Genes and Immunity (2015) 16, 43–53 © 2015 Macmillan Publishers Limited All rights reserved 1466-4879/15 www.nature.com/gene

ORIGINAL ARTICLE

Cervical cancer-associated promoter polymorphism affects akna expression levels GA Martínez-Nava1, K Torres-Poveda1, A Lagunas-Martínez1, M Bahena-Román1, MA Zurita-Díaz1, E Ortíz-Flores1, A García-Carrancá2, V Madrid-Marina1,3 and AI Burguete-García1,3 Cervical cancer (CC) is responsible for 4260 000 deaths worldwide each year. Efforts are being focused on identifying genetic susceptibility factors, especially in genes related to the immune response. Akna has been proposed to be one of them, but data regarding its functional role in the disease is scarce. Supporting the notion of akna as a CC susceptibility gene, we found two polymorphisms associated with squamous intraepithelial lesion (SIL) and CC; moreover, we identified an association between high akna expression levels and CC and SIL, but its direction differs in each disease stage. To show the potential existence of a cis-acting polymorphism, we assessed akna allelic expression imbalance for the alleles of the − 1372C4A polymorphism. We found that, regardless of the study group, the number of transcripts derived from the A allele was significantly higher than those from the C allele. Our results support the hypothesis that akna is a CC susceptibility genetic factor and suggest that akna transcriptional regulation has a role in the disease. We anticipate our study to be a starting point for in vitro evaluation of akna transcriptional regulation and for the identification of transcription factors and cis-elements regulating AKNA function that are involved in carcinogenesis. Genes and Immunity (2015) 16, 43–53; doi:10.1038/gene.2014.60; published online 6 November 2014

INTRODUCTION Cervical cancer (CC) is the fourth major mortality rate cancer in females worldwide, with an estimated 264 000 deaths in 2012 and accounting for 7.5% of all female cancer deaths.1 Around 11% of these deaths occurred in Latin America and the Caribbean region, making this disease a serious public health problem for all the countries of the area, including Mexico.1 It is well established that high-risk human papillomavirus (HPV) infection is a necessary factor for the development of CC. Nevertheless, this factor alone is not sufficient as a significant percentage of infected women will succeed in HPV infection clearance, but a minor percentage (0.1–14.2%) with certain environmental, lifestyle and genetic factors will not and by consequence will develop persistent infection and later on CC.2–4 All such factors that predispose women to CC have not been completely identified yet, and given the heritability of this disease (27%), which is higher than the one seen in other types of cancer (like colorectal and lung cancer), efforts are mostly focused in identifying CC susceptibility genetic factors.5 The majority of the polymorphisms associated with CC, beside the known tumorsuppressor genes, oncogenic genes and those involved in cell cycle regulation, are located in genes related to the immune response.6 This is not surprising given the fact that the immune response triggered against the HPV infection by the host is determinant in CC development. It has been reported that the 9q32 region contains a susceptibility locus for CC.7 There are five candidate genes for CC susceptibility related to antigen-dependent immune response

and inflammation located in that particular region. Among them is akna, a gene that encodes a transcription factor present in the germinal center of secondary lymphoid organs and immune system cells, such as B and T cells, natural killer and dendritic cells.8 The human akna gene is 61-kb long, contains 24 exons and encodes 9 different transcripts as the result of alternative promoter usage, splicing and two polyadenylation sites; the F1 isoform was the first one to be described, and one of the few functionally tested.8,9 It contains a N- and a C-terminus AT-hook domain that enables the protein to bind to AT-rich DNA regions such as the High Mobility Group (HMG) proteins family, where this kind of domains was first described.10 In vitro experiments have shown the ability of the N-terminus AT-hook domain of AKNA F1 isoform to bind to the AT-rich promoter regions in both CD40 and CD40 ligand (CD40L), activating their expression. This, and the notion that B lymphocytes within germinal centers are destined to die unless they are positively selected by antigens and signals initiated by co-stimulatory molecules interactions, such as CD40–CD40L, suggests the important role of AKNA in achieving an efficient immune response.8 In addition, the generation of an akna knockout animal model showed that, in its absence, the expression of inflammatory cytokines (interferon-γ, and interleukin-1β), neutrophil-specific chemoattractants (NGP, CRAMP and S100A9) and a neutrophil collagenase (matrix metalloproteinase-9) is enhanced, which proves the implication of this gene in the negative regulation of inflammatory processes.11,12 Even though akna is located in a common fragile site linked to loss-of-function mutations (FRA9E) associated with neoplastic

1 Área de Infecciones Crónicas y Cáncer, Centro de Investigación sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Cuernavaca, Mexico and 2Unidad de Investigación Biomédica en Cáncer, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México and Instituto Nacional de Cancerología, Secretaría de Salud, Distrito Federal, Mexico. Correspondence: Dr V Madrid-Marina or Dr AI Burguete-García, Area de Infecciones Crónicas y Cancer, Centro de Investigación sobre Enfermedades Infecciosas, Instituto Nacional de Salud Publica, Av. Universidad 655, Santa María Ahuacatitlán, Cuernavaca, Morelos 62508, Mexico. E-mail: [email protected] or [email protected] 3 These authors contributed equally to this work. Received 27 June 2014; revised 2 September 2014; accepted 24 September 2014; published online 6 November 2014

