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. 2013 Jul 1;2(7):e54. doi: 10.1038/oncsis.2013.15

More targets, more pathways and more clues for mutant p53

S Garritano 1, A Inga 2, F Gemignani 1, S Landi 1,*
PMCID: PMC3740285  PMID: 23817466

Abstract

Mutations in the transcription factor p53 are among the most common genetic alterations in human cancer, and missense p53 mutations in cancer cells can lead to aggressive phenotypes. So far, only few studies investigated transcriptional reprogramming under mutant p53 expression as a means to identify deregulated targets and pathways. A review of the literature was carried out focusing on mutant p53-dependent transcriptome changes with the aims of (i) verifying whether different p53 mutations can be equivalent for their effects, or whether there is a mutation-specific transcriptional reprogramming of target genes, (ii) understanding what is the main mechanism at the basis of upregulation or downregulation of gene expression under the p53 mutant background, (iii) identifying novel candidate target genes of WT and/or mutant p53 and (iv) defining cellular pathways affected by the mutant p53-dependent gene expression reprogramming. Nearly 600 genes were consistently found upregulated or downregulated upon ectopic expression of mutant p53, regardless of the specific p53 mutation studied. Promoter analysis and the use of ChIP-seq data indicate that, for most genes, the expression changes could be ascribed to a loss both of WT p53 transcriptional activation and repressor functions. Pathway analysis indicated changes in the metabolism/catabolism of amino acids such as aspartate, glutamate, arginine and proline. Novel p53 candidate target genes were also identified, including ARID3B, ARNT2, CLMN, FADS1, FTH1, KPNA2, LPHN2, PARD6B, PDE4C, PIAS2, PRPF40A, PYGL and RHOBTB2, involved in the metabolism, xenobiotic responses and cell differentiation.

Keywords: p53, microarray, target genes

Introduction

The tumor suppressor p53 is a 393-amino-acid nuclear phosphoprotein that responds to numerous stress stimuli, including DNA damage1 and hypoxia.2 Following homotetramerization, it acts as a transcription factor3 and modulates the expression of a variety of genes, leading to enhanced DNA repair, control of cell cycle and apoptosis, and maintaining cellular homeostasis.4, 5, 6 The p53 targets are only partially known, with assessments suggesting their number to be nearly 2000 genes.7

CDKN1A, MDM2, BAX, GADD45 and BBC3 are paradigmatic examples of upregulated target genes where p53 exerts its activity via evolutionarily conserved cis-response elements (p53RE).8 The importance of p53 for the biology of cancer is evident by the fact that colorectal, breast and most other human solid tumors show a high frequency of somatic mutations within the TP53 gene (www.iarc.fr/p53). Moreover, germline mutations within TP53 cause the Li–Fraumeni syndrome, a dominantly inherited cancer proneness syndrome with an elevated risk of developing adrenocortical carcinoma, choroid plexus carcinomas, sarcomas and other types of cancer in multiple sites at a young age.9 So far, nearly 2000 different single amino-acid changes in p53 have been reported in tumors,10 and their frequencies vary markedly: next to exceedingly rare mutations, strong hotspots are evident.11, 12 This latter group of mutations affects, in particular, codons 175, 248, 249, 273 or 282. The impact of mutations on p53 functions can vary from a wild-type-like activity, for example, the R337H mutations associated with predisposition to adrenocortical carcinoma,13 to a partial function or to a suspected complete loss of function (LOF).12, 14

