key: cord-1052287-0ozzbp85 authors: Marriott, Andrew S.; Vasieva, Olga; Fang, Yongxiang; Copeland, Nikki A.; McLennan, Alexander G.; Jones, Nigel J. title: NUDT2 Disruption Elevates Diadenosine Tetraphosphate (Ap(4)A) and Down-Regulates Immune Response and Cancer Promotion Genes date: 2016-05-04 journal: PLoS One DOI: 10.1371/journal.pone.0154674 sha: ceab18d97f09ef0d9030f0c808f7ab5ab1c7d583 doc_id: 1052287 cord_uid: 0ozzbp85 Regulation of gene expression is one of several roles proposed for the stress-induced nucleotide diadenosine tetraphosphate (Ap(4)A). We have examined this directly by a comparative RNA-Seq analysis of KBM-7 chronic myelogenous leukemia cells and KBM-7 cells in which the NUDT2 Ap(4)A hydrolase gene had been disrupted (NuKO cells), causing a 175-fold increase in intracellular Ap(4)A. 6,288 differentially expressed genes were identified with P < 0.05. Of these, 980 were up-regulated and 705 down-regulated in NuKO cells with a fold-change ≥ 2. Ingenuity(®) Pathway Analysis (IPA(®)) was used to assign these genes to known canonical pathways and functional networks. Pathways associated with interferon responses, pattern recognition receptors and inflammation scored highly in the down-regulated set of genes while functions associated with MHC class II antigens were prominent among the up-regulated genes, which otherwise showed little organization into major functional gene sets. Tryptophan catabolism was also strongly down-regulated as were numerous genes known to be involved in tumor promotion in other systems, with roles in the epithelial-mesenchymal transition, proliferation, invasion and metastasis. Conversely, some pro-apoptotic genes were up-regulated. Major upstream factors predicted by IPA(®) for gene down-regulation included NFκB, STAT1/2, IRF3/4 and SP1 but no major factors controlling gene up-regulation were identified. Potential mechanisms for gene regulation mediated by Ap(4)A and/or NUDT2 disruption include binding of Ap(4)A to the HINT1 co-repressor, autocrine activation of purinoceptors by Ap(4)A, chromatin remodeling, effects of NUDT2 loss on transcript stability, and inhibition of ATP-dependent regulatory factors such as protein kinases by Ap(4)A. Existing evidence favors the last of these as the most probable mechanism. Regardless, our results suggest that the NUDT2 protein could be a novel cancer chemotherapeutic target, with its inhibition potentially exerting strong anti-tumor effects via multiple pathways involving metastasis, invasion, immunosuppression and apoptosis. Nudix hydrolases regulate the levels of a wide variety of canonical and modified nucleotides and some non-nucleotide phosphorylated substrates as well as participating in essential processes such as mRNA decapping [1, 2] . One of the best studied is mammalian NUDT2. This enzyme has been isolated from many sources [3, 4] and its principal substrate is believed to be diadenosine 5 0 ,5 000 -P 1 ,P 4 -tetraphosphate (Ap 4 A). In animal cells, Ap 4 A can be synthesized by most aminoacyl-tRNA synthetases, DNA ligases, firefly luciferase and acyl-CoA synthetases while a further range of enzymes is able to do so in plants, fungi and bacteria [5] [6] [7] . Synthesis usually involves transfer of AMP from an acyl-AMP or enzyme-AMP reaction intermediate to an ATP acceptor. It can also be degraded by a number of enzymes in addition to NUDT2, including FHIT [8] , aprataxin [9] and non-specific phosphodiesterases [3] . However, NUDT2 is believed to be principally responsible for maintaining the low level of intracellular Ap 4 A [10] [11] [12] . An increase in Ap 4 A resulting from activation of synthesis, inhibition of degradation or both has been implicated in several intracellular processes. Genotoxic, thermal and other stresses lead to increased Ap 4 A [13] [14] [15] [16] [17] and so Ap 4 A has been implicated in the regulation of DNA replication after DNA damage and in promoting apoptosis [17] [18] [19] . Ap 4 A may also be raised in response to external ligands and act as an intracellular second messenger [20] [21] [22] . It also acts as an extracellular messenger through its interaction with a number of P2-type receptors [23] . Ap 4 A is also a ligand for a number of proteins including a multiprotein complex containing DNA polymerase-α [24, 25] , protein kinases [26] [27] [28] , uracil-DNA glycosylase [29] , protein chaperones [30] , the HINT1 tumor suppressor [31] , 5 0 -nucleotidase II [32] , CBS domain proteins [33, 34] and CFIm25 [35] , but in most cases the significance of this binding is not clear. Of particular interest, however, is the possibility that Ap 4 A may act as a transcriptional regulator. It has been suggested that an increased level of Ap 4 A induced in mast cells by external factors activates the expression of a subset of genes controlled by the MITF and USF2 transcription factors by binding to and displacing the inhibitory HINT1 protein from these factors [10, 31, 36] . In order to determine whether transcriptional regulation by Ap 4 A is confined to relatively few genes or is more widespread, we have analysed the transcriptome of a knockout derivative of the KBM-7 chronic myeloid leukaemia (CML) cell line [37] in which the intracellular level of Ap 4 A has been increased 175-fold by disruption of the NUDT2 gene (KBM-7-NuKO, referred to hereafter as NuKO). These cells show profound changes in gene expression compared to the parent KBM-7 cell line with a total of 6288 significantly differentially expressed genes (DEGs) identified. Ingenuity 1 Pathway Analysis was used to highlight the gene networks and metabolic and signaling pathways affected, revealing down-regulation of interferon, inflammatory and innate immune responses and up-regulation of processes involving MHC class II antigens. In addition, many of the most strongly affected genes have roles in promoting cancer metastasis and invasion, suggesting that NUDT2 may offer a novel, pleiotropic target for cancer chemotherapy. modified Eagle medium (IMEM, Sigma) supplemented with 10% (v/v) Foetal Bovine Serum (Sigma), 2 mM L-glutamine (Sigma) and 100 μg mL -1 penicillin-streptomycin (Sigma). The level of intracellular Ap 4 A in log phase KBM-7 and NuKO cells was determined as previously described using a sensitive luminometric assay with slight modifications for use with suspension cells [17, 39] . Cells were harvested from suspension by centrifugation at 500 g for 5 min and used for nucleotide extraction. Ap 4 A was also measured in the growth medium supernatant from these cells, which was filtered through a 0.2 μm Millipore filter, deproteinized with 10% TCA, then assayed as above. ADP-ribosylated derivatives of Ap 4 A (ADPR-Ap 4 A) were separated by ion-exchange chromatography and identified and assayed as previously described [17] . Cells (2 x 10 5 ) were seeded into 25 cm 2 flasks containing 7 mL of growth medium. Chemical agents were added as stated and cells grown for 96 h at 37°C after which cultures were centrifuged at 500 g for 5 min, cells resuspended in fresh medium, and counted using a haemocytometer. Average counts were normalized to the cell count of the untreated culture. Three independent samples of total RNA were prepared from both KBM-7 and NuKO cells. RNA extraction was performed using a Qiagen RNeasy mini kit with QIAshredder, and the quantity and quality determined using a Nanodrop and Agilent Bioanalyzer. For each of the six samples, 10 μg of RNA was DNase-treated using an Ambion TURBO DNA-free™ kit and subsequently purified using AMPure XP beads. 