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A long-range interactive DNA methylation marker panel for the promoters of HOXA9 and HOXA10 predicts survival in breast cancer patients

Background Most DNA cancer methylation markers are based on the transcriptional regulation of the promoter-gene relationship. Recently, the importance of long-range interactions between distal CpGs and target genes has been revealed. Here, we attempted to identify methylation markers for breast cancer that interact with distant genes. Results We performed integrated analysis using chromatin interactome data, methylome data, transcriptome data, and clinical information for breast cancer from public databases. Using the chromatin interactome and methylome data, we defined CpG-distant target gene relationships. After determining the differences in methylation between tumor and paired normal samples, the survival association, and the correlation between CpG methylation and distant target gene expression, we selected CpG methylation marker candidates. Using Cox proportional hazards models, we combined the selected markers and evaluated the prognostic model. We identified six methylation markers in HOXA9 and HOXA10 promoter regions and their long-range target genes. We experimentally validated the chromatin interactions, methylation status, and transcriptional regulation. A prognostic model showed that the combination of six methylation markers was highly associated with poor survival in independent datasets. According to our multivariate analysis, the prognostic model showed significantly better prognostic ability than other histological and molecular markers. Conclusions The combination of long-range interacting HOXA9 and HOXA10 promoter CpGs predicted the survival of breast cancer patients, providing a comprehensive and novel approach for discovering new methylation markers. Electronic supplementary material The online version of this article (doi:10.1186/s13148-017-0373-z) contains supplementary material, which is available to authorized users.

Background
Breast cancer is both the most common cancer and the most frequent cause of cancer-related deaths among women [1]. Based on the expression level of hormone receptors, such as the estrogen receptor (ER) and progesterone receptor (PR), or human epidermal growth factor receptor (Her2), breast cancers are divided into several subtypes, and small molecules or antibodies targeting ER, PR, and Her2 have been used in breast cancer therapies [2]. Breast cancer is conventionally diagnosed by mammography, but this method cannot be applied to some cases, including women with premenopausal breast cancer [3]. Molecular markers and reference laboratory tests for breast cancer diagnosis and prognosis have been developed, but the methods are limited to specific subtypes, such as node-negative and ER-positive breast cancer [4,5]. Thus, novel approaches for the diagnosis and prognosis of breast cancer are still needed.
DNA methylation is one of the most well-known aberrations in human cancers [6]. During tumor progression from normal tissue to invasive cancer, the total level of DNA methylation gradually decreases, but the frequency of hypermethylated CpG islands on promoters increases, causing the transcriptional silencing of tumor-suppressive genes [7,8]. DNA methylation markers have advantages compared to other molecular markers. For example, hypermethylation of promoter CpGs is a common and early event during the progression of various tumors [9,10], and DNA methylation is more chemically and biologically stable than RNA or most proteins [6]. DNA methylation markers for cancer diagnosis and prognosis have been discovered, and some of them have been used in clinical trials [11,12]. For breast cancer, researchers have also reported particular DNA methylation markers [13,14], some of which need further development for clinical application.
The DNA methylation of promoters and CpG islands is known to inhibit target gene expression by regulating the binding of transcription modulators to the promoter [15,16]. The long-range interaction between CpGs and target genes has been reported [17,18]. A recent genomic study revealed that the correlation between DNA methylation at distal regulatory sites and long-range target gene expression is significantly stronger than the correlation with promoter methylation and that differences in DNA methylation between cancer and normal tissues at distal regulatory sites are significantly greater than differences in promoter methylation among various cancer types [19]. Nevertheless, most DNA methylation markers for cancer diagnosis and prognosis have been developed based on promoter-gene relationships because of the difficulty of defining the relationship between distal CpGs and target genes. The long-range action of distal CpG-target gene interaction and transcriptional regulation can be specified by chromatin interactome data, particularly data from RNA polymerase II (Pol II) chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) [20]. Thus, novel approaches could be based on the long-range interaction between CpGs and their target genes.
In this study, we identified DNA methylation markers for breast cancer and the putative target genes that had long-range interactions using an integrated analysis incorporating the chromatin interactome, methylome, and transcriptome data for breast cancer from public databases. We tried to validate the chromatin interaction, methylation status, and transcriptional regulation. Selected marker candidates were combined to establish a prognostic model and evaluated as markers for breast cancer.

Public data analysis
The Cancer Genome Atlas (TCGA) methylome (Illumina Infinium Human Methylation 450k BeadChip microarray data, Infinium HM450k) and transcriptome (high-throughput RNA sequencing, RNA-seq) data containing clinical information were downloaded from the International Cancer Genome Consortium (ICGC) data portal (http://icgc.org/). The chromatin interactome (chromatin interaction analysis by paired-end tag sequencing, ChIA-PET) data were downloaded from the Encyclopedia of DNA Elements (ENCODE) databases (https://genome.ucsc.edu/ENCODE/). Another Infinium HM450k methylome dataset for validation was downloaded from the NCBI GEO database (http:// www.ncbi.nlm.nih.gov/geo/) (GSE39004). The expression microarray (Affymetrix Human Genome U133 Plus 2.0 microarray, affyU133P2) data for MCF7 breast cancer cells after 5-azacytidine (5-aza C) treatment and the untreated control data were downloaded from the NCBI GEO database (GSE22250). The methylome data were globally normalized using β values (methylation ratio). The RNA-seq data were normalized based on the RPKM (reads per kilobase per million mapped reads) values. The affyU133P2 data were globally normalized using the Robust Multi-array Average (RMA) method.
The genomic positions were defined by the human hg19 reference genome. Genomic loci from 2000 bp upstream to 500 bp downstream of the transcription start sites (TSS) were defined as promoters. ENCODE MCF7 Pol II ChIA-PET data deposited in the UCSC genome browser database (http://genome.ucsc.edu/) were used to define CpG-target gene relationships. Genes whose promoters were anchored by ChIA PET reads were defined as target genes, and CpGs that overlapped with opposite ends of promoter-anchored ends were defined as distal CpGs.
Statistical tests were performed using the R program (https://www.r-project.org/). Graphs and heatmaps were prepared using Excel (Microsoft) and R.
Cell culture and AZA treatment of the MCF7 breast cell line The MCF7 cell line was purchased from the American Type Culture Collection (ATCC). MCF7 was maintained in complete Dulbecco's modified Eagle medium (DMEM, HyClone) at 37°C in a humidified 5% CO 2 incubator. The complete medium was supplemented with 10% fetal bovine serum (HyClone), 100 U/ml penicillin/ streptomycin (WelGENE), and 2 mM L-glutamine (HyClone).

Chromosome conformation capture (3C)
For this process, 5.0 × 10 6 cells were cross-linked with 2% formaldehyde for 10 min at 25°C. Five milliliters of NP-40 buffer (10 mM Tris-HCl, pH 7.5, 10 mM NaCl, 0.2% NP-40, and a protease inhibitor cocktail) was added to the cells, and the cells were incubated at 4°C for 2 h.
The mixture was then centrifuged, and the pellet was resuspended in 0.5 ml of 1.2× DpnII restriction enzyme buffer (NEB). Fifteen microliters of 10% SDS was added to the sample, and the mixture was incubated at 37°C for 1 h. Forty microliters of 25% Triton-X100 was added, and the sample was incubated at 37°C for 1 h. The sample was digested with 400 units of DpnII restriction enzyme at 37°C for approximately 18 h. To deactivate DpnII, 80 μl of 10% SDS was added to the sample, and the mixture was incubated at 65°C for 20 min. Then, 6.125 ml of 1.15× ligation buffer and 300 μl of 25% Triton-X100 were added, and the sample was incubated at 37°C for 1 h. The digested samples were ligated with 100 units of T4 DNA ligase (Promega) at 16°C for 4 h and then at 25°C for 30 min. Reverse cross-linking and proteinase K treatment were performed overnight at 65°C. The chromatin was then treated with RNase A for 1 h at 37°C. The DNA was purified using a phenol/chloroform extraction or with a QIAquick PCR Purification Kit.
3C-PCR assays were performed using amfiXpand PCR Master Mix. The data were normalized to "internal" primers for the GAPDH gene. At least three independent biological replicates were included for each 3C-PCR assay. The primer sequences are listed in Additional file 1: Table S1.

Reverse transcription PCR
The total RNA was extracted using the RNeasy Mini Kit (QIAGEN) according to the manufacturer's instructions. Reverse transcription was performed with 1 μg of total RNA as the template and M-MLV Reverse Transcriptase (Promega). RT-PCR assays were performed using AmfiXpand PCR Master Mix (GenDEPOT). The cDNA expression was normalized to the levels of GAPDH. The primers used for the PCR reactions were designed either manually or using the Primer3 program (http://biotools.umassmed.edu/bioapps/primer3_www.cgi). All primer sequences are listed in Additional file 1: Table S1.

Pyrosequencing
The total DNA was extracted using a QIAamp DNA Blood Mini Kit (QIAGEN) according to the manufacturer's protocol. In total, 0.5 μg of total DNA from each of the samples was used for bisulfite conversion using an EZ DNA Methylation Lightning kit (Zymo Research). The bisulfite-converted DNA was amplified using TOPsimple Premix (Enzynomics). Pyrosequencing was performed using the PyroMark Q96 ID (PSQ 96MA, QIAGEN) system according to the manufacturer's protocol. Pyrosequencing primers (forward, reverse, and sequencing) were designed using the PSQ Assay Design program (version 1.0.6). All primer sequences are listed in Additional file 1: Table S1.