Akna in cervical cancer GA Martínez-Nava et al

44

diseases,9,13–16 the only evidence of its direct association with CC is the one reported by our group in 2010.17 In that work, we found a significant, high magnitude association (odds ratio (OR) = 3.66, 95% confidence interval (CI) = 1.35–9.94) of a single-nucleotide polymorphism (SNP) within the N-terminus AT-hook motif (rs3748178, R1119Q) with an increased risk of CC in a Mexican population. That work links AKNA directly, as a transcription factor of immune response co-stimulatory molecules, and as a negative regulator of inflammation processes, with CC. Taking all this together, it is possible to place akna as a potential CC susceptibility genetic factor. However, functional data concerning AKNA and its complex transcriptional regulation is scarce, not to mention the data about its role along CC natural history. To fully support the notion of akna as a CC susceptibility genetic factor, we need to better understand its function and the role of its polymorphisms (coding and non-coding) in CC. Therefore, we aimed to assess the association of AKNA F1 isoform promoter region SNPs with squamous intraepithelial lesion (SIL) and CC, as well as their effect over akna mRNA expression levels in peripheral blood mononuclear cells (PBMC) in both stages of the disease by measuring the number of transcripts coming from each of both alleles simultaneously (feature known as allelic expression imbalance (AEI)). RESULTS Known reproductive and sexual lifestyle risk factors for CC were confirmed to be statistically different between study groups: on average, women with CC were older, had higher parity, had their first sexual intercourse at a younger age and reported a higher frequency of not using contraceptive methods than women with SIL and HPV-positive (HPV+) patients with non-cervical lesions (NCL) (Table 1). Most of the women had high-risk HPV infection, had less than three lifetime sexual partners and reported a higher frequency of not having any cancer family history independently of the diagnosis (Table 1). The 79% of the women with SIL had a low-grade dysplasia (cervical intraepithelial neoplasia 1), and most of the women with CC had stage II tumors. We found a significant positive association with age at first sexual intercourse, age at first childbirth, contraceptive method used and the number of childbirths and both SIL and CC diagnosis (Table 2). History of previous sexually transmitted diseases (STDs) behaved as a risk factor only for SIL, whereas that for CC behaved in the opposite way, as well as cancer family history (Table 2). Given the fact that these two covariates did not behave as expected, and that the best score in the goodness-of-fit tests performed for all the logistic models evaluated was achieved upon incorporating these two variables, plus patient age and HPV type, further multinomial logistic regression analyses were adjusted by these four covariates. For the SNP selection, we first performed a bioinformatics analysis with the Neural Network Promoter Prediction (NNPP) software to confirm the six putative transcription initiation sites (TIS) predicted by Sims-Mourtada et al.9 and found two additional TIS and their corresponding TATA boxes for the AKNA F1 isoform in particular. To delimit the F1 isoform promoter region, we took 2 kb further upstream of the last TIS predicted, which left us with a 2.2-kb long sequence spanning from the 9:117124595 to the 9:117122379 base (this sequence was recently updated to 9:114362315-114360099). We then input this sequence in the remaining three transcription factor (TF) response element (RE) predictor programs (SiteGA, TF Search and P-Match) and obtained a total of 1546 REs for 50 TFs. Detailed information about all the RE predicted that had a validated SNP in its sequence is shown in the Supplementary Table S1. Of the SNPs present in the delimited promoter region, 16 were validated and fell in at least one of the predicted RE. Two of these were not polymorphic, and 12 were in linkage disequilibrium (LD) with at least one other candidate SNP. Genes and Immunity (2015) 43 – 53

Four groups of SNPs were automatically formed by LD, as SNPs within each group presented LD only with other SNPs within that group. We selected one SNP of each LD group, following the selection criteria mentioned in Materials and methods section, and the two SNPs that were not in LD with any other candidate SNP. Following this procedure, we ended up with six SNPs to evaluate (rs115287453, rs73656049, rs2636898, rs10122672, rs10817594 and rs10817595). Three SNPs (rs115287453, rs73656049 and rs2636898) resulted to be non-polymorphic with a minor allele frequency o 1%. We could not carry out multinomial logistic regression analysis with them, so we only show the OR with SIL and with CC for the genotypes and alleles of the three remaining selected SNPs (rs10122672 (−392C4T), rs10817594 (−1116C4T) and rs10817595 (−1372C4A); Table 3). We found a highly significant negative association for CC with both − 392C4T and − 1372C4A minor allele homozygotes (OR = 0.31, 95% CI 0.12–0.80; and OR = 0.31, 95% CI 0.12–0.79, respectively), with a significant negative trend for both (P = 0.017 and P = 0.014, respectively). For − 1372C4A minor allele homozygotes, we also found a significant negative association with SIL (OR = 0.37, 95% CI 0.16–0.86), along with a significant negative trend (P = 0.016), but we did not find a significant association for the − 392C4T minor allele homozygotes. These associations were maintained when the analysis by alleles of these two variants was performed. More importantly, this analysis revealed a significant negative association with SIL and − 392C4T minor allele (OR = 0.64, 95% CI 0.42–0.99). On the other hand, we did not find a significant association for either genotypes or alleles of the − 1116C4T polymorphism with SIL or with CC (Table 3). Upon doing the haplotype analysis, we observed that the negative associations seen in the genotype and allele analysis were enhanced in the simultaneous presence of − 392C4T and − 1372C4A minor alleles (TCA haplotype). However, in the haplotype where the three minor alleles for the three SNPs of interest are present (TTA haplotype), this negative association did not prove to be significant (Table 3). We observed that the SNPs of interest were in LD between − 1372C4T polymorphism with a D′ value of 0.99 and 0.80 for − 392C4T and − 1116C4T, respectively. Akna mRNA expression level distribution normalized with HPRT-1 (hypoxanthine-guanine phosphoribosyltransferase-1) mRNA expression levels (expressed by relative expression units (REU)) and stratified by the study group in PBMC and cervix are shown in Figure 1. Akna expression levels in PBMC were higher in patients with SIL than in HPV+ NCL patients (P = 0.003); in contrast, in PBMC from CC patients, akna expression levels were lower (median of 1.04 REU) than in PBMC of HPV+ NCL patients (P o 0.001). Akna expression level tendency across the study groups seen at the systemic level was maintained at cervix; however, the REU magnitude was significantly lower, and the difference between HPV+ NCL and SIL and between the former and CC was not pronounced and was not significant. Nonetheless, the difference of akna expression levels between SIL (median of 1.02 REU) and CC patients (median of 0.45 REU) was statistically significant (P o0.001) (Figure 1). After performing the multinomial logistic regression analysis for akna REU tertiles at the systemic and cervix levels with SIL and CC diagnosis, we observed a significant high-magnitude positive association for the higher PBMC akna expression tertile and SIL (OR = 4.03, 95% CI 1.87–8.69) along with a significant positive trend across the tertiles (P o0.001). Contrarily, for CC there were significant high-magnitude negative associations for median and high PBMC akna expression tertiles (OR = 0.21, 95% CI 0.09–0.49; OR = 0.005, 95% CI 0.0005–0.04; respectively), also with a significant negative trend (P o 0.001). Even though there was no significant difference between akna expression levels distribution in cervix across the diagnosis groups (Figure 1); we found a significant OR of 0.23 (95% CI 0.06–0.88) between the higher akna expression tertile in cervix and CC (Table 4). © 2015 Macmillan Publishers Limited