2 μg of the DNase-treated total RNA was then subjected to rRNA depletion using the Ribo-Zero Gold (Human/Mouse/Rat) kit and purified again with Ampure XP beads. Successful depletion was assessed using a Qubit fluorometer and Agilent 2100 Bioanalyzer and all of the depleted RNA was used for the RNA-Seq library preparation using the ScriptSeq v2 protocol. Following 15 cycles of amplification the libraries were purified using Ampure XP beads. Each library was quantified using Qubit and the size distribution assessed using the Agilent 2100 Bioanalyzer. The final libraries were pooled in equimolar amounts using the Qubit and Bioanalyzer data. The quantity and quality of each pool was assessed with the Bioanalyzer and by qPCR using the KAPA Library Quantification kit for Illumina platforms on a Roche LC480II Light Cycler according to manufacturer's instructions. The template DNA was denatured according to the protocol described in the Illumina cBot user guide and loaded at a concentration of 9 pM. Sequencing was carried out on one lane of an Illumina HiSeq 2000 with version 3 chemistry generating 2 × 100 bp paired end reads. Quality control was maintained with a 1% PhiX spike-in. used produced a second-strand library type (R1 is expected to map on the 5 0 !3 0 strand and R2 on the 3 0 !5 0 strand). Reads aligning to the reference in more than one position were discarded and FKPM values (fragments per kilobase transcript per million reads mapped) calculated. Differential gene expression analysis was conducted in the R environment using the edgeR package [42] . The count data were normalised across libraries using the Trimmed Mean M-values (TMM) method in edgeR with default parameters. Tagwise dispersion parameters were estimated and then used for log 2 FC (log 2 Fold Change) estimation and testing in edgeR using the Likelihood Ratio (LR test) [43] . P values associated with log 2 FC were adjusted for multiple testing using the False Discovery Rate (FDR) approach [44] . Significant DEGs were defined as those with an FDR-adjusted P value < 0.05. All original RNA-Seq data produced in this study have been submitted to the EMBL-EBI ArrayExpress database under accession number E-MTAB-4104. RT-PCR analysis of selected genes RNA extraction was performed using a Qiagen RNeasy mini kit with QIAshredder and cDNA was synthesized using a Bioline Tetro cDNA synthesis kit, both according to the manufacturer's instructions. The cDNA was then quantitated by PCR using Maxima SYBR Green master mix (Thermo) and a StepOnePlus™ Real Time PCR system (Applied Biosystems). Primers were obtained from Sigma and are listed in S1 Table. The 2 -ΔΔCt method was used to determine relative transcript levels using the housekeeping GAPDH gene to normalize the data [45] . Genes showing 2-fold up-or down-regulation with an FDR-adjusted P value < 0.05 were analyzed through the use of QIAGEN Ingenuity 1 Pathway Analysis software (IPA 1 , QIA-GEN, Redwood City, http://www.ingenuity.com) in order to assign them to different functional networks. IPA 1 uses the manually curated Ingenuity1 Knowledge Base, which contains information from several gene and protein expression, interaction and annotation databases such as IntACT, BIND and MiPs, as well as from the published literature [46] . We also used IPA to identify functionally related genes that correspond to specific canonical pathways that were most significant to the data set from a collection of 200 curated metabolic, cell-signaling cascade and disease-associated pathways. Fisher's exact test of independence was used to calculate the probability that the association between the genes in the dataset and the canonical pathway can be explained by chance alone. Finally, we used the IPA upstream regulator analysis to identify factors that may control the genes and pathways highlighted by network analysis to provide testable hypotheses for gene regulation by Ap 4 A. The parent KBM-7 line used in this study contains the BCR-ABL1 gene fusion and potentially inactivating mutations in TP53 and NOTCH1, but lacks the other common genetic aberrations found in myeloid malignancies [38] . It expresses the majority of annotated proteins from a wide range of signaling pathways, making it a suitable cell line for this study. The complete absence of NUDT2 protein from the NuKO NUDT2 disruptant was confirmed by Western blotting (Fig 1) . The steady-state concentration of intracellular Ap 4 A in unstressed mammalian cells is typically in the range 0.1-1.0 pmol/10 6 cells (0.05-0.5 μM), the exact amount being species-and cell type-dependent [17, 47] . Log phase KBM-7 cells had a level of 0.21±0.02 (n = 3) pmol/10 6 cells. However, the NuKO derivative had a 175-fold increased level of 36.9±0.3 (n = 3) pmol/10 6 cells, providing the clearest evidence yet that Ap 4 A is an important NUDT2 substrate in vivo and that this enzyme plays an essential role in maintaining the low background level of Ap 4 A. Note that an Ap 4 A content of 1 pmol/10 6 cells equates roughly to an intracellular concentration of 0.5 μM if uniformly distributed [17] so the level in NuKO cells will be around 20 μM. Regarding whether this high level and the resulting changes in the cells reported here are biologically relevant, we have previously measured up to 20 μM Ap 4 A in DNA repair-defective cells treated with mitomycin C [17] while a concentration as high as 775 μM has been reported in FCεR1-activated mast cells [31] . Chromatographic analysis of the Ap 4 A from NuKO cells showed that about 35% was present in the form of ADP-ribosylated derivatives (ADPR-Ap 4 A), mainly mono-ADPR-Ap 4 A (Fig 1) . We have previously shown that ADP-ribosylation of Ap 4 A by PARP1 and PARP2 in Chinese hamster EM9 cells and mouse embryo fibroblasts occurs in response to DNA damage [17] ; however, it appears that the high level of Ap 4 A here is subject to constitutive ADP-ribosylation. Ap 4 A has been reported to activate the transcription of subsets of genes controlled by the transcription factors MITF and USF2 [31, 36] . In view of this, and to further explore the phenotype of the NUDT2 knockout cells, we carried out a comparative analysis of the transcriptomes of KBM-7 and NuKO cells by RNA-Seq to identify DEGs. An average of 46.1 million pairs of 100 bp paired-end reads per sample were generated that aligned to the reference human genome. Alignment results are summarized in Table 1 , showing the number and percentage of reads mapped for each sample. Mapping percentages for the six samples were between 80.2 and 81.3%. 