Abstract
Microglial dysfunction and inflammation have recently been shown to be related to the development of Alzheimer’s disease (AD). Inositol polyphosphate-5-phosphatase (INPP5D) functions broadly as a negative regulator of immune signaling, and its locus was associated with development of AD in a large-scale genome-wide association study. Thus, we examined INPP5D mRNA expression and methylation rates of the CpG sites in the upstream region of INPP5D exon 1 in peripheral leukocytes in 50 AD and age- and sex-matched control subjects. INPP5D mRNA expression in AD subjects was significantly higher than that in control subjects (1.16±0.39 versus 1.0±0.23, p = 0.049) and was correlated with the Mini-Mental State Examination score (p = 0.002, r = 0.426) and the total score of the Alzheimer’s Disease Assessment Scale (p < 0.001, r = –0.697). Methylation rates in the upstream region of INPP5D exon 1 were not significantly different between AD and control subjects (average rate: 3.5±3.0 versus 2.8±1.3, p = 0.551). Our results suggested that INPP5D mRNA expression was elevated in the early stage and decreased with cognitive decline in AD. INPP5D mRNA expression in leukocytes may be a useful biomarker for the early stage of AD.
INTRODUCTION
Alzheimer’s disease (AD) is the most common cognitive disorder, and is characterized not only by progressive deterioration in cognition but also by psychological and behavioral changes. In 2015, 46.8 million patients had AD, and the number is expected to increase to 131.5 million by 2050 [1]. Pathologically, the presence of amyloid plaques that contain amyloid-β (Aβ), a peptide fragment of the amyloid-β protein precursor, is partially responsible for causing AD [2]. Other reports indicate that extracellular deposits of Aβ [3–5] and neurofibrillary tangles, which occur following intracellular accumulation of hyperphosphorylated microtubule-associated protein tau [6, 7], play a key role in the pathogenesis of AD.
Microglial dysfunction and inflammation were recently reported to cause or modulate the development of AD [8]. Increased phagocytic ability of microglia can reduce Aβ, and tau-induced microglial activation can promote the clearance of Aβ [9]. Triggering receptor expressed on myeloid cell 2 (TREM2) is highly expressed in microglia [10], and single nucleotide polymorphisms (SNPs) [11], mRNA expression changes [12], and epigenetic changes in DNA methylation in TREM2 [13] are associated with AD. Furthermore, large-scale genome-wide association studies revealed that genes involved in microglial function and inflammation are associated with the development of AD, including ATP binding cassette subfamily A member 7 (ABCA7) [14], Myocyte-specific enhancer factor 2C (MEF2C) [15], and others.
Inositol polyphosphate-5-phosphatase (INPP5D) is highly expressed in microglia and encodes a negative regulator of the phosphoinositide 3-kinase/Akt (protein kinase B) pathway [16, 17]. INPP5D is related to regulation of plasmacytoid dendritic cell receptor signaling [18] and is a candidate gene for AD. rs35349669 at INPP5D intron 13 was associated with AD in large-scale genome-wide association studies [19, 20], and the association was confirmed with a meta-analysis [21]. In addition, rs1057258 in the 3′-untranslated region of INPP5D is associated with AD progression [22]. A relationship between neuroinflammation and the peripheral immune system regarding amyloid plaques may be associated with AD pathogenesis [23]. Expression of neuroinflammation-associated genes such as TREM2 [12], Synuclein alpha (SNCA) [24], and Translocase of outer mitochondrial membrane 40 (TOMM40) [25] in leukocytes is significantly changed.
The DNA methylation ratio in the CpG islands of the promoter region is altered in neuropsychiatric conditions [26, 27] including AD [28, 29]. An association between the methylation ratio and gene expression has been generally reported [30, 31]. Our group previously reported that the methylation ratio of SNCA may be a useful biomarker for both AD [24] and diffuse Lewy body disease [32].
The aims of our current study were to measure the INPP5D mRNA expression and DNA methylation ratio in the upstream region of INPP5D exon 1 in peripheral leukocytes of AD and control subjects and to investigate the relationships between these factors and clinical parameters.
