical roles in hepatocyte physiology by regulating cell death and proliferation, and deletion of JNK1 was shown to suppress HCC (Hui et al., 2008; Sakurai et al., 2006; Schwabe, 2006). Thus, the JNK pathway may be involved in NLRP12-mediated regulation of oncogene expression.

We hypothesized that JNK activation in Nlrp12-/- HCC occurs in parenchymal tumor cells. To test this hypothesis, we isolated parenchymal cells from tumor tissue and measured the activation of JNK and other signaling pathways by Western blotting. Nlrp12-/- tumor cells exhibited significantly increased phosphorylation of JNK, while no remarkable difference in the activation of ERK, p38, NF-κB pathways was observed (Figure 4G and H). Along with higher JNK activation, there was significantly increased expression of inflammatory cytokines Cxcl1 and Ccl2, and pro-proliferative molecules including Survivin, Myc, Ccnd1, and MKi67 and reduced expression of Cdkn1a (Figure 4I). These levels coincide with the expression profiles of these molecules in the liver tumors (Figures 2C and 4A).

To examine the role of NLRP12 in the regulation of JNK in non-parenchymal cells, such as Kupffer cells and hepatic stellate cells, in the tumor microenvironment, we isolated hepatocytes, Kupffer cells, and hepatic stellate cells from WT and Nlrp12-/- HCC tissues. The purity of hepatocytes, Kupffer cells and hepatic stellate cells was confirmed by real-time qPCR of Alb, Emr1, and Pdgfr-b respectively (Figure 4—figure supplement 1A). NLRP12 was seen expressed in all these cell types (Figure 4—figure supplement 1B). Cells were then stimulated with LPS and activation of JNK was measured by Western blotting. Interestingly, higher JNK activation was seen only in hepatocytes but not in Kupffer cells and hepatic stellate cells (Figure 4J). LPS stimulation of tumor hepatocytes further enhanced the expression of inflammatory cytokines and chemokines Ccl2, Cxcl1, Cxcl2, Tnfa, and Il6 (Figure 4—figure supplement 1C). Taken together, these findings suggest that higher activation of JNK in Nlrp12-/- HCC contributes to increased expression of cytokines and chemokines, which help recruitment of macrophages and dendritic cells in the tumor microenvironment (Figure 2E), and tumor proliferation (Figure 3A).

NLRP12 attenuates PAMPs-mediated hepatic inflammation and oncogenesis

HCC develops in a microenvironment of chronic liver injury, inflammation and fibrosis. However, existing evidence points to the critical contribution of PAMPs derived from gut microbiota in promoting HCC pathogenesis (Tandon and Garcia-Tsao, 2008). Sensing gut-derived LPS by TLR4 in the liver promotes inflammation, fibrosis and carcinogenesis (Dapito et al., 2012; Fukui et al., 1991; Paik et al., 2003; Rutenburg et al., 1957; Seki et al., 2007). Our data also suggest that LPS triggers inflammatory responses in tumor parenchymal cells (Figure 4J; Figure 4—figure supplement 1C). To understand whether increased HCC susceptibility of Nlrp12-/- mice is due to the defect in the downregulation of PAMPs-mediated HCC pathogenesis, we fed Nlrp12-/- mice with the regular drinking water supplemented with a cocktail of antibiotics that eliminates commensal bacteria (Rakoff-Nahoum et al., 2004) and thereby reduces systemic levels of PAMPs (Seki et al., 2007). 4 weeks following antibiotics treatment, mice were treated with DEN and HCC development was monitored at 38 weeks of age (Figure 5A). Depletion of microbiota was confirmed by colony forming assay and real time PCR analysis of universal bacterial 16S rRNA (data not shown). Antibiotic-treated Nlrp12-/- mice exhibited a profound reduction of tumor burden and expression of AFP after DEN-induced tumorigenesis (Figure 5B and C). The expression of proinflammatory cytokines and chemokines was also significantly reduced in livers from antibiotic-treated mice compared to untreated controls (Figure 5D). Moreover, the expression of proproliferative molecules including Myc, Ccnd1, Ccnb1, Survivin, and MKi67 in Nlrp12-/- livers was significantly reduced upon antibiotics treatment (Figure 5E). These results suggest that gut-derived PAMPs, particularly LPS, promotes HCC development which is downregulated by NLRP12.

Figure 5 with 1 supplement see all
NLRP12 suppresses gut microbiota-dependent inflammatory responses and HCC pathogenesis.

(A) Nlrp12-/- mice were treated with antibiotics in their drinking water starting at 4 weeks after birth and continued until the end of the experiment. Control groups were left untreated. At 4 weeks following antibiotics treatment, all mice (n = 10/group) were injected with DEN (100 mg/kg body weight). (B) At 38 weeks, mice were sacrificed and liver tumor development was monitored. Representative images of DEN-treated mouse livers are shown here. Number of tumors were counted. Data represent means ± SEM (n = 10). Statistical difference was determined by two-tailed unpaired t-test. (C) The expression of HCC marker AFP in the liver was measured by real-time qPCR. Data represent means ± SEM (n = 10). Statistical difference was determined by two-tailed unpaired t-test. (D–E) Liver tissues were analyzed for the expression of cytokines and chemokines (D) and pro-proliferative genes (E) by real-time qPCR. (D–F) Data represent means ± SEM (n = 10). Statistical difference was determined by two-tailed unpaired t-test.

https://doi.org/10.7554/eLife.40396.018

LPS is the major cell wall component of gram-negative bacteria and most potent ligand for NF-κB and MAPK activation. To understand whether gram-negative bacteria are abundant in Nlrp12-/- mice, we analyzed gut microbiota composition by 16S rRNA gene sequencing (Figure 5—figure supplement 1A and B). The phylum Bacteroidetes, which are gram-negative and most abundant in mouse gut, are equally abundant in WT and Nlrp12-/- mice (Figure 5—figure supplement 1A). There was also reduced levels of gram-negative phylum Proteobacteria but a higher abundance of gram-positive phylum Firmicutes in Nlrp12-/- mice (Figure 5—figure supplement 1A), suggesting that increased TLR4 responses in Nlrp12-/- HCC may not be due to increased gram-negative bacteria in their gut. Since relative abundance of several bacterial species was different in WT and Nlrp12-/- mice (Figure 5—figure supplement 1A and B), we next verified the effect of altered microbiota composition in immune responses in the liver at homeostasis. Hence, we measured the expression of inflammatory molecules, counted the number of immune cells, and analyzed the activation of signaling pathways in healthy WT and Nlrp12-/- livers. Our data show similar levels of Il6, Tnfa, Cxcl1, Cxcl2, and Ccl2 in the liver of healthy WT and Nlrp12-/- mice (Figure 5—figure supplement 1C). The number of immune cells including T cells, Kupffer cells, dendritic cells, and neutrophils infiltrated in the liver of healthy WT and Nlrp12-/- mice was comparable (Figure 5—figure supplement 1D). There was also no difference in the activation of JNK, NF-κB, ERK, and STAT3 in healthy WT and Nlrp12-/- livers (Figure 5—figure supplement 1E). These data suggest that NLRP12 deficiency doesn’t have immunomodulatory effect on the liver in the absence of injury or carcinogenesis.

NLRP12 negatively regulates JNK signaling in hepatocytes

Since JNK plays critical roles in hepatocyte physiology and HCC (Hui et al., 2008; Schwabe, 2006; Schwabe et al., 2003), we further investigated whether NLRP12 plays a role as an intrinsic regulator of JNK in hepatocytes. To elucidate the role of NLRP12 in hepatocyte-specific JNK activation, we cultured primary hepatocytes from healthy WT and Nlrp12-/- mouse livers (Figure 6A and Figure 6—figure supplement 1A) and stimulated them with LPS. The activation of JNK was enhanced in Nlrp12-/- hepatocytes relative to WT; but ERK, p65, and p38 activation was unchanged (Figure 6A). Consistent with JNK activation, the expression of Ccl2, Cxcl1, Cxcl2, Ccnd1, and Myc was significantly higher in LPS-stimulated Nlrp12-/- hepatocytes compared to WT hepatocytes (Figure 6B). A pharmacological JNK inhibitor (SP600125) significantly diminished the expression of these molecules, indicating JNK dependency (Figure 6B). In addition to LPS, other stimuli such as peptidoglycan (PGN) and TNFα, but not IL-6, induced higher expression of Cxcl1, Cxcl2, and Ccl2 in Nlrp12-/- hepatocytes (Figure 6—figure supplement 1B). Consistently, overexpression of NLRP12 in HepG2 cells led to markedly reduced p-JNK levels and subsequent expression of CXCL1, CXCL2, and CCL2 (Figure 6C and Figure 6—figure supplement 1C).

Figure 6 with 1 supplement see all
NLRP12 downregulates JNK activation in hepatocytes.

(A) Primary hepatocytes from healthy WT and Nlrp12-/- mouse livers were isolated and cultured. Hepatocytes were stimulated with LPS for the indicated time and analyzed for the activation of JNK, p38, ERK, and p65 by Western blotting. (B) Primary hepatocytes from healthy WT and Nlrp12-/- mouse livers were stimulated with LPS in the presence of absence of JNK inhibitor. The expression of inflammatory and proliferative molecules was measured by real-time qPCR. Data represent means ± SD (n = 3 replicates). Statistical difference was determined by two-tailed unpaired t-test. (C) HepG2 cells stably expressing either GFP or NLRP12 were stimulated with LPS and analyzed for the activation of JNK, p38, ERK, and p65 by Western blotting. (D) Primary hepatocytes isolated from untreated WT and Nlrp12-/- mouse livers were stimulated with LPS for the indicated time periods and analyzed for cMyc, P-cJun, Cyclin d1, and P-JNK by Western blotting. (E–F) Primary hepatocytes grown on coverslip were treated with or without LPS for 1 hr and immunostained for P-cJun (green) and cMyc (green). Cellular morphology was visible with filamentous actin (F-actin) staining (red). DAPI (blue) was used for nuclear staining. (F) P-cJun and cMyc fluorescence intensities were measured by Image J software. Data represent means ± SD (n = 20) and is representative of three independent experiments. Statistical difference was determined by two-tailed unpaired t-test. (G–H) Nlrp12-/- primary hepatocytes were transiently transfected with either GFP or Nlrp12 constructs followed by stimulation with LPS. The levels of P-JNK, cMyc, P-cJun, and P-p65 were measured by Western blotting (G) and the expression KC (Cxcl1), MIP2 (Cxcl2), and MCP1 (Ccl2) was analyzed by real-time qPCR (H). Data represent means ± SD (n = 3 replicates) and is representative of three independent experiment. Statistical difference was determined by two-tailed unpaired t-test.

https://doi.org/10.7554/eLife.40396.022

Activated JNK phosphorylates cJun and cMyc, and thereby inhibiting their proteasomal degradation (Alarcon-Vargas and Ronai, 2004; Noguchi et al., 1999). In agreement, the levels of cMyc and P-cJun were markedly higher in LPS-stimulated Nlrp12-/- hepatocytes as compared to WT (Figure 6D). Immunofluorescence staining showed increased numbers of cMyc and P-cJun-positive cells in Nlrp12-/- hepatocytes at basal levels and after stimulation with LPS compared with WT hepatocytes (Figure 6E and F). Notably, both cMyc and P-cJun were present in the nucleus consistent with their participation in gene transcription (Figure 6E). Increased levels of cMyc and P-cJun in LPS-treated Nlrp12-/- hepatocytes (Figure 6D) was JNK dependent as inhibition of JNK markedly reduced their levels (Figure 6—figure supplement 1D). The role of NLRP12 in regulating JNK, cMyc, and cJun was further confirmed by their rescue after the transient overexpression of NLRP12 in Nlrp12-/- hepatocytes (Figure 6G). Likewise, the expression of Cxcl1, Cxcl2, and Ccl2 was suppressed in Nlrp12-/- hepatocytes following overexpression of NLRP12 (Figure 6H). Finally, we confirmed the role of NLRP12 in the downregulation of JNK by knocking down NLRP12 with CRISPER/Cas9 in HepG2 cells. Suppression of NLRP12 resulted in higher activation of JNK in LPS-stimulated HepG2 cells (Figure 6—figure supplement 1E). Taken together, these results suggest that NLRP12 suppresses the expression of inflammatory and oncogenic molecules in hepatocytes via negative regulation of JNK signaling.