HOXA9 and HOXA10 promoter CpG selection from public data
To identify DNA methylation markers for breast cancer that physically interact with distant genes, we performed an integrated analysis and stepwise selection of the DNA methylation markers using publicly available chromatin interactome, methylome, transcriptome, and clinical information (Fig. 1a). The following data sets were used: the chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data for the MCF7 breast cancer cell line from the ENCODE database, Illumina Infinium Human Methylation 450k BeadChip (Infinium HM450k) microarray data, high-throughput RNA sequencing (RNA-seq) data and clinical information for breast cancer patients from TCGA database, and Affymetrix Human Genome U133 Plus 2.0 microarray (affyU133P2) data for MCF7 breast cancer cells after 5-aza C treatment from the NCBI GEO dataset (GSE22250). For the CpG sites probed by Infinium HM450k, CpGs that had missing values in the TCGA breast cancer dataset were removed. To select CpGs with long-range interactions, we used the MCF7 ChIA-PET data for RNA polymerase II (Pol II) and selected CpGs with genomic positions that overlapped with the ChIA-PET reads. Subsequent analyses were performed using the data from TCGA breast cancer patients who had both tumor and the paired solid normal tissue samples because we tried to select CpG methylation marker candidates that have both diagnostic and prognostic value. To determine the diagnostic value, the methylation difference of each CpG between the tumor and paired normal samples (Δβ value) was calculated, and hypermethylated CpGs (average Δβ > 0.2) were selected. To determine the prognostic value, after calculating the association between CpG methylation and the survival of breast cancer patients, we selected significantly associated CpGs (p < 0.05, logrank test). To identify putative target genes of the selected CpGs, we paired the hypermethylated CpGs and distant gene promoters using the MCF7 Pol II ChIA-PET data. Using this information, we calculated the Pearson correlation coefficient (R) between the methylation of the CpGs and the expression of paired genes. To identify the putative relationship between the hypermethylated CpGs and distant target genes, we selected CpG-target gene relationships that had a highly negative correlation (R < −0.35). To test the DNA methylation sensitivity of the putative target genes, we examined the expression change in the genes after 5-aza C treatment using the GSE22250 dataset. Differentially expressed (fold change >1.5) genes (DEGs) and the paired CpGs were selected. Thus, eight putative CpG-target gene relationships that were supposed to compose long-range regulation modules were selected (Fig. 1b). Interestingly, six existed on Homeobox gene 9 and 10 (HOXA9 and 10) promoter loci, and four CpGs on the HOXA9 promoter and two CpGs on the HOXA10 promoter physically interacted with one another in the single ChIA-PET read (Fig. 1c). Thus, we subsequently focused on the methylation status of four CpGs on the HOXA9 promoter and two CpGs on the HOXA10 promoter.
Long-range interplay of HOXA9 and HOXA10 promoter methylation markers HOXA9 and HOXA10 have been reported to be tumor suppressor genes in breast cancer [21][22][23]. The methylation of the HOXA9 and HOXA10 promoters is associated with cancer progression in various cancers [24,25]. Previous analyses identified HOXA9 and HOXA10 promoter methylation marker candidates and their chromatin interactions. Thus, we further investigated the methylation status and the association with the survival of breast cancer patients using TCGA Infinium HM450k data and clinical information for paired tissues. First, we examined the DNA methylation differences between the normal and tumor samples. Comparing the DNA methylation percentage of each of the six CpGs on the HOXA9 and HOXA10 promoters in tumor tissues and their paired normal samples revealed that the methylation was significantly higher in the tumor samples than their paired normal samples (p < 1.0 × 10 −14 for all six CpGs, n = 90, paired t test) (Fig. 2a). Using the survival data, we examined the association between the survival rate of breast cancer patients after surgery and the DNA methylation of each of the six CpGs on the HOXA9 and HOXA10 A B C Fig. 1 Selection of long-range interacting CpG methylation markers for the diagnosis and prognosis of breast cancer patients. a Scheme for the marker selection using pubic data (from the TCGA breast cancer dataset: Infinium 450k array data (methylome), RNA-seq data (transcriptome), and clinical information; from ENCODE: ChIA-PET data (chromatin interactome) for MCF7 breast cancer cells; from NCBI GEO: expression microarray data (transcriptome, GSE22250) for MCF7 breast cancer cells after 5-Aza C treatment). b Selected CpG marker candidates and long-range interacting genes. c Gene structure, the selected CpG loci and chromatin status near the HOXA9 and HOXA10 locus (UCSC genome browser) A B C Fig. 2 Estimation of CpGs on HOXA9 and HOXA10 promoters as diagnostic and prognostic markers for breast cancer. a Differences in the methylation status of the CpGs between tumor and paired normal tissues (diagnostic value). b Association between survival and the CpG methylation status (prognostic value, black: high, gray: low). c Correlation between methylation level of the CpGs and expression level of the HOXA9, HOXA10, and HOXA11 genes promoters. We divided the patients into two groups based on the median Δβ value. Survival was significantly lower in the patient group with higher methylation percentages than the patient group with lower methylation percentages (p < 0.05 for all six CpGs, n = 82, log-rank test). These results suggested that the six CpGs on the HOXA9 and HOXA10 promoters show possibility as diagnostic and prognostic markers for breast cancer. After paring and integrating the TCGA Infinium HM450k and RNA-seq data, we investigated the target gene expression status, survival association, and correlation with neighbor genes. Comparing the expression level of HOXA9, HOXA10, and HOXA11 in tumors and their paired normal samples revealed that the expression of each HOXA9 and HOXA10 gene was significantly lower in the tumor samples than their paired normal samples (p = 1.3 × 10 −14 for HOXA9, p = 7.6 × 10 −9 for HOXA9, n = 49, paired t test) (Additional file 1: Figure  S1A). Survival was not significantly associated with the expression of HOXA9, HOXA10, and HOXA11 (Additional file 1: Figure S1B). To define the CpG-target gene relationships, we examined the correlation among the six CpG methylations and the gene expression of HOXA9, HOXA10, and HOXA11 (Pearson correlation, n = 142). The methylation of all six CpGs was negatively correlated with the expression of HOXA9 (Fig. 2c). Interestingly, the methylation of the two HOXA10 promoter CpGs showed a higher correlation with HOXA9 expression than HOXA10 expression, whereas the four HOXA9 promoter CpGs highly correlated with HOXA9 expression (Fig. 2c). These results implied that the major target gene of the six CpGs is HOXA9 and that the two HOXA10 promoter CpGs regulate HOXA9 expression through a long-range interaction.

Validation of the long-range interplay between the promoters of HOXA9 and HOXA10
Considering previous analyses, we hypothesized that CpGs on the HOXA9 and HOXA10 promoters interplayed through a long-range interaction. Using a cellbased model, we tried to validate the chromatin interaction between the HOXA9 and HOXA10 promoters. By performing 3C PCR assays in MCF7 and MDA-MB-231 breast cancer cells and MCF10A normal breast cells, we confirmed that the HOXA9 and HOXA10 promoters physically interacted each other, while the HOXA11 promoter did not interact with the HOXA9 or HOXA10 promoter ( Fig. 3a and Additional file 1: Figure S2). Next, we tried to investigate whether the gene expression of HOXA9, HOXA10, and HOXA11 could be influenced by methylation status. We treated MCF7 breast cancer cells with 5-aza C during culture. Using a pyrosequencing assay with 5-aza C-treated cells and the controls, we confirmed that 5-aza C treatment decreased the methylation levels of four CpGs (Fig. 3b). Because we could not design good primers for two CpG sites on the HOXA9 promoter, the methylation of only two HOXA9 promoter CpG sites were validated. Consequently, we examined the gene expression change of HOXA9, HOXA10, and HOXA11 by performing a RT-PCR assay with 5-aza C-treated cells and the controls. We found that the expression of HOXA9 and HOXA10 increased with 5-aza C treatment, while the expression of HOXA11 did not (Fig. 3c). These results suggested that the CpGs on the HOXA9 and HOXA10 promoters regulated the target gene expression levels through longrange interactions.

Prognostic value of HOXA9 and HOXA10 methylation marker combinations
As previously shown, the methylation of each HOXA9 and HOXA10 promoter CpG showed potential as a prognostic marker (Fig. 2b). To increase the prognostic ability, we tried to combine the CpGs and evaluated the prognostic abilities of the combinations. By grouping neighboring CpGs based on the genomic loci, we made two combinations, the HOXA9 promoter CpG (H9) group and the HOXA10 promoter CpG (H10) group. After calculating the risk scores (RSs) of H9 and H10 based on the Cox proportional hazards model, we performed survival analyses with the previously used TCGA paired sample dataset (n = 82). We divided the patients into two groups based on the median of the Δβ value. The RS of the separate H9 and H10 combinations significantly predicted poor survival, but it did not better predict poor survival compared with the single CpG methylations shown in Fig. 2b (Fig. 4a top and middle). However, the RS of all CpG (H9 + H10) combinations highly significantly predicted and better predicted poor survival compared with the single CpG methylations shown in Fig. 2b (p = 1.9 × 10 −4 , n = 82, log-rank test) (Fig. 4a bottom).
The TCGA breast cancer dataset contains information about more than paired samples, but they do not have paired normal samples. However, this dataset could be used to evaluate the robustness of the combination of H9 and H10. In the case of this dataset, it is impossible to calculate the Δβ value because of the lack of paired normal samples. Thus, we divided the patient into two groups based on the median of the β value (methylation ratio) of the tumor samples. After the missing data were removed, the Infinium HM450k data and survival data of 781 patients were available (TCGA tumor). Using the dataset, we evaluated the prognostic abilities of the combinations of H9 and H10. Performing the same analysis as for Fig. 4a, we found that the RS of all CpG (H9 + H10) combinations showed better prognostic ability than H9 or H10 alone (p = 7.1 × 10 −5 , n = 781, log-rank test) (Fig. 4b). Additionally, we performed survival analysis using another dataset from the NCBI GEO database (GSE39004). For survival analysis, we divided the patients into two groups based on the median of the β value of tumor samples. The RS of all CpG (H9 + H10) combination also significantly predicted poor survival in the GSE39004 dataset (p = 2.1 × 10 −3 , n = 62, log-rank test) (Fig. 4c). Thus, we hypothesize that the combination of HOXA9 and HOXA10 promoter CpG methylation markers are enhanced, robust prognostic markers.

Subtype independency of the combination of HOXA9 and HOXA10 methylation markers
Breast cancer patients are divided into several molecular subtypes based on the expression level of hormone receptors, such as ER positive, PR positive, Her2 positive, and triple negative [2]. Using Infinium HM450k data and clinical information, including molecular subtype markers in the TCGA tumor dataset, we divided the patients into molecular subtype groups. Breast cancer patients can also be divided into several molecular subtypes based on a well-known gene expression signature (PAM50) [26]. We also divided the patients of the TCGA tumor dataset into PAM50 subtype groups. Then, we examined the association between subtypes and the combination of HOXA9 and HOXA10 promoter CpG methylations (RS). RS also separated the poor survival patients in each subtype except the Her2-positive type in both the TCGA tumor and GSE39004 datasets (Additional file 1: Figure S3). For the Her2-positive type, we observed a similar tendency as for the other subtypes, but this tendency was not significant because of the small number of patients (n = 46). To assess the value of the combination of the HOXA9 and HOXA10 promoter CpG methylation markers as a prognostic marker for breast cancer, we performed a multivariate Cox proportional hazards analysis with other subtype markers. For multivariate analysis, we selected patients from TCGA tumor dataset who had a distinct subtype annotation as positive or negative (n = 249). The RS better predicted poor survival in the TCGA tumor dataset than for any other marker (Table 1). For validation, we performed the same analysis using the GSE39004 dataset after removing samples with missing values (n = 58). The RS also better predicted poor survival in the GSE39004 than any other markers A B C Fig. 3 Validation of the CpG-gene relationship of HOXA9 and HOXA10 promoters. a Validation of chromatin interaction between HOXA9 and HOXA10 promoters using 3C PCR assays. b Validation of the HOXA9 and HOXA10 promoter CpG methylation decrease by 5-Aza C treatment. c Validation of the HOXA9 and HOXA10 gene expression increase by 5-Aza C treatment (Table 2). Thus, we suggest that the combination of HOXA9 and HOXA10 methylation markers is an independent prognostic marker for breast cancer.