According to Resnick et al., different mutant p53s retaining a partial activity (for example, T123A or S215C) show specific effects on the transactivation of target promoters, leading to mutation-specific altered regulation of hundreds of genes (the ‘piano model'), resulting in a variety of biological consequences.15, 16 Cells can show lack of control of their cell cycle and weakened apoptosis and DNA repair. However, in selected examples, separation of p53 functions was observed, with defective apoptotic control, but wild-type function in cell cycle arrest.17 Moreover, knockin mouse models showed varied phenotypes, suggesting the occurrence of mutation-specific gene expression reprogramming also in vivo.18, 19 However, most studies related to mutant p53 activity were performed on hotspot mutations, using reconstituted assays12, 14, 20 or other in vitro models. Following these experimentations, it was observed that hotspot mutations have the least transactivating activity of common targets and therefore they were suggested to cause a p53 LOF. Hotspot p53 mutations were reported to be associated with more aggressive malignancies and could confer novel phenotypes in vivo, including an increased metastatic capacity and resistance to chemotherapies.21, 22, 23, 24, 25, 26, 27 The acquired phenotypes of specific mutant p53s are generally referred to as gain-of-function properties,28 but it is unclear if these features are restricted to or distinct among specific p53 hotspot mutations. Examining the impact of hotspot p53 mutations at a transcriptome level, a large number of genes are downregulated. However, there are also a restricted number of WTp53 targets whose transactivation seems not to be hampered by p53 mutations.7 Moreover, there are also genes that are upregulated under mutant but not WT p53 expression. It is not clear whether different mutants can lead to similar transcriptional changes or have different impact on it (like an extension of the ‘piano model') and whether the gained phenotypes can be related to specific genes upregulated in the p53 mutant background. Thus, in this work, the focus was placed on cancer-associated p53 hotspot mutations that exhibit a loss of transactivation function in reconstituted assays,12, 14 and a review of the literature was performed, with the following aims: (1) to verify whether different p53 mutations can be equivalent for their effects, or whether there is a mutation-specific transcriptional reprogramming of target genes, (2) to understand what is the main mechanism at the basis of upregulation or downregulation of gene expression under the p53 mutant background, (3) to identify the novel candidate target genes of WT and/or mutant p53 and (4) to define cellular pathways affected by the mutant p53-dependent gene expression reprogramming.

Selection of the published literature

In order to identify genes differentially modulated upon the expression of mutant p53, only potentially unbiased transcriptome studies published in the literature were collected. In fact, in transcriptome studies, target genes are analyzed without formulating any a priori hypothesis and, virtually, all the genes are evaluated with the same relevance. An extensive literature search was carried out using PubMed (http://www.ncbi.nlm.nih.gov/pubmed) to collect original papers. Articles were selected by screening title, abstract and full text, and only those reporting the effects of ectopic expression of p53 mutants on the transcriptome were considered further.

Out of over 2000 known p53 mutations reported by the IARC (www.iarc.fr/p53) or UMD TP53 databases,10, 29 only 12, falling in 11 different codon sites (Figure 1), were studied through global gene expression changes. Those 11 mutated codons lie within the sequence-specific DNA-binding domain, correspond to hotspot mutations in tumors and result in LOF in functional assays. Studies on p53-dependent transcriptomes were few and heterogeneous in their experimental design, with a variable p53 status of the cell lines used, thus limiting the strength of the comparisons. Therefore, conservatively, conclusions on mutant p53-dependent gene deregulation were drawn only when at least three independent p53 mutations showed a coherent effect on the same target. Now on, genes upregulated under the ectopic expression of at least three different mutant p53 genetic backgrounds are defined as UMB, whereas DMB are the genes downregulated under at least three different mutant p53s. The results were obtained relying on the statistical analyses imbedded within each study, and a list of differentially expressed genes was compiled for further analysis (Supplementary Data S1). For each chosen article, in Table 1 the p53 missense mutation studied, and the cell lines used to perform the experiments were reported.

Figure 1.

Figure 1

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Number of genes (Log10) upregulated (black bars) and downregulated (white bars) following in vitro studies where a mutant form of p53 is overexpressed. Only few codons were assayed and for each mutation it is shown that the number of genes going overexpressed is approximately similar to those downregulated. The missense mutations falling within the codon 248 (R248Q and R248W) were considered as a unique one, in order to empower the study.

Table 1. List of selected article.

Article Cells used Mutation tested
88 TKS, WTK1 M237I
89 HCT 116 A138P; R175H
90 H1299 R175H
91 HME1 R175H; R273H; R280K; R249S
65 H1299 R175H; R273H; D281G
92 U2OS R175H; V157F; R248Q
93 LNCaP G245S; R248W; R273H; R273C
94 H1299 R175H; R248W; R273H; D281G
95 H1299 D281G
96 H1299 R175H; R273H; D281G
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For each article, the investigated p53 missense mutations and the cell lines used to perform the experiments are reported.