Akna in cervical cancer GA Martínez-Nava et al

45 Table 1.

Clinical and clinicopathological characteristics of the study population Total (n = 420)

NCL (n = 109)

SIL (n = 149)

CC (n = 162)

P-value

Age (years) Median (5–95%)

39 (22–66)

34 (22–66)

34 (21–66)

51 (32–76)

o0.01a

Onset of menarche (years) Median (5–95%)

13 (11–15)

13 (11–15)

13 (10–15)

13 (11–16)

0.27a

Age at first sexual intercourse (years) Median (5–95%) 18 (14–25)

20 (15–27)

18 (14–24)

17 (13–24)

o0.01a

Variable

Age at first childbirth (years) Nulliparous o19 19–21 ⩾ 22

12.75 39.38 23.51 24.36

52.73 14.55 7.27 25.45

7.25 44.2 27.54 21.01

3.75 43.75 25.62 26.88

o0.01b

Parity (%) Nulliparus ⩽3 43

12.53 47.47 40

33.96 55.66 10.38

6.76 52.7 40.54

3.73 37.27 59.01

o0.01b

Number of lifetime sexual partners (%) ⩽3 84.95 4–9 12.86 ⩾ 10 2.18

79.61 18.45 1.94

87.16 9.46 3.38

86.34 12.42 1.24

0.20b

Contraceptive method None Non-hormonal Hormonal

26.67 38.52 34.81

11.11 51.52 37.37

11.72 35.86 52.41

49.69 32.92 17.39

o0.01b

History of previous STD None Other STD HPV

55.94 34.9 9.16

53.33 35.24 11.43

24.49 60.54 14.97

88.16 9.87 1.97

o0.01b

Cancer family history (%) No Yes

70.05 29.95

58.88 41.12

71.43 28.57

76.25 23.75

o0.01b

HPV type (%) Low risk High risk

8.49 91.51

14.58 85.42

15 85

0 100

o0.01b

Dysplastic degree (only for the SIL group) CIN 1 — CIN 2 — CIN 3 —

— — —

79.19 10.07 10.74

— — —

—

Cancer stage (only for the CC group) I II III IV

— — — —

— — — —

12.5 56.25 25 6.25

—

— — — —

Abbreviations: CC, cervical cancer; CIN, cervical intraepithelial neoplasia; HPV, human papillomavirus; NCL, non-cervical lesions; SIL, squamous intraepithelial lesion; STD, sexually transmitted disease. Bold values denotes significant P-values (Po 0.05). aKruskal–Wallis test P-value. bΧ2 test P-value.

In order to explore whether the SNPs of interest had a relationship with the different akna expression levels seen in CC patients, we stratified them by polymorphism genotypes (Figure 2). The difference in akna REU level distribution between minor allele homozygous and ancestral allele homozygous CC patients was only statistically significant for − 392C4T and − 1372C4A polymorphisms; as for the − 1116C4T polymorphism, the carriers of one minor allele had higher levels than the carriers of two copies of the ancestral allele (Figure 2). The mean estimated difference of akna REU in CC patients was 1.97 (0.54–3.41) REU for both − 392C4T and − 1372C4A minor allele homozygotes and 2.12 (0.45–3.78) REU for − 1116C4T minor © 2015 Macmillan Publishers Limited

allele homozygotes. Consistent with the analysis done by genotypes, we observed a mean estimated difference of almost one REU for the minor allele of the three polymorphisms of interest in CC patients. This mean estimated difference was of more than one REU (β = 1.14; 95% CI 0.30–1.99) when the three minor alleles of the SNPs of interest were simultaneously present and exhibited a significant positive trend as the number of minor alleles present increased (P = 0.008) (Table 5). On the other hand, we did not find a statistically significant association between the presence of the minor allele of the three SNPs of interest and akna expression levels in SIL patients; however, for the − 1116C4T polymorphism we found that the Genes and Immunity (2015) 43 – 53

Akna in cervical cancer GA Martínez-Nava et al

46 Table 2. Association analysis of conventional reproductive and sexual lifestyle risk factors with squamous intraepithelial lesions and cervical cancer in the study population Risk factor

ORa (95% CI)

NCL/SIL/CC (n = 109/149/163)b

Squamous intraepithelial lesion (SIL)

Cervical cancer (CC)

12/30/2029 95/118/131

1 0.50 (0.24–1.02)

1 0.47 (0.22–1.01)

Age at first sexual intercourse (years) ⩾ 18 o18

77/75/78 27/73/83

1 2.77 (1.61–4.79)c

1 3.22 (1.77–5.84)c

Age at first childbirth (years) ⩾ 22 Nulliparous 19–21 o19

14/29/43 29/10/6 4/38/41 8/61/70

1 0.12 (0.04–0.34) 3.92 (1.15–13.38) 3.29 (1.23–8.81)c

1 0.17 (0.05–0.57) 4.71 (1.36–16.26) 3.05 (1.13–8.23)c

Parity Nulliparous ⩽3 43

36/10/6 59/78/60 11/60/95

1 7.10 (3.13–16.11) 49.01 (16.57–144.96)c

1 3.95 (1.48–10.54) 20.87 (6.63–65.68)c

Number of lifetime sexual partners ⩽3 43

82/129/139 21/19/22

1 0.57 (0.28–1.13)

1 0.91(0.43–1.91)

Contraceptive method Non-hormonal Hormonal None

51/52/53 37/76/28 11/17/1980

1 2.00 (1.14–3.46) 1.58 (0.67–3.71)