Discussion
Many CpG methylations have been identified as diagnostic or prognostic markers for cancer [13,14]. Some of them have been used in the clinical field [11,12]. The methylation of CpGs on promoters has been known to inhibit the gene expression of the nearest target. Recently, the function of trans or long-range actions of CpG methylation has been revealed by genome-wide scale analyses [19], but distal CpGs are still excluded from methylation marker development because of the difficulty in defining CpG-target gene relationships. The trans or long-range action of the CpG-target gene interaction can be specified by chromatin interactome data, and Pol II ChIA-PET data can specify transcriptional regulation. To define the relationship between CpG and long-range target genes, we used Pol II ChIA-PET data. After testing the diagnostic and prognostic maker value, we selected several long-range CpG-gene interactions that could play an important role in breast cancer tumorigenesis and progression. The representative markers are the CpGs on the HOXA9 and HOXA10 promoters. Homeobox (HOX) genes are highly conserved gene clusters that encode transcription factors mediating the development process [21][22][23]. In humans, HOX genes are divided into four clusters, HOXA through HOXD [27]. The HOX gene loci are reported to form three-dimensional A C B Fig. 4 Combination of HOXA9 and HOXA10 promoter CpG methylation markers to enhance the prognostic ability. a Evaluation of the CpG combinations using the TCGA paired sample dataset. b Evaluation of the CpG combinations using the TCGA all sample dataset. c Evaluation of the combination of six HOXA9 and HOXA10 promoter CpGs using an independent dataset (NCBI GEO GSE39004) (black: high, gray: low)   [28][29][30][31]. An association has been reported between the methylation of several CpGs on HOXA loci and breast cancer progression [32,33]. The expression of many HOX genes is associated with tumorigenesis and cancer progression in various cancers [34][35][36][37]. In breast cancer, HOXA9 and HOXA10 act as tumor suppressor genes [21][22][23]. The methylation of the HOXA9 and HOXA10 promoters is associated with cancer progression in various cancers [24,25]. These associations imply that studies of HOXA loci will provide good models for the long-range interactions between distal CpG methylation markers and target genes. According to our analysis of the correlation between CpG methylation and target gene expression, HOXA10 promoter CpGs showed a different correlation pattern with HOXA9 promoter CpGs. HOXA9 promoter CpG methylation tended to correlate with HOXA9 gene expression, but HOXA10 promoter CpG methylation tended to correlate with long-range HOXA9 gene expression rather than the expression of the nearer HOXA10 gene. In other words, the HOXA10 promoter had an enhancer-like function, whereas the HOXA9 promoter had a promoter function. We suggest that the promoter-promoter interaction between HOXA9 and HOXA10 is important in breast cancer progression through the enhancer-like action of HOXA10 promoter CpGs.
To select both diagnostic and prognostic markers, we started marker selection from paired samples. In the case of the expression level of HOXA9 and HOXA10, there was the possibility of only a diagnostic marker but not a prognostic marker. This result could be caused by the instability of RNA markers. The HOXA9 and HOXA10 promoter methylation markers showed potential as both diagnostic and prognostic markers. The initial selection was performed based on the Δβ value, and the combination of the six HOXA9 and HOXA10 promoter methylation markers showed a highly significant prognostic value. Based on the tumor β value, the combination also showed a highly significant prognostic value in two independent datasets. Molecular markers that are clinically used can be applied to specific subtypes, such as node-negative and ER-positive breast cancer [4,5]. The combination of the HOXA9 and HOXA10 promoter methylation markers showed a subtype-independent effect. Multivariate analysis indicated that the combination acted like an independent variable in the prediction of the prognosis of breast cancer. Thus, we suggest that the long-range interplay of HOXA9 and HOXA10 promoter CpGs is an efficient and robust methylation marker for breast cancer.
Molecular markers using single gene expression or CpG methylation have been identified [11], but more marker panels of multiple genes or CpGs have been developed due to advantages in efficiency and robustness [4,5]. Many multiple marker panels have been identified by unsupervised methods without considering biological mechanisms [12][13][14]. The HOXA9 and HOXA10 promoter CpGs were identified by a supervised method based on long-range chromatin interactions, and the detailed action is specified in the HOXA9 and HOXA10 transcriptional regulation module. With respect to translational and biological relevance, this method has advantages for specifying therapeutic targets and strategies. We suggest that our method provides a comprehensive and novel approach for the development of molecular markers for personalized medicine and facilitating the precise determination of cancer prognosis.

Conclusions
Breast cancer is both the most common cancer and the most frequent cause of cancer-related deaths among women. Mammography and some molecular markers have been used for its diagnosis and prognosis, but these techniques have limitations in premenopausal breast cancer or specific subtypes. In this study, we show that a combination of HOXA9 and HOXA10 promoter methylation markers is significantly associated with the prognosis of breast cancer patients in independent datasets and compose a transcriptional regulation module through long-range chromatin interactions. In contrast to other clinically used methylation markers applied to specific subtypes, the combination of the HOXA9 and HOXA10 promoter methylation markers showed a subtype-independent manner. Therefore, we suggest that the prognostic model using the HOXA9 and HOXA10 promoter CpG combination has translational potential to facilitate determination of breast cancer prognosis and therapeutic strategies targeting a specific molecular regulation module.

Additional file
Additional file 1: Figure S1. Estimation of HOXA9 and HOXA10 expression as diagnostic and prognostic markers for breast cancer. Figure S2. Validation of chromatin interaction between HOXA9 and HOXA10 promoters using 3C PCR in MDA-MB-231 breast cancer cells and MCF10A normal breast cells. Figure S3. Association of HOXA9 and HOXA10 methylation marker combinations and survival in various patient subtype groups. Table S1. Primers used in the study. (DOCX 218 kb)


extfieldsofstudy: Biology Medicine

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Degradation of cofilin is regulated by Cbl, AIP4 and Syk resulting in increased migration of LMP2A positive nasopharyngeal carcinoma cells

Expression of cofilin is directly associated with metastatic activity in many tumors. Here, we studied the role of Latent Membrane Protein 2 A (LMP2A) of Epstein-Barr Virus (EBV) in the accumulation of cofilin observed in nasopharyngeal cancer (NPC) tumor cells. We used LMP2A transformed NPC cell lines to analyze cofilin expression. We used mutation analysis, ectopic expression and down-regulation of Cbl, AIP4 and Syk in these cell lines to determine the effect of the LMP2A viral protein on cofilin degradation and its role in the assembly of a cofilin degrading protein complex. The LMP2A of EBV was found to interfer with cofilin degradation in NPC cells by accelerating the proteasomal degradation of Cbl and Syk. In line with this, we found significantly higher cofilin expression in NPC tumor samples as compared to the surrounding epithelial tissues. Cofilin, as an actin severing protein, influences cellular plasticity, and facilitates cellular movement in response to oncogenic stimuli. Thus, under relaxed cellular control, cofilin facilitates tumor cell movement and dissemination. Interference with its degradation may enhance the metastatic potential of NPC cells.

Results
LMP2A binds cofilin and interferes with its proteasomal degradation. In Fig. 1A, we show expression of cofilin in immunoblots of WCL from LMP2A positive (lane 1) and LMP2A negative cells (lane 2) probed with anti-cofilin antibody. Equal input of protein from LMP2A positive and negative cells is shown by the actin controls in the lower panel of Fig. 1A (lanes 1 and 2 respectively). It demonstrates that the steady state levels of cofilin are increased in LMP2A positive cells as compared to LMP2A negative cells. In lanes 3 and 4 we show that cofilin is detected in LMP2A immunoprecipitates from LMP2A expressing cells (lane 3) but not from LMP2A negative cells (lane 4) and is not detected in immunoprecipitates with an irrelevant antibody (lane 5). This provides confirmation that LMP2A binds cofilin. We also show that cofilin is up-regulated in LMP2A positive cells already at the gene expression level (Fig. S1).
The accumulation of cofilin in LMP2A positive cells was also confirmed by FACS analysis (panel B) and fluorescence imaging (panel C). Panel B in Fig. 1 shows FACS analysis results demonstrating that LMP2A expressing cells have an increased RFP-cofilin expression (red curve) as compared to LMP2A negative cells (black curve). The staple diagram on the left ( Fig. 1 panel B) summarizes the quantitative difference in mean fluoresce values between the two cell populations. Since overexpression of cofilin was observed in LMP2A expressing cells ( Fig. 1), we asked whether the known interaction of LMP2A with two ubiquitin ligases, AIP4 and Cbl might alter the stability of cofilin in LMP2A expressing cells. We first observed that cofilin, in LMP2A negative CNE2 cells, treated with the proteasome inhibitor MG132, underwent a modification that increased its MW to approximately 27 kDa (Fig. 2, panel A, right lane). LMP2A expressing CNE2 cells, however, lacked this additional 27 kDa band (Fig. 2, panel A, left lane). This indicated that cofilin, in LMP2A negative cells, underwent a modification that increased its MW by about 8 kDa. We asked whether cofilin might be modified differently by ubiquitin in LMP2A expressing and control cells. To this end, we undertook to analyze the patterns of K48 linked and K63 linked ubiquitin complexes in cofilin immunoprecipitates from CNE2 cells after inhibition of proteasome activity with MG132.
Inhibition of proteasomal degradation induces accumulation of additional, higher order molecular forms, which are recognized by K48 and K63 specific ubiquitin antibody (Fig. 2 panels B and C respectively). As expected, the patterns of K48 and K63 branched ubiquitin chains in the cofilin immunoprecipitates are significantly different. There are also quantitative differences between the cofilin immunoprecipitates from LMP2A positive and negative epithelial cells.
When probed with K48 specific ubiquitin antibody, the LMP2A positive and shSyk transfected cells (Fig. 2, panel B, lanes labelled LMP2A and shSyk) showed a reduced amount of K48-linked mono Ub-conjugates of cofilin, in particular with respect to the 27 kDa band (Fig. 2B), as compared to control cells treated with scrambled shRNA (Fig. 2B, panel B, lane shScr). Conversely, there was an increase in (poly-) ubiquitinated cofilin species in the LMP2A positive NPC cells as recognized by the K63 specific ubiquitin antibody (Fig. 2C).

Two E3 ubiquitin ligases control cofilin expression.
To further understand the regulation of cofilin protein stability, we investigated the involvement of two E3 ligases, AIP4 and Cbl, and the Syk tyrosine kinase, all of which are components of the LMP2A protein complex. We found that exogenous expression of wt Cbl resulted in decreased cofilin levels in LMP2A expressing CNE1 cells (Fig. 3A), while exogenous expression of AIP4 led to stabilization of cofilin at the protein level (Fig. 3B). Analysis of cofilin peptides after tryptic digestion (Fig. S2) also support the involvement of these two ubiquitin ligases.
In Fig. 3C, we show, by fluorescence imaging of individual cells, that siRNA-mediated down-regulation of Cbl (middle panel) or Syk (left panel) results in increased levels of RFP-cofilin as compared to cells treated with irrelevant siRNA (right panel). These siRNA experiments in LMP2A negative HEK293 cells show that cofilin is stabilized by siRNA-mediated downregulation of either Cbl (middle image) or Syk (right image) but not by a scrambled control siRNA (left image). This reproduces the stabilizing effect on cofilin effected by LMP2A-mediated downregulation of Cbl and Syk. This effect is also observed by western blot (Fig. S3A) and by FACS analysis (Fig. S3B). The rapid disappearance of Cbl upon inhibition of proteasomal degradation in LMP2A positive cells (Fig. 3D, left panel) as compared to vector transfected cells (Fig. 3D, right panel) suggests that the increased turnover of Cbl in LMP2A cells depends on a different protein degradation system. Panel E shows quantitation of the pixel density ratio of Cbl over actin in the WB shown in panel D using the Image J program, showing that while Cbl levels were fourfold increased over 8 hours in LMP2A negative cells in the presence of the proteasome inhibitor MG132, there was a fivefold reduction in Cbl levels in LMP2A expressing cells. Panel F shows the reduction in the ratio of Cbl over actin expression in LMP2A expressing (grey bars) as compared to vector transfected CNE1 NPC cells.
In Fig. 3, panel G we show that IP with anti-Cbl antibodies also detects an additional, larger form of Cbl in stably LMP2A expressing CNE1-cells (lane 1) as well as in CNE1-cells stably expressing an shSyk shRNA from an integrated retroviral construct (lane 2) but not in CNE1 cells stably expressing a scrambled control shRNA (Fig. 3, panel G, lane 3) where only the normal (100 kDa) size of Cbl is detected. This suggests that Cbl undergoes posttranslational modifications specific for LMP2A expressing cells, or else where Syk expression (and thus also Cbl activation) is suppressed. Of note, it was previously reported 12 that AIP4 targets Cbl for lysosomal degradation. Consequently, the stabilization of cofilin levels observed by us, is likely due to the reduced Cbl levels in LMP2A expressing cells. Thus, the disappearance of the 27 kDa form of cofilin in LMP2A expressing TWO3 NPC cells (Fig. 3H, right panel), correlates with a reduced expression of Cbl, shown in the left-hand panel of Fig. 3H. This likely reflects the previously established effect of LMP2A expression on the stability of Syk 9 . Cbl was also reported as a target for proteasomal degradation 27 . Thus, the two E3 ligases, Cbl and AIP4, have opposite effects on cofilin expression. While wt Cbl negatively affects cofilin expression, expression of wt AIP4 in HEK293 cells, on the contrary, correlates with increased cofilin expression (Fig. 3 panels A and B). These studies confirm that Cbl plays an important role in the regulation of cofilin stability.