31,177 (48.5%) of the 64,253 reference genes had at least one read aligned while 33,076 genes had no read aligned from any of the six samples. The difference in gene expression profiles between the two cell types is illustrated in the Principal Component Analysis (PCA) plot of log2 gene expression data shown in Fig 2A. The triplicate samples of each cell type are grouped well away from each other, indicating a high degree of differential gene expression between them. Furthermore, the heatmap of the Pearson correlation coefficients in Fig 2B indicated that the expression profiles for the three samples from the same cell type were much more closely correlated than samples from different cell types, showing that the effect of NUDT2 knockout on gene expression was much stronger than the influence of any technical or biological variations between samples. The heatmap also shows a very high correlation (R >0.99) among samples from the same cell type. Thus, we can conclude that the differential gene expression detected here is statistically very robust. Of the 31,177 reads mapped (S2 Table) a total of 6,288 DEGs were identified with a P-value (FDR-adjusted) < 0.05, of which 2,550 were up-regulated and 2,285 down-regulated with a fold-change 1.2 ( Fig 2C and S3 Table) . The MA plot in Fig 2C shows a fairly symmetrical distribution of up-and down-regulated genes at all levels of expression. Of these genes, 980 were up-regulated and 705 down-regulated with a fold-change 2. In both cases, 88% had FPKM 0.3 for one or both of the WT and KO datasets. The 40 most strongly down-and upregulated annotated genes are shown in Tables 2 and 3 respectively. Note that many of these genes had zero read counts for either the WT or KO datasets necessitating the addition of a small zero-offset pseudocount by the edgeR software in order to calculate log 2 FC [42] . The RNA-Seq analysis was validated by performing real-time qRT-PCR on a selection of genes representing various affected pathways (Fig 3) . These results confirmed the direction of regulation (up or down) for all genes studied. The magnitude of change was also similar for the majority of genes, with a correlation coefficient of 0.83 between the two data sets (Fig 3, inset) . However, for some genes with a zero value of FPKM for one of the samples in the RNA-Seq analysis, the use of the pseudocount method by edgeR to calculate a fold-change has led to a significantly different value, e.g. GFRA1 and TNF. Nevertheless, the values calculated by edgeR are used in the following discussions as they are available for all genes and are still a good relative indication of the change in expression. In order to show that the observed differential gene expression correlates solely with increased Ap 4 A rather than the related ADPR-Ap 4 A derivatives, qRT-PCR analysis was also performed with RNA extracted from NuKO cells grown in the presence of 100 nM KU-0058948, a PARP1 and PARP2 inhibitor that prevents the synthesis of ADPR-Ap 4 A species [17] . The results were very similar to those obtained in the absence of KU-0058948 (Fig 3) , showing that, for these genes at least, ADPR-Ap 4 A is not the cause of the differential expression. The function of ADPR-Ap 4 A, if any, is still unclear. In order to place the gene expression data into a biological context, Ingenuity 1 Pathway Analysis (IPA 1 ) software was used to assign the DEGs to known canonical pathways and functional networks in order to predict the biological functions of the transcriptional changes. For simplicity, the initial analysis included only genes that were up-or down-regulated by 2-fold (P < 0.05); however, where present in the resulting pathways and networks, genes up-or down-regulated by 1.2 were also considered to be of potential interest as there is no biological justification for a cut-off value of 2. It was found that the DEGs mapped to a large number of pathways with a significant enrichment score (-log(P-value)) (S4 Table) . Top ranked within both up-and down-regulated gene sets were signaling pathways related to immunity and inflammation. Pathways associated primarily with the innate immune response, such as activation of interferon regulatory factors (IRFs) by pattern recognition receptors (PRRs), and inflammation were specifically enriched in the down-regulated set of genes while functions associated with MHC class II antigens were specific for the set of up-regulated genes ( Table 4 ). The predominance of these pathways in the dataset may reflect the myeloid nature of the KBM-7 cell line [37] . These pathways are discussed in detail below. Interferon response and innate immunity. Interferons are important mediators of the innate immune response, which provides an initial vital defence against invading pathogens (viruses, bacteria, protozoa) following interaction of pathogen components with PRRs in various cellular compartments. They can also inhibit cell proliferation, modulate the adaptive immune response, and be pro-or anti-inflammatory, depending on context [48] [49] [50] [51] . Submission of the set of 4,835 DEGs with fold change 1.2 to the Interferome database (v2.01) [52] revealed a subset of at least 1,038 DEGs known to be regulated by Type I IFNs (IFNα and IFNβ) in other systems. Roughly half of these overlapped with the set of 944 showing known regulation by Type II IFNs (IFNγ). Some (56) also showed Type III (IFNλ) regulation with 15 of these potentially unique to Type III (S5 Table) . Table) . The JAK-STAT signaling pathways are central to the interferon response. In Type II interferon signaling, activated STAT1 homodimers bind to the GAS (Interferon Gamma Activated Sequence) promoter and induce gene expression while Type I signaling involves the combination of STAT1-STAT2 heterodimers with IRF9 (Interferon Response Factor 9) forming ISGF3 (Interferon Stimulated Gene Factor), which then binds to the ISRE (Interferon-Stimulated Response Element) promoter. STAT1, STAT2 and IRF9 were all down-regulated (1.4-, 1.8-and 3.7-fold respectively) while the pathway suppressors SOCS1 and PTPN2 were up-regulated (1.2-1.4-fold). Though individually slight, the combined effect of these changes could nevertheless be significant. The increases in SOCS1 and PTPN2 also show that there is not just a general suppression of gene expression but that negative feedback via these genes is preserved. Finally, several of the STATcontrolled genes that are down-regulated are themselves activators of further IFN response genes, e.g. IRF1, IRF7 and IRF9 (1.2-, 3.8-and 3.7-fold respectively). The