1 0.80 (0.41–1.55) 4.38 (2.00–9.58)c

History of previous STD None Other STDs HPV

56/36/134 37/89/15 12/22/2003

1 3.75 (2.11–6.65) 2.85 (1.26–6.48)c

1 0.22 (0.11–0.47) 0.14 (0.04–0.56)c

Cancer family history No Yes

63/ 105/122 44/42/38

1 0.57 (0.34–0.97)

1 0.44 (0.24–0.79)

Onset of menarche (years) o12 ⩾ 12

Abbreviations: CI, confidence interval; HPV, human papillomavirus; NCL, non-cervical lesions; OR, odds ratio; STD, sexually transmitted disease. Bold values denotes significant P-values (Po0.05). aAdjusted by age. bSome numbers do not equal the total sample size because of missing values. cStatistically significant P-values for trend (Po0.05).

mean estimated difference of akna REU in NCL HPV+ patients was of 24.02 (2.88–45.16) for the minor allele and of 43.39 (11.67–75.11) for this allele homozygotes. For the other two polymorphisms of interest, there was no significant association with akna expression levels in NCL HPV+ patients (Table 5). To further explore the putative − 1372C4A polymorphism effect over akna transcriptional regulation, we carried out an AEI analysis in the heterozygous patients. We could only perform the AEI analysis in this SNP, because we did not find suitable synonymous SNPs for − 392C4T and − 1116C4T polymorphisms. We found a clear difference in the number of mRNA coming from the two alleles of interest (Log2 (A allele/C allele) = 1.58; 95% CI 1.48–1.69). On average, the higher AEI was seen in SIL patients followed by HPV+ NCL and CC patients; however, there was no significant statistical difference between the diagnosis groups (P = 0.44) (Figure 3). DISCUSSION The main findings of this study were (i) the identification of a significant negative association between SIL and CC with akna − 392C4T, and − 1372C4A polymorphisms, (ii) together with a Genes and Immunity (2015) 43 – 53

significant association of akna expression levels at the cervix and in PBMC with CC diagnosis, and (iii) the fact that its expression differs across SNP genotypes in CC patients. Even though the precise role of AKNA in CC has not been reported, the evidence showing the linkage of its locus with this disease and the previous report of a coding SNP in its amino terminal AT-hook domain that increases the risk for CC place akna as a potential genetic susceptibility factor.7,17 Furthermore, the capacity to regulate the expression of immune response costimulatory molecules and its role in the negative regulation of inflammation processes entail AKNA loss of function with known emerging and enabling hallmarks of cancer (specifically, avoiding immune destruction and tumor promoting—inflammation hallmarks, respectively).18 In agreement with this notion, we have found two non-coding akna polymorphisms, − 392C4T (rs10122672) and − 1372C4A (rs10817595), which are negatively associated with SIL and CC. In addition, these associations are not masked by the previous reported associated SNP (rs3748178), as these two polymorphisms are not in LD with it, as the 1000 genomes project reports.19 This finding supports the idea of akna being a CC susceptibility gene. © 2015 Macmillan Publishers Limited

Akna in cervical cancer GA Martínez-Nava et al

47 Table 3.

Association analysis of polymorphisms of interest with squamous intraepithelial cervical lesion and cervical cáncer

Polymorphism

ORa (95% CI)

Frequency (%) NCL (n = 109)

SIL (n = 149)

CC (n = 162)

SIL

CC

− 392C4T (rs10122672) C/C C/T T/T C (0.55) T (0.45) P-valuec

26.6 45.74 27.66 49.47 50.53 0.7

36.51 44.44 19.05 58.73 41.27

28.22 53.37 18.4 54.91 45.09

1 0.62 (0.30–1.31) 0.43 (0.18–1.01) 1 0.64 (0.42–0.99)

1 0.71 (0.31–1.60) 0.31 (0.12–0.80)b 1 0.56 (0.35–0.90)

− 1116C4T (rs10817594) C/C C/T T/T C (0.64) T (0.36) P-valuec

42.31 45.19 12.5 64.9 35.1 0.82

46.67 38.52 14.81 65.93 34.07

34.57 54.32 11.11 61.73 38.27

1 0.74 (0.39–1.41) 0.88 (0.36–2.12) 1 0.88 (0.58–1.35)

1 1.37 (0.68–2.75) 0.59 (0.21–1.64) 1 0.91 (0.57–1.44)

− 1372C4A (rs10817595) C/C C/A A/A C (0.55) A (0.45) P-valuec

25 46.15 28.85 48.08 51.92 0.67

37.96 43.8 18.25 59.85 40.15

27.61 53.99 18.4 54.91 45.09

1 0.54 (0.26–1.11) 0.37 (0.16–0.86)b 1 0.60 (0.39–0.90)

1 0.69 (0.31–1.55) 0.31 (0.12–0.79)b 1 0.57 (0.36–0.90)

Haplotypes CCC TCA TTA

48.48 17.58 33.94

61.09 6.79 32.13

55.47 6.2 38.32

1 0.33 (0.15–0.71) 0.74 (0.45–1.23)

1 0.18 (0.08–0.41) 0.63 (0.36–1.10)

Abbreviations: CC, cervical cancer; CI, confidence interval; NCL, non-cervical lesions; OR, odds ratio; SIL, squamous intraepithelial lesion. Bold values denotes significant P-values (Po 0.05). aAdjusted by age, human papillomavirus type, history of previous sexually transmitted disease and cancer family history. b Statistically significant P-value for trend (P o0.02). cHardy–Weinberg equilibrium test in the study population.