LMP2A exempts cofilin from degradation to mediate increased cellular motility.
To study whether the LMP2A-mediated stabilization of cofilin turn-over affects cofilin function, we investigated the cellular localization of cofilin. HEK293 cells were transfected with a LMP2A coding plasmid in combination with either a red fluorescent control vector (pTomato) or a RFP-cofilin encoding plasmid. Transfected cells were detected with red fluorescence (568 nm wavelength). Coexpression of LMP2A and cofilin induced morphological changes ( , and to the early demonstration that LMP2A is expressed in clusters in the membrane 28 , lipids droplets were also localized in clusters at the cellular periphery in the LMP2A transfected cells as compared to the uniform distribution in the control cells. In line with this, we observed that cofilin floated closer to the top of discontinuous Optiprep gradients in LMP2A positive cells, while in LMP2A negative cells, cofilin remained closer to the bottom of the gradient (Fig. 4C).
The effect of LMP2A and cofilin on cellular migration was measured by a scratch wound assay in stably LMP2A transfected or control CNE2 cells, which were transiently transfected with RFP-cofilin. At 24 hrs post-transfection a scratch wound was introduced.
LMP2A-positive cells, transfected with cofilin ( Fig. 5A, plates 3 and 4) migrated fastest, resulting in a rapidly closing gap as compared to similarly treated cofilin transfected CNE2 control cells (Fig. 5A, plates 1 and 2). The red fluorescence in RFP-cofilin expressing cells was also more intense in the LMP2A background (Fig. 5B, right panel) than in CNE2 control cells (Fig. 5B, left panel), in line with our finding that LMP2A expression stabilizes cofilin in HEK293 cells (Fig. 1). The LMP2A and RFP-cofilin positive cells were both larger in size and accumulated at the border of the gap to a higher degree than did the control cells (Fig. 5B, right panel). Figure 5C (Fig. 6).

Discussion
We demonstrate that the viral LMP2A protein associates with cofilin, resulting in cofilin stabilization (Fig. 1). We hypothesize that this effect of the viral protein on cofilin half-life facilitates cellular motility. In this article we explore the mechanism of the LMP2A-mediated stabilization of cofilin.
Cofilin was recently reported to undergo proteasomal degradation [23][24][25] , although a cofilin specific E3 ubiquitin ligase was not identified. Here we confirm the conventional K48 ubiquitin modification of cofilin, and report the finding of the novel K63 ubiquitin chain extension for cofilin, which suggests that two E3 ubiquitin ligases are involved in ubiquitin modifications of cofilin. We suggest that the E3 ubiquitin ligase Cbl catalyzes addition of a conventional K48-linked ubiquitin chain to cofilin. The Cbl level in LMP2A positive cells is reduced as a result of the assembly, by the viral protein, of a specific protein complex consisting of the two ubiquitin ligases Cbl and AIP4 and the tyrosine kinase Syk 8 , where Cbl is targeted for degradation.
The E3 ubiquitin ligases Cbl and AIP4 are known to jointly orchestrate the final steps in the process of cell surface receptor internalization 29 . During this process, AIP4 ubiquitinates Cbl, providing the targeting signal for its degradation 27 . The process is initiated by tyrosine phosphorylation of Cbl 10, 30, 31 . Tyrosine kinase activity, in turn, is also an inducible event 10 . While at a steady-state, kinases may separate the two ubiquitin ligases, protecting them from untimely degradation. We speculate that the Syk tyrosine kinase, was early reported to phosphorylate Cbl 32 and found in a complex with AIP4 33 , functions as such a spacer. Our observations of higher molecular weight isoforms of Cbl in western blot analyses of cell lysates from cell lines where Syk is down-regulated or absent (Fig. 3G), are suggestive of its ubiquitination. In LMP2A positive cells, Syk is constitutively but weakly activated and simultaneously degraded 8 which may contribute to an elevated rate of Cbl degradation leading to a predominance of AIP4 over Cbl activity 34 . AIP4 is known to specifically catalyze K63 ubiquitin conjugation 35 . Indeed, we detect unique species of lysine K63 Ub labelled cofilin in LMP2A positive or shSyk treated cells (Fig. 2C). The K63 ubiquitin-branched modification was previously reported to modulate signaling complexes assembled on the targeted proteins 36 . This type of ubiquitination was not previously described for cofilin. The K48 linked, mono-ubiquitinated 27 kDa form of cofilin, which is also detected at a low level in LMP2A positive and shSyk expressing cells Fig. 2B, can be attributed to residual Cbl expression in these cells. Our data on differential sensitivity of cofilin immunoprecipitates from LMP2A negative and positive CNE2 NPC cells to deubiquitinating enzymes (DUBs) are in line with this (Fig. S4). Cofilin immunoprecipitates from LMP2A cells are more sensitive to AMSH, the K63 specific DUB, while cofilin immunoprecipitates from LMP2A negative cells are more sensitive to Cezanne, the K11 specific DUB as judged by appearance of mono-, di-, tri-and tetra-ubiquitin species. Recently, K11 ubiquitination was associated with protein degradation 37 while the K63 ubiquitination pattern serves to modify cellular signaling 36 .
We suggest that the two ubiquitin ligases, Cbl and AIP4, modify cofilin in different manners. Cofilin ubiquitylation catalyzed by Cbl is a prerequisite for cofilin degradation by the ubiquitin-proteasome system (UPS), whereas the AIP4 mediates addition of K63-linked multi-or oligo-ubiquitin chains. The latter type of ubiquitylation, is associated with activation of various pathways, e.g. cofilin targeting into the MVB organelle, as was shown for ERBB4 29 , or sorting to the lysosomes as in the case EGFR 38, 39 , i.e. processes that are involved in different aspects of protein metabolism and transport and proceed at different rates than proteasomal degradation.
In addition, the reduced expression and activity of Syk in LMP2A expressing cells may contribute to cofilin activation as it was reported that Syk negatively affects cofilin activity 26 .
Beyond mediating an increase in the levels of cofilin, LMP2A expression may influence its localization in the cells, due to the excessive accumulation of lipids that we observe at the periphery of LMP2A positive cells (Fig. 4B). Lipids may physically sequester cofilin at the protruding ends. This suggests that cofilin may acquire modifications in LMP2A expressing cells that favor its reversible association with lipid domains. Based on available tools for prediction of lipid modifications, cysteines 39 and 147 may be targets for palmitoylation 40 . Interestingly, it was reported that the periplasmic fraction of cofilin is active in redirecting apoptotic signaling into migratory 41,42 . Thus, the effects of LMP2A on lipid accumulation and relocation of cofilin to lipid domains are in line with our observation of a more migratory phenotype in migration (scratch) assays (Fig. 5A).
Apart from the LMP2A-mediated interference with proteasomal degradation of cofilin, its interaction with the LMP2A protein complex may also interfere with other cofilin inactivating processes such as binding to PIP2 and phosphorylation by LIMK1 kinase 14,16 .
Thus, in addition to mediating an increase in steady state levels of cofilin protein, association with LMP2A may also contribute to maintaining cofilin in an active state. Interference with cofilin and actin organization may be a mechanism behind increased cell surface expression in LMP2A positive cells of a number of receptors like CXCR4, VEGFR, EGFR (our preliminary data and 42 . Most probably, cofilin affects the delivery of cell surface receptors through the endocytic vesicle fusion machinery, as is the case for cofilin-assisted expression of GLUT4 43 . In the presence of growth factors, chemokines or other stimulatory signals, LMP2A-mediated regulation of cofilin expression can facilitate directional cellular movement, due to increased actin turnover.
The stabilization of cofilin by the LMP2A complex may affect immune responses. Cell surface expression of cofilin was reported recently as an autoantigen during apoptosis, capable of inducing the production of autoantibody 44,45 .
In summary, we show that the LMP2A protein complex confers stabilizing K63-branched ubiquitin chains on cofilin through the action of AIP4, resulting in increased steady-state levels of cofilin. This effect is mediated by Syk catalytic activity which induces AIP4 dependent Cbl degradation. Targeting the cellular ubiquitination machinery is one way by which EBV, like many other viruses, interferes with cellular functions as part of their survival strategies 46 . Analysis of cofilin expression in histological sections of normal nasopharyngeal or NPC tissue (Fig. 6) supports our conclusion that cofilin expression is dysregulated in NPC tumor tissue. We propose that cofilin dysregulation may serve as a tumor hallmark of NPC, the cellular mechanism for which is suggested by the involvement of the viral LMP2A protein in dysregulation of ubiquitin-mediated cofilin turnover in the tumor cells.
Cells with increased cofilin expression have a propensity to form more cellular protrusions, contributing to increased cellular motility, phagocytosis and responses to growth factors.
Our finding on the LMP2A mediated inhibition of cofilin degradation warrants further investigation of the ubiquitylation pattern of cofilin and its functional relevance.

Materials and Methods
Regulatory and safety statements. All experimental protocols and procedures were performed in accordance with the regulations issued by the Swedish Environment Authority and the Environmental Authority of the Peoples Republic of China regarding recombinant DNA and microbiological work with genetically modified organisms (BSL1 and 2, where applicable). All work was carried out using experimental protocols approved by the respective Biosafety Committees of Karolinska Institutet and Guangxi Medical University, respectively, performed in laboratory facilities designed for this purpose. Work performed at the Institute of Biology and Biomedicine in Russia consisted of biophysical analyses of biologically inactivated protein preparations prepared at Karolinska institute for these analyses. Coauthors with other addresses performed their work while at the Karolinska Institute. All human materials were obtained with informed consent from the donors (see below). In total, NPC tissues from 9 patients (42.11 ± 10.06 years old, 4 males, 5 females) and epithelial tissues from 10 patients (52.3 ± 12.63 years old, 5 males, 5 females) with chronic nasopharyngitis, considered as controls, were obtained at the Department of Otolaryngology Head & Neck Surgery, First Affiliated Hospital of Guangxi Medical University (Nanning, China). Written informed consent was obtained individually from each participant in the study. Diagnoses were made by experienced pathologists according to the World Health Organization (WHO) classification. All NPC samples were nonkeratinizing carcinomas. All the tissue samples were collected before radiotherapy. Tissue blocks were formalin-fixed and paraffin-embedded (FFPE). Standard immunohistochemical methods were used to examine the distribution of cofilin in NPC tissues and normal controls as described previously 47 . Immunohistochemical (IHC) grades were assigned based on intensity and frequency of staining results and was performed by two independent investigators. Staining intensity was scored as negative (0), weak (+1), moderate (+2), or strong (+3). The frequency of positive cells in specific areas was scored as negative (0), less than 25% (+1), 25-50% (+2), 51-75% (+3), or more than 75% (+4). IHC grades were assigned by multiplying the intensity score by the frequency score as described previously 48  The pCDNA 4xFlag-LMP2A wt construct was designed and kindly provided by Dr Rob Ingham, Edmonton, Canada and described in ref. 9. The wt AIP4 construct is described in ref. 9. The Cbl and AIP4 constructs were kindly provided by Dr. Gerry Gish, Toronto. The authenticity of all the constructs was confirmed by DNA sequencing.