In silico analyses of promoters and pathways

COMPASSS (COMplex PAttern of Sequence Search Software),30 a software that allows to perform custom pattern searches in entire genomes, was used to analyze the promoters. Given that most of the deregulated genes are not well-established p53 target genes, the focus was placed on the identification of non-canonical p53REs,31, 32 particularly a half-site RE motif. In the exploratory search, a conservative approach was used by limiting inspection to 2-kb upstream of annotated transcriptional start sites, not allowing mismatches in the half-site decameric motif, and requiring the presence of a cluster of at least two half-sites within one nucleosome.32 Hence, the following input were used: RRRCWWGYYY(N0-50)RRRCWWGYYY and NRRCWWGYYN(N0-50)NRRCWWGYYN. Two closely spaced p53 half-sites either in a direct orientation (RRRCWRRRCW) or lacking the CWWG core (WGYYYRRRCW), or having a relaxed motif definition (RRNCNNGNYN) (all sequence features that have been associated with genes repressed by WTp53),8 were also queried. Thus, COMPASSS was used to analyze the promoters of UMB and DMB genes and to measure the ‘baseline' number of p53REs found in the whole human genome. Then, a binomial distribution-based statistics (approximated as normal distribution) was used, in order to verify whether the promoters were enriched for the input motifs, as compared with the baseline level.

The complex pattern of gene transcription changes was further analyzed with the tool Database for Annotation, Visualization and Integrated Discovery, in order to detect whether mutations within p53 could affect specific biological pathways.33 Database for Annotation, Visualization and Integrated Discovery uses all the human genes as background to perform the comparisons, and if a group of genes is enriched within a specific biological process or pathway, the P-value of the modified Fisher's exact test will be lower than the cutoff (0.05). First, the short lists of UMB and DMB genes, either separated or combined, were used as input, but the total number of genes was not large enough to obtain statistically significant results. Thereafter, the analyses were repeated with a broadened input list, that is, the list of genes changing their expression under at least one p53 mutant background (that is, those reported in Supplementary Table S1).

Results and Discussion

Similar deregulation profile of genes by distinct p53 mutation hotspots

By observing UMB and DMB genes, consistent trends emerged (Table 2, see also Supplementary Table S1). A total of 401 genes were found downregulated under the ectopic expression of at least three different mutant p53s, whereas 260 genes were found upregulated (reported in Tables 3 and 4, respectively). Given the heterogeneity among studies, it is likely that consistent findings reveal true p53 target genes. Similar, although less confident, results could be obtained when the expression of genes was evaluated comparing at least two p53 mutants: 446 genes were found to be downregulated, whereas 503 genes were found to be upregulated. Thus, overall, the gene expression reprogramming did not seem to differ in relation to the mutant p53 hotspot analyzed. However, it should be also noticed that a small share of genes (48 out of 1846, 2.6%) was described as behaving discordantly, in relation to the p53 mutant assayed. It should be acknowledged that a systematic comparison of all mutant p53s under the same experimental conditions was not found in the literature, and thus subtle mutation-specific differences cannot be ruled out.

Table 2. Number of genes upregulated and downregulated following in vitro studies where a mutant form of p53 is overexpressed.

Number of genes % Of the total number of genes Number of mutations on different p53 codons, leading to upregulation Number of mutations on different p53 codons, leading to downregulation
151 8.2% 0 1
698 37.8% 1 0
446 24.2% 0 >1
503 27.2% >1 0
401 22% 0 ⩾3
260 14% ⩾3 0
       
Inconsistent
 48 2.6% ⩾1 ⩾1
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Only 48 out of 1846 were described as behaving differently according to the p53 mutated codon assayed. The greatest majority of genes (949) showed a reproducible upregulation or downregulation when various p53 mutations were assayed.

Table 3. List of genes consistently downregulated where at least three independent mutations were assayed.