Regarding the akna expression levels, the only previous report of their indirect association in vivo with a human disease is with an autoimmune disorder, Vogt–Koyanagi–Harada syndrome. In a case control study, Mao et al.20 found significant lower levels of AKNA protein in CD4+ T cells obtained from patients than in the cells from healthy controls. This syndrome is characterized by a chronic inflammation of highly pigmented tissues, and the decrease in AKNA protein concentration seen in these patients is consistent with the lack of inflammatory response intensity regulation observed in the akna knockout mice. Nonetheless, the molecules and mechanisms involved in akna expression regulation under normal and pathological conditions have not been identified to date. This study presents the first evidence of akna expression behavior in CC patients at a systemic level as well as in tumor biopsies. We found lower akna expression levels in PBMC as well as in tumor biopsies of CC patients than in PBMC and in cervix scrapings of HPV+ NCL patients. It has been reported that interaction of CD4+ T-cell CD40L with CD40 of dendritic cells is needed for CD4+ T cells to help generate an efficient CD8+ T cells’ response in vivo and consequently is crucial for the development of antitumor immunity.21 Furthermore, in the particular case of CC, it has been shown that despite the presence of infiltrating CD8+ T cells, they have poor activity.22 Low levels of akna expression could compromise the amount of CD40 and CD40L (or other costimulatory molecules) present on the surface of dendritic cells and CD4+ T cells, implicating a deficient antitumor immunity in CC patients. In a somewhat opposite way, there is evidence suggesting that chronic inflammation, with sustained inflammatory infiltrates of macrophages and neutrophils, is associated with HPV persistence © 2015 Macmillan Publishers Limited

and increased risk of high-grade HPV lesions.23–25 This is consistent with the phenotypes observed in the akna knockout and constitutes another potential mechanism that explains the low expression levels seen in CC patients in comparison to HPV+ NCL patients. Having lower levels of akna expression could enhance the expression of proinflammatory cytokines and neutrophil-specific chemokines promoting a chronic inflammation that favors HPV persistence, resulting in a higher risk of CC. The increase in infiltrated inflammatory cells concentration (that is, neutrophils) could result in a greater release of reactive oxygen species, well-known mutagenic chemicals, promoting the transition of nearby cancer cells to a heightened malignant state.18 On the other hand, we found high akna expression levels in PBMC of SIL patients compared with HPV+ NCL patients. This can be explained by the fact that immune response in patients with SIL is still fully active trying to achieve clearance of the HPV lesion; after all, it is well established that the majority of HPV lesions eventually regress and, even though all the immune response mechanisms that have a role in this phenomenon are not completely understood, there is evidence of key elements of the cellular immune response (that is, high CD4:CD8 ratio, a more prominent T helper type 1 response, T-cell response against early viral antigens and so on) that favors the cytological remission of HPV lesions in patients.22,23,26–30 In contrast, in CC this response has been counteracted by the action of tumor cells. Previous reports have shown that the tumor cells are capable of secreting anti-inflammatory cytokines (interleukin-10, transforming growth factor-β and interleukin-4) suppressing the cellular immune response.22,31 Moreover, it is highly likely that a great percentage of women with SIL still have an active HPV infection, meaning that the viral Genes and Immunity (2015) 43 – 53

Akna in cervical cancer GA Martínez-Nava et al

48 genome has not integrated yet into the genome of host cells, unlike in CC, where the integration of the viral genome is presumed. The high expression of akna in PBMC of SIL and HPV+ NCL patients is probably counteracting not only the lesion produced by HPV but also the active viral infection. It has been shown that in Huh 7.5 cells infected with hepatitis C virus akna expression is substantially upregulated 72 h. after infection; this supports the idea that akna expression is differentially regulated

*

AKNA REU in PBMC

400

* 300

200

100

0 NCL

SIL

CC

*

AKNA REU in cervix

40

30

20

10

0 NCL

SIL

CC

Figure 1. Akna REU level distribution over the diagnosis group in PBMC (a) and in cervix-scraped cells and tumor-biopsied cells (b). Asterisk represents a statistically significant P-value for the Kruskal–Wallis test adjusted by multiple comparisons (P o0.01). Table 4.

during viral infections. Nonetheless, it is not completely clear whether the increase of akna expression seen is a general mechanism activated by virus infection.32 Even though we did not aim to identify the cells expressing akna in cervical scrapings and tumor biopsies, we hypothesize that, as akna REU levels were considerably lower in the cervix than in PBMC, the number of cells expressing akna present in the scrapings and biopsies was significantly small. We based this reasoning on the fact that expression of this gene has been reported only in lymphoid secondary organs and in immune system cells. We speculate that infiltrating lymphocytes, dendritic cells or macrophages present in the cervix and the tumor microenvironment, and not in the epithelial cells or tumor cells, are the ones responsible for akna expression levels seen in situ. Efforts should be addressed to elucidate which cells are expressing akna in the cervix in order to clarify the potential role of this gene in the pathology of the disease, as the functions attributed to akna are highly likely cell type and environment dependent. Interestingly, carriers of the two minor alleles for each of the three SNPs of interest had higher akna expression levels than carriers of the respective two ancestral alleles, suggesting that the presence of these polymorphisms enhance akna expression. The biological impact of having one more akna REU given the haplotype TTA is not easily inferred in CC; that is because of the intuitively opposite functions reported for AKNA, the complex role that CD40 has along cervical carcinogenesis33–35 and because of the controversial role of inflammation in HPV persistence and CC.23–25 AEI assessment allows us to know if there is a difference in the number of transcripts coming from each allele of a certain polymorphism. It is expected that in a heterozygote both alleles (DNA strands) are transcribed at the same rate, unless the presence of a certain allele favors its transcription over the other one. In this last case, the SNP responsible has a functional role on the nearby gene expression regulation, meaning that it is a cisexpression quantitative trait locus (eQTL). There are plenty of SNPs in several promoter gene regions associated with CC; however, few are assessed to be a functional cis-acting polymorphism. Recently, Chen and Gyllensten36 evaluated a cis-eQTL of HLA-DRB1 gene in CC patients, and their findings provide a possible explanation for the variable patterns of associations with HLAB*07 and DRB1*1501-DQB1*0602 and the consistent pattern of association with DRB1*1301-DQA1*0103-DQB1*0603 in CC. Nonetheless, there is no other study in CC patients that, by assessing AEI, points out the existence of a potential cis-acting polymorphism. It is of high relevance to identify those noncoding

Odds ratios for akna REU and squamous intraepithelial lesion and cervical cáncer

akna REU

ORa (95% CI)

NCL/SIL/CC (n = 109/149/163)b

SIL

CC

PBMC (n = 412) o2.35 2.35–20.16 420.16

35/27/132 35/37/26 34/85/1

1 1.99 (0.87–4.56) 4.03 (1.87–8.69)c

1 0.21 (0.09–0.49) 0.005 (0.0005–0.04)c

Cervix (n = 269) o0.39 0.39–1.61 41.61

11/27/1949 11/57/36 11/47/20

1 1.54 (0.47–5.09) 1.36 (0.41–4.46)

1 0.33 (0.09–1.18) 0.23 (0.06–0.88)

Abbreviations: CC, cervical cancer; CI, confidence interval; NCL, non-cervical lesions; OR, odds ratio; PBMC, peripheral blood mononuclear cells; REU, relative expression units; SIL, squamous intraepithelial lesion. Bold values denotes significant P-values (Po 0.05). aAdjusted by age, human papillomavirus type, history of previous sexually transmitted disease and cancer family history. bSome numbers do not equal the total sample size because of poor quality of the sample. c Statistically significant P-value for trend (Po0.02).