Ethics Statement. Ethical permission for this study was approved by the Research Ethics
The total amount of plasmids was kept constant throughout all transfections and corrected with empty vector plasmid if needed. Pre-designed siRNAs, siCbl ID 133804, siSyk ID 138814 and an irrelevant siRNA ID 4390843 (Ambion, Life Technology), were transfected at 100 pmol/well. and analyzed after 24 hr. The Lipofectamine2000 Transfection Reagent was used for all plasmid and siRNA transfections (cat No11668027, Thermo Fisher Scientific).
Treatment with the proteasomal inhibitor MG132 was performed as described in ref. 9.
Optiprep gradient fractionation. Cell lysates were mixed with Optiprep reagent (AXIS-Shield PoC AS, Oslo, Norway) and loaded on a discontinuous density gradient as described previously for floatation analysis of membrane proteins 49 . Centrifugation was performed at 25 000 rpm for 16hr at 4 C. One milliliter fractions were harvested from the top. Proteins were concentrated by Trichloroacetic acid (TCA) precipitation. The TCA traces were washed away with acetone, the fractions were dried and dissolved in Western blot sample buffer and analyzed by Dotblot. A total number of 50 000 cells were seeded in 6 wells and allowed to attach overnight. Adherent cells were fixed with 4% formaldehyde for 15 minutes, permeabilized with 0.5% Triton X-100 for 10 minutes. Cell nuclei were counterstained with DAPI. Images were acquired using a Leica DMRE microscope with HiPic software (Leica, Bensheim, Germany).
Lipid droplet specific fluorescent dye BODIPY (D3922, Molecular Probes; Carlsbad, Calif., US) was diluted in DMSO and applied on slides for 30 minutes at room temperature, followed by two washing steps with PBS. Slides were mounted with VECTASHIELD (Vector Laboratories,Burlingame, CA, US).
Slides were examined with epifluorescence optics. Images were acquired using the Apotome Zeiss microscope Observer Z.1 (Carl Zeiss, Jena, Germany).
Immunoprecipitation, electrophoresis and immunoblotting. Protein lysates were prepared and analysed by a conventional Western blot (WB) assay as described elsewhere 6 . Signals from enhanced chemiluminescence reagent (ECL, Amersham, USA), used in the WB assay, were acquired by a ChemiDoc XRS + (Bio-Rad, Scratch wound assay. HEK293 cells, were seeded in a 6 well plate 24 hrs before transfection to achieve 70% confluency at the day of treatment. Cells were then transfected with 1 mkg of LMP2A, RFP-cofilin or empty vector constructs in combinations as indicated. A scratch with a P10 pipette tip was made 16 hr post-transfection. Images of scratches were recorded with a Olympus CKX 41 microscope at 4x magnification and processed using DpxView Pro [1.14.3] software at 16hr after the scratch. The remaining width at 15 positions along the scratch gap at 16 hours was normalized to the gap measured at 0 hours. The bar graph in Fig. 5C shows average width of the scratch at 0h, normalized to 1 (bars 1 and 3) with standard deviation. Bars 3 and 4 represent the ratio of the 0h value (=1) divided by the average scratch width at 16 h, resulting in a measure that reflects cell migration rate.


text:
Toxoplasma gondii GRA7-Targeted ASC and PLD1 Promote Antibacterial Host Defense via PKCα

Tuberculosis is a global health problem and at least one-third of the world’s population is infected with Mycobacterium tuberculosis (MTB). MTB is a successful pathogen that enhances its own intracellular survival by inhibiting inflammation and arresting phago-lysosomal fusion. We previously demonstrated that Toxoplasma gondii (T. gondii) dense granule antigen (GRA) 7 interacts with TNF receptor-associated factor 6 via Myeloid differentiation primary response gene 88, enabling innate immune responses in macrophages. To extend these studies, we found that GRA7 interacts with host proteins involved in antimicrobial host defense mechanisms as a therapeutic strategy for tuberculosis. Here, we show that protein kinase C (PKC)α-mediated phosphorylation of T. gondii GRA7-I (Ser52) regulates the interaction of GRA7 with PYD domain of apoptosis-associated speck-like protein containing a carboxy-terminal CARD, which is capable of oligomerization and inflammasome activation can lead to antimicrobial defense against MTB. Furthermore, GRA7-III interacted with the PX domain of phospholipase D1, facilitating its enzyme activity, phago-lysosomal maturation, and subsequent antimicrobial activity in a GRA7-III (Ser135) phosphorylation-dependent manner via PKCα. Taken together, these results underscore a previously unrecognized role of GRA7 in modulating antimicrobial host defense mechanism during mycobacterial infection.

Introduction Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB) [1]. The World Health Organization reported that in 2014, 9.6 million cases and 1.5 million deaths were globally [2]. Recent developments in TB drug-development strategies (including new and repurposed antimicrobials and host-directed drugs) have produced new regimens to shorten treatment duration, improve outcomes of TB treatment such as, prevent resistance, reduce lung injury by promoting autophagy, antimicrobial peptide production, and other macrophage effector mechanisms, as well as inhibiting mechanisms causing lung inflammation and matrix destruction [1,[3][4][5]. A wide range of candidate host-directed therapies (HDTs)-including new and repurposed drugs, biologics, and cellular therapies-have been proposed to accelerate eradication of infection and overcome the problems associated with current treatment regimens.
Recent studies have revealed the intracellular signaling pathways that govern the outcome of the innate immune response to mycobacteria infection and antibacterial defense [6][7][8][9][10][11]. First, the NLRP3 inflammasome complex, an intracellular protein complex consisting of the sensor NACHT, LRR and PYD domains-containing protein 3 (NLRP3), the adaptor apoptosis-associated speck-like protein containing a carboxy-terminal CARD (ASC), and pro-caspase-1 regulates IL-1β and IL-18 processing [10][11][12]. Jayaraman et al. showed that IL-1β directly promotes antimicrobial immunity in murine and human macrophages by regulating TNFR signaling and caspase-3 activation against MTB infection [10]. Verway et al. showed that 1-25-Dihydroxyvitamin D (1,25D) enhances IL-1β signaling from MTB-infected macrophages, inducing antimicrobial peptide DEFB4/HBD2 in primary lung epithelial cells, which in turn helps control MTB [11]. Second, host phospholipids play a critical role in the activation of the antimicrobial innate immune response [13]. Phospholipase D (PLD), which has two isoforms (PLD1 and PLD2) catalyzes the hydrolysis of the membrane phospholipid, phosphatidylcholine, to generate the metabolically active phosphatidic acid (PA) [14]. PLD1 is activated by arf-, ral-, and rho-family GTPases, and protein kinase C (PKC) α, while PLD2 activity is elevated by fatty acids [15]. Interestingly, MTB, unlike the nonpathogenic M. smegmatis, inhibits PLD activation during phagocytosis, a process that is associated with intracellular survival of the pathogen [6]. Garg et al. showed that Natural lysophospholipids promote MTB-induced in vitro PLD-dependent phagolysosome maturation and PLD-dependent intracellular killing of MTB in human macrophages [8] and the type II alveolar epithelial cell line A549 [9]. Third, recent studies have highlighted the role of protein kinases in the biology and pathogenesis of mycobacteria. The members of the PKC-family of proteins are classified into three groups, based on the mechanisms regulating their activation in response to different stimuli [7,16]. Holm et al. showed that PKCα regulates phagocytosis and the biogenesis of phagolysosomes by promoting the interaction of phagosomes with late endosomes and lysosomes [16]. Furthermore, PKCα also plays an important role in the killing of MTB in human macrophages [7]. Collectively, these infection-induced signaling pathways suggest possibilities for the development of novel therapeutic modalities for tuberculosis that target the intracellular signaling pathways permitting the replication of this nefarious pathogen. However, the roles of MTBinfection signal-dependent HDTs involved in host innate immune responses and their regulatory mechanisms have not yet been fully elucidated.
In a previous study, we demonstrated that T. gondii GRA7/MyD88-dependent NF-κB activation is essential for the activation of TNF receptor-associated factor 6 (TRAF6) and ROS generation, and enhances the release of inflammatory mediators. We also found that GRA7 stimulation led to physical and functional associations between GRA7 and TRAF6, resulting in crucial protective efficacy against T. gondii infection in vivo [17]. It remains to be seen whether GRA7 targeting can be used as a therapeutic strategy for infectious diseases. In this study, we further investigated the intracellular regulatory network of T. gondii GRA7-induced ASC, PLD1, and PKCα signaling pathways to help identify novel therapeutic modalities for tuberculosis. We found that the PKCα-mediated phosphorylation of GRA7 was essential for interaction between GRA7 and ASC or PLD1, which contributes to antimicrobial defense against MTB in vitro and in vivo. Our findings demonstrate that GRA7-I and -III play finetuning roles in the activation of HDTs and innate immune machineries through direct binding with ASC or PLD1 and may provide a unique opportunity for urgently needed therapeutic interventions against tuberculosis.

Ethic statement
All animal experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee of Hanyang University (protocol 2014-0207) and Bioleaders Corporation (Daejeon, Korea, protocol BLS-ABSL3-13-11). All animal experiments were performed in accordance with Korean Food and Drug Administration (KFDA) guidelines.

M. tuberculosis culture
Cultures of MTB H37Rv (provided by Dr. R. L. Friedman, University of Arizona, Tucson, AZ) were prepared as described previously [1]. The effective concentration of lipopolysaccharide was <50 pg/ml in those experiments, with a bacterium-to-cell ratio of 10:1. For all assays, midlog phase bacteria (absorbance 0.4) were used. Bacterial strains were divided into 1-ml aliquots and stored at -70˚C.

M. tuberculosis infection in vitro and in vivo
For in vitro experiments, cells were infected with MTB for 2-4 h. Then, cells were washed with PBS to remove extracellular bacteria, supplied with fresh medium, and incubated at 37˚C for indicated time points. For in vivo experiments, C57BL/6 mice were i.v. injected with MTB (1×10 6 CFU/mouse). After 3 wks of infection, mice injected intraperitoneally with rGRA7 proteins for 7 consecutive days. After 1 wk of treatment, mice were sacrificed for harvesting of the lungs, spleens, and livers. Mice were maintained in biosafety level 3 laboratory facilities.

Immunoblot analysis and immunoprecipitation
THP-1, 293T, and BMDMs were treated as indicated and processed for analysis by Western blotting, co-immunoprecipitation, and GST pulldown as previously described [17,18].

Confocal fluorescence microscopy
Immunofluorescence analysis was performed as described previously [1]. The cells were fixed on coverslips with 4% (w/v) paraformaldehyde in PBS and then permeabilized for 10 min using 0.25% (v/v) Triton X-100 in PBS at 25˚C. PLD1 or His was detected using a 1/100 dilution of the primary Ab for 1 h at 25˚C. After washing, the appropriate fluorescently labeled secondary Abs were incubated for 1 h at 25˚C. Slides were examined using laser-scanning confocal microscopy (model LSM 800; Zeiss). For colocalization analysis, the co-distribution of the PLD1 and GRA7 were quantified and validated statistically by Pearson coefficient, as specified by the ZEN 2009 software (version 5.5 SP1; Zeiss).