ABAT C15orf41 COBL EDNRB GABARAPL1 GTPBP2 KIAA1211 MARVELD2 P9 PSA SHC4 TBXA2R VCAN
ABCA12 C16orf5 COL18A1 EFS GAD1 H19 KIAA1324 MCC PADI3 PSEN2 SHROOM2 TEAD3 VSNL1
ABCG1 C17orf103 COL2A1 EGR2 GADD45A HAGH KIAA1751 MDFIC PAK6 PSTPIP2 SI TFAP2E WDR8
ABHD15 C19orf59 CPE ENC1 GAGEB1 HES2 KITLG MDM2 PALMD PTGES SLC2A13 TGFBR1 WFDC5
ABHD4 C1orf187 CPN2 EPB49 GALE HHATL KLRK1 MEF2A PARD6G PTPN22 SLC2A8 TINAGL1 WNT5A
ABHD6 C2orf3 CRISPLD2 EPHB4 GAMT HIC2 KREMEN2 MGC16703 PARP10 PVRL4 SLC2A9 TINP1 WWP1
ABTB2 C4A CRYAB EPPK1 GATA3 HIST1H2AE KRT78 MGC4248 PARP14 PYHIN1 SLC35D1 TLR3 XLalphas
ACPP C4orf18 CSNK1G1 ETNK1 GCH1 HLA-DMB KRTAP2-1 MIB2 PCBP4 RALGDS SLC35E4 TM7SF3 YPEL3
AHSA2 C5orf4 CST1 ETV7 GCLC HOXC13 KSR MICALL2 PCLO RASAL1 SLC39A8 TMEM144 ZBTB1
AIFM2 C6orf204 CST11 F2RL2 GDAP1L1 HOXD10 LAMP3 MIR PDE3A RASGEF1B SLC4A11 TMEM27 ZMAT3
AK1 C7orf10 CTSH FAM105A GDF15 HSDL2 LCE1B MLF2 PDE4C RASSF6 SLC4A4 TMEM63B ZNF197
AKR1B10 C7orf57 CYFIP2 FAM134B GDF9 HSPA5BP1 LDLRAP1 MME PDE4D RDH10 SLC9A1 TMG4 ZNF236
ALDH7A1 C9orf100 CYLC1 FAM13C GGT1 HTR2A LHX3 MPZL2 PENK RET SLC9A3R1 TMTC3 ZNF385A
AMACR C9orf98 CYP2C9 FAM43A GGT6 IDUA LIN54 MRAS PERP retII SLCO2B1 TNFRSF10B ZNF441
ANK1 CABC1 CYP2S1 FAM46C GH2 IFIT3 LOC132671 MRPL44 PF4V1 RGS12 SMARCD3 TNFRSF14 ZNF492
ANO4 CABYR CYP3A7 FAM69B GHR IFITM1 LOC203274 MSX1 PGF RHOD SOM TNP2 ZNF746
APAF1 CACNA2D2 D4S234E FAM82A1 GJC1 IL24 LOC283585 MT1G PLA2G2A RNASE7 SORBS1 TNRC6C ZNF786
APOBEC3C CALML3 DDB2 FAM87A GLS IL2RB LOC284837 MTMR6 PLAC8 RNF128 SORL1 TP53I11  
ARAP1 CAPN1 DDC FBXO2 GLS2 INPP1 LOC286434 MYO6 PLAT RNF144B SPAG1 TP53I3  
ARG1 CARD18 DFNB31 FBXO22 GM2A INPP5D LOC348938 MYO7A PLEK RNF4 SPN TP53INP1  
ARHGAP6 CASP6 DGKZ FGF1 GNA11 ISG15 LOC80154 NADSYN1 PLXNB3 RPL36 SSB2 TP53TG1  
ARHGEF3 CBFA2T3 DHRS2 FITM2 GNA14 ISYNA1 LRDD NCRNA00085 PMAIP1 RPS27L STAR TP73  
ASPA CCNA1 DISP1 FLJ14312 GNG2 ITFG1 LRP10 NEFL PODXL RPS6KA1 STARD5 TRAF4  
ATP11B CCT6B DKK2 FLJ32065 GPC1 KAT2B LTB4R NHLH2 POLH RRM2B STAT4 TREM2  
BCHE CDH10 DMRTC1 FLJ36336 GPR126 KCNB1 LUM NIFU POMZP3 RTN1 STOX2 TRIM11  
BCL11B CDKN1A DNAJC18 FLJ40773 GPR137B KCNC4 MAB21L1 NLRP1 POU3F1 SAA2 SULF2 TRIM2  
BDKRB1 CDKN1C DNAJC21 FLNC GPR155 KCNJ12 MAD1L1 NOTCH1 PPM1F SAC3D1 SYK TRIM22  
BDNF CEACAM1 DPYSL4 FMN1 GPR56 KIAA0247 MAEL NOTCH3 PPP2R2B SCN3B SYNC TRIM3  
BLNK CES2 DSG3 FREQ GPR87 KIAA0284 MAFB NRCAM PPP2R2C SCNN1G SYTL2 TSGA10  
BTF3L3 CLCA2 DUOX1 FRMD8 GRAMD2 KIAA1026 MAGEA4 NRP2 PRKX SEC14L5 TAF3 TSPAN14  
BTG2 CLDN19 DUSP13 FXYD2 GREB1 KIAA1052 MAP2K3 OIP106 PRKY SERPINB5 TAGLN TSPY1  
C13orf31 CNNM4 EDAR G6PC GRN KIAA1199 MAPK13 OTP PRRX1 SESN1 TAP1 ULBP2  
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The listed genes are reproducibly deregulated irrespectively on the mutated codon. Note that all the assayed mutations fall within the p53 DNA-binding domain. Bolded genes are in common with the functional assay proposed by Riley et al.3 Underlined are genes for which a high-confidence p53 occupancy sites had been mapped.