Genes and Immunity (2015) 43 – 53

© 2015 Macmillan Publishers Limited

Akna in cervical cancer GA Martínez-Nava et al

49 *

* 30

AKNA REU in PBMC

AKNA REU in PBMC

30

20

10

20

10

0

0 C/C

C/T

T/T

C/C

C/T

T/T

*

AKNA REU in PBMC

30

20

10

0 C/C

A/C

A/A

Figure 2. PBMC akna REU level distribution stratified by genotypes of the SNPs of interest in CC patients. Minor allele homozygotes of − 392C4T (a) and − 1372C4A (c) polymorphisms exhibited higher levels of akna REU. For the − 1116C4T polymorphism genotypes (b), the distribution of akna REU was statistically different only between the heterozygotes and homozygotes of the ancestral allele. Asterisk represents a statistically significant P-value for the Kruskal–Wallis test adjusted by multiple comparisons (P o0.01).

Table 5.

Estimated mean difference of PBMC akna REU between polymorphism genotypes and alleles stratified by diagnosis βa coefficient (95% CI)

Polymorphisms Total

NCL

SIL

CC

− 392C4T (rs10122672) C/C — C/T 6.64 (−3.39–16.68) T/T 1.81 (−10.43–14.06) C — T 1.41 (−4.67–7.49)

— 30.77 (−10.47–72.02) 9.85 (−35.06–54.76) — 2.41 (−19.20–24.03)

— − 0.08 (−15.66–15.49) − 8.12 (−28.19–11.96) — − 3.53 (−13.17–6.11)

— 0.96 (−0.14–2.06) 1.97 (0.54–3.41)b — 0.93 (0.25–1.62)b

− 1116C4T (rs10817594) C/C — C/T 5.40 (−4.06–14.86) T/T 6.91 (−6.99–20.81) C — T 3.93 (−2.45–10.32)

— 43.39 (11.67–75.11) 38.20 (−7.08–83.47)b — 24.02 (2.88–45.16)

— − 9.06 (−23.88–5.76) − 10.75 (−30.80–9.30) — − 6.83 (−16.56–2.90)

— 1.00 (−0.05–2.04) 2.12 (0.45–3.78)b — 0.92 (0.21–1.63)

− 1372C4A (rs10817595) C/C — C/A 6.79 (−3.02–16.60) A/A 3.07 (−8.88–15.02) C — A 2.03 (−3.93–7.99)

— 32.16 (−7.10–71.41) 15.22 (–27.03–57.48) — 5.52 (−15.13–26.17)

— 2.19 (−12.66–17.05) − 6.60 (−25.53–12.35) — − 2.62 (−11.85–6.62)

— 0.96 (−0.14–2.06) 1.97 (0.54–3.41)b — 0.93 (0.25–1.62)b

Haplotypes CCC TAC TAT

— − 21.57 (53.25–10.11) 13.96 (−12.20–40.11)

— 2.01 (−19.30–23.32) − 6.38 (−17.89–5.13)

— 0.78 (−0.92–2.48) 1.14 (0.30–1.99)c

— − 3.02 (−15.17–9.14) 3.08 (−4.40–10.56)

Abbreviations: CC, cervical cancer; CI, confidence interval; NCL, non-cervical lesions; PBMC, peripheral blood mononuclear cells; REU, relative expression units; SIL, squamous intraepithelial lesion. Bold values denotes significant P-values (Po0.05). aAdjusted by age, human papillomavirus type, history of previous sexually transmitted disease and cancer family history. bStatistically significant P-value for trend (Po 0.02), assessed by genotypes. cStatistically significant P-value for trend (Po 0.02), assessed by haplotypes.

© 2015 Macmillan Publishers Limited

Genes and Immunity (2015) 43 – 53

Akna in cervical cancer GA Martínez-Nava et al

50

Log2(A allele/ C allele)

6

4

2

0 NCL

SIL

CC

Mean Log2(A allele/ C allele)

1.8

1.7 1.61 1.6

1.58

1.56

1.5

1.4

1.3 NCL

SIL

CC

Figure 3. Akna AEI in − 1372C4A polymorphism heterozygotes. (a) Distribution of the number of transcripts ratio coming from each − 1372C4A polymorphism alleles, stratified by diagnosis status. (b) On average, the number of transcripts derived from the A allele Aallele Aallele ¼ 2log 2 ðCalleleÞ ¼ 21:58 ¼ 2:99Þ times larger than was almost three ðCallele the number of transcripts derived from C allele. Error bars represent the s.d. in each group.