NLRP12 regulates hepatocyte proliferation via JNK

We finally sought to investigate whether NLRP12-mediated regulation of JNK activation affects hepatocyte proliferation. To this end, we isolated and cultured primary hepatocytes from WT and Nlrp12-/- mouse livers and followed the cellular proliferation using IncuCyte live cell image analyzer. The proliferation rate of Nlrp12-/- hepatocytes was seen significantly higher than WT (Figure 7A and B; Figure 7—figure supplement 1A), and was further increased by LPS but suppressed by JNK inhibitor (Figure 7A and B; Figure 7—figure supplement 1A). Consistent with higher proliferation, Nlrp12-/- hepatocytes with or without LPS stimulation showed markedly higher Ki67 staining (Figure 7—figure supplement 1B and C). To observe cell cycle progression in WT and Nlrp12-/- hepatocytes, we incubated WT and Nlrp12-/- hepatocytes with BrdU, which incorporates into newly synthesized DNA at S phase. BrdU-positive cells were analyzed by flow cytometry and microscopy. There was a trend of increased BrdU-positive S phase hepatocytes in the Nlrp12-/- group compared to WT which was further increased significantly with LPS stimulation (Figure 7E and F). Similarly, Nlrp12-/- hepatocytes showed markedly higher BrdU immunoreactivity upon stimulation with LPS (Figure 7E and F). These results indicate that NLRP12 controls hepatocytes proliferation via regulation of JNK.

Figure 7 with 1 supplement see all
NLRP12 regulates hepatocyte proliferation via JNK activation.

(A–B) Primary hepatocytes from WT and Nlrp12-/- mouse livers were treated with LPS in the presence or absence of a JNK inhibitor. The proliferation of hepatocytes was monitored in real-time by IncuCyte live cell image analyzer. (A) Representative images of hepatocytes captured by IncuCyte are shown. (B) The changes in cell confluence were used as a surrogate marker of cell proliferation. Data represent means ± SD (n = 5 replicates) and is representative of three independent experiments. Statistical difference was determined by two-tailed unpaired t-test. (C) WT and Nlrp12-/- hepatocytes were treated with LPS for 24 hr followed by 1 hr incubation with BrdU. Cells were then immunostained with anti-BrdU antibody and BrdU incorporation (S phase) was analyzed by flow cytometry. (D) Percentage of S phase cells as analyzed by flow cytometry was quantitatively analyzed. Data represent means ± SD (n = 3 replicates) and is representative of two independent experiments. Statistical difference was determined by two-tailed unpaired t-test. (E–F) Hepatocytes stained with anti-BrdU (green) were observed and counted under the 20X microscopic objective. DAPI (blue) was used for nucleus staining. Data represent means ± SD (n = 30 replicates) and is representative of two independent experiments. Statistical difference was determined by two-tailed unpaired t-test. (G) The proposed mechanism of NLRP12-mediated regulation of HCC.

https://doi.org/10.7554/eLife.40396.026

In summary, our data suggest that NLRP12 plays a central role in hepatocyte function by dampening the expression of cytokines, chemokines, growth factors, and oncogenes via negative regulation of the JNK pathway (Figure 7G). NLRP12 deficiency, therefore, enhances hepatocyte-specific inflammatory and proliferative responses during DEN-induced liver injury, leading to higher incidence of HCC (Figure 7G).

Discussion

Despite the high mortality rate, effective treatment options for HCC remain limited, emphasizing the importance of finding therapeutic measures to impede carcinogenesis. It is commonly viewed that HCC is initiated and promoted with consistent and nonspecific activation of the immune system in the liver (El-Serag and Rudolph, 2007; Farazi and DePinho, 2006). Gut microbiota have recently emerged as a critical pathogenic trigger for HCC (Dapito et al., 2012; Yoshimoto et al., 2013). TLR4-mutant mice, which lack responsiveness to LPS, are resistant to liver fibrosis, cirrhosis, and HCC (Dapito et al., 2012; Seki et al., 2007). Although the liver is not in a direct contact with gut microbiota, microbes and their PAMPs are transported to the liver through the hepatic portal vein after crossing the gut epithelial barrier (Son et al., 2010). Cirrhosis facilitates intestinal barrier permeability and cirrhotic patients are vulnerable for bacterial infection which increases mortality (Campillo et al., 1999; Cirera et al., 2001; Fukui et al., 1991; Pascual et al., 2003; Rutenburg et al., 1957; Yoneyama et al., 2002). However, the precise mechanism of gut microbiota-mediated augmentation of HCC pathogenesis remains to be determined. Here we show that NLRP12 is a potent molecular checkpoint for gut microbiota-dependent inflammation and carcinogenesis in the liver.

The pathogenesis of HCC is a complex process because of its genetic heterogeneity. Genome sequencing and transcriptomics analyses have identified CTCNB1, WNT, AXIN, TP53, CCND1, CDKN2A, TERT, ARID1A, and ARID2 as major genes whose alterations cause HCC induction (Huang et al., 2012; Marquardt et al., 2015). Since somatic mutations in these driver oncogenes and tumor suppressors occur in the liver with the exposure of carcinogen, chronic inflammation, and oxidative stress, these pathological stimuli also alter many other genes (Marquardt et al., 2015). Genome sequencing of tumors from an HBV-infected HCC individual has identified more than 11,000 somatic substitutions (Totoki et al., 2011). Significant genetic alterations in NLRs have been identified in a genome sequence study (Everson et al., 2013). Although at low frequency, mutations in passenger genes promote tumorigenesis in multiple ways including hyperactivation of inflammatory signaling pathways such as NF-κB, MAPK, AKT, and JAK/STAT (Nault and Zucman-Rossi, 2011). As a regulator of NF-κB and MAPK pathways, therefore, NLRP12 mutations may play a critical role in HCC pathogenesis. Increased HCC susceptibility of Nlrp12-/- mice suggests that Nlrp12 contributes to the suppression of HCC, which is also supported by human HCC genomics data showing NLRP12 mutations in 1–2% HCC. Notably, like many other cancer-related genes, amplification of NLRP12 as seen in less than 0.5% HCC cases may also contribute to HCC. It is possible that higher NLRP12 activity suppresses JNK to a level that promotes HCC. In fact, JNK and NF-κB pathways play both tumor promoting and tumor suppressive roles (Das et al., 2011; Hui et al., 2008; Inokuchi et al., 2010; Luedde et al., 2007; Maeda et al., 2005; Sakurai et al., 2006).

Growing evidence implicates the role of NLRP12 in diverse pathophysiological conditions. Variants of NLRP12 have been linked to autoinflammatory diseases, including periodic fever syndrome, atopic dermatitis, and arthritis (Borghini et al., 2011; Jéru et al., 2008; Jéru et al., 2011). Experimental studies demonstrated that NLRP12 protects mice from colitis, colorectal tumorigenesis, and experimental autoimmune encephalomyelitis (EAE) (Allen et al., 2012; Lukens et al., 2015; Zaki et al., 2014; Zaki et al., 2011). Mechanistically, NLRP12 regulates these diseases via down-regulation of the canonical and non-canonical NF-κB pathways (Allen et al., 2012; Zaki et al., 2011). However, NLRP12-mediated regulation of NF-κB under different pathophysiological contexts appears to be cell type-dependent. For example, NLRP12-mediated inhibition of NF-κB in myeloid cells contributes to protection against intestinal inflammation and tumorigenesis, in T cells it is implicated in experimental autoimmune encephalomyelitis (Lukens et al., 2015), and in osteocytes it is involved in osteoclast differentiation (Krauss et al., 2015). This study shows that NLRP12 is expressed in liver parenchymal and non-parenchymal cells and suppresses HCC by preventing JNK signaling, particularly in the hepatocyte. Thus, our findings elucidate a novel function of NLRP12 in the liver and underscore its versatile physiological functions.

Although we could not find a difference in the activation of NF-κB and ERK between WT and Nlrp12-/- HCC, the involvement of NLRP12 in the downregulation of these pathways in myeloid and T cells during HCC and a contribution of such processes in the suppression of HCC cannot be completely excluded. It might be possible that NLRP12 suppresses NF-κB and ERK in immune cells during acute but not in a chronic inflammatory environment. Thus, NLRP12-mediated downregulation of NF-κB and ERK activation as well as inflammatory responses in immune cells may occur during the early phase of HCC development. The contribution of the immune cell-specific function of NLRP12 in the suppression of HCC needs to be further investigated using Nlrp12 conditional knockout mice in future studies.

In this study, we show that NLRP12 attenuates HCC development though multiple mechanisms. First, NLRP12 protects the liver from injury and hepatocyte death. The liver is a unique organ with regenerative capacity. Hepatocyte death due to cytotoxic insult or inflammatory responses triggers rapid proliferation to compensate for apoptotic or necrotic hepatocyte cell death (Sakurai et al., 2008). Such a rapid cell cycle progression in an inflammatory environment results in accumulation of mutations leading to neoplastic transformation of hepatocytes. Secondly, NLRP12 downregulates hepatocyte-specific expression of cytokines such as IL-6 and TNFα which promote proliferation and tumor growth, and chemokines CXCL1, CXCL2 and CCL2, which enhance inflammatory cell infiltration and thereby augment inflammation in the tumor milieu (Kamata et al., 2005; Maeda et al., 2005; Sakurai et al., 2008). Tumor infiltrated immune cells, particularly macrophages, play critical roles in HCC induction and progression by producing many tumor-promoting factors such as iNOS, Cox2, and ROS (Capece et al., 2013; Grivennikov et al., 2010). Third, NLRP12 inhibits the expression of cJun and cMyc, which are major oncogenes seen in HCC (Lin et al., 2010). These oncogenic transcription factors regulate the expression of molecules involved in proliferation and cell cycle progression, such as Ccnb1, Ccnd1, and Cdkn1a. Consistently, we observed higher induction of Ccnb1 and Ccnd1 but reduced expression of Cdkn1a in Nlrp12-/- hepatocytes and HCC. Notably, NLRP12-mediated regulation of these processes occurs within the context of PAMP-stimulation as inhibition of gut microbiota abolishes the HCC susceptibility of Nlrp12-/- mice.