canonical pathway in Fig 5 highlights the roles of the three RIG-1-like helicase PRRs of the innate immune response, RIG-1 (DDX58), MDA5 (IFIH1) and LGP2 (DHX58) in the activation of IFNB following stimulation by viral double-stranded RNAs and the feedback provided by IFNβ on the expression of these PRRs. All three receptor genes are down-regulated (3.1-, 2.3-and 3.0-fold respectively) (S3 Table) in NuKO cells. In addition, IFITM2 and IFITM3, whose products restrict the entry of many viruses [53] , and all four antiviral IFIT family members that bind viral components (IFIT1, 2, 3 and 5) [54] are down-regulated between 1.6-and 6.8-fold. Other down-regulated anti-viral genes include PKR, GBP1 and TLR10 [55-58] (S3 Table) . Cytokine signaling, inflammation and NF-κB. Interleukin-1 (IL-1) signaling is flagged by IPA 1 as a top down-regulated pathway (Table 4 ) with reduced expression of important pro-inflammatory members of the IL-1 superfamily [59] . For example, the mRNAs for IL-1β, its receptor IL-1R1 and accessory protein IL-1RAP are decreased 1.5-, 11.7-and 1.2-fold respectively while IL-18, IL-18R1 and IL-18RAP are down 2.3-, 3.0-and 4.0-fold respectively. Expression of pro-inflammatory IL32 is also reduced 5-fold, while the expression of Tumor Necrosis Factor (TNF or TNFα), which can activate both Type I IFNs and the inflammatory mediator NF-κB, is down-regulated 30-fold (S3 Table) . The canonical pathway leading to transcriptional activation by NF-κB through IL-1, TNFα and other ligands is shown in Fig 6. The NF-κB complex is an important mediator of inflammatory and immune responses and responds to PRRs and pro-inflammatory cytokines [60] . It can synergize with STAT signaling with the increased induction of target genes resulting from coordinate binding of STATs and NF-κB to GAS and NF-κB promoters. The p50 and p52 components of the NF-κB complex and the RELB transactivator are all down-regulated as are many components of signaling pathways that lead to NF-κB activation. It is known that Type I IFNs and TNF can mutually suppress each other's expression, and it has been suggested that changes in the cross-regulation of these pathways might affect the balance between the potential destructive and protective roles of these cytokines in the pathogenesis of autoimmune inflammatory diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) [61] . IPA 1 identifies signaling in RA as a top-ranked affected canonical pathway (Table 4 ) and the list of DEGs associated with RA and SLE are shown in S6 Table. Although the modest changes in expression in some other cytokine receptors could potentially be pro-inflammatory (e.g. IL10RA and IL23R), the overall picture is one of the Up-regulated canonical pathways and MHC class II antigens. KBM-7 cells can be regarded as immature precursors to professional antigen-presenting cells such as macrophages and dendritic cells and almost all of the top 20 up-regulated canonical pathways flagged by IPA 1 involve functions associated with the adaptive immune response, including antigen presentation, OX40 signaling, allograft rejection and B cell development (Table 4 and S4 Table) . However, it should be emphasized that this is largely because these pathways all involve one of the most prominent up-regulated gene sets, the inducible MHC class II antigens (MHC-II). MHC-II molecules are mainly concerned with the presentation of antigens derived from extracellular pathogens resulting in CD4+ T helper cell priming and the production of antibodies by B cells [62] . Almost all class II subtype genes show a significant increase in expression, with some showing a large increase, e.g. HLA-DOA 47-fold and HLA-DPA1 12-fold. Thus, the extent to which these canonical pathways can be regarded as up-regulated as a whole is open to question. Nevertheless, in addition to MHC-II, a number of other genes involved in promotion of aspects of the adaptive immune response are up-regulated in NuKO cells. OX40 (CD134, TNFRSF4) is a member of the TNF receptor superfamily expressed by activated T helper cells and other cells and when engaged by the OX40L ligand promotes the clonal expansion of effector and memory T cells responding to an antigen [63] . It is up-regulated 5-fold. CD86 (up 2.2-fold) is expressed on antigen-presenting cells and interacts with T cell surface ligands. It is crucial for effective T cell activation and survival [64] and several studies have shown that down-regulation or blockade of CD86 can improve allograft survival [65] . FCER1G (up 8-fold) encodes the γ chain (FcRγ) of the high affinity IgE receptor FCεR1 [66] . FcRγ is also a component of several other Fc receptors and the T-cell receptor, which may explain its association by IPA 1 with several immune functions. Interestingly, activation of mast cells by aggregation of FCεR1 with IgE-antigen complexes has been reported to promote synthesis of Ap 4 A by lysyl-tRNA synthetase and the consequent transcriptional changes [10, 31, 36] . Up-regulation of FCER1G might therefore provide a mechanism to potentiate and prolong Ap 4 A synthesis. Together, these data suggest reinforcement of elements of the adaptive immune response by elevated Ap 4 A. As far as KBM-7 cells are concerned, this may indicate that Ap 4 A is promoting their differentiation from a blast-like phenotype to a more mature stage [67] . There are some apparent exceptions, such as the down-regulation of the recombination activating genes RAG1 and RAG2 (down 4.4-and 107-fold respectively). The RAG proteins catalyze VDJ recombination and are essential for the generation of mature, functional T and B cells [68] . These genes are moderately expressed in KBM-7 cells and can be highly expressed in other blast-phase CML-derived cell lines, e.g. NALM-1 [69] . However, their relevance to immune function is in lymphocytes, in which their expression is normally confined, and in that context they may not be down-regulated by high Ap 4 A. Tryptophan catabolism. Canonical pathway analysis also shows that a number of metabolic pathways including tryptophan (Trp) catabolism, and consequently de novo NAD+ biosynthesis (derived from Trp [70] ), are strongly associated with the set of down-regulated genes (Fig 7 and S4 Table) while creatine phosphate biosynthesis, melatonin degradation (also a Trp derivative) and NAD+ phosphorylation are associated with the set of up-regulated genes. The strong down-regulation of both major pathways of Trp catabolism, particularly the key enzymes kynureninase (KYNU, 65-fold), indoleamine 2,3-dioxygenase (IDO1, 19-fold) and DOPA decarboxylase (Trp decarboxylase, DDC, 16-fold) is of particular note. Expression of the rate-limiting IDO1 is induced in myeloid-lineage cells by IFNs, particularly Type II, and TNF can act synergistically to increase IDO1 expression [71] , so the observed down-regulation of these pathways in NuKO cells combined