Peptide spot arrays
Peptide arrays were synthesized using the SPOTs synthesis method and spotted onto a derivatized cellulose membrane (Intavis) in the presence of [γ-32 P]ATP and calcium as described previously [19,20]. Peptide spot phosphorylation was quantified using phosphoimaging.

Histology
For immunohistochemistry of tissue sections, mouse lungs were fixed in 10% formalin and embedded in paraffin. Paraffin sections (4 μm) were cut and stained with hematoxylin and eosin (H&E) [21].
In vitro PLD activity assay PLD activity was measured using the Amplex Red PLD assay kit (Molecular Probes, A12219) according to the manufacturer's protocol. The resulting fluorescence was detected using a fluorescence microplate reader at an excitation of 530 nm and an emission of 590 nm.

Supplemental Information
Supplemental experimental procedures and supplemental references.

Statistical analysis
All data were analyzed by Student's t-test with Bonferroni adjustment or ANOVA for multiple comparisons, and are presented as mean ± SD. Grubbs' test was used for evaluating the outliers. Differences were considered significant at p <0.05.

GRA7 associates with ASC and PLD1
To establish a role for GRA7 in intracellular signaling pathways as a therapeutic strategy for infectious diseases in macrophages, we investigated whether GRA7 interacts with molecules involved in innate immunity. GRA7 complexes were subjected to co-immunoprecipitation (co-IP) of recombinant GRA7 protein with THP-1 lysates. The purified GRA7 complexes retrieved several endogenous proteins selectively, as identified by mass spectrometry analysis, including PLD1 (124 K), PKCα (76 K), ASC (21 K), and TRAF6 (60 K) (Fig 1A and S1 Fig). Endogenous co-IP showed that GRA7 interacted strongly, although temporarily (from 15 to 60 min), with endogenous PLD1, TRAF6, and ASC but not with PLD2, NLRP3, or NLRC4 after stimulation with rGRA7 in THP-1 cells, and vice versa (Fig 1B, S2A and S2B Fig). As previously reported [17], GRA7 associated with TRAF6, based on their molecular weights and co-IP (Fig 1A-1C, S1 and S2A Figs).
To determine the mechanism by which rGRA7 interact with the intracellular protein, THP-1 cells were preincubated with cytochalasin D, which inhibits actin polymerization. Pretreatment with cytochalasin D completely blocked the phagocytic activities of rGRA7 and it binding with intracellular proteins (S2C and S2D Fig).
Structurally, GRA7 contains a signal sequence, N-terminal domains (I-IV), a transmembrane, and a C-terminal domain (V) (Fig 1C) [17]. In 293T cells, detailed mapping using various mammalian glutathionine S-transferase (GST)-GRA7 fusions and truncated mutants of V5-ASC indicated that the N-terminal I-domain (aa26-80) of GRA7 exhibited only minimal binding affinity to ASC and ASC carrying the N-terminal PYD domain (aa1-91) bound GRA7 as strongly as ASC WT (Fig 1C and 1D). GST pull-down assays using truncated mutants of GST-GRA7 mammalian fusions and AU1-PLD1 showed that the N-terminal III-domain (aa120-150) of GRA7 is required for its interaction with PX (aa81-212) of PLD1 (Fig 1C and 1E and S2E Fig), indicating that the interactions of GRA7 with ASC, PLD1, and TRAF6 are genetically separable (Fig 1). These results show that GRA7 interacts with ASC and PLD1 through its N-terminal I-and III-domains in macrophages, respectively. and truncated mutant constructs together with V5-ASC (left), V5, or V5-ASC constructs together with GST-GRA7 (right), 293T cells were used for GST pulldown, followed by IB with αV5. WCLs were used for IB with αGST, αV5 or αActin. (E) 293T cells were co-transfected with GST or GST-GRA7 and truncated mutant constructs together with AU1-PLD1 (left), or AU1 or AU1-PLD1 constructs together with GST-GRA7 (right), and subjected to GST pulldown, followed by IB with αAU1. WCLs were used for IB with αGST, αAU1 or αActin. The data are representative of four independent experiments with similar results (A, B, D, E).

GRA7 interaction with ASC and PLD1 via PKCα
In addition to ASC and PLD1 binding, GRA7 also interacted with PKCα. Endogenous co-IP revealed a robust interaction between GRA7 and PKCα, but not PKCβI or PKCγ, after stimulation with rGRA7 in THP-1 cells, and vice versa (Fig 2A).
A large-scale proteomics analysis of the human kinome [23] and computational sequence analysis [24] predicted five PKC phosphorylation residues (S 52 LR, T 121 DR, S 135 FK, T 204 TR, S 209 PR) within the GRA7 N-terminal I, III domains, and C-terminal V-domain. To confirm that GRA7 was phosphorylated by PKCα, we used several strategies. First, we performed Phostag gel electrophoresis, which involves the use of a Phos-tag biomolecule that specifically binds , followed by IP with αHis-agarose bead (left) or αPKCα (right) and IB with αPKCα, αPKCβI, αPKCγ, αHis, and αActin. (B) Phos-tag and SDS-PAGE analyses of GST-GRA7, GRA7-I WT , GRA7-I S52A , GRA7-III WT , GRA7-III T121A , or GRA7-III S135A expressed together with Flag-tagged PKCα in 293T cells left untreated (CIP-) or treated with calf intestinal alkaline phosphatase (CIP+), and subjected to GST pulldown, followed by IB with αGST. WCLs were used for IB with αFlag or αActin. (C) Mapping of PKCα phosphorylation sites on GRA7 by tiled peptide array analysis using purified recombinant PKCα. Phosphorylation intensity of 15-amino acid peptides that span full-length GRA7 and are each shifted by 3 amino acids was detected using MultiGauge version 3.0. The serines in the two peptides that showed a phosphorylation signal stronger than 100 PSL/mm 2 are indicated above the corresponding peaks. (D) 293T cells were co-transfected with GST or GST-GRA7-I and truncated mutant constructs together with V5-ASC (left), or GST or GST-GRA7-III and truncated mutant constructs together with AU1-PLD1 (right), and subjected to GST pulldown, followed by IB with αV5 or αAU1. WCLs were used for IB with αGST, αV5, αAU1, αPKCα, or αActin. (E) BMDMs from PKCα +/+ and PKCα -/were stimulated with rGRA7 for 30 min., followed by IP with αHis-agarose bead and IB with αASC, αPLD1, αPKCα, and αActin. The data are representative of four independent experiments with similar results (A to E). phosphorylated proteins and retards their migration in the gel [25]. The results showed that GRA7 of wild-type, I-, and III-domains migrated more slowly and produced an 'up-shifted' band (as visualized by the Phos-tag labeling system for the analysis of phosphorylation, followed by SDS-PAGE) when co-expressed with PKCα, but when co-expressed with PKCβ, PKCδ, or PKCξ ( Fig 2B and S3A Fig). Furthermore, we performed an in vitro phosphorylation assay using purified recombinant PKCα and a non-biased overlapping peptide array covering the entire GRA7 sequence [19,20]. From GRA7, two peptides ( 49 PVDSLRPTNAGVDSK 73 and 121 TDRKVVPRKSEGKRS 135 ) showed a phosphorylation signal >200 PSL/mm 2 (Fig 2C). In contrast, none of the peptides spanning the C-terminal of GRA7 showed a significant phosphorylation signal. GRA7 has serine/threonine residues, and two peptides of GRA7 that were phosphorylated contained three potential phosphorylation sites (Fig 2C and 2D). Interestingly, the specific point mutation forms (I S52A and III S135A ) of GRA7 markedly decreased phosphorylation in Phos-tag gel electrophoresis, whereas the mutant III T121A of GRA7 did not (Fig 2B).
These results indicate that PKCα can specifically phosphorylate S52 and S135 residues of GRA7, demonstrating that GRA7 is a substrate of PKCα.
We next investigated whether phosphorylation of S52 and S135 of GRA7 was necessary for binding with ASC and PLD1, respectively. The point mutation (I S52A and III S135A ) of GRA7 markedly abolished its interaction with ASC and PLD1, suggesting that this interaction is S52and S135-phosphorylation dependent ( Fig 2D and S3B Fig). Furthermore, because phosphomimetic residues (aspartic acid or glutamic acid) do not fully approximate the electronegativity produced by phosphorylation, we employed the strategy of mutating amino acids to overcome the charge differential [20,26]. The GST pull-down assay showed that phosphomimetic mutants of GRA7 strongly bound to ASC and PLD1 compared to GRA7 WT, indicating that mimicking constitutively phosphorylated GRA7 overrode the need for PKCα function in the innate immune pathway. Consistent with the findings shown in Fig 2A-2D, GRA7 interaction with ASC and PLD1 was markedly decreased in BMDMs from PKCα -/mice, THP-1 from knock down with shRNA specific for PKCα (Fig 2E and S3C Fig), and BMDMs treated with a pharmacological inhibitor of PKCα upon rGRA7 stimulation (S3D Fig). Taken together, these data indicate that PKCα-mediated phosphorylation of GRA7 at Ser52 or Ser135 is essential for interactions between GRA7 and ASC or PLD1, respectively.