Previous studies implicated the NF-κB pathway in NLRP12-mediated regulation of inflammatory disorders. However, for the first time, we show that NLRP12 regulates JNK activation in hepatocytes. JNK has critical functions in liver physiology and contributes to HCC pathogenesis (Das et al., 2011; Hui et al., 2008; Sakurai et al., 2006; Schwabe, 2006). JNK is highly activated in human HCC and mouse deficient in JNK1 develop reduced tumor burden in the liver following DEN treatment (Hui et al., 2008; Sakurai et al., 2006). Similar to our observation that NLRP12 regulates hepatocyte death and proliferation, previous studies relate JNK1-dependent HCC pathogenesis to hepatocyte death and proliferation (Hui et al., 2008; Sakurai et al., 2006). The mechanism of JNK-mediated regulation of cellular proliferation involves activation of c-Jun, JunD, and cMyc, depending on the cell type and stimuli (Alarcon-Vargas and Ronai, 2004; Bogoyevitch and Kobe, 2006). The role of cJun and cMyc in hepatocellular carcinogenesis is well documented (Eferl et al., 2003). In agreement, we observed that increased HCC pathogenesis in Nlrp12-/- mice is associated with increased cMyc and cJun activity. Moreover, NLRP12-deficiency links JNK activation to higher inflammatory responses during DEN-induced liver tumorigenesis. Through in vitro biochemical studies using primary hepatocytes, we clearly demonstrated that NLRP12 regulates JNK activation in the hepatocyte, particularly in the context of TLR stimulation. It is worthwhile to mention that higher JNK activation in Nlrp12-/- liver is only seen during HCC; JNK activation as well as inflammatory responses in the liver of healthy WT and Nlrp12-/- mice were comparable. It seems that tumor induction sensitizes hepatocytes to TLR ligands, leading to increased activation of JNK. It is also possible that HCC development increases intestinal epithelial barrier permeability, allowing increased translocation of gut-derived PAMPs into the liver. Higher levels of plasma endotoxin and bacterial translocation are seen in patients with liver cirrhosis (Campillo et al., 1999; Cirera et al., 2001; Fukui et al., 1991). Thus, NLRP12 may play a functional role in suppressing JNK activation in the hepatocyte in the context of liver injury, fibrosis, and tumorigenesis.

In summary, this study demonstrates that the innate pathogen sensor NLRP12 is a molecular checkpoint for HCC. NLRP12 suppresses HCC by attenuating JNK-mediated inflammatory and proliferative responses in the hepatocytes, particularly in the context of stimulation with microbial pattern molecules. Thus, this study suggests that inducing or activating NLRP12 or its downstream signaling could be potential therapeutic options for HCC. Considering the critical role of gut microbiota and TLR pathways in inflammatory liver disorders (Guo and Friedman, 2010; Seki et al., 2007), future studies should investigate the role of NLRP12 in liver fibrosis, cirrhosis, and non-alcoholic fatty liver syndrome using appropriate animal models.

Materials and methods

Key resources table
Reagent type or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent
(M. musculus)
C57BL/6JJackson LabRRID:MGI:3028467
JAX:000664
Genetic reagent
(M. musculus)
Nlrp12-/- C57BL/6JPMID:12563287RRID: MGI:2676630
Cell line
(Homo Sapiens)
HepG2ATCCRRID:CVCL_0027
Recombinant
DNA reagent
pcDNA4/TOInvitrogenCat#V1020-20
Recombinant
DNA reagent
pcDNA4/TO-Nlrp12This paperFull length Nlrp12
(mouse) cDNA was
cloned into pcDNA4/TO
Recombinant
DNA reagent
pcDNA4/TO-NLRP12This paperFull length NLRP12
(human) cDNA was
cloned into pcDNA4/TO
Recombinant
DNA reagent
NLRP12 sgRNA
CRISPR/Cas9
All-in-One Lentivector
(Human)
ABM IncCat # K1434706
Recombinant
DNA reagent
Scrambled sgRNA
CRISPER/Cas9
All-in-one Lentivector
ABM IncCat # K010
AntibodyRabbit polyclonal
anti-NLRP12
Aviva Systems
Biology
RRID:SCR_001456
Cat # OAAB04256
WB: 1:500
AntibodyRabbit monoclonal
anti-p44/42 (Erk1/2)
Cell SignalingRRID:AB_390779
Cat # 4695,
WB: 1:2000
AntibodyRabbit monoclonal
anti-Phospho-
SAPK/JNK
Cell SignalingRRID:AB_823588
Cat # 4668
WB: 1:1000
AntibodyRabbit monoclonal
anti-p38 MAPK
Cell SignalingRRID:AB_10999090
Cat # 8690
WB: 1:1000
AntibodyRabbit monoclonal
anti-Phospho-AKT
Cell SignalingRRID:AB_2315049
Cat # 4060
WB: 1:1000
AntibodyRabbit monoclonal
anti-cMyc
Cell SignalingRRID:AB_1903938
Cat # 5605
WB: 1:1000,
IF:1:100
AntibodyMouse monoclonal
anti b-actin
SigmaRRID:AB_476697.
Cat # A2228
WB: 1:10000
AntibodyRabbit polyclonal
anti-SAPK/JNK
Cell SignalingRRID:AB_2250373
Cat # 9252
WB: 1:5000
AntibodyRabbit monoclonal
anti-Phospho-p44/42
(ERK1/2)
Cell SignalingRRID:AB_2315112
Cat # 4370
WB: 1:2000
AntibodyRabbit monoclonal
anti-Phospho-cJun
Cell SignalingRRID:AB_2129575
Cat # 3270
WB: 1:1000,
IF:1:100
AntibodyRabbit monoclonal
anti-Phospho-p38
MAPK
Cell SignalingRRID:AB_331762
Cat # 9215
WB: 1:1000
AntibodyRabbit monoclonal
anti-Phospho-STAT3
Cell SignalingRRID:AB_2491009
Cat # 9145
WB: 1:1000
AntibodyRabbit monoclonal
Phospho-NF-kB p65
Cell SignalingRRID:AB_331284
Cat # 3033
WB: 1:2000
AntibodyRabbit monoclonal
anti-Cyclin d1
Cell SignalingRRID:AB_2259616
Cat # 2978
WB: 1:1000
AntibodyRabbit polyclonal
anti-Akt
Cell SignalingRRID:AB_329827.
Cat # 9272
WB: 1:1000
AntibodyRabbit monoclonal
anti-Ki67
abcamRRID:AB_302459
Cat # ab16667
IF:1:100
AntibodyRat monoclonal
anti-F4/80 (Clone CI:A3-1)
BioRadRRID:AB_323806
Cat # MCA497GA
IF:1:100
AntibodyMouse monoclonal
anti-FlagM2
SigmaRRID:AB_262044
Cat # F1804
WB: 1:10000
AntibodyMouse monoclonal
anti-α-BrdU
Cell signalingRRID:AB_10548898
Cat # 5292
IF: 1:200
AntibodyRat monoclonal
anti-CD16/CD32
(clone 2.4G2)
BioLegendRRID:AB_394656
clone 2.4G2
1 μg/ 106 cells
AntibodyMouse monoclonal
Pacific Blue
anti-CD45.2 Antibody
BioLegendRRID:AB_492873
Cat # NC0123437
1:100
AntibodyRat monoclonal
PerCP-Cyanine5.5
Anti-Human/Mouse
CD11b (M1/70)
Tonbo BioscienceRRID:AB_2621885
Cat# 65–0112
1:100
AntibodyRat monoclonal APC
Anti-Mouse F4/80
Antigen (BM8.1)
Tonbo BioscienceRRID:AB_2621602
Cat # 20–4801
1:100
AntibodyRat monoclonal
In Vivo Ready Anti-Mouse
Ly-6G (Gr-1) (RB6-8C5)
Tonbo BioscienceRRID:AB_2621463
Cat # 40–5931
1:100
AntibodyMonoclonal
Anti-CD11c conjugated
with PE
Tonbo BioscienceRRID:AB_2621747
Cat # 50–0114
1:100
AntibodyMonoclonal PE/Cy7
anti-mouse TCR β chain
BiolegendRRID:AB_893627
Cat # 109221
1:100
Chemical
compound
ZeocinInvivogenCat # ant-zn-1(100 μg/ml)
Chemical
compound
Lipofectamine 3000Thermo FisherCat # L3000015
Chemical
compound
Mycoplasma KitSigmaCat#11663925910
Chemical
compound
Ultrapure Escherichia
coli-derived LPS
InvivogenCat # tlrl-smlps
Chemical
compound
PGNInvivogenCat # tlrl-pgnsa
Recombinant
protein
Recombinant
Human IL-6
PeprotechCat # 200–06
Recombinant
protein
Recombinant
Murine TNFα
PeprotechCat # 315-01A
Commercial kitIn Situ Cell Death
Detection Kit-Fluorescein
RocheCat # 11684795910
Commercial kitPathscan Inflammation
Multi-target ELISA kit
Cell Signaling
Technology
Cat # 7276
Commercial kitPathsan Phospho-p44/42
MAPK ELISA kit
Cell Signaling
Technology
Cat # 7177C
Software,
algorithm
BD FACS Diva softwareBD Bioscience
Software,
algorithm
Flowjo v10Treestar, IncRRID:SCR_008520
Software,
algorithm
GraphPad Prismgraphpad.comRRID:SCR_002798
Software,
algorithm
QIIME 1.8.0Qiime.orgRRID:SCR_008249
PMC3156573

Mice

Wild-type (C57BL6/J) mice were purchased from Jackson Laboratory. Nlrp12-/- was generated by Millenium Pharmaceuticals and backcrossed for 10 generations with C57BL6/J mice. All mice were bred and maintained in a specific pathogen free (SPF) facility at UT Southwestern Medical Center. Unless otherwise stated, mice of different genetic backgrounds were housed in separate cages, maintained in same animal room, and used for in vivo and in vitro experiments. This study was performed under the protocol #2016–101683 which was approved by the Institutional Animal Care and Use Committee (IACUC). All animal experiments were conducted in accordance with the IACUC guidelines and the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All experiments were conducted with age and sex-matched mice and treatment groups were allocated randomly.

Cell culture

HepG2 cell line was collected from ATCC. The cells were authenticated by UT Southwestern genomics core facility using Short Tandem Repeat (STR) DNA profiling. The cells were cultured in DMEM with 10% FBS plus 1X Pen/Strep (Thermo Scientific). Cells were tested for mycoplasma contamination using mycoplasma testing kit (Sigma).