with the moderate up-regulation of the negative effectors DAP12 (TYROBP) and BIN1, a tumor suppressor, (S3 Table) [72] would be expected to reduce IDO1 expression substantially. There is strong evidence that Trp catabolism and IDO1 expression in dendritic cells, which have a myeloid lineage, are important for the suppression of T-cell responses and the promotion of immune tolerance [73] . The reduction of extracellular Trp, the generation of metabolites via the kynurenine pathway and the signaling function of tyrosine-phosphorylated IDO1 all contribute to immunosuppression and protection against autoimmune disease and allograft rejection by inhibiting the proliferation of T cells and NK cells and promoting autophagy and anergy [74, 75] . The strong down-regulation of Trp catabolism caused by elevated Ap 4 A would therefore be expected to promote T-cell responses and suppress tolerance. This is consistent with the up-regulation of adaptive immune functions in NuKO cells predicted by IPA 1 (Table 4 ). Furthermore, overexpression of IMPACT, an inhibitor of the GCN2-kinase (EIF2AK4) stress-signaling pathway that represses translation and proliferation in response to amino acid starvation, protects T-cells from Trp depletion [76] . IMPACT is up-regulated 13-fold in NuKO cells (S3 Table) and this would contribute further to the promotion of T-cell responses if reproduced in T cells. Cancer. Introduction of Ap 4 A by cold shock into some cell lines has been reported to induce cell cycle arrest and apoptosis [19, 77] . It has also been shown that the level of NUDT2 expression positively correlates with lower survival and increased lymph node metastases in breast carcinoma [78] . This suggests that low Ap 4 A might promote and/or high Ap 4 A might inhibit cancer progression. Given the multifactorial nature of the disease, it is not surprising that IPA 1 classifies 1,108 of the 1,685 DEGs with 2-fold change and P 0.05 as being increased, decreased or affected (usually by mutation association) in cancer. So, to investigate further whether the level of Ap 4 A might correlate with cancer cell proliferation, survival or metastasis, we have further filtered this DEG set to exclude those with FPKM values < 0.3 for both WT and KO samples to focus attention on the more abundant transcripts and avoid any uncertainties about the biological impact of DEGs with low level expression. We then conducted a literature search on the 40 most strongly up-regulated and down-regulated annotated protein-coding genes satisfying these conditions (S7 Table) . Existing experimental evidence suggests that reduced expression of 14 of the top 40 down-regulated genes would lead to a significant anti-cancer effect while reduced expression of only 2 might have a promotional effect. A further 3 could be pro-or anti-cancer depending on cellular context. Of the top 40 up-regulated genes, increased expression of 8 restricts cancer progression in other systems, while only 2 promote it and one could possibly do either (S7 Table) . Thus, increased intracellular Ap 4 A seems to be associated overall with a strong anti-cancer effect. A more detailed appraisal of these genes follows. It is worth noting that the well-characterized FHIT tumor suppressor protein that binds both Ap 3 A and Ap 4 A [8, 79] is not expressed in KBM-7 cells, probably as a result of gene deletion [38] . Several genes showing a high degree of differential expression have been associated in other cell systems with the epithelial-mesenchymal transition (EMT)-the loss of cell-cell adhesion that initiates metastasis-and the reverse process, mesenchymal-epithelial transition (MET), which stabilizes and integrates the cancer cells into tissues: 1. GFRA1 encodes a receptor for glial cell line-derived neurotrophic factor (GDNF) and is classified by edgeR as the most strongly down-regulated gene in the dataset (3221-fold). The proliferation of prostate cancer cells and their resistance to genotoxic treatment correlate directly with the level of GFRA1 expression [80] . It is also up-regulated in breast carcinoma [81] while GFRA1 released by cells can promote cancer cell migration and invasion [82] . 2. Expression of the transcription factors OVOL1 and OVOL2 in mesenchymal prostate cancer and poorly differentiated breast cancer cells induces MET and so inhibits their metastatic potential [83] . It has been proposed that the EMT/MET balance is regulated by the ratio of OVOL1/2 (promote MET) to ZEB1/2 (promote EMT) expression. OVOL1 is up-regulated 719-fold in NuKO cells (although OVOL2 expression is decreased 8-fold) while ZEB1 and ZEB2 are slightly down 1.4-and 1.2-fold respectively. 3. The secreted glycoprotein EDIL3 has recently been identified as a novel inducer of EMT in hepatocellular carcinoma. It promotes cell migration, invasion and angiogenesis [84] . It is also up-regulated in oral squamous cell carcinoma [85] but is down-regulated 590-fold in NuKO cells. 4. The mRNA binding protein IGF2BP1 promotes EMT while its knockdown reduces cell migration in various mesenchymal-like tumor cells [86] . It is down 122-fold. 5. GJA-1 was recently characterized as a key gene for cervical cancer invasion and metastasis [87] and is down 120-fold in NuKO cells. 6. The transmembrane protein TM4SF1 is overexpressed in many cancers and in the tumor vascular endothelium [88] with its level correlating with poor prognosis in glioblastoma [89] . Its down-regulation by an endogenous miRNA in prostate cancer cells inhibited migration and invasion [90] . It is down 70-fold. 7. BRINP3 overexpression in pituitary gonadotrope cells promotes proliferation, migration, and invasion [91] . It is down 136-fold. 8. MPZL2 expression is significantly decreased in breast carcinoma cells growth-arrested by siRNA knockdown of the migration and invasion regulatory PACE4 proprotein convertase [92] . Its expression is reduced 260-fold in NuKO cells. 9. The RAC1-activating guanine nucleotide exchange factor PREX2, which is frequently mutated in cancer and which promotes migration and invasion of various neoplasias [93, 94] is down 44-fold. 10 . High expression of the HSP40 family member DNAJC12 has been found to correlate with colorectal tumor progression and invasion and with a poor response to neoadjuvant concurrent chemoradiotherapy [95] . It is down-regulated 41-fold in NuKO cells. 11. Down-regulation of the transcription factor FOXD3 promotes an EMT phenotype in breast cancer cells, causing proliferation and invasion both in vivo and in vitro while overexpression inhibits this phenotype [96, 97] . Similar results have been found with other cancers [98, 99] . It is up-regulated 16-fold in NuKO cells. 