variants that have a function over the abnormal spatiotemporal expression patterns seen in CC patients. This will give insights of molecules and genes not yet associated with the disease, and a better understanding of the role of known susceptibility genetic factors in the neoplastic process. In the present study, we have shown that the number of mRNA copies from the − 1372 A allele is nearly three times larger than the mRNA copies from the C allele. This finding shows that akna expression is enhanced by the presence of the − 1372C4A minor allele, meaning that there is an AEI, which is a key signature of cisacting polymorphism.37 The motifs predicted in the bioinformatics analysis in this particular location correspond to myeloid zinc finger 1 (MZF1), CCAAT/enhancer binding protein beta (C/EBPB) and caudal type homeobox 1 (Cdx1) TFs (Supplementary Table S1); the latter is an intestine-specific TF, so it is highly unlikely to be responsible for akna transcriptional regulation in PBMC. MZF1 and C/EBPB are TFs known to be related to immune system cells. Both TFs, MZF1 and C/EBPB, have been associated with different types of cancer38–41 but only MZF1 is known to participate in the invasiveness of human CC cells.42 However, with the evidence generated in this study we cannot conclude which TF is the one regulating akna expression. Further in vitro analyses are required to corroborate the presence of this cis-regulating element, to know which TF may be regulating akna transcription and in which way it is doing so (as a repressor or an activator). Regarding methodological strengths and limitations, one of the study strengths lies in the fact that the associations reported are Genes and Immunity (2015) 43 – 53

indeed with CC risk and not with HPV infection risk; this is due to the incorporation of only HVP+ patients to the study sample. This selection allowed us to determine the role of akna polymorphisms and its different expression profiles over what distinguishes patients with an active HPV infection and those that developed SIL or CC. Furthermore, confirmation of the presence of known reproductive risk factors for CC (age at first sexual intercourse, age at first childbirth, contraceptive method and number of childbirth) supports the external validity of our results. The fact that history of previous STD and cancer family history variables in our population behaved in an opposite way as have been reported could be a sign of a potential measurement bias. As we used a previously built database, we could not control the data collection processes; therefore, we could not prevent such bias by study design. However, all multinomial logistic regression analyses performed were adjusted by these two variables (HPV type and patient age) in order to control the potential confounding effect that history of previous STD and cancer family history covariates could be providing, so it seems unlikely that the results obtained are being affected by this fact. Moreover, the SNPs of interest were all in Hardy–Weinberg equilibrium, meaning that the allelic frequencies seen in our study population are equal to the ones expected given no selection pressure or genetic drift. Additionally, the minor allele frequency observed for each SNP of interest were consistent with the frequency reported for Mexican ancestry population in the 1000 genomes project,19 so we can assure there was no bias that favored the presence of the polymorphism in the study sample. Regarding limitations, given the epidemiological design of this study we cannot assure the akna expression level differences seen here are a cause or an effect of the diagnosis status. However, the possibility that the akna expression levels are partially due to the presence of the polymorphism indirectly gives temporality to the association observed between akna expression levels and CC. The last thing to consider is that given the close proximity of the three SNPs of interest and the LD between them seen in the study population, the synonymous SNP selected for the AEI assays was also in LD with − 1116C4T (rs10817594) and − 392C4T (rs10122672) SNPs; so the real functional cis-eQTL could be any of the three SNPs of interest. However, we consider that, given the consistent results obtained for the − 1372C4A (rs10817595) polymorphism throughout the entire study, and the fact that running the bioinformatics analysis with the same software used for the SNP selection with the respective minor allele in this particular location eliminates the predicted MZF1 motif, it is highly likely that this polymorphism is the responsible for the AEI observed. In conclusion, the present study suggests that transcriptional regulation of akna has an important role in the natural history of CC disease; however, its effect is likely to be spatiotemporal dependent, and further studies are needed to comprehend AKNA function in normal conditions and cancer. Supporting the notion of akna being a genetic susceptibility factor for CC, we have found an allelic imbalance in akna expression. We suggest that the significant negative association observed for the − 1372C4A (rs10817595) variant and the high levels of akna expression with CC are explained by the effect of its minor allele over akna transcriptional regulation. We believe this work will set the basis for in vitro evaluation of akna transcriptional regulation in carcinogenesis and other immune response-related diseases. MATERIALS AND METHODS Study population This study employed samples from a previously built biological sample bank described elsewhere.43 Briefly, all women who assisted to the Centro de Atención para la Salud de la Mujer de los Servicios de Salud del Estado © 2015 Macmillan Publishers Limited

Akna in cervical cancer GA Martínez-Nava et al

51 de Morelos, México (CAPASAM) between June 2008 and December 2011, and to the gynecology service of the Instituto Nacional de Cancerología de México (INCan) between September 2010 and December 2011, and met the inclusion criteria (recent cytological, colposcopic and histopathological diagnosis; period of residence in Mexico 45 years; aged ⩾ 18 years; and had not initiated treatment) were invited to participate. After the women signed the informed consent and answered a lifestyle, socio-demographic and hormonal factors’ questionnaire, peripheral blood was collected to obtain genomic DNA (gDNA) and total RNA. Also, cervical scrapings and tumor biopsies were obtained from women diagnosed with NCL or SIL and CC, respectively, in order to further extract the total RNA. Complementary DNA (cDNA) synthesis was carried out from total RNA extracted from PBMC, cervical scrapings and tumor biopsies. Women who suffered from autoimmune or chronic inflammation diseases or had a STD active infection (besides HPV) during the sampling were excluded from the original study (0.54%). Because we aimed to identify association with higher CC risk, and not with HPV infection, the present study was carried out only in the HPV+ women from the described biological bank. Of these 420 HPV+ women, 109 had NCL, 149 had SIL and 162 had CC. The protocol of this study was approved by the National Institute of Public Health Institute of Mexico Bioethics and Research committees.

SNP selection Due to the absence of previous association reports of regulatory SNPs or eQTL in the akna gene region, we decided to perform a bioinformatics analysis to help us select the SNPs that most likely affect akna F1 isoform transcription rate. We used four distinct free online programs, each one with a different approach to predict both TATA boxes and TIS (NNPP44) or TF RE (SiteGA,45 TF Search46 and P-Match47). Because we wanted to validate the previously TIS predicted for the AKNA F1 isoforms, we used the same bioinformatics tool (NNPP) and the whole genomic sequence of akna as Sims-Mourtada et al.9 did. This way we delimited the sequence used for the three remaining programs; we took 2 kb upstream from the last TIS predicted by the NNPP program, which left us with a 2.2-kb promoter sequence to analyze further. We used only the matrices for vertebrate organisms, when the program had such an option, and a minimal score of 0.8 as a cutoff point for each bioinformatic tool employed. Also, specifically for the SiteGA tool we opted to use both strands (forward and reverse) and to include all the TF available for analysis. For P-Match, we selected the minimizing falsenegative option to establish the cutoff point and the option to use only the high-quality matrices. As we could use specific profile matrices for distinct cell types with P-Match, we performed two separate analyses: one without a specific profile and one using the predefined immune system cell profile. We then crossed the data obtained from the bioinformatics analysis with the information of all SNPs reported in the dbSNP48 and Ensembl49 databases on the delimited promoter region (data accessed on November 2012). We selected the SNPs with the higher RE prediction score of each group of SNPs in LD that were previously validated by the HapMap project or the 1000 genomes project and that had a minor allele frequency ⩾ 1% in a Mexican ancestry population.