Induction of hepatocellular carcinoma (HCC)

Mice were injected with DEN (25 mg/kg i.p.) intraperitoneally (i.p.) at day 14 postpartum. 10 months following DEN injection, mice were euthanized with CO2. In another approach, HCC was induced by the combination of DEN (25 mg/kg i.p.) given at day 14 postpartum followed by weekly injections of CCl4 (0.5 ml/kg i.p., dissolved in corn oil) for 8 weeks starting at 10 weeks after birth. Mice were sacrificed at the age of 6 months.

In antibiotics study, 4-week-old mice were treated with a combination of antibiotics including ampicillin (1 g/L), neomycin (1 g/L), metronidazole (1 g/L), streptomycin (1 mg/L) and vancomycin (500 mg/L) in drinking water. At 8 weeks of age, mice were injected with DEN (100 mg/kg, i.p.). Antibiotics treatment was continued until the end of the study. Elimination of gut microbiota was confirmed by measuring eubacterial 16S rDNA by real-time PCR and culturing of fecal homogenates on brain heart infusion (BHI) agar.

Histopathology and immunohistochemistry

Liver tissue samples were fixed in 4% paraformaldehyde and embedded in paraffin. The tissue sections were stained with hematoxylin and eosin (H and E). Histopathological scoring was done in a blinded fashion by a pathologist. Steatosis was scored as: 0: 0% to 5%; 1: 5% to 33%; 2: 33% to 66%; and 3: greater than 66% of liver shows steatosis. Inflammation was scored as: 0: no foci/20x field; 1:<2 foci/20x field; 2: 2–4 foci/20x field; and 3:>4 foci/20x field. Fibrosis was defined as: 0: no fibrosis; 1: mild fibrosis, focally or extensively present; 2: moderate fibrosis, extensive periportal fibrosis; 3: severe fibrosis, extensive bridging fibrosis; and 4: cirrhosis. HCC was scored as a percentage of area covered by tumor.

For immunohistochemistry, 4% paraformaldehyde-fixed and paraffin-embedded tissue sections were de-paraffinized and hydrated through decreasing concentrations of ethanol. Antigen retrieval was done in 10 mM sodium citrate solution (pH 6.0) for 20 min at 95°C. Tissue sections were blocked with 5% goat serum for 30 min and stained for Ki67 using rabbit anti-Ki67 (ab16667; Abcam), anti-F4/80 (Clone CI:A3-1; MCA497GA, BioRad), and anti-cleaved caspase-3 (Asp175; 5A1; 9664; Cell Signaling Technologies). After overnight incubation at 4°C, the tissue sections were washed three times and incubated with HRP-conjugated anti-rabbit antibody for 1 hr at room temperature. The images were taken using bright field microscopy.

Immunofluorescent staining

For immunofluorescent staining of hepatocytes, cells were seeded on cover slips and fixed in 4% paraformaldehyde for 15 min at RT, washed with PBS 3 times for 5 min each and blocked with PBS containing 5% goat serum and 0.3% Triton-X100 for 1 hr. Cells were then incubated with primary antibodies against Ki-67 (ab16667; Abcam), albumin (NB600-41532, Novus), phospho-cJun (Ser73, D47G9, 3270, Cell Signaling), cMyc (D84C12, 5605, Cell Signaling) overnight at 4°C. After washing in PBS, cells were incubated with anti-rabbit secondary antibodies conjugated with Alexa Fluor 564 (Invitrogen), Alexa Fluor 488 (Invitrogen), or FITC (Sigma), for 1 hr at RT. The cells were washed in PBS and incubated with Flash Phalloidin Red 594 antibody (424203, Biolegend) for 30 min at RT. Following three washes in PBS, cells were mounted with mounting media containing DAPI. Images were taken by fluorescence microscope (Zeiss).

Hepatocytes isolation and culture

Hepatocytes were isolated from mouse livers by a collagenase perfusion technique. In brief, after the left ventricle was cannulated and right atrium was cut, the liver was perfused with 20 ml PBS followed by 30 ml HBSS (without Mg2+ or Ca2+) supplemented with 0.2 mM EDTA at 10 ml min−1 speed through the left ventricle. Next, the liver was perfused with 20 ml collagenase type IV (Sigma Aldrich) containing medium (0.5 mg/ml collagenase type IV in DMEM supplemented with 5 mM HEPES, Penicillin/Streptomycin). The liver was dissociated and the liver suspension was passed through a 70 μm sterile filter. The hepatocytes were separated from non-parenchymal cells by low-speed centrifugation (50xg for 5 min). The hepatocytes were further purified using Percoll gradient separation. The living hepatocytes were counted using Trypan blue and cultured on collagen-coated plate having DMEM medium supplemented with 10% FBS, 1x Penicillin/Streptomycin, 1x Insulin (sigma), and EGF (40 ng/ml). For priming, hepatocytes were cultured in collagen-coated 6- or 12-well cell culture plates overnight and stimulated with TLR ligands: ultrapure Escherichia coli-derived LPS (Invivogen), PGN (Invivogen), IL-6 (Peprotech), or TNFα (Peprotech).

Isolation and culture of hepatic stellate cells and Kupffer cells

The liver was dissociated as described above and the liver suspension was passed through a 70 μm sterile filter. The hepatocytes were separated from non-parenchymal cells by low-speed centrifugation (50xg for 5 min) as described above. The supernatant was collected and centrifuged at 640xg for 10 min and resuspended in washing buffer followed by pass through a 70 µm sterile filter. The pellet was resuspended in 10 ml of 35% Percoll (GE Healthcare, Pittsburgh, PA, USA) with an overlay of 1 ml PBS. After centrifugation at 1130xg for 30 min, hepatic stellate cells are in the layer located between the PBS and 35% Percoll. Kupffer cells were separated from the remainder of non-parenchymal cells by Percoll gradient centrifugation at 800xg for 30 min. After centrifugation, Kupffer cells are in the layer located between the 70% and 40% Percoll. The hepatic stellate cells were cultured in RPMI 1640 medium supplemented with 10% FBS, 1x Penicillin/Streptomycin, 1x Insulin (sigma). The Kupffer cells were cultured on six-well plate having DMEM medium supplemented with 10% FBS, 1x Penicillin/Streptomycin. For priming, the cells were cultured in 6- or 12-well cell culture plates overnight and stimulated with ultrapure Escherichia coli-derived LPS (Invivogen).

Flow cytometric analysis of liver non-parenchymal cells

The harvested livers were cut into small pieces (1–2 mm) and digested with liver digestion media (0.5 mg/ml Collagenase Type IV, 5 mM HEPES, and 1X Penicillin-streptomycin in HBSS) for 30 min at 37°C while shaking in a water bath. HBSS media supplemented with 2% heat-inactivated FBS and 5 mM EDTA was added into the digested liver to stop the digestion followed by filtering through a 70 μm sterile filter. The filtered liver suspension was centrifuged at 450xg for 8 min at 4°C. The cell pellet was resuspended in ice-cold RBC lysis buffer, kept on ice for 3 min and the lysis buffer was neutralized by adding RPMI media supplemented with 5% FBS. Following centrifugation at 450xg for 8 min at 4°C, the cell pellet was washed three times with RPMI media supplemented with 5% FBS. The cell pellet was resuspended in RPMI media supplemented with 5% FBS and the living cells were counted using Trypan blue. 1 × 106 live cells per sample were stained with Live/Dead fixable yellow dead cell stain kit (Life Technologies) according to the manufacturer’s instructions. Cells were then blocked with Fc Block (anti-mouse CD16/CD32; clone 2.4G2; 1ug/106 cells; BioLegend) in FACS Buffer (2% FBS in PBS) for 20 min at 4°C. Following blocking, cells were stained for 30 min at 4°C with anti-CD45.2 conjugated with Pacific blue (1:100 dilution, NC0123437, BioLegend), anti-CD11b conjugated with PerCP-Cy5.5 (Clone M1/70, 1:100 dilution, 65–0112 U025, Tonbo Bioscience), anti-F4/80 conjugated with APC (Clone BM8.1, 1:100 dilution, 20–4801 U025, Tonbo Bioscience), anti-Ly6G (Gr.1) conjugated with FITC (1:100 dilution, 40–5931 U100, Tonbo Bioscience), anti-CD11c conjugated with PE (1:100 dilution, 50–0114 U025, Tonbo Bioscience), and anti-TCRb conjugated with PE-Cy7 (1:100 dilution, 109221, Biolegend). After staining, cells were washed and fixed with 4% paraformaldehyde in PBS. Data were acquired with a BD LSRII Fortessa flow cytometer using BD FACSDiva software (BD Bioscience). Compensation was performed on the BD LSRII flow cytometer at the beginning of each experiment. Data were analyzed by using Flowjo v10 (Treestar, Inc).

In vivo BrdU incorporation assay

BrdU incorporation assay was done using BrdU In-Situ Detection Kit (550803, BD Pharmingen) according to the manufacturer’s instructions. In brief, 2 hr prior to euthanasia mice were injected intraperitoneally with BrdU (B5002, Sigma Aldrich), at a dose of 50 mg/kg body weight of mice. Tissue samples were fixed in 4% paraformaldehyde and embedded in paraffin. The paraffin-embedded liver tissue sections were de-paraffinized by washing with xylene 2 times for 5 min each time at room temperature. The tissue sections were dehydrated by incubation in 100% ethanol 2 times for 5 min followed by once in 95% ethanol for 3 min at room temperature. The tissue sections were treated with 0.3% H2O2 to block endogenous peroxidase, followed by antigen retrieval with ‘BD Retrievagen A’ (#550803, BD Pharmingen) in a microwave oven to 89°C for 10 min. The tissue sections were incubated in biotinylated anti-BrdU antibody (#550803, BD Pharmingen) at 1:10 in diluent buffer for 1 hr at room temperature, and then in HRP-conjugated streptavidin for 1 hr at room temperature. The tissue sections were stained with DAB substrate solution followed by hematoxylin counterstaining. Slides were examined by bright field microscopy.

In vitro proliferation assay using BrdU incorporation

For measuring proliferation using BrdU incorporation in vitro, cells were incubated with BrdU (10 μM) in culture medium for 2 hr at 37°C with 5% CO2. The cells were then fixed in 70% ethanol for 5 min, treated with 1.5 N HCl for 30 min at RT followed by washing with PBS. Following blocking with 5% normal goat serum in PBS plus 0.3% Triton-X100 for 1 hr the cells were incubated with anti α-BrdU antibody (Bu20a, 5292, Cell signaling) overnight at 4°C. After washing in PBS, the cells were treated with anti-mouse antibody conjugated with Alexa flour 488 for 2 hr at RT. Finally, the cells were washed with PBS and mounted with mounting media having DAPI. Images were taken by fluorescence microscope (Zeiss). For flow cytometric analysis, the cells were fixed in ice-cold 70% ethanol, followed by treatment with 1.5 HCl plus 0.5% Triton X-100 to permeabilize the cell and denature the DNA. The cells were neutralized with 0.1 M sodium tetraborate followed by rinse with PBS and incubated with anti α-BrdU antibody (Bu20a, 5292, Cell signaling). Anti-mouse antibody conjugated with Alexa flour 488 was used for counterstaining. DNA was stained with 2.5 μg/ml 7-AAD in the presence of 10 μg/mL RNase A. Samples were analyzed on a BD LSRII Fortessa flow cytometer using BD FACSDiva software (BD Biosciences).