12. Overexpression of NKD2, a negative regulator of Wnt signaling, in metastatic osteosarcoma and breast carcinoma significantly reduces tumor growth and metastasis in vivo and decreases cell proliferation, migration and invasion in vitro, while down-regulation has the opposite effect [100, 101] . It is up-regulated 19-fold. 13 . The leukemia inhibitory factor receptor LIFR has been shown to act as a suppressor of metastasis in hepatocellular carcinoma [102] . Increased LIFR activity has also been correlated with a reduction in the pool of breast cancer stem cells [103] . It is up-regulated 11-fold. Several other prominent DEGs are known to affect growth and apoptosis in other systems. The Wilms' tumor transcriptional regulator WT1 can exhibit both oncogenic and tumor suppressor activities depending on its association with specific co-regulators [104, 105] . For example, the co-repressor BASP1 interacts with WT1 in a complex with PHB and BRG1 to favor growth arrest and the induction of apoptosis over proliferation [106] . All these genes are well expressed in KBM-7 cells, with BASP1 exhibiting 35-fold up-regulation in NuKO cells. Up-regulation of IFI44L is associated with melanoma and prostate cancer [107, 108] while overexpression of NKX2-2 is associated with Ewing's sarcoma and fibromatosis [109] . They are down 224-and 125-fold respectively in NuKO cells. The homeobox transcription factor NKX3-1 is a prostate tumor suppressor [110] and its expression is increased 13-fold in NuKO cells. Overexpression of the coiled coil domain protein CCDC68 decreased proliferation and tumorigenicity of pancreatic ductal adenocarcinoma cells while allelic loss was found in about half the tumors examined [111] . It has also been identified as a possible tumor suppressor in colorectal cancer [112] and is up-regulated 11-fold in NuKO cells. Even genes with a more modest change in expression could have a profound anti-cancer effect; for example, Interferon Regulatory Factor 4 (IRF4), an important NF-κB-activated regulator of immune system development and the innate immune response [113] , also plays an essential role in many lymphoid malignancies, and knockdown of its expression by only 50% is lethal to multiple myeloma cells [114, 115] . It is down-regulated 7-fold in NuKO cells. Mixed results have been reported for a few of these DEGs in other contexts. For example, high expression of the cysteine-rich secretory protein CRISP3 (down 130-fold in NuKO cells) has been found in certain subtypes of prostate cancer [116] but down-regulation has been associated with oral squamous cell carcinoma [117] . A similar pattern has been found for the JCHAIN component of IgA and IgM (down 75-fold), which is up-regulated in prostate [108] but down-regulated in colorectal cancer [118] . The chemokine CXCL10 is another factor with both tumor-promoting and anti-tumor effects, the latter largely through its immunogenic action. It is overexpressed up to 40-fold in most types of cancer and can promote tumor cell growth and metastasis [119] but is down 66-fold in NuKO cells. Mixed results have also been reported for the Ig superfamily protein CD200 and the transmembrane co-receptor syndecan-4 (SDC4), with both pro-and anti-cancer roles suggested in different situations [120] [121] [122] . They are down 51-and up 35-fold in NuKO cells respectively. The change in expression of a small number of genes in NuKO cells could potentially promote cancer. For example, reduced expression of early B-cell factor 1 (EBF1) has been found in Hodgkin lymphoma and appears to contribute to the loss of B-cell phenotype and consequent malignancy [123] . There is also evidence for a tumor suppressor role in mouse leukemia [124] . It is down-regulated 38-fold. Expression of the TNF family member CD70 is normally restricted to activated T and B-cells but it is activated in a wide variety of tumors where it promotes tumor cell expansion and survival [125] . It is up-regulated 17-fold. Finally, type II transglutaminase (TGM2) is up-regulated 10-fold in NuKO cells and may have an important role in maintaining survival, invasion and the metastatic behavior of a variety of tumors and cancer stem cells [126] while sphingosine-1-phosphate phosphatase 2 (SGPP2, up 63-fold) may be a target for the tumor suppressor miRNA-31 [127] . If the above set of prominent DEGs were expressed and responded in the same way to increased Ap 4 A in carcinomas, the overwhelming net effect could be the strong suppression of tumor growth and, particularly, metastasis. Furthermore, as indicated previously, IDO1 and several other enzymes of Trp catabolism are strongly down-regulated in NuKO cells. Increased IDO1 expression is a characteristic of many cancer cells and assists them in avoiding clearance by the immune system, with the level of expression often correlating with poor prognosis [72, 128] and so inhibitors of IDO1 have been considered as novel immunotherapeutic adjuvants to conventional anti-cancer drugs [129, 130] . Taken together, there seems to be sufficient evidence to support NUDT2 as a novel chemotherapeutic target that could conceivably exert an anti-cancer effect via multiple pathways involving apoptosis, metastasis, invasion and immunosuppression. One additional benefit of targeting NUDT2 could be the 158-fold down-regulation of O 6 -methylguanine-DNA methyltransferase (MGMT). The MGMT protein dealkylates toxic and mutagenic O 6 -alkylguanine lesions in DNA exposed to alkylating agents [131] and its reduced expression in NuKO cells renders them much more sensitive to growth inhibition by methylmethane sulfonate (MMS), N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) and N-methylnitrosourea (MNU) (Fig 8) . While down-regulation by high Ap 4 A would in theory promote the carcinogenic effect of environmental alkylating agents, it would also render cancer cells more sensitive to alkylation therapies such as temozolomide, a common treatment for glioblastoma and astrocytoma. MGMT status is an important determinant of the success of these therapies [132] . This sensitivity to methylating agents also provides good phenotypic confirmation of the transcriptomic data. IPA 1 prediction of upstream regulatory factors IPA 1 prediction of key upstream regulators for the DEGs suggests numerous factors controlling large sets of down-regulated genes with fewer factors controlling small groups of up-regulated genes. Of the top 100 most significant potential regulators, 97 are proposed to contribute to gene down-regulation (S8 Table) although the total number of genes that are up-and downregulated is similar. The inter-relationships between the top-ranked transcription factors implicated in gene down-regulation and their major identified targets are shown in Fig 9. The functions of several of these have already been described. In addition, CNOT7 (hCAF1) is a STAT1-binding negative regulator of Type I and Type II IFN signalling [133] , while the transcription factor IRF3 is an important responder to PRR activation ( Fig 5) and co-operates with NF-κB and IRF7 in the transcription