Genotyping The allelic discrimination assays for the six SNPs selected were carried out from PBMC gDNA by traditional PCR using TaqMan probes in an Applied Biosystems 79000 Real-Time PCR System instrument, according to the manufacturer’s instructions. All assays were done in duplicate with a concordance of 100% and were analyzed and determined with the ‘Sequencing Detection System’ software (SDS 2.3, Applied Biosystems, Life Technologies, Carlsbad, CA, USA) using a quality call rate of no o0.99.

Akna expression level assessment Quantitative PCR (qPCR) was performed from PBMC, cervical epithelialscraped cells and tumor-biopsied cDNA using TaqMan probes for gene expression assays (Applied Biosystems) to measure akna mRNA (probe ID: Hs00363936_m1) in both levels (systemic and in cervix). We decided to use the HPRT-1 gene (probe ID: Hs01003267_m1) as a endogenous control, for it showed no variation among the three study groups. Both Taqman probes used were carefully selected to ensure that one of their respective primers fall on an exon junction to prevent gDNA amplification. The qPCR assays for both genes (target and endogenous) were carried out in triplicate with a variation coefficient (VC) of o2% in an Applied © 2015 Macmillan Publishers Limited

Biosystems 79000 Real-Time PCR System instrument. If the triplicate showed a VC42%, the experiment was repeated. With the equation 2 − Δct, we calculated the REU of akna mRNA at the cervix and systemic level.

AEI assessment For each associated SNP, we selected a synonymous SNP in LD (D′ ⩾ 0.99) with each one of them to evaluate akna AEI. We found a suitable synonymous SNP (rs3748177) only for the − 1372C4A (rs10817595), therefore, we could only determine the allelic imbalance for one of the three associated SNPs. We designed a TaqMan probe for the selected synonymous SNP, taking the F1 isoform transcript sequence as template, where the reverse primer fell on the 9 and 10 exon junction, assuring nongDNA amplification. PBMC cDNAs from the heterozygous women for the − 1372C4A polymorphism were used to perform, by duplicate, qPCR assays with custom-designed TaqMan probes for each one of the SNP alleles in an Applied Biosystems StepOnePlus Real-Time PCR System instrument, following the manufacturer’s instructions. The results were analyzed with the Applied Biosystems Step One Software v2.2.2 (Life Technologies). According to the method proposed by Chen et al.50 to calculate the ratio of mRNA expression between two alleles, we used the following equation:   alleleY ¼ a ´ ðCt 2 - Ct1 Þ þ ðb2 - b1 Þ log 2 alleleX In order to do so, we generated a standard curve with 8:1, 4:1, 2:1, 1:1, 1:2, 1:4 and 1:8 ratio dilutions of cDNAs from two samples with homozygous genotypes (−1372 C/C, and − 1372 A/A) that had similar (±0.5) HPRT1 Ct values in the previous qPCR assay. The linear equation of the standard curve generated was f(x) = 1.436x+0.060 and yielded a R2 of 0.97.

Statistical analysis Known reproductive and sexual lifestyle factors for CC risk (that is, age at first sexual intercourse, age at first childbirth, parity, STD history, contraceptive method and HPV type) were first confirmed in the study population by bivariate multinomial logistic regression models adjusted by age. Hardy–Weinberg equilibrium was corroborated using the allelic frequencies of the whole study population. LD between the selected SNPs was calculated according to the standardized values proposed by Lewontin;51 likewise the ORs for the genotypes, the alleles and haplotypes of interest with SIL and CC were calculated using the multinomial logistic regression models adjusted by age, HPV type, history of previous STD and family history of cancer. PBMC and cervix akna REU were compared across the groups of study using the Kruskal–Wallis test adjusted by multiple comparisons. Tertiles for both akna REU were created according to the respective observed distribution in the HPV+ NCL group to evaluate their association with SIL and CC by multinomial logistic regression models. Distribution of akna REU in PBMC was compared by genotypes and alleles of interest to determine whether those variants modified the corresponding akna REU levels observed in the CC group with the Kruskal–Wallis test and the Wilcoxon– Mann–Whitney test in the case of the alleles. The mean estimated difference of PBMC akna REU between alleles of interest and haplotypes, stratified by study group, was assessed by linear regression models adjusted by age, HPV type, history of previous STD and cancer family history. The potential AEI was assessed by calculating the mean value of the base two logarithms of the allele2/allele1 ratio and their respective 95% CI across the groups of study. We also compared their distribution across the groups with the Kruskal–Wallis test. In all the regression models performed, only the observations with complete information of the variables of interest were included. All the statistical analyses were performed using the STATA program, version 12.1 (StataCorp, College Station, TX, USA).

CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGEMENTS We thank all the patients for their participation in the study and MD Guillermina López-Estrada, Karina Delgado-Romero and David Cantú for the gynecological

Genes and Immunity (2015) 43 – 53

Akna in cervical cancer GA Martínez-Nava et al

52 sampling and patient care that constitute the biological sample bank used. This work was supported by the Instituto Nacional de Salud Pública de México (INSP) and grants from CONACYT--FONSEC SSA/IMSS/ISSSTE-2011-01-161710, Mexico. GAM-N was recipient of a PhD fellowship from CONACYT. This study was also supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT) and the Fondo Sectorial de Investigación en Salud y Seguridad Social, México. The supporting agency had no role in the study design; in the collection, analysis and interpretation of the data; in the preparation of the report; or in the decision to submit the article for publication. This work was submitted in partial fulfillment of the requirements for the PhD degree of GAM-N from the Doctoral Program in Health Sciences of the School of Public Health of Mexico.

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