Table 4. List of genes consistently upregulated where at least three independent mutations were assayed.

ABLIM1 CA9 DRG1 HIST1H4C LPP MYO5B ProSAPiP1 SLCO4A1 XRCC5
ACTA2 CARS E2F3 HMGB2 LRRFIP1 NAP1L1 PRPF40A SLPI YARS
ADAMTSL4 CBR4 E2F5 HOMER1 LRRK1 NARS PRSS7 SMA4 ZBTB45
ADPRTL2 CCNB1IP1 EBAG9 HOMER2 LYN NBEA PRSS8 SMTN ZNF217
AGGF1 CCNB2 EFHA1 HSP90AB1 MAD2L1 NDC80 PVRL3 SNTB2 ZNF238
AKT2 CCNH EXPH5 HYAL3 MAL2 NDUFA4L2 PYGL SPAG5 ZNF24
ALDH1A3 CCNL1 F2R ID1 MAP2K5 NFATC2IP QARS SQSTM1 ZNF273
ALDH2 CD14 FADS1 ID3 MAP4 NFKBIA RAD51C SRM ZNF415
ALDH3A1 CD44 FAM169A IL1RL1 MAPKAPK2 NKTR RBBP6 SS18 ZNF44
ANGPT1 CDC2 FBXO31 IMPDH2 MAPKAPK3 NLRX1 RBBP8 STAMBP ZNF579
APS CDC6 FDFT1 INADL MAPKAPK4 NMI RHOBTB2 STAT3 ZNF580
AR CDH1 FECH INF2 MARCH_6 NPC1 RHOG STATH ZNF652
ARHGEF2 CDKL3 FTH1 INPPL1 MARCKS NUP153 RLN1 TAF1A  
ARID3B CDS1 FZD3 ITGA6 MAZ PAQR3 RNF44 TARS  
ARNT2 CEBPB GAGE3 ITPR3 MCL1 PARD6B RNF6 TFAP2A  
ASB13 CKS1B GAPVD1 JUNB MCM3 PARP1 RP3-402G11.5 TMC6  
ATF3 CLEC18C GARS JUP MCM6 PCGF2 RPGRIP1 TNFRSF9  
ATIC CLMN GHDC KIAA0516 ME1 PDE3B RPS6KA3 TRAF3IP2  
BAG2 CPT1B GINS1 KIF13B MED13 PDLIM5 RRM1 TREM1  
BARD1 CPVL GLI2 KIF24 MEF2D PGAP3 SARNP TRIM29  
BCAN CREB1 GMIP KIF2C MELK PGM1 SEC31A TROVE2  
BMP6 CTPS GNB2L1 KLF16 MEST PHKB SEZ6L2 TUBB  
BPTF CTSF GOSR1 KPNA2 MFGE8 PI3 SF3A2 TUSC3  
BTG3 CUL5 GPR153 KRAS MINK PIAS2 SFPQ TXNIP  
BTN3A3 CUL7 GTF3A KRT16 MMP28 PIK3CA SGPP2 TXNL4A  
C10orf116 DAAM1 GUCY1A3 LARP MNX1 PLEKHH1 SIRT6 UCN  
C13orf1 DCAF4 HAS3 LOC115871 MOCOS POLA2 SLC16A4 UGT1A10  
C16orf45 DHFRL1 HAX1 LOC120450 MRPL46 POLD2 SLC26A2 UGT2B28  
C1orf63 DHRS9 HBA2 LOC139376 MRPS6 POLR2E SLC29A2 UGT2B7  
C21orf63 DICER1 HHL LOC81691 MTAP PRDM15 SLC4A7 UPF1  
CA2 DMXL1 HIBCH LPHN2 MYC PRKCI SLC6A8 VIM  
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The listed genes are reproducibly deregulated irrespectively on the mutated codon. Note that all the assayed mutations fall within the p53 DNA-binding domain. Bolded genes are in common with the functional assay proposed by Riley et al.3 Underlined are genes for which a high-confidence p53 occupancy sites had been mapped.