of IRF3-and NF-κB-dependent genes [113] . SP1 controls the transcription of multiple genes, many of which have been described as promoting the 'hallmarks' of cancer: proliferation, independence from growth signals, avoidance of apoptosis and immune destruction, invasion and metastasis, and angiogenesis. It is overexpressed in many tumors, making it a target for chemotherapy [134, 135] . Control of up-regulated genes appears to be less integrated with different factors potentially regulating small subsets of genes (S8 Table) . Furthermore, of the top five listed regulators (SMC3, PDLIM2, EBI3, MYOC and NEUROG1, Table 4 ), little or no expression of EBI3, Methods. Data are presented as means ± SEM of the results obtained from three independent experiments. When no error bar is shown, the error is smaller than the symbol. Statistical significance was assessed by Student's two-tailed t-test; *P < 0.05, **P < 0.01, ***P < 0.001 versus WT control. doi:10.1371/journal.pone.0154674.g008 MYOC and NEUROG1 was detected so their significance is hard to determine. In the case of MHC-II genes, SMC3, a subunit of the cohesin protein complex, was identified as a possible regulator. Cohesin co-operates with MHC-II specific transcription factors RFX5 and CIITA (up 1.3-fold) to activate MHC-II expression [136] . SMC3 has an ATP-binding domain but whether cohesin binding to MHC-II transcriptional insulators could be promoted by Ap 4 A is unknown. The ubiquitin E3 ligase PDLIM2 (SLIM) is predicted by IPA 1 to both up-regulate and down-regulate different sets of genes (S8 Table) . This prediction appears to be based largely on the results of one previous investigation into PDLIM2-modulated gene expression in DU145 prostate carcinoma cells [137] and the direction of expression change of about half the genes in that study is different from that in ours. Given that the z-scores for the predictions are both below the significance threshold of 22.0 or <-2.0 is significantly predictive, the P-value and the molecules for which there is documented evidence for regulation. The top transcription factors examined in The Nudix hydrolase superfamily Substrate ambiguity among the nudix hydrolases: biologically significant, evolutionary remnant, or both? Specific and nonspecific enzymes involved in the catabolism of mononucleoside and dinucleoside polyphosphates Dinucleoside polyphosphates-friend or foe? Enzymatic synthesis of mono and dinucleoside polyphosphates Synthesis of dinucleoside polyphosphates catalyzed by firefly luciferase and several ligases The duality of LysU, a catalyst for both Ap 4 A and Ap 3 A formation Fhit, a putative tumor suppressor in humans, is a dinucleoside 5',5‴-P 1 ,P 3 -triphosphate hydrolase Aprataxin forms a discrete branch in the HIT (histidine triad) superfamily of proteins with both DNA/RNA binding and nucleotide hydrolase activities Diadenosine tetraphosphate hydrolase is part of the transcriptional regulation network in immunologically activated mast cells Cloning, characterisation and crystallization of a diadenosine 5',5‴-P 1 ,P 4 -tetraphosphate pyrophosphohydrolase from Caenorhabditis elegans Lanterns of the firefly Photinus pyralis contain abundant diadenosine 5',5‴-P 1 ,P 4 -tetraphosphate pyrophosphohydrolase activity Alteration of adenyl dinucleotide metabolism by environmental stress Non-adenylylated bis(5'-nucleosidyl) tetraphosphates occur in Saccharomyces cerevisiae and in Escherichia coli and accumulate upon temperature shift or exposure to cadmium Is Ap 4 A involved in DNA repair processes? AppppA, heat-shock stress, and cell oxidation Diadenosine 5', 5‴-P 1 ,P 4 -tetraphosphate (Ap 4 A) is synthesized in response to DNA damage and inhibits the initiation of DNA replication Diadenosine 5', 5‴-P 1 , P 4 -tetraphosphate: a pleiotropically acting alarmone? The involvement of diadenosine 5 ',5‴-P1,P4-tetraphosphate in cell cycle arrest and regulation of apoptosis Diadenosine tetraphosphate-gating of cardiac K-ATP channels requires intact actin cytoskeleton Intracellular diadenosine polyphosphates: a novel second messenger in stimulus-secretion coupling Ryanodine receptor modulation by diadenosine polyphosphates in synaptosomal and microsomal preparations of rat brain The purinergic neurotransmitter revisited: a single substance or multiple players? Characterization of the HeLa cell DNA polymerase α-associated Ap 4 A binding protein by photoaffinity labeling Adenosine(5')tetraphospho (5')adenosine-binding protein of calf thymus Effect of several nucleotides on the phosphorylating activities of the Rous-sarcomavirus transforming protein pp60v-src and its cellular homologue, pp60c-src Inhibition of casein kinase-II by dinucleoside polyphosphates Inhibition of protein kinase activity of phorboid and ingenoid receptor by di(adenosine 5') oligophosphate Uracil DNA-glycosylase/glyceraldehyde-3-phosphate dehydrogenase is an Ap 4 A binding protein Investigation into the interactions between diadenosine 5',5‴-P 1 ,P 4 -tetraphosphate and two proteins: molecular chaperone GroEL and cAMP receptor protein The function of lysyl-tRNA synthetase and Ap 4 A as signaling regulators of MITF activity in FceRI-activated mast cells Structural basis for the allosteric regulation and substrate recognition of human cytosolic 5'-nucleotidase II The CBS Domain: A Protein Module with an Emerging Prominent Role in Regulation Cystathionine β-synthase (CBS) domain-containing pyrophosphatase as a target for diadenosine polyphosphates in bacteria Structural basis of UGUA recognition by the Nudix protein CFI m 25 and implications for a regulatory role in mRNA 3 ' processing Nonconventional involvement of LysRS in the molecular mechanism of USF2 transcriptional activity in FceRI-activated mast cells Isolation and characterization of a near-haploid human cell line A reversible gene trap collection empowers haploid genetics in human cells The Fhit tumor suppressor protein regulates the intracellular concentration of diadenosine triphosphate but not diadenosine tetraphosphate Cutadapt removes adapter sequences from high-throughput sequencing reads TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions edgeR: a Bioconductor package for differential expression analysis of digital gene expression data The large-sample distribution of the likelihood ratio for testing composite hypotheses Controlling the false discovery rate: a practical and powerful approach to multiple testing Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method A survey of current software for network analysis in molecular biology Determination of dinucleoside polyphosphates Fine tuning type I interferon responses Regulation of type I interferon responses Interferon-stimulated genes: a complex web of host defenses Type I interferons as anti-inflammatory mediators Interferome v2.0: an updated database of annotated interferon-regulated genes