Real-time cell proliferation assay

The cell proliferation was measured in real time by using a label-free, non-invasive cellular confluence assay by IncuCyte Live-Cell Imaging Systems (Essen Bioscience, Ann Arbor, MI, USA). 3000 hepatocytes/well were seeded on a collagen-coated 96-well plate, placed in the incubator maintained at 37°C with 5% CO2 supply. The IncuCyte system scanned the plate and collected live cell images every 2 hr until the end of each experiment. The cell confluence was calculated using IncuCyte software and the cell proliferation is expressed as the percentage of confluence.

Western blot analyses

Mouse liver tissues or cultured cells were homogenized in RIPA lysis buffer containing complete protease inhibitor cocktail and phosphatase inhibitor cocktail (Roche), resolved by SDS-PAGE, and transferred onto a PVDF membrane. The membranes were immunoblotted with antibodies against ERK (4695, Cell Signaling), Phospho-ERK (4370, Cell Signaling), JNK (9252, Cell Signaling), Phospho-JNK (4668, Cell Signaling), Phospho-cJun (3270, Cell Signaling), p38 (8690, Cell Signaling), Phospho-p38 (9215, Cell Signaling), AKT (9272, Cell Signaling), Phospho-AKT (4060, Cell Signaling), β-catenin (8480, Cell Signaling), Phospho-β-catenin (9565, Cell Signaling), Phospho-STAT3 (9145, Cell Signaling), Phospho-NF-κB p65 (3033, Cell Signaling), cMyc ( 5605, Cell signaling), Cyclin d1 (2978, Cell signaling), Anti-FlagM2 (F1804, Sigma-Aldrich), NLRP12 (Aviva Systems Biology, #OAAB04256), and β-actin (A2228, Sigma). Finally, immunoreactive proteins were detected using ECL super signal west femto substrate reagent (Thermo Scientific).

Sandwich ELISA for quantitative measurement of cell signaling pathways

The activation of p65, ERK, JNK, p38, and STAT3 in the HCC of WT and Nlrp12-/- mice were measured by Pathscan Inflammation Multi-target Sandwich ELISA kit (Cell Signaling technology; #7276) and Pathscan phosphor-p44/42 MAPK Sandwich ELISA kit (Cell Signaling technology; #7177C) according to manufacturer’s protocol. Briefly, HCC tissue lysates at a concentration of 0.5 mg/ml were added into antibody pre-coated ELISA plates and incubated overnight at 4°C. After washing, plates were incubated with HRP-conjugated detection antibodies. HRP substrate TMB was used to develop color and the absorbance was measured at 450 nm.

Real-time PCR

Liver tissues were preserved in RNA later (Invitrogen). Total RNA was extracted using TRIzol (Invitrogen) according to the manufacturer’s instructions. Isolated RNA was reverse transcribed into cDNA using iScript (Bio-Rad). Real-time PCR was performed using iTaq Universal SYBR Green Supermix (Bio-Rad). Expression data were normalized to GAPDH as described earlier (Hu et al., 2015). Primers used for real-time PCR are listed in Supplementary file 1-Table 1 and 2.

Transient transfection and preparation of stable cell lines

The human NLRP12 cDNA was prepared from Jurkat cell cDNA library by PCR using oligonucleotide primers acttAAGCTTatgctacgaaccgcaggcagg and gtcgGATATCtgcagccaatgtccaaataa. The human NLRP12 cDNA was cloned into flag-tagged pcDNA4/TO vector, a CMV expression vector, at HindIII and EcoRV sites. The mouse NLRP12 cDNA was obtained from mouse macrophage by primers gtcgGATATCtgcagccaatgtccaaataag and tcgaGCGGCCGCccacacccaatatccaggtacgg. Then mouse NLRP12 cDNA was cloned into Flag-tagged pcDNA4/TO vector at KpnI and NotI sites. As a control, GFP was cloned into the pcDNA4/TO:Flag vector at BamHI and NotI sites. Mouse primary hepatocytes and HepG2 hepatocellular carcinoma cells were transiently transfected with mouse and human NLRP12 construct respectively or GFP construct using lipofectamine 3000 reagent (Invitrogen). To make stable HepG2 cells expressing human NLRP12 or GFP, the cells were transfected with NLRP12 or GFP construct using lipofectamine 3000 reagent (Invitrogen), and were selected on Zeocin (100 μg/ml) and confirmed by observing GFP under fluorescence microscope and western blot analysis of Flag. HepG2 cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin and streptomycin in a 5% CO2 incubator at 37°C. The stable HepG2 cells expressing NLRP12 or GFP were maintained in Zeocin (100 μg/ml) containing DMEM supplemented with 10% FBS and 1% pen/strep.

Knock down of NLRP12 in HepG2 cells

The human liver cancer cell line HepG2 was cultured in Dulbecco’s Modified Eagle’s medium (high glucose, Sigma) supplemented with 10% FBS and 1% penicillin and streptomycin (Sigma) and maintained in a 5% CO2 incubator at 37°C. At 50–60% confluency, cells were transfected with either NLRP12 sgRNA CRISPR/Cas9 (K1434706, abm) or Scrambled sgRNA CRISPR/Cas9 (K010, abm) plasmids using Lipofectamine 3000 reagent (Invitrogen) according to manufacturer`s instructions. 48 hr post-transfection, CRISPR/Cas9 plasmid-transfected HepG2 cells were selected using media containing 2 μg/ml puromycin (A1113803, Gibco). Knock down of NLRP12 in CRISPR/Cas9-transfected cells was confirmed by Western blot analysis. The NLRP12 or scrambled sgRNA transfected HepG2 cells were seeded in 12-well plate, incubated for overnight and stimulated with LPS (1 μg/ml). The activation of signaling pathways was measured by Western blotting.

TUNEL assay

TUNEL staining was performed by TUNEL assay kit (In Situ Cell Death Detection Kit- Fluorescein, Cat # 11684795910, Roche) according to manufacturer’s instruction. In brief, primary hepatocytes grown on cover slip were fixed with 4% paraformaldehyde. After permeabilization with 0.1% Triton X-100 in 0.1% sodium citrate, cells were incubated with TUNEL antibody. For liver tissues, 4% paraformaldehyde-fixed and paraffin-embedded tissue sections were de-paraffinized and hydrated through decreasing concentrations of ethanol. Antigen retrieval was done in 10 mM sodium citrate solution (pH 6.0) for 20 min at 95°C followed by treatment with TUNEL reagents. Nuclei of cells were counterstained with the DAPI reagent. Images were taken using Zeiss fluorescence microscope.

Sequencing of 16S rRNA gene amplicons and analysis

Fecal samples were collected from 10-month-old WT and Nlrp12-/- mice. Using the Fecal DNA isolation kit (Qiagen, USA), total genomic DNA were extracted from fecal pellets. The concentration and purity of DNA (A260/280) were estimated spectrophotometrically. The quality of DNA was assessed by agarose gel electrophoresis. Bacterial primers 341F 5'-CCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') targeting the V3-V4 hyper-variable region of 16S rRNA gene was used for PCR amplification. In library preparation, sample-specific barcode sequences were incorporated to the primers, and then barcoded 16S rRNA gene amplicons were pooled at equimolar concentration prior to sequencing using MiSeq platform (Illumina, Inc, San Diego, California). The raw sequence reads were demultiplexed according to the sample-specific barcodes, which followed by primer trimming and quality filtering, were assigned to operational taxonomic units (OTUs). The quality filtering steps include the removal of sequences with anonymous bases, chimera sequences using vsearch method (Rognes et al., 2016). In addition, the phred quality cutoff was set to 30 to maintain high quality sequences. We employed uclust method (Edgar, 2010) to cluster the quality-filtered sequence reads at a minimum of 97% sequence similarity using open reference OTU-picking approach, and assigned taxonomy to each sequence of the representative set using the GreenGenes database (DeSantis et al., 2006). Following the removal of the singleton OTUs, the sequence reads, which ranged from 12,194 to 26,028, were used for estimating the relative abundances of the bacterial taxa at phylum and family levels. All the upstream and downstream analyses of Illumina sequences were carried out in the QIIME 1.8.0 environment (Caporaso et al., 2010).

Statistical analysis

Data for all in vivo study are presented as means ± SEM. In vitro experimental data are presented as means ± SD. Statistical significance was determined by two-tailed unpaired Student’s t-test, and p<0.05 was considered statistically significant. To compare the relative frequencies of bacterial taxa between control and treated groups, the nonparametric t-test was performed. GraphPad Prism eight software was used for statistical analyses.

16S rRNA raw sequence data accessibility

The 16S rRNA gene sequence data have been deposited to Sequence Read Archive (SRA) of National Center for Biotechnology Information (SRA accession SRP175050). Detailed information on the individual sample can be accessed at https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP175050 with NCBI BioProject accession PRJNA512540.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
    Intestinal permeability is increased in patients with advanced cirrhosis
    1. S Pascual
    2. J Such
    3. A Esteban
    4. P Zapater
    5. JA Casellas
    6. JR Aparicio
    7. E Girona
    8. A Gutiérrez
    9. F Carnices
    10. JM Palazón
    11. J Sola-Vera
    12. M Pérez-Mateo
    (2003)
    Hepato-Gastroenterology 50:1482–1486.
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
    Liver tumour promotion by chemicals: models and mechanisms
    1. DS Sarma
    2. PM Rao
    3. S Rajalakshmi
    (1986)
    Cancer Surveys 5:781–798.
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67
  68. 68
  69. 69

Decision letter

  1. Xuetao Cao
    Reviewing Editor; Zhejiang University School of Medicine, China
  2. Tadatsugu Taniguchi
    Senior Editor; Institute of Industrial Science, The University of Tokyo, Japan

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "NLRP12 suppresses hepatocellular carcinoma via downregulation of cJun N-terminal kinase activation in the hepatocyte" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tadatsugu Taniguchi as the Senior Editor. The reviewers have opted to remain anonymous.

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

Summary:

NLRP12 negatively regulates HCC pathogenesis and prevents HCC by maintaining a stable immunolandscape via downregulation of JNK-dependent inflammation and proliferation of hepatocytes.

Essential revisions:

1) More accurate quantitative methods are required to confirm the activation of JNK in NLRP12-inhibited HCC pathogenesis by excluding the involvement of the well-known NF-B and STAT3 pathways in this process.

2) The roles of NLRP12 in immune modulation have been ignored to some extent. The contribution of NLRP12-mediated modulation of immune response should be properly evaluated.