Hypothesized mechanisms at the basis of DMB and UMB phenotypes

In order to better understand the possible mechanisms related to the changes of expression caused by mutations within p53, DMB genes were first compared with the information from Riley's list,3 who reported 126 experimentally validated p53 target genes. These genes were crossed with those reported in Table 3 and 26 in common were found (bolded in Table 3). Almost all of them, 25, were genes normally activated by the WTp53. Then, COMPASSS was used (the detailed statistics are reported for each chromosome and for each p53RE motif in Supplementary Table 2) and it was observed that DMB genes were enriched for p53RE motifs typically found in genes transactivated by WTp53. This was expected and was consistent with the comparison made with Riley's data. Thus, it is conceivable that, for most of the DMB genes, the lack of expression is related to the LOF of p53.

When Riley's list was compared with the UMB genes (Table 4), only three were in common (bolded), preventing to draw any conclusion. According to COMPASSS, UMB genes were specifically enriched for a pair of the p53RE variant motif (RRNCNNGNYN) that was previously related to WTp53-dependent gene repression.8 Out of 260 UMB genes, 242 contained a putative repressor element. It is, however, important to note that the p53RE variant pattern search may retrieve false-positive results. This motif was also enriched over the baseline for the DMB genes (this because it represents a more degenerated version of the canonical p53RE) confirming the difficulty in separating p53-upregulated and p53-downregulated genes purely on the basis of the cis-regulatory elements.3, 8, 32 The fact that WTp53 could bind p53RE within specific UMB genes is reinforced by studies of chromatin immunoprecipitation followed by DNA sequencing (ChiP-seq) coupled to transcriptome analysis.34 In fact, high-confidence p53 occupancy sites have been mapped not only for 57 DMB genes but also for 23 UMB genes (underlined in Tables 3 and 4). In summary, also UMB genes could be explained with the loss of activity, that is, a loss of transcriptional repression, toward specific targets. Actually, it was shown recently that p53 bears a repressor activity for genes such as CHEK1, BCL2, ARF and FOS,8 MDR1 and Lasp1.8, 35 Moreover, experiments in the yeast assays showed that mutant p53s lose the transactivating capability towards several target REs from target genes.12, 14, 20

Three generally accepted mechanisms of direct p53-mediated transcriptional repression are known: (1) steric interference by masking overlapping transcription factor binding sites,36, 37 (2) sequestration of transcription activators38 and (3) recruitment of histone deacetylases.3, 39 Moreover, other indirect mechanisms were suggested, such as the transcriptional activation of microRNAs, known inhibitors of mRNA translation and stability.39, 40 Thus, a ‘full-loss-of-function hypothesis' could explain both the UMB and the DMB genes. However, alternatives are discussed further in final remarks section.

Novel targets for WTp53

Previous analyses were also useful to detect novel putative direct p53 targets. In fact, a short list of highly likely candidate p53 targets was obtained applying the in silico analysis of p53REs within the DMB and UMB genes, crossed with the results from a ChIP-seq study.34 In Supplementary Table S3, all the UMB and DMB genes positive for a p53REs within the promoter (through COMPASSS) were listed. Following the cross with the ChIP-seq study, known p53 targets were found (including ATF3, BTG2, BTG3, MYC, CDKN1A, ENC1, TP53I3 and TP53INP1). However, interestingly, a restricted number of novel potential p53 targets were also suggested. These are: ARID3B, ARNT2, CLMN, FADS1, FTH1, KPNA2, LPHN2, PARD6B, PDE4C, PIAS2, PRPF40A, PYGL and RHOBTB2. Intriguingly, some of them, belonging to the UMB category, were shown to be in causal relationship with features of the malignant phenotype and their upregulation in tumor correlates with a worsening of the prognosis. For example, an overexpression of ARID3B in human neuroblastoma cell lines is more common in stage IV neuroblastoma than in stages I–III, indicating its role in the progression of malignant neuroblastoma.41 The upregulation of ARNT2 is also common in neuronal-derived tumors. ARNT2 forms complexes with HIF-1a (Hypoxia-inducible factor 1-alpha), and it allows for initiating hypoxia/nutrient deprivation-induced vascular endothelial growth factor expression, therefore permitting tumor angiogenesis.42 FTH1 was found to be overexpressed in tumorspheres, and its upregulation has an important anti-apoptotic role.43, 44 Moreover, FHIT overexpression was shown to have a role in increasing the multidrug resistance of cancer cells,45 whereas its silencing caused an increased sensitivity.46 KPNA2 was found to be highly expressed in different types of cancer, and its aberrant expression is often linked to a poor prognosis.47 Finally, PARD6B was found amplified and overexpressed in a high number of breast cancer cell lines. The encoded protein, PAR6B, has a central role in tight junction assembly, maintenance of cell polarity, all features important for tumor progression and invasion.48 Although the precise mechanism at the basis of the upregulation is not established (a loss of transcriptional repression is likely, as stated before), the increase in gene expression could, at least in part, explain some of the novel phenotypes gained by cancer cells, including angiogenesis, drug resistance and altered cell–matrix and cell–cell interactions.