GRA7-I induces activation of ASC-dependent inflammasome
To examine the role of T. gondii GRA7-I in innate immune responses of macrophages, we generated bacterially purified His-tagged GRA7-I and its mutant proteins, as described previously [17,22]. The purified rGRA7-I (10 kDa) was confirmed through SDS-PAGE and immunoblotting analysis (Fig 3A). No significant difference compared to vector control observed for rGRA7-induced cytotoxicity in macrophages [17].
We showed previously that rGRA7-induced expression of pro-inflammatory cytokine genes and proteins including IL-1β, in macrophages [17] and NLRP3 inflammasomes involves a multimeric protein complex containing NLRP3 interacting the adaptor ASC and caspase-1 to induce the maturation of IL-1β and IL-18 [10,12]. To investigate the role of GRA7-I in the regulation of inflammasome activation, BMDMs from PKCα +/+ and PKCα -/mice were stimulated with rGRA7-I and its mutant proteins. In response to rGRA7-I and -WT, PKCα-deficient BMDMs showed significantly attenuated IL-1β and IL-18 production than WT BMDMs, but the phosphomimetic mutant (I S52D )-induced markedly increased secretion of IL-1β and IL-18 ( Fig 3B and S4A Fig). Consistent with these results, the caspase-1 activation and IL-1β and IL-18 maturation observed in response to rGRA7-I and -WT proteins were significantly decreased in PKCα-deficient BMDMs, and the constitutively active form (I S52D ) of GRA7 'rescued' the PKCα deficiency ( Fig 3C). Notably, the PKCα non-phosphorylatable mutant (I S52A ) and shRNA-mediated reduction of endogenous ASC expression led to significant attenuation of IL-1β and IL-18 production (Fig 3B and S4B Fig) in an ASC-binding dependent manner.
Next, we determined whether ASC is substantially oligomerized and if the intracellular formation of ASC specks is dependent on GRA7-I interaction. In correlation with secretion of active caspase-1 and IL-1β, PKCα-deficient BMDMs showed markedly attenuated ASC oligomerization and speck formation compared to WT BMDMs, but the phosphomimetic mutant (I S52D ) markedly increased both (Fig 3D and 3E and S4C Fig). Further, the intracellular interaction of GRA7 and ASC was confirmed by their co-localization after stimulation with rGRA7. Subcellular fractionation and co-IP analysis showed that GRA7-I associated with ASC and PKCα in the mitochondrial fraction in PKCα +/+ BMDMs. Notably, these binding patterns were increased by the phosphomimetic mutant (I S52D ) in BMDMs from PKCα +/+ and PKCα -/mice (S4D Fig). These data suggest that GRA7-I acts as a positive regulator of ASC-dependent inflammasome activation via PKCα in mitochondria.
GRA7-I-induced activation of ASC-dependent inflammasomes is important for resistance against MTB infection IL-1β and IL-18 are cytokines that play crucial roles in host defense and inflammation [11,12]. We first measured caspase-1 activation and maturation of IL-1β and IL-18 induced by rGRA7 and its mutants in MTB-infected macrophages. rGRA7-I treatment increased inflammasome activity in MTB-infected macrophages in a dose, ASC-binding and PKCα phosphorylationdependent manner. Importantly, treatment with the phosphomimetic mutant (I S52D ) of GRA7 markedly amplified inflammasome activity in MTB-infected conditions in BMDMs from PKCα +/+ and PKCα -/mice ( Fig 4A). The PKCα non-phosphorylatable mutant (I S52A ) and the shRNA-mediated reduction of endogenous ASC expression led to significant attenuation of caspase-1 activation and maturation of IL-1β and IL-18 (Fig 4A and 4B) in an ASC-binding dependent manner.
IL-1β directly activates MTB-infected macrophages to restrict intracellular bacterial replication [10,27]. We examined whether rGRA7-induced antimicrobial activity was dependent on ASC-dependent inflammasome activation via PKCα in macrophages. The rGRA7-WT and -I-induced antimicrobial responses against MTB were significantly downregulated in BMDMs from PKCα -/mice and cells transduced with shASC in a dose-dependent manner (Fig 4C and  4D). Notably, the PKCα non-phosphorylatable mutant (I S52A ) of GRA7 did not induced antimicrobial responses of MTB, compared with the WT-and rGRA7-I treatment, in BMDMs from PKCα +/+ mice. The phosphomimetic mutant (I S52D ) of GRA7 markedly increased antimicrobial responses to MTB in dose-dependent manner, indicating that the constitutively active form (I S52D ) of GRA7 partially 'rescued' the PKCα deficiency. No significant difference was observed for MTB growth in 7H9 broth with or without rGRA7 (S5 Fig), indicating that ASC-dependent inflammasome-derived IL-1β controls the outcome of MTB infection and is functionally linked via PKCα in macrophages.

GRA7-III induces activation of PLD1
To examine the role of T. gondii GRA7-III in innate immune responses by macrophages, we generated bacterially purified His-tagged GRA7-III and its mutant proteins, as described previously [17,22]. The purified rGRA7-III (5 kDa) was confirmed through SDS-PAGE and immunoblotting analysis ( Fig 5A).
As GRA7 associates with PLD1 but not PLD2 (Fig 1 and S1 Fig), we sought to determine whether PLD1 activation by GRA7-III was regulated by phosphorylation events in many cellular processes [15,28]. PLD1 activity is regulated by phosphorylation of Thr147 in the PX domain and Ser561 in the negative regulatory loop region of PLD1 by PKCα [15,28]. We first measured rGRA7-III-induced phosphorylation of PLD1 at Thr147 and Ser561 but not the PKCα nonphosphorylatable mutant (III S135A ) of GRA7 in macrophages. Importantly, the phosphomimetic mutant (III S135D ) of GRA7 treatment markedly amplified PLD1 activation in BMDMs from PKCα +/+ and PKCα -/mice (Fig 5B). Consistent with these results, PLD activity was significantly decreased by the PKCα non-phosphorylatable mutant (III S135A ) and increased by the phosphomimetic mutant (III S135D ) of GRA7 in BMDMs from PKCα +/+ and PKCα -/mice ( Fig  5C). However, PLD activity was at the basal level in PLD1 -/macrophages with the phosphomimetic mutant, indicating that phosphorylated GRA7-III interacted with activated PLD1 by PKCα and stimulated its enzymatic activity through the phosphorylation of PLD1 Thr147 and Ser561. Further, the intracellular interaction of GRA7 and PLD1 was confirmed by their colocalization after stimulation with rGRA7, as documented by immunostaining and image overlay (Fig 5D and S6 Fig). GRA7-III localized with PLD1 and PKCα in the cytoplasm, appearing as small speckles and punctate spots. Notably, these co-localization patterns were increased by the phosphomimetic mutant (I S135D ) in BMDMs from PKCα +/+ and PKCα -/mice, but not

GRA7-I-induced inflammasome activation was required for antimicrobial activity in MTB-infected macrophages. (A and B)
BMDMs from PKCα +/+ and PKCα -/mice (A) and BMDMs were transduced with lentivirus-shRNA-NS or lentivirus-shRNA-ASC for 2 days (B) infected with MTB (MOI = 1) for 4 h and then stimulated with rGRA7 (1, 5, 10 μg/ml) and its mutants for 18 h. IB analysis for IL-1β p17, IL-18 p18, or caspase-1 p10 in supernatants (SN), ASC, NLRP3, pro-IL-1β, pro-IL-18, or pro-caspase-1 in whole-cell lysates (WCL). Actin was used as a loading control. (C and D) Intracellular survival of MTB was assessed by CFU assay. BMDMs were infected with MTB for 4 h, followed by treatment with rGRA7, and then lysed to determine intracellular bacterial loads. The data are representative of five independent experiments with similar results (A and B). Data shown are the mean ± SD of five experiments (C and D). Significant differences (*P < 0.05; **P < 0.01; ***P < 0.001) compared with rVector. CFU, colony-forming units. ns, not significant.

GRA7-III-induced phagosome maturation of PLD1 is important for resistance against MTB infection
PLD1 activity regulates the actin cytoskeleton, vesicle trafficking for secretion and endocytosis, and receptor signaling. With the emerging concept of dynamic cycling of PLD1 inside the cell, some of the varying reports of localization may be due to differential rates and numbers of vesicles cycling in the cell lines used and thus differential regulation of PLD1 localization [14,15,28]. MTB preferentially infects alveolar macrophages, although mycobacteria allow only early endosome membrane fusion and induce phagosome arrest by selective Rab GTPase recruitment to avoid fusion with late endosomes and lysosomes [29,30]. To investigate the subcellular fractionation of PLD1, we treated MTB-infected BMDMs with rGRA7 and its mutants, and then examined the induction of protein levels of Rab5, Rab7, LAMP1, and LAMP2 regulators of phagosomal maturation in mycobacteria-containing phagosome fractions (phagosome and phago-lysosome) subsequently purified by sucrose-step-gradient-ultra-centrifugations. Interestingly, GRA7-induced MTB-containing phagosomes were recruited to late endosome and lysosome marker Rab7, LAMP1, and LAMP2, indicating that GRA7 facilitates mycobacterial phagosome-lysosome fusion in macrophages in a PKCα-and PLD1-dependent manner (Fig 6A). Furthermore, GRA7 associated with PLD1 in phagosomal fractions in a bindingdependent manner, and the phosphomimetic mutant (I S135D ) of GRA7 markedly increased and then stimulated with rGRA7 (5 μg/ml) and its mutants for 18 h. Mycobacteria-containing phagosome fractions were subsequently purified by sucrose-step-gradient-ultra-centrifugations, followed by IB to detect αRab5, αRab7, αLAMP1, αLAMP2, αPLD1, αPKCα, and αActin (A) or IP with αPLD1 and IB with αHis, αPLD1, and αActin (B). Quantitative analysis of the PLD1 interacts with His-rGRA7 in MTB-containing phagosomes band normalized to Actin is shown (lower). (C and D) Intracellular survival of MTB was assessed by CFU assay. BMDMs were infected with MTB for 4 h, followed by treatment with rGRA7, and then lysed to determine intracellular bacterial loads. (D, right) IB with αPLD1, αΑSC, and αActin in BMDMs. The data are representative of five independent experiments with similar results (A and B). Data shown are the mean ± SD of five experiments (C and D). Significant differences (*P < 0.05; **P < 0.01; ***P < 0.001) compared with PKCα+/+ and PKCα-/-(B) or rVector (C and D) phagosomal trafficking and binding to PLD1, indicating that the constitutively active form (I S135D ) of GRA7 'rescued' PKCα deficiency (Fig 6B). Consistently, the viability and growth rate of intracellular MTB decreased following treatment with the phosphomimetic mutant (I S135D ) in BMDMs from PKCα +/+ and PKCα -/mice, but not PLD1 -/mice in dose-dependent manner (Fig 6C and 6D). These results collectively indicate that GRA7 facilitates phagosomal maturation through interactions with PLD1 and thereby, exerts marked control of bacterial killing activity against intracellular mycobacteria in a binding-dependent manner via PKCα.

GRA7-I and-III-dependent host protective effects against MTB infection in vivo
Drawing on the observation that GRA7-I and -III associate with ASC and PLD1, respectively, and which contributes to antimicrobial defense against MTB in macrophages (S7A Fig), we next evaluated the in vivo efficacy of rGRA7 and its binding mutants in a mouse model of established tuberculosis [31]. MTB-infected mice were given rGRA7 and its mutants, starting three weeks after infection. Mice treated with rGRA7-WT alone, rGRA7-I WT +III WT , or rGRA7-I S53D +III S135D , but not the binding deficient mutant (rGRA7-I S53A +III S135A ) had significantly reduced bacillary load in the lung, liver, and spleen, and reduced formation of lung granulomatous lesions in size and number of foci, compared with vector-treated mice (Fig 7A and 7B). Notably, the phosphomimetic mutant (I S53D +III S135D ) of rGRA7 drastically reduced bacillary load, at a level similar to rGRA7-WT in PKCα +/+ and PKCα -/mice.
We further investigated the effect of GRA7-III on in vivo host responses to MTB infection. As shown in Fig 7C and 7D, treatment with PLD1-binding domain (III WT ) and the phosphomimetic mutant (III S135D ) of rGRA7 markedly increased bacterial killing effects and number of granulomatous foci in PLD1 +/+ mice, but not PLD1 -/mice. Treatment with the PLD1-binding deficient mutant (III S135A ) of rGRA7 had no significant effect on bacterial killing or granulomatous lesions in either PLD1 +/+ or PLD1 -/mice, indicated that the anti-mycobacterial effect of GRA7-III acts in a PLD1-binding dependent manner via PKCα in vivo. However, No significant difference was observed for inflammation score in lung (S7B and S7C Fig). The pharmacokinetics of therapeutic rGRA7 proteins were localized in alveolar macrophages was maintained for up to 7 days and gradually cleared until 25 days was studied by the fluorescence of the fluorophore Alexa 488-conjugated with the proteins (S8 Fig). These results unambiguously show that host defenses against MTB infection are substantially affected by GRA7-I and GRA7-III.