Reviewer #1:

In general the study provides a straight forward concept: Nlrp12 plays an important role in maintaining a stable immunolandscape through the regulation of JNK signaling to prevent HCC. However, the clinical patient data demonstrates that NLRP2 is more likely to be oncogenic in HCC than downregulated, in contrary to their model as there are three independent dataset that show a higher propensity for gene amplification in HCC tumors than deletions, which focus on how the KO of NLRP12 increases the tumor burden of chemically induced HCC. How do the authors reconcile that gene amplification also contributes to HCC (its potential role as oncogenic), while the main message is that the loss of NLRP2 is important for HCC (tumor suppressive)? While Figure 1B addresses that the down-regulation of NLRP12 is found in HCC, what is the likelihood of this data something that is important for HCC and not a specific event found in only in the UALCAN webportal? In addition, the tumors shown are only liver tumors, is this something specific to the liver or is are there nodules in other organs as well? Since DEN induces malignancy in other tissues as well. Is this likely to liver specific event as eluded by the author? Why would CCL4 treatment in DEN induced NLRP12 KO mice decrease the tumor size of DEN NLRP12 KO mice (Figure 1E vs. Figure 1J)? In addition, the mice seem to weigh less as well. These data indicate that CCL4, further hepatic injury, almost decreases tumor burden in DEN mice, and making them sicker. This doesn't make sense to the main paper. Have the authors looked at NLRP12 KO mice + CCL4 alone? Do these NLRP12 KO mice + CCL4 also have a higher mortality rate than NLRP12 KO mice +DEN alone? Does NLRP12 KO Mice +CCL4 also have a higher tumor burden then WT? These are more appropriate questions to ask as a secondary model. Survival is important indicator of tumor burden, does the NLRP12 KO+DEN mice get sicker and die sooner than others after the 10 months? These data would suggest that the viability of the tumor type is specific to NLRP12 KO and not the DEN itself.

Reviewer #2:

In the manuscript by Udden and colleagues, the authors report that NLRP12 negatively regulates HCC pathogenesis via downregulation of JNK-dependent inflammation and proliferation of hepatocytes. The role of NLRP12 as a negative regulator in multiple immune cells has been widely explored. However, the role and significance of NLRP12 in HCC pathogenesis have not been completely elucidated. The current study revealed a novel role of NLRP12 in hepatocyte during HCC pathogenesis. Moreover, the authors proposed that NLRP12-mediated JNK inhibition, but not NF-κB and ERK signaling, was critical for HCC prevention. Therefore, the study is of significance and novelty in the field of both immunology and oncology. Generally, the study is well designed and presented. Most of the data are convincing and impressive. However, there are several concerns may need to be further clarified.

1) The authors proposed a novel role of NLRP12 in hepatocytes during HCC pathogenesis while they also linked the effects to intestinal microbiota. However, since the model used is the NLRP12 knockout mice, the effects of NLRP12 in HCC pathogenesis definitely involved the immune microenvironment. While the data in Figures 1-3 are convincing, the roles of NLRP12 in immune modulation have been ignored to some extent. This is a major concern. The contribution of NLRP12-mediated modulation of immune response has not been properly evaluated. Additionally, whether NLRP12 KO affected the microbiota (leading to enhanced inflammation of liver)? Hepatocyte-specific knockout model may improve the convincingness of the study.

2) In experiments regarding responsiveness of hepatocytes to PAMPs, LPS stimuli were used as standard treatment. Although I understand LPS derived from microbiota may be the major PAMPs, the other major or well-known endogenous PAMPs generated during HCC pathogenesis may also be taken into consideration.

3) In Figure 4, the authors highlighted the JNK activation in NLRP12 KO tumors and cells. However, these data cannot convincingly exclude the activation of NF-κB and ERK pathway and the activation of STAT3 during HCC pathogenesis, as demonstrated in previous studies. Could the authors more quantitatively analyze the activation status of these related signaling mediators (WB is not sufficient as exclusion experiments)?

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

Thank you for resubmitting your work entitled "NLRP12 suppresses hepatocellular carcinoma via downregulation of cJun N-terminal kinase activation in the hepatocyte" for further consideration at eLife. Your revised article has been favorably evaluated by Tadatsugu Taniguchi (Senior Editor), a Reviewing Editor, and two reviewers.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

It would be nice if the authors could provide data that NLRP12 is downregulated in hepatocytes of DEN- or DEN plus CCL4-induced HCC in mice or that NLRP12 protein level is downregulated in human HCC tissues. Please discuss the weaknesses of this study: the lack of hepatocyte-specific Nlrp12 KO models and the fact that the conclusion on the contributions of inflammatory cell populations and cytokines to HCC pathogenesis in this model would need to be supported by more data in the future.

Reviewer #1:

The authors investigated the role of Nlrp12 as a protective protein against carcinogen-induced HCC via animal models. The authors show that in DEN Nlrp12 KO mice had high levels of proto-oncogenes and activation of JNK signaling. Inhibition of JNK signaling reduced proliferation and hepatocyte inflammatory responses.

The authors indicate in the rebuttal that the both genetic mutations and gene amplifications can occur and play contradicting roles in cancer. I agree. But the authors suggest that NRPL12 knockout enhances HCC progression. The concern here is the interpretation of what these genomic data means. A low frequency of alterations (<3%) in NRPL12 indicate that in humans, NRPL12 genomic alterations are rare and that NRPL12 has the potential to play both roles. In addition, the authors work shows that NRPL12 KO enhances HCC progression, therefore its likely not important for tumor initiation (as the NRPL12 KO alone doesn't produce much tumors). In addition, the author did not reconcile that in humans, NRPL12, with its low frequency in changes and a 1% vs. 2.5% in TCGA, but a 1% and 1% in AMC, or mutated less 2% in Inserm, that NRPL12 potentially could also be oncogenic and potentially play a minimal role in HCC progression? What is the likelihood that all other gene mutations with similar alterations in human samples are also functionally important, if not more important? While I appreciate that it's not easy to do a holistic approach, this data was not well explained nor was it reconciled in the manuscript or carefully and thoughtfully rebutted.

In addition, the RNASeq data (using a different data platform (UALCAN instead of cbioportal, which collects and processes TCGA differently, also is an issue as it may skew data). The authors indicate that because they used UALCAN, a platform for TCGA analysis, for RNASeq values data, the graph 1B is considered unbiased? I'm not sure what that means. This figure shows that in the TCGA data, there is a difference between tumor and non-tumor (not the same as normal livers, since TCGA patients are more likely to be HBV, HCV, NAFLD and fatty liver disease as underlying liver disease) with unequal numbers of samples. What is the likelihood of this difference being specific to this dataset (could be background noise due to the low number of non-tumor samples) as opposed to be a robust HCC specific observation? To answer this question, the authors have to check other datasets that are available to confirm that these observations are stable (not specific to this cohort, not specific to the low numbers of non-tumors). The authors did so in 1A, but did not in 1B.

In response to the authors comment about DEN-induced mice not producing tumors in other organs, DEN is also widely used as a carcinogen in other animal models for other cancer types. Where are the data to demonstrate this model doesn't? I don't agree that because its well-used in the liver field, the authors shouldn't show controls. At least at the timepoint in which the authors sacrificed the mice.

Reviewer #2:

The authors have performed additional experiments to address my concerns, such as comparison of microbiota, ELISA confirmation of JNK activation, and clarification of inflammatory factors in HCC pathogenesis. More importantly, the authors have clearly described the limitations and provided possibilities for the current data. So generally I agree that the authors have made essential revisions for the manuscript. Considering the importance and novelty of the current study, I recommend to accept the paper for publication.

https://doi.org/10.7554/eLife.40396.037

Author response

Essential revisions:

1) More accurate quantitative methods are required to confirm the activation of JNK in NLRP12-inhibited HCC pathogenesis by excluding the involvement of the well-known NF-B and STAT3 pathways in this process.

To understand what signaling pathways contribute to increased HCC pathogenesis in Nlrp12-/- mice, we measured the activation status of different signaling pathways including NF-κB (p65), ERK, JNK, p38, and STAT3 in tumor lysates from wild-type and Nlrp12-/- mice by Western blotting (Figure 4D and 4E). Our data show that all these pathways are activated in both WT and Nlrp12-/- HCC. However, the intensity of P-JNK was significantly higher in Nlrp12-/-HCC compared to that of WT (Figure 4D and 4E). We would like to clarify that we are not implying that JNK is the only pathway activated in Nlrp12-/- HCC tissues and other pathways do not contribute to HCC. While p65, ERK, p38, and STAT3 pathways are also activated, there was no statistically significant difference in their levels between WT and Nlrp12-/- tumors (Figure 4E), implicating a dominant role of P-JNK in higher HCC pathogenesis in Nlrp12-/- mice.

Western blot is a gold standard method for measuring the activation of cell signaling pathways. However, as the reviewer insisted on measuring the activation of these pathways in a different quantitative approach, we used an ELISA-based method (Pathscan Inflammation Multi-Target ELISA; Cell Signaling Technology) (Figure 4F). The new data showing higher P-JNK in Nlrp12-/- HCC lysates compared to WT is consistent with our Western blot analysis. There was no significant difference in the activation of other pathways (Figure 4F). Therefore, this quantitative analysis further strengthens our observation that NLRP12 down-regulates JNK activation in the liver during HCC development.

2) The roles of NLRP12 in immune modulation have been ignored to some extent. The contribution of NLRP12-mediated modulation of immune response should be properly evaluated.

To understand the role of NLRP12 in immunomodulation, we measured the expression of several proinflammatory cytokines and chemokines in HCC tissues by real-time qPCR (Figure 2C, Figure 2—figure supplement 1D), and quantify different immune cell population including macrophages, dendritic cells, T cells, and neutrophils by flow cytometry (Figure 2D and 2E). We observed that IL-6, TNFα, Cxcl11 (KC), Cxcl2 (MIP2), and Ccl2 (MCP1) are significantly highly expressed in the tumors of Nlrp12-/- mice. In addition to these, we measured several others cytokines and chemokines but no difference was observed in their levels between the groups. In the interest of space, we didn’t include those non-significant data. The flow cytometric analysis of tumor infiltrated immune cells show a higher number of macrophages and dendritic cells in Nlrp12-/- tumors, while there was no major difference in T cells and neutrophils (Figure 2D and 2E). Consistently, we could not see any difference in IFNγ, IL-17 and IL-4 (Figure 2—figure supplement 1F), suggesting that NLRP12 doesn’t regulate T cell responses in HCC. Since we didn’t observe any difference in T cell number or T-cell dependent cytokines between wild-type and Nlrp12-/- HCC, we didn’t further investigate the role of NLRP12 in T cell responses in the liver. Instead, we focused on understanding why Nlrp12-/- HCC exhibited higher levels of proinflammatory cytokines and chemokines. To this end, we analyzed signaling pathways and observed that Nlrp12 deficiency leads to hyper activation of JNK in the HCC. We further clarified what cell types regulate JNK activation in Nlrp12-dependent manner by isolating hepatocytes, Kupffer cells, and hepatic stellate cells. Our data suggest that Nlrp12 regulates JNK activation in the hepatocyte but not in macrophages and stellate cells (Figure 4J). All these data led us to focus on the hepatocyte-specific function of NLRP12 which we described in Figure 6 and Figure 7. With all these data, we believe that we investigate the role of NLRP12 in immune modulation to an extent what is necessary to explain the underlying mechanism of higher HCC susceptibility in Nlrp12-/- mice.