Pathway analysis of deregulated genes using Database for Annotation, Visualization and Integrated Discovery

The possible pathways and biological functions modulated by mutant p53s were evaluated in silico using the tool Database for Annotation, Visualization and Integrated Discovery and in Table 5 the main results (with a KEGG-pathways based analysis) are reported. As expected, the p53 signaling pathway (P=8.6 × 10−8), and pathways related to the control of the cell cycle (P=1.3 × 10−3), is among the most significant semantic terms. Moreover, an over-representation of genes encoding for enzymes in the metabolism of xenobiotics (P=1.3 × 10−4) was found, where, in general, the cytochrome p450 genes are overexpressed. This might be related to the known resistance to chemotherapeutic drugs associated with p53 mutation status of patients' cancer cells.49 Intriguingly, an enrichment of deregulated genes in pathways devoted to the catabolism of amino acids was also found (for example, ARG1, arginase; PRODH, proline oxidase; GLS2, glutaminase; GAD1, glutamate decarboxylase 1, all downregulated). The amino-acid catabolism leads to the formation of α-ketoglutarate, one of the key substrate for the tricarboxylic acid cycle, which in turn results in enhanced mitochondrial respiration and ATP generation. It is worth to stress here that p53 was shown to have a role not only in the regulation of cell cycle, apoptosis, differentiation, senescence, angiogenesis,50 antioxidant response51 and glutaminolysis52, 53 but also in the modulation of both glycolysis54, 55, 56 and mitochondrial respiration.57, 58, 59, 60 Metabolic enzymes including glucose transporters (such as GLUT1 and GLUT4), glycolytic enzymes (such as PGM5 and HK2) and tricarboxylic acid cycle enzymes are downstream targets of p53 (ref. 61), and WTp53 was shown to slow the glycolysis. The inhibition of glycolysis can also be achieved by p53-dependent transcriptional activation of synthesis of cytochrome C oxidative 2, resulting in enhanced mitochondrial respiration.62 Thus, mutations within p53 could lead to an increase of the glycolysis, characteristic of cancer cells.63 One of the most important genes linking the energy metabolism with p53 was proposed to be GLS2, encoding for glutaminase. It was shown that GLS2 is transactivated by WTp53, and it regulates the cellular energy metabolism by increasing the production of α-ketoglutarate, the mithochondrial respiration and the ATP generation.52 It is noteworthy that GLS2 expression was shown to be decreased in hepatocellular carcinomas, whereas its overexpression reduced tumor cell colony formation in an in vitro assay. GLS2 downregulation in cancer could be obtained by LOF mutations within p53, and this is consistent with the fact that GLS2 was found among the DMB genes. In addition, PRODH (proline dehydrogenase) was found downregulated in various transcriptome studies (Supplementary Table S1). Interestingly, PRODH functions as a tumor suppressor, and it suppresses hypoxia-inducible factor signaling by increasing α-ketoglutarate.64 Thus, overall, p53 mutations could lead to increasing levels of glycolysis and, in parallel, to reduced mitochondrial respiration. This suggests a role of mutant p53s in the Warburg effect. The modulation of α-ketoglutarate production, through the alteration of amino-acid catabolism, could be one possible mechanism to be considered in a putative p53-dependent metabolic shift.

Table 5. The tool DAVID groups cluster of genes into biological pathways.