IFITM-family proteins: The cell's first line of antiviral defense Interferon-induced Ifit proteins: their role in viral pathogenesis Impact of protein kinase PKR in cell biology: from antiviral to antiproliferative action Antiviral effects of the interferon-induced protein guanylate binding protein 1 and its interaction with the hepatitis C virus NS5B protein Toll-like receptor 10 is involved in induction of innate immune responses to influenza virus infection IFIT1 is an antiviral protein that recognizes 5'-triphosphate RNA The interleukin (IL)-1 cytokine family-Balance between agonists and antagonists in inflammatory diseases Complex modulation of cell type-specific signaling in response to type I interferons Autoimmunity through cytokine-induced dendritic cell activation The ins and outs of MHC class II-mediated antigen processing and presentation The significance of OX40 and OX40L to T-cell biology and immune disease What's the difference between CD80 and CD86? Graded function of CD80 and CD86 in initiation of T-cell immune response and cardiac allograft survival The high-affinity IgE receptor (Fc epsilon RI): from physiology to pathology Differential expression of MHC class II antigens in myelomonocytic leukemia cell lines Biochemistry of V(D)J recombination The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity NAD + metabolism in health and disease Synergistic transcriptional activation of indoleamine dioxygenase by IFN-γ and tumor necrosis factor-α Role of indoleamine 2,3-dioxygenase in health and disease IDO expression by dendritic cells: tolerance and tryptophan catabolism Tryptophan-derived catabolites are responsible for inhibition of T and natural killer cell proliferation induced by indoleamine 2,3-dioxygenase GCN2 kinase in T cells mediates proliferative arrest and anergy induction in response to indoleamine 2,3-dioxygenase High expression of IMPACT protein promotes resistance to indoleamine 2,3-dioxygenase-induced cell death Ap 4 A induces apoptosis in cultured human cells Nudix-type motif 2 in human breast carcinoma: a potent prognostic factor associated with cell proliferation The FHIT gene product: tumor suppressor and genome "caretaker DNA damage induces GDNF secretion in the tumor microenvironment with paracrine effects promoting prostate cancer treatment resistance BEX2 is overexpressed in a subset of primary breast cancers and mediates nerve growth factor/nuclear factor-kappaB inhibition of apoptosis in breast cancer cell lines GFRalpha1 released by nerves enhances cancer cell perineural invasion through GDNF-RET signaling Transcription factors OVOL1 and OVOL2 induce the mesenchymal to epithelial transition in human cancer EDIL3 is a novel regulator of epithelialmesenchymal transition controlling early recurrence of hepatocellular carcinoma Identification of two distinct carcinoma-associated fibroblast subtypes with differential tumor-promoting abilities in oral squamous cell carcinoma IGF2BP1 promotes mesenchymal cell properties and migration of tumor-derived cells by enhancing the expression of LEF1 and SNAI2 (SLUG) Cervical squamous cancer mRNA profiles reveal the key genes of metastasis and invasion TM4SF1: a new vascular therapeutic target in cancer Transmembrane-4-L-six-family-1, a potential predictor for poor prognosis, overexpressed in human glioma hsa-miR-141 downregulates TM4SF1 to inhibit pancreatic cancer cell invasion and migration Bone morphogenetic protein and retinoic acid-inducible neural specific protein-3 is expressed in gonadotrope cell pituitary adenomas and induces proliferation, migration, and invasion PACE4 regulates proliferation, migration and invasion in human breast cancer MDA-MB-231 cells PTEN inhibits PREX2-catalyzed activation of RAC1 to restrain tumor cell invasion Molecular pathways: P-Rex in cancer Overexpression of DNAJC12 predicts poor response to neoadjuvant concurrent chemoradiotherapy in patients with rectal cancer FoxD3 deficiency promotes breast cancer progression by induction of epithelial-mesenchymal transition Downregulation of the transcription factor, FoxD3, is associated with lymph node metastases in invasive ductal carcinomas of the breast FOXD3 is a novel tumor suppressor that affects growth, invasion, metastasis and angiogenesis of neuroblastoma FoxD3 is a novel, epigenetically regulated tumor suppressor in gastric carcinogenesis Epigenetic silencing of NKD2, a major component of Wnt signaling, promotes breast cancer growth NKD2, a negative regulator of Wnt signaling, suppresses tumor growth and metastasis in osteosarcoma LIFR functions as a metastasis suppressor in hepatocellular carcinoma by negatively regulating phosphoinositide 3-kinase/AKT pathway. 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Purification and properties of a diadenosine 5',5‴-P 1 ,P 4 -tetraphosphate (symmetrical) pyrophosphohydrolase from Escherichia coli K12 PrpE, a PPP protein phosphatase from Bacillus subtilis with unusual substrate specificity Variation in intracellular P 1 ,P 4 -bis(5'-adenosyl) tetraphosphate (Ap 4 A) in virus-infected cells SARS coronavirus protein 7a interacts with human Ap4A hydrolase Viruses transfer the antiviral second messenger cGAMP between cells Transmission of innate immune signaling by packaging of cGAMP in viral particles Identification of Escherichia coli K1 genes contributing to human brain microvascular endothelial cell invasion by differential fluorescence induction The Rickettsia prowazekii invasion gene homolog (invA) encodes a Nudix hydrolase active on adenosine (5')-pentaphospho-(5')-adenosine Regulation of dinucleoside polyphosphate pools by the YgdP and ApaH hydrolases is essential for the ability of Salmonella enterica serovar Typhimurium to invade cultured mammalian cells The invA gene of Brucella melitensis is involved in intracellular invasion and is required to establish infection in a mouse model P 1 ,P 3 -bis(5'-adenosyl)triphosphate (Ap 3 A) as a substrate and a product of mammalian tryptophanyl-tRNA synthetase Differential regulation of the human, interferon inducible tryptophanyl-tRNA synthetase by various cytokines in cell lines Interferons induce accumulation of diadenosine triphosphate (Ap 3 A) in human cultured cells Regulation of indoleamine 2,3-dioxygenase and tryptophanyl-tRNA-synthetase by CTLA-4-Fc in human CD4+ T cells Stereospecific targeting of MTH1 by (S)-crizotinib as an anticancer strategy An organometallic inhibitor for the human repair enzyme 7,8-dihydro-8-oxoguanosine triphosphatase The authors wish to thank Charlotte Nelson, Anita Lucaci, Luca Lenzi and Sam Haldenby for assistance with the RNA-Seq analysis and Mark Wilkinson for provision of and help with hplc. Conceived and designed the experiments: ASM NJJ AGM. Performed the experiments: ASM. Analyzed the data: ASM AGM OV YF NAC. Contributed reagents/materials/analysis tools: OV. Wrote the paper: AGM NJJ.