Discussion
The central finding of this study is that the PKCα-mediated phosphorylation of T. gondii GRA7 is essential for the interaction between GRA7 and ASC or PLD1, which contributes to antimicrobial defense against MTB (S9 Fig). Specifically, we found that (1) PKCα specific phosphorylation of Ser52 and Ser135 of GRA7 in vitro and in vivo was functionally required for ASC and PLD1 interactions with GRA7, respectively, (2) GRA7 was a novel substrate of PKCα, (3) the N-terminal of GRA7 (GRA7-I) was sufficient for interaction with the PYD domain of ASC in mitochondria, leading to ASC oligomerization and inflammasome activation, and subsequent antimicrobial activity, (4) GRA7-III interacted with the PX domain of PLD1 in cytosol, facilitating its enzyme activity, phago-lysosomal biogenesis, and subsequent antimicrobial activity, (5) GRA7-I and -III-dependent host protective effects against MTB infection were demonstrated in vivo, and (6) a phosphomimetic mutant that constitutively activated GRA7 'rescued' PKCα deficiency both in vitro and in vivo. Collectively, these observations indicate that T. gondii GRA7-mediated HDTs leading to an antimicrobial response, as a novel host defense mechanism may provide a unique opportunity for urgently needed therapeutic intervention strategies for TB and other infectious diseases.
Although it is well established that dense granule protein GRA7 is important for immunodiagnosis of toxoplasmosis in patients [32,33], new candidates for further effective vaccine development against T. gondii infection is the need [17,34,35]. Recent reports showed that GRA7 is associated with T. gondii ROP5 was required for efficient phosphorylation of Irga6 and additional component of the ROP5/ROP18 kinase complex [22,36] and binding of ROP2 and ROP4 was shown [37] in T. gondii. However, the modulation of host innate immunity by GRA7 in the early phases of infection is critical for the establishment of both the initial invasion and the subsequent maintenance of latent infection is have not been fully elucidated. Growing evidence suggests that host-pathogen interactions have led to the coevolution of toxoplasmosis-causing T. gondii with its host [17,22,38]. GRA7 binds to poly(rC) binding protein 1/PCBP1 along with PCBP2 and hnRNPK, corresponding to the principal cellular poly (rC) binding proteins according to yeast two-hybrid analysis. PCBP1 plays a part in the formation of a sequence-specific α-globin mRNP complex that is associated with the stability of αglobin mRNA [38]. Additionally, GRA7 directly binds to the active dimer of Irga6 in a GTPdependent manner. The binding of GRA7 to Irga6 led to enhanced polymerization, rapid turnover, and eventual disassembly, which contributed to acute virulence in the mouse [22]. We recently showed that the GRA7-V (aa 201-236) domain led to physical and functional associations with TRAF6. Furthermore, GRA7-V-induced Th1 immune responses and protective efficacy were crucial for T. gondii infection in vivo [17]. In this study, we showed that host cell ASC, PLD1, and PKCα bind to GRA7. The GRA7 protein interacted with a number of host cell proteins including enzymes, and a broad spectrum of structural and functional subcellular organellar proteins revealing a new facet of the role of GRA7 in the regulation of innate host immune responses.
Our results correlate with those of previous studies showing that T. gondii is a novel activator of NLRP1 and NLRP3 inflammasomes by activating caspase-1, an enzyme that mediates cleavage and release of the proinflammatory cytokines IL-1β and IL-18 in vitro and in vivo, thereby establishing a role for these sensors in host resistance to toxoplasmosis [39][40][41]. Furthermore, Millholland et al. showed that a Gα subunit (Gα)q-coupled host-signaling cascade is required for the egress of T. gondii. Gαq-coupled signaling results in PKC-mediated loss of the host cytoskeletal protein adducin and weakening of the cellular cytoskeleton. This cytoskeletal compromise induces catastrophic Ca 2+ influx mediated by the mechanosensitive cation channel TRPC6, which activates host calpain that in turn proteolyzes the host cytoskeleton allowing parasite release [42]. T. gondii induces prostaglandin E2 biosynthesis in macrophages by regulating arachidonic acid production through a Ca 2+ -dependent pathway and induction of cyclooxygenase-2 expression by a PKC-dependent pathway [43,44]. Reinforcing the feasibility of targeting host proteins as an antiparasitic strategy, mammalian PKC inhibitors demonstrate activity in murine models of toxoplasmosis. In this study, we focused on the role of GRA7-I and -III-dependent innate immunity. Future studies will aim to clarify the precise molecular mechanisms of GRA7 and GRA7-II and -IV-related signaling pathways in inflammatory responses and host defense.
HDTs aim to modulate immune responses in the TB lung [45,46]. Neutralization of proinflammatory cytokines such as IL-6, TNF-α, VEGF, and IFN-α/β, as well as anti-inflammatory IL-4, during severe pulmonary disease may help reduce ongoing parenchymal damage in the MTB-infected lung [27,[45][46][47]. Alternatively, suboptimal activation of anti-TB immune responses due to regulatory T cell activity can be reversed by the use of the anti-cancer drug cyclophosphamide. Drugs with anti-TB potential, such as metformin, imatinib, ibuprofen, zileuton, valproic acid, and vorinostat as well as nutraceuticals such as 1,25D, may not only abate the bacterial burden via host-dependent mechanisms, but also fine-tune the immune response to MTB. These drugs increase phagocytosis of extracellular bacteria, improve emergency myeloid response, and increase autophagic and apoptotic killing of bacteria, subsequently editing the T cell response in favor of the host. Immune checkpoint inhibition with blockade of the PD-1/PD-1 ligand 1, CTLA-4/cytotoxic T lymphocyte-associated antigen 4, LAG3/lymphocyte-activation gene 3, and TIM3/T cell immunoglobulin pathways may improve the quality of the cellular immune response to MTB epitopes, as seen in cancer immunotherapy [4,5,[45][46][47]. Our results partially correlate with those of previous studies showing that host-directed immunotherapy with clinically approved drugs that augment prostaglandin E2 level prevents acute mortality of MTB-infected mice. Thus, IL-1 and type I IFNs represent two major counter-regulatory classes of inflammatory cytokines that control the outcome of MTB infection and are functionally linked via eicosanoids [27], and IL-1β either directly or via enhancement by 1,25D promotes antimicrobial immunity against MTB infection [10,11]. Greco et al. showed that PKC-mediated Ca 2+ mobilization, PLD activity, and (auto)phagolysosome maturation represent effector processes induced by apoptotic body-like liposomes carrying PA that concur with the intracellular killing of MTB [14]. The MTB-containing phagosomes is involved in arresting phagosome maturation and inhibiting phagolysosome biogenesis [6,8,9], however, rGRA7-induced PKCα regulates phagocytosis, PLD-dependent the biogenesis of phagolysosomes (Rab5 conversion to Rab7) by promoting the interaction of phagosomes with late endosomes and lysosomes, and Rab7 regulated phagosomal acidification, which is important for the killing of MTB in human macrophages [7,16]. Our current observations based on the study of GRA7-III co-localized with PLD1 and PKCα in the cytoplasm (Fig 5D and S6 Fig) have the proposal the localized on phagolysosomes, appearing as speckles and punctate spots, because of an artifact of rGRA7 overexpression. Further studies are needed to localization organelle population.
The rGRA7 have a function of biologicals as potential therapeutics. However, these rGRA7 do not fulfil the requirements of direct anti-mycobacterial agent, which represent feasible alternatives to conventional chemotherapy to TB, due to the still unclear specificity and selectivity does not enable linking the effects of rGRA7s to host immune systems, as well as limitation of animal experimental model, unknown off-target effects, pharmacokinetics, safety data, and their potential feasibility for in vivo proof-of-concept studies. Further analyses are required to find out whether rGRA7s can be translated to the in vivo situation or be observed in the presence of physiological condition to patient with TB.
In conclusion, we provide evidence of a critical role of PKCα-mediated phosphorylation of T. gondii GRA7 in the interaction between GRA7 and ASC or PLD1, which contributes to antimicrobial defense against MTB (S9 Fig). GRA7-I and -III-dependent host protective effects worked against MTB infection in vivo, and a phosphomimetic mutant that constitutively activated GRA7 'rescued' PKCα deficiency. These observations reveal a new role for GRA7 in regulating innate immune responses in host protective immunity. Our findings establish proof of concept for HDT strategies that manipulate host GRA7-mediated immune networks. Further studies are needed to develop more effective GRA7-based potential therapeutic targets and to understand how GRA7 regulates host defense strategies against TB and other infectious diseases. THP-1 cells were stimulated with rGRA7 (5 μg/ml) for 30 min., followed by IP with αPLD1, αTRAF6, or αASC and IB with αPLD1, αTRAF6, αASC, αHis, and αActin. (B) THP-1 cells were stimulated with rGRA7 for the indicated times, followed by IP with αHis-agarose bead and IB with αPLD2, αNLRP3, αNLRC4, αHis, and αActin. (C) THP-1 cells were pre-incubated with Cytochalasin D (Cyto D, 10 μM), phagocytosis inhibitor or solvent control for 30 min before treated with Alexa488-conjugated rGRA7 for the indicated times, followed by flow cytometry analysis to detect internalized Alexa488-rGRA7. The mean fluorescence intensity values of Alexa488-rGRA7 from flow cytometry were used to generate phagocytosis rate kinetics (bottom). (D) THP-1 cells were pre-incubated with Cytochalasin D (Cyto D, 5, 10, 20 μM) and stimulated with rGRA7 for the indicated times, followed by IP with αHis-agarose bead and IB with αPLD1, αTRAF6, αASC, αHis, and αActin. BMDMs from PKCα +/+ and PKCα -/were stimulated with rGRA7 (5 μg/ml) and its mutants for the indicated times and culture supernatants were harvested and analyzed for cytokine ELISA for TNF-α and IL-6. (B) BMDMs was transduced with lentivirus-shRNA-NS or lentivirus-shRNA-ASC (MOI = 100) with polybrene (8 μg/mL) (right) for 2 days, followed by IB with αASC, αPLD1, and αActin. (C) The number of ASC pyroptosome was counted using a fluorescent microscope and the ASC speck-containing cells were represented as a relative percentage compared to the total cell number related to Fig 3E. (D) BMDMs from PKCα +/+ and PKCα -/were stimulated with rGRA7-I and its mutants for 18 h. The cells were then subcellularly fractionated, subjected to co-IP with αHis, followed by IB analysis with αASC and αPKCα. Levels of tubulin (cytosolic), calnexin (endoplasmic reticulum (ER) and mitochondria-associated membrane (MAM)), fatty acid CoA ligase 4 (FACL4, MAM) and voltage-dependent anion channels (VDAC, mitochondrial) protein in each fraction were determined by IB analysis. Data shown are the means ± SD of five experiments (A and C). The data are representative of three independent experiments with similar results (B and D). (B) BMDMs from PKCα +/+ and PKCα -/were stimulated with rGRA7-III and its mutants for 18 h. The cells were then subcellularly fractionated, subjected to co-IP with αHis, followed by IB analysis with αPLD1 and αPKCα. Levels of tubulin (cytosolic), calnexin (endoplasmic reticulum (ER) and mitochondria-associated membrane (MAM)), fatty acid CoA ligase 4 (FACL4, MAM) and voltage-dependent anion channels (VDAC, mitochondrial) protein in each fraction were determined by IB analysis. The data are representative of three independent experiments with similar results. (TIF)

S7 Fig. Effects of GRA7-induced anti-mycobacterial activity and inflammation score in vivo.
(A) Intracellular survival of MTB was assessed by CFU assay. BMDMs were infected with MTB for 4 h, followed by treatment with rGRA7, and then lysed to determine intracellular bacterial loads. Data shown are the mean ± SD of five experiments. Significant differences ( ÃÃ P < 0.01; ÃÃÃ P < 0.001) compared with rVector. (B and C) Whole lung photo (B, left, related to Fig 7B) and immunopathology scores were obtained from H&E stained lung sections (B, right and C), as described in Methods. The data are representative of three independent experiments with similar results (B, left, 12.5 X). n = 10 (B, right and C). CFU, colonyforming units. ns, not significant.