Category Term Count % P-value Benjamini
KEGG_PATHWAY p53 signaling pathway 26 1.9 8.6 × 10−8 1.6 × 10−5
KEGG_PATHWAY Metabolism of xenobiotics by cytochrome P450 19 1.4 1.3 × 10−4 7.9 × 10−3
KEGG_PATHWAY Progesterone-mediated oocyte maturation 23 1.7 3.2 × 10−4 0.015
KEGG_PATHWAY Ascorbate and aldarate metabolism 9 0.7 3.5 × 10−4 0.013
KEGG_PATHWAY Alanine, aspartate and glutamate metabolism 12 0.9 5.5 × 10−4 0.017
KEGG_PATHWAY Cell cycle 28 2 1.3 × 10−3 0.029
KEGG_PATHWAY Beta-alanine metabolism 9 0.7 2.7 × 10−3 0.052
KEGG_PATHWAY Arginine and proline metabolism 15 1.1 2.8 × 10−3 0.05
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The p53 signaling pathway and pathways related to the control of the cell cycle are deregulated, as expected (analysis carried out including all 1846 genes collected among all the published studies).

Final remarks

As stated before, collected data seem to be in favor of a general lack of activity at the basis of UMB (LOF) and DMB (loss of transcriptional repression) genes. Given that all the p53 mutants taken into consideration here were classified as LOF in in vitro reporter assays, their expected impact corresponds to the lack of the hand in the ‘piano model' analogy15 and, therefore, the consequence of this gene expression reprogramming could result as a ‘sound of silence'. However, several other aspects deserve discussion and open to the possibility of other mechanisms at play. In fact, the majority of p53 mutations encountered within tumors are of missense type. Moreover, tumors commonly retain and overexpress the full-length mutant p53 (ref. 65) and mutations whose effect is a true ablation of the gene sequence, such as large deletions, nonsense substitutions or in/del frame-shifts, account for only about 16% of the cases (www.iarc.fr/p53). This is in striking contrast to the majority of tumor suppressors (for example, RB1, APC, NF1, NF2 and VHL), where the primary mutations are deletion or nonsense, leading to little or no expression of the respective proteins. Dominance or dominant-negative potential of mutant p53s when heterozygous with the WT allele has been considered as an underlying reason for the high preponderance of p53 missense mutations in cancer. However, one should wonder whether the classification of the hotspot mutations as ‘inactivating' based on in vitro assays (commonly performed on yeast systems) is completely correct. The fact that missense mutations are preferred to the abrogation of the locus suggests that these two possibilities are not equivalent. p53 knockin mutant mouse models produced an altered tumor spectrum as compared with the knockout models, with more metastatic tumors.66, 67 Similarly, in an analysis of Li–Fraumeni patients, germline missense mutations in TP53 have been shown to be associated with an earlier age of onset (9 years) when compared with germline deletions, suggesting a gain-of-function effect of missense p53 mutants in human tumors.68 In addition, tumors with mutant p53 proteins may be more aggressive (for example, conferring a poor prognosis) as compared with tumors where p53 is lost.18, 69, 70, 71 Thus, it could be hypothesized that these mutations alter profoundly, but not completely abrogate, some of the p53 functions. Various authors suggested a gain-of-function at molecular level for mutant p53s.65, 72, 73, 74, 75, 76 Mutant p53s can directly bind promoters of various targets such as it is for miR-130b,77 miR-128-2,78 Axl79 or NF-kB2.80 It was also shown that they form aberrant protein complexes with interacting partners, such as NF-Y, Sp1, Ets-1 or VDR, perturbing their activity.81 More interactions of this type were reported in a recent review.82 Among them, it is noteworthy to underline that, although WTp53 does not form heterotetramers with p63,83 mutant p53s have been shown to bind and sequester TAp63 away from its target genes, hampering its anti-metastatic capacity.84, 85, 86, 87 Furthermore, other studies showed that mutant p53s bind to p63 and use it as chaperone for the transactivation of novel targets.7 In this regard, it should be considered that the enrichment of the degenerated motif detected with COMPASSS within the promoters of UMB genes could be also a marker of the presence of p63-responsive elements, given the sequence similarities with the p53REs. Thus, we cannot rule out that some of the UMB genes are directly/indirectly transactivated by mutant p53s.

In summary, the elaboration of data already present in the literature allowed to gain novel insights in the biology of p53 and to define novel targets. Further studies are warranted in order to better define the extent of differences in the transactivating activities of different mutant p53s and to validate the novel targets suggested here.

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies this paper on the Oncogenesis website (http://www.nature.com/oncsis).

Supplementary Material

Supplementary Table 1
Click here for additional data file. (68.4KB, pdf)
Supplementary Table 2
Click here for additional data file. (45.9KB, pdf)
Supplementary Table 3
Click here for additional data file. (265KB, doc)
Supplementary Table Legends
Click here for additional data file. (22.5KB, doc)

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