As the second reviewer stressed to further analyze the role of NLRP12 in the regulation of immune responses, we performed additional experiments and included the new data in the revised manuscript. At first, we measured the key cytokines IL-6, TNFα, and KC in the lysate of WT and Nlrp12-/- tumors by ELISA. We observed that, consistent to real-time qPCR analysis, these proinflammatory cytokines were significantly highly expressed in Nlrp12-/-HCC (Figure 2—figure supplement 1E). Next, we addressed whether NLRP12 deficiency affects immune responses in the liver at homeostasis. To this end, we measured cytokines and chemokines in age and sex-matched healthy WT and Nlrp12-/- mouse livers. The expression levels of IL-6, TNFα, CXCL1, CXCL2, and CCL2 were seen comparable between WT and Nlrp12-/- livers (Figure 5—figure supplement 1C). We also measured the number of different immune cell population in the liver of age-matched healthy WT and Nlrp12-/- mice by flow cytometry and observed no difference in the number of macrophages, dendritic, T cells, and neutrophils between the two groups (Figure 5—figure supplement 1D). This data is supported by real-time qPCR analysis of F4/80 in the healthy liver, showing no major difference between the groups (Figure 2—figure supplement 1G). Finally, we measured the activation status of different signaling pathways in healthy livers. Interestingly, no difference in the activation of JNK, as well as other signaling pathways, was observed between healthy WT and Nlrp12-/- livers (Figure 5—figure supplement 1E). All these data suggest that Nlrp12 deficiency doesn’t induce any immune dysregulation in the liver without any pathological trigger. We have discussed these observations in the last paragraph of the subsection “NLRP12 attenuates PAMPs-mediated hepatic inflammation and oncogenesis”.

In summary, our data suggest that NLRP12-mediated regulation of JNK in the hepatocyte play the key role in the suppression of HCC. Previously, we and others have shown that NLRP12 regulates inflammatory responses in myeloid and T cells in different disease contexts (Zaki et al., 2011; Lukens et al., 2015). It is possible that NLRP12 regulates different signaling pathways in cell type and tissue-specific manner. However, we do not completely exclude the involvement NLRP12-dependent regulation of myeloid and T cell-mediated immune responses in HCC pathogenesis. To further characterize NLRP12-dependent regulation of immune responses during HCC, we need to conditionally knockout NLRP12 in different immune cells. Due to resource limitation, we are not able to extend our investigation to that extent in this current study, but our future study will focus on this aspect.

Reviewer #1:

In general the study provides a straight forward concept: Nlrp12 plays an important role in maintaining a stable immunolandscape through the regulation of JNK signaling to prevent HCC. However, the clinical patient data demonstrates that NLRP2 is more likely to be oncogenic in HCC than downregulated, in contrary to their model as there are three independent dataset that show a higher propensity for gene amplification in HCC tumors than deletions, which focus on how the KO of NLRP12 increases the tumor burden of chemically induced HCC. How do the authors reconcile that gene amplification also contributes to HCC (its potential role as oncogenic), while the main message is that the loss of NLRP2 is important for HCC (tumor suppressive)?

We thank the reviewer for the valuable comments. With due respect to the reviewer comments, we disagree with the reviewer’s opinion that NLRP12 is more likely to be oncogenic. The data presented in Figure 1A shows that mutation in NLRP12 is seen in 1.5-2.5% HCC, while amplification is observed in less than 1% patients. It is not uncommon that both mutation and amplification of a particular gene are associated with carcinogenesis. While loss-of-function mutations of a particular gene are commonly associated with carcinogenesis, amplification of the same gene is also linked with cancer susceptibility. Nlrp12 amplification may reduce JNK activation to a point that dysregulates normal liver physiology and promotes tumorigenesis. Indeed, inhibition of JNK1 and JNK2 in hepatocytes was seen to promote HCC burden (Das et al., 2011), while Jnk1-/- mice were protected from HCC (Hui et al., 2008). Similarly, TCGA database analyses through cBioportal show that both mutation and amplification of JNK are linked with HCC. Thus, it is not surprising that both mutations and amplifications of NLRP12 are associated with increased HCC.

While Figure 1B addresses that the down-regulation of NLRP12 is found in HCC, what is the likelihood of this data something that is important for HCC and not a specific event found in only in the UALCAN webportal?

UALCAN is a platform for analysis of publicly available RNAseq database of cancer. The graph presented here (Figure 1B) was based on TCGA data, which is a great resource of cancer genomics and commonly used by the research community. Therefore, the analysis shown in Figure 1B should be considered unbiased.

In addition, the tumors shown are only liver tumors, is this something specific to the liver or is are there nodules in other organs as well? Since DEN induces malignancy in other tissues as well. Is this likely to liver specific event as eluded by the author?

DEN-induced HCC is widely used model for HCC study. To our knowledge, the dose of DEN we used does not induce tumor in other organs. Indeed, we didn’t observe any tumor development in other organs.

Why would CCL4 treatment in DEN induced NLRP12 KO mice decrease the tumor size of DEN NLRP12 KO mice (Figure 1E vs. Figure 1J)? In addition, the mice seem to weigh less as well. These data indicate that CCL4, further hepatic injury, almost decreases tumor burden in DEN mice, and making them sicker. This doesn't make sense to the main paper.

The differences in tumor size and mouse body between DEN plus CCl4 vs DEN alone model is due to the difference in time points when HCC was evaluated. As outlined in these two models in Figure 1C and 1H, the endpoints of the DEN model and DEN +CCl4 model were 10 and 6 months after birth respectively. No visible tumor lesion was observed in mice treated with DEN alone until 7 months post DEN administration (data not shown). Therefore, had we compared livers of DEN-treated mice at 6 months, we would not see any tumors in the earlier group while DEN+CCl4-treated mice developed an appreciable number of visible tumors. Similarly, if we would have allowed DEN+CCl4-treated mice to live until 10 months, there would have a higher number and much bigger tumors in the liver. Even some mice may not survive up to that time point due to severe HCC related complication. Therefore, the data presented here don’t imply that CCl4 suppresses DEN-induced HCC, rather it accelerates HCC development. For the same reason, the reviewer might be confused with the data representing liver to body weight ratios (Figure 1E and 1J). Since DEN-treated mice were aged (10 months) compared to DEN+CCl4-treated mice (6 months), the body weight of the former group was higher. Furthermore, mice received CCl4 for 2 months starting at 2.5 months after their birth. CCl4 is a hepatotoxin which induces liver injury and affects liver function. Thus DEN+CCl4-treated mice gained body weight at a slower rate than DEN-treated mice. However, both groups of mice remained apparently healthy until the endpoint and there was no incidence of death during the experiment. Finally, we would like to mention that both of these two models are commonly used in HCC studies. The number and size of tumor lobes depend on the dose of CCl4and the duration of the experiment. Thus, although the tumor size and other morphometric features of HCC obtained from two different models are not comparable, the conclusion that Nlrp12-/- mice are more susceptible to HCC compared to WT mice is same.

Have the authors looked at NLRP12 KO mice + CCl4 alone? Do these NLRP12 KO mice + CCl4 also have a higher mortality rate than NLRP12 KO mice +DEN alone? Does NLRP12 KO Mice +CCl4 also have a higher tumor burden then WT? These are more appropriate questions to ask as a secondary model. Survival is important indicator of tumor burden, does the NLRP12 KO+DEN mice get sicker and die sooner than others after the 10 months? These data would suggest that the viability of the tumor type is specific to NLRP12 KO and not the DEN itself.

Thanks for asking some intriguing questions. Without DEN, CCl4 alone doesn’t induce any tumor and no DEN or DEN+CCl4-injected mouse died during the experiment. The role of NLRP12 on CCl4-induced liver fibrosis is an important concern, but out of the scope of this current study. A role for NLRP12 in liver fibrosis should be investigated in depth in a separate study using appropriate animal models (we discussed in the last paragraph of the subsection “NLRP12 regulates hepatocyte proliferation via JNK”). We agree that it would be interesting to see whether DEN-treated Nlrp12-/-mice die faster than WT mice. However, our IACUC protocol does not permit to leave the experimental mice until they die. With the help of published literature and our own preliminary experiments, we optimized treatment doses and experimental scheme which would allow us to evaluate HCC pathogenesis without reaching to a condition when mice would become moribund. We are required by the IACUC to terminate the study before mice suffer from pain and distress.

Reviewer #2:

[…] 1) The authors proposed a novel role of NLRP12 in hepatocytes during HCC pathogenesis while they also linked the effects to intestinal microbiota. However, since the model used is the NLRP12 knockout mice, the effects of NLRP12 in HCC pathogenesis definitely involved the immune microenvironment. While the data in Figures 1-3 are convincing, the roles of NLRP12 in immune modulation have been ignored to some extent. This is a major concern. The contribution of NLRP12-mediated modulation of immune response has not been properly evaluated. Additionally, whether NLRP12 KO affected the microbiota (leading to enhanced inflammation of liver)? Hepatocyte-specific knockout model may improve the convincingness of the study.

Please see our reply in response to editorial comment #2 regarding the concern on the role of NLRP12 in the modulation of immune response.

To address the reviewer’s concern regarding microbiota, we measured the microbiota composition of healthy wild-type and Nlrp12-/- mice by 16S rRNA gene sequencing. We observed that the composition of certain bacterial species is different between WT and Nlrp12-/- mice. However, such a difference in microbiota composition didn’t impact on immune homeostasis in the liver as inflammatory responses in the livers of the healthy WT and Nlrp12-/- mice are comparable (Figure 5—figure supplement 1C-E). We have discussed the new data in the last paragraph of the subsection “NLRP12 attenuates PAMPs-mediated hepatic inflammation and oncogenesis”.

2) In experiments regarding responsiveness of hepatocytes to PAMPs, LPS stimuli were used as standard treatment. Although I understand LPS derived from microbiota may be the major PAMPs, the other major or well-known endogenous PAMPs generated during HCC pathogenesis may also be taken into consideration.

We have shown that in addition to LPS, other PAMPS such as peptidoglycan (PGN) and cytokine TNFa also regulate JNK in an Nlrp12-dependent manner (Figure6—figure supplement 1B and 1C). Several molecules and danger signals produced in the tumor microenvironment may also activate JNK. However, examining the role of NLRP12 in activating JNK in response to all of these endogenous ligands and implicating those data in our current study is out of context.

3) In Figure 4, the authors highlighted the JNK activation in NLRP12 KO tumors and cells. However, these data cannot convincingly exclude the activation of NF-κB and ERK pathway and the activation of STAT3 during HCC pathogenesis, as demonstrated in previous studies. Could the authors more quantitatively analyze the activation status of these related signaling mediators (WB is not sufficient as exclusion experiments)?

Please see our reply to the editorial comment #1.

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

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

It would be nice if the authors could provide data that NLRP12 is downregulated in hepatocytes of DEN- or DEN plus CCL4-induced HCC in mice or that NLRP12 protein level is downregulated in human HCC tissues. Please discuss the weaknesses of this study: the lack of hepatocyte-specific Nlrp12 KO models and the fact that the conclusion on the contributions of inflammatory cell populations and cytokines to HCC pathogenesis in this model would need to be supported by more data in the future.

We thank the editors and reviewers to review our revised manuscript favorably. As suggested by the editors, we measured Nlrp12 expression in healthy and HCC livers and presented the data in the revised manuscript (Figure 1C). The new data show that Nlrp12 is downregulated in HCC tissue compared to healthy livers. This data supports the RNAseq data showing that the expression of NLRP12 is significantly reduced in human HCC (Figure 1B).

We have discussed the weakness of this study as the reviewer and editors pointed out in the revised manuscript. Please see the fourth paragraph of the Discussion for relevant changes.

Reviewer #1:

[…] The authors indicate in the rebuttal that the both genetic mutations and gene amplifications can occur and play contradicting roles in cancer. I agree. But the authors suggest that NRPL12 knockout enhances HCC progression. The concern here is the interpretation of what these genomic data means. A low frequency of alterations (<3%) in NRPL12 indicate that in humans, NRPL12 genomic alterations are rare and that NRPL12 has the potential to play both roles. In addition, the authors work shows that NRPL12 KO enhances HCC progression, therefore its likely not important for tumor initiation (as the NRPL12 KO alone doesn't produce much tumors). In addition, the author did not reconcile that in humans, NRPL12, with its low frequency in changes and a 1% vs. 2.5% in TCGA, but a 1% and 1% in AMC, or mutated less 2% in Inserm, that NRPL12 potentially could also be oncogenic and potentially play a minimal role in HCC progression? What is the likelihood that all other gene mutations with similar alterations in human samples are also functionally important, if not more important? While I appreciate that it's not easy to do a holistic approach, this data was not well explained nor was it reconciled in the manuscript or carefully and thoughtfully rebutted.

We thank the reviewers for critically reviewing our manuscript, which helped improve our manuscript and better explain the role of NLRP12 in HCC. We agree with the reviewer that NLRP12-deficiency alone doesn’t initiate HCC. During tumor promotion, NLRP12 acts as a tumor suppressor, at least in our mouse model. Therefore Nlrp12-deficient mice develop a higher number and faster progression of the tumor. We don’t consider NLRP12 a potential oncogene or tumor suppressor, rather it is a regulator of signaling pathways, such as JNK, which are involved in HCC pathogenesis. Similar to NLRP12, many other innate immune molecules are involved in the regulation of HCC. Previous studies described the role of JNK, IKKb, NEMO, RIG-I, TLR4, etc. in HCC pathogenesis (Dapito et al., 2012; Hou et al., 2014; Hui et al., 2008; Luedde et al., 2007; Maeda et al., 2005). None of these innate molecules are seen to be altered at a higher frequency in human HCC (please see Author response image 1), but their roles in the protection against HCC are appreciated. In fact, HCC is a genetically heterogeneous disease. While several genes such as TP53, CTCNB1, WNT, ARID1A, TERT, etc. are frequently mutated in HCC, genetic alterations in many other genes are associated with HCC and has been shown to promote the tumorigenesis process. Therefore, many genes with lower frequency mutations are considered critical regulators of HCC pathogenesis. As the reviewer may understand, comparison of NLRP12 with other related genes in HCC pathogenesis is out of scope of this study. However, we have added a discussion on this issue in light of the reviewer’s comments in the revised manuscript (please see Discussion, second paragraph). We hope that with these changes and discussion in this rebuttal letter, the reviewer will find that we have adequately addressed the reviewer’s concerns.

Author response image 1
Analysis of gene alterations of different innate immune genes using cBioportal.

In addition, the RNASeq data (using a different data platform (UALCAN instead of cbioportal, which collects and processes TCGA differently, also is an issue as it may skew data). The authors indicate that because they used UALCAN, a platform for TCGA analysis, for RNASeq values data, the graph 1B is considered unbiased? I'm not sure what that means. This figure shows that in the TCGA data, there is a difference between tumor and non-tumor (not the same as normal livers, since TCGA patients are more likely to be HBV, HCV, NAFLD and fatty liver disease as underlying liver disease) with unequal numbers of samples. What is the likelihood of this difference being specific to this dataset (could be background noise due to the low number of non-tumor samples) as opposed to be a robust HCC specific observation? To answer this question, the authors have to check other datasets that are available to confirm that these observations are stable (not specific to this cohort, not specific to the low numbers of non-tumors). The authors did so in 1A, but did not in 1B.

The major focus of this study is to demonstrate a biological function of NLRP12 in inflammation and carcinogenesis in the liver using animal models of HCC. The reviewer may appreciate that we have demonstrated compelling data based on animal models and cell culture systems which support a critical role for NLRP12 in the suppression of HCC. We are very sincere about the robustness and reproducibility of our experimental data. Therefore, we performed every key experiment in multiple ways. For example, we showed the HCC phenotype in two different model – DEN and DEN plus CCl4. We measured the signaling pathways in whole liver, tumor tissues, isolated tumor cells, and primary hepatocytes. In addition, we overexpressed and knocked down NLRP12 in vitro and confirmed the function of NLRP12 in hepatocytes. Similarly, we established the role of NLRP12 in hepatocyte proliferation in multiple approaches including Ki67 staining of tumor tissue and hepatocyte culture, IncuCyte live imaging, and BrdU staining. Including mutation and expression analysis of NLRP12 in human HCC (Figure 1A and 1B) is a part of our effort to support the findings of our experimental study. However, genome sequencing and bioinformatics are not our expertise. We, therefore, relied on publicly available online platforms for genomics and transcriptomics analyses of NLRP12. We analyzed the frequency of alterations of NLRP12 in human HCC based on available datasets in cBioportal. However, cBioportal is not a suitable platform for the analysis of expression profile of a gene of interest and comparison between normal vs tumor samples. We found that UALCAN web-portal, which uses only TCGA datasets, is a helpful platform in this regard. We are sorry that we are not able to provide RNAseq data analysis from other sources as the reviewer asked; UALCAN doesn’t have any resource/option to analyze other datasets. On the other hand, cBioportal doesn’t provide a comparative analysis of gene expression in tumor vs non-tumor samples. However, as the editor suggested, we now provide Nlrp12 expression profile in healthy and HCC mouse livers. Our data demonstrate that Nlrp12 expression is significantly reduced in liver tumors compared to healthy livers (Figure 1C). We hope that the reviewer will find this new data is supportive of human NLRP12 expression data (Figure 1B).

We understand the importance of a large scale clinical data analysis to establish the role of a gene in a particular disease. However, we believe that our study will be used as a platform for future research focusing on genomics and transcriptomics analyses of NLRP12 in HCC and other liver disorders.

In response to the authors comment about DEN-induced mice not producing tumors in other organs, DEN is also widely used as a carcinogen in other animal models for other cancer types. Where are the data to demonstrate this model doesn't? I don't agree that because its well-used in the liver field, the authors shouldn't show controls. At least at the timepoint in which the authors sacrificed the mice.

As we explained previously, we opted to use DEN model because it is widely used for HCC study (Bakiri and Wagner, 2013). We acknowledge that as a DNA alkylating agent, DEN may have a potential for inducing tumor in other organs. However, DEN-induced carcinogenesis is dependent on dose, time of administration, and duration of the treatment (Verna et al., 1996). For example, a low dose of DEN administration in mice at 2 weeks of age induces tumor development in the liver because liver cells are rapidly proliferating at that age allowing DNA modification. DEN-induced HCC model was developed by other researchers about 5 decades ago (Rajewsky et al., 1966) and optimized over time to make it an ideal model for studying HCC. We followed HCC studies published by eminent scientists like Dr. Michael Karin and Dr. Robert Schwabe. In their published literature, we could not find any description of tumor development in other organs during DEN-induced HCC (Dapito et al., 2012; Maeda et al., 2005; Sakurai et al., 2008; Sakurai et al., 2006). In our own study, we also didn’t notice any apparent tumor development in other organs like kidney, liver, and gastrointestinal tract. We are showing some representative images here taken during the sacrifice of DEN-treated mice (please see Author response image 2). We are not excluding the possibility of tumor metastasis in other organs at an advanced stages of HCC. However, honestly speaking, we did not focus on this aspect in this current study. To test such a possibility, we need to examine different organs histopathologically. In the future study, we will investigate whether Nlrp12-deficiency promotes tumor metastasis.

Author response image 2
Gross anatomy of DEN and DEN plus CCL4-treated mice.
https://doi.org/10.7554/eLife.40396.038

Article and author information

Author details

  1. SM Nashir Udden

    Department of Pathology, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Writing—original draft
    Competing interests
    No competing interests declared
  2. Youn-Tae Kwak

    1. Department of Pathology, UT Southwestern Medical Center, Dallas, United States
    2. Department of Biochemistry, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  3. Victoria Godfrey

    Department of Pathology, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  4. Md Abdul Wadud Khan

    Department of Surgical Oncology, MD Anderson Cancer Center, Houston, United States
    Contribution
    Formal analysis, Analyzed 16S rRNA sequencing
    Competing interests
    No competing interests declared
  5. Shahanshah Khan

    Department of Pathology, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Nicolas Loof

    Children's Research Institute, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  7. Lan Peng

    Department of Pathology, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation, Performed histopathological scorings
    Competing interests
    No competing interests declared
  8. Hao Zhu

    1. Children's Research Institute, UT Southwestern Medical Center, Dallas, United States
    2. Department of Pediatrics, UT Southwestern Medical Center, Dallas, United States
    3. Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, United States
    4. Department of Internal Medicine, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Resources, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8417-9698
  9. Hasan Zaki

    Department of Pathology, UT Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    hasan.zaki@utsouthwestern.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9002-5399

Funding

Cancer Prevention and Research Institute of Texas (RP160169)

  • Hasan Zaki

UT Southwestern Medical Center

  • Hasan Zaki

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

Acknowledgements

We would like to thank the UT Southwestern Animal Resource Center (ARC) for maintenance and care of our mouse colony. We are thankful to Millennium Pharmaceuticals and Dr. Thirumala-devi Kanneganti at St. Jude Children’s Research Center for sharing Nlrp12-/- mice. We also thank Dr. James S Malter at the Department of Pathology, UT Southwestern Medical Center, for critically reviewing the manuscript. This work was supported by Cancer Prevention and Research Institute of Texas (CPRIT) Individual Investigator Award (RP160169), and UT Southwestern funding given to Hasan Zaki. Hao Zhu was supported by NCI (R01CA190525) and CPRIT (RP180268) 

Ethics

Animal experimentation: This study was performed under the protocol #2016-101683 which was approved by the Institutional Animal Care and Use Committee (IACUC). All animal experiments were conducted in accordance with the IACUC guidelines and the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Senior Editor

  1. Tadatsugu Taniguchi, Institute of Industrial Science, The University of Tokyo, Japan

Reviewing Editor

  1. Xuetao Cao, Zhejiang University School of Medicine, China

Publication history

  1. Received: July 24, 2018
  2. Accepted: March 25, 2019
  3. Accepted Manuscript published: April 16, 2019 (version 1)
  4. Version of Record published: April 25, 2019 (version 2)

Copyright

© 2019, Udden et al.

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

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