MATERIAL AND METHODS
Subjects
Demographic data for each group of participants are shown in Table 1. We enrolled 50 AD patients (11 males and 39 females, mean age±standard deviation (SD) = 77.7±6.0 years) who were outpatients at Ehime University Hospital, Zaidan Niihama Hospital, and Kuroda Hospital, Ehime, Japan. AD subjects were diagnosed with probable AD dementia according to the Aging/Alzheimer’s Association criteria [33] and were living with at least one caregiver. According to interviews, we found that some AD subjects had comorbidities (hypertension (HT, n = 5), hyperlipidemia (HL, n = 9), anddiabetes mellitus (DM, n = 0). All AD subjects had no cerebral vascular lesions as determined with either head computed tomography or magnetic resonance imaging. AD subjects were evaluated with the Mini-Mental State Examination (MMSE, n = 50) [34] and the Alzheimer’s Disease Assessment Scale (ADAS, n = 33) [35] to assess their cognitive function, the Montgomery-Åsberg Depression Rating Scale (MADRS, n = 34) to assess their depression symptoms [36], Clinical Dementia Rating (CDR, n = 46), which was assessed by family caregivers [37], and the Neuropsychiatric Inventory (NPI, n = 47) to assess their psychological symptoms [38]. Control subjects were 50 elderly participants (11 males and 39 females, mean age±SD = 76.3±6.0 years) without cognitive impairment, psychiatric signs, or a past history of mental disorders, and who were diagnosed as mentally and cognitively normal by at least two certified psychiatrists based on clinical interviews. All participants were unrelated, of Japanese origin, and provided written informed consent forms approved by the institutional ethics committees of Ehime University Hospital, Zaidan Niihama Hospital, and Kuroda Hospital.
Demographic and clinical data for Alzheimer’s disease subjects
Values are the mean±standard deviation. HT, hypertension; HL, hyperlipidemia; DM, diabetes mellitus; MMSE, Mini-Mental State Examination; ADAS, Alzheimer’s Disease Assessment Scale; NPI, Neuropsychiatric Inventory; MADRS, Montgomery-Åsberg Depression Rating Scale; CDR, Clinical Dementia Rating.
Blood sample collection and processing of complementary DNA (cDNA) and genomic DNA (gDNA)
Total RNA samples were obtained from whole peripheral blood samples, which were collected in PaxGene Blood RNA Systems tubes (BD, Tokyo, Japan) according to the manufacturer’s protocol. The RNA concentration and purity were measured with the NanoDrop-1000 (Thermo Fisher Scientific, Yokohama, Japan) system, and acceptable 260/280 ratios were in the range of 1.8–2.0. For each 40-μl reaction, 1.0 μg RNA was used as a template to synthesize cDNAs with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Whole peripheral blood samples were collected in potassium EDTA tubes, and gDNA was extracted from these samples using the QIAamp DNA Blood Mini Kit (Qiagen, Tokyo, Japan) according to the manufacturer’s protocol.
PCR procedure
For the mRNA expression analysis, real-time quantitative reverse transcription-PCR (RT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems). The specific TaqMan probes were Hs00183290_m1 for INPP5D and Hs99999905_m1 for GAPDH (Applied Biosystems). GAPDH is suitable for normalization of quantitative RT-PCR and was used as the reference gene in previous studies [39–41]. The final volume of each reaction was 10 μl, and each contained TaqMan Universal Master Mix (Applied Biosystems). Expression levels were examined in duplicate. The ΔΔCt method and StepOne software (Applied Biosystems) were used to determine relative expressionlevels.
Genotyping
Genotyping of SNPs (rs1057258, rs429358, and rs7412) in INPP5D and apolipoprotein E (APOE) was conducted using the TaqMan 5′-exonuclease allelic discrimination assay (Assay ID: rs1057258; C–___8741744_10, rs429538; C___3084793_20, and rs7412, Applied Biosystems) using the StepOnePlus real-time PCR system (Applied Biosystems). We did not analyze rs35349669 because the minor allele frequency in Asian populations is too low (0.01, dbSNP) for association analysis. Genotyping call rates were 96.0% (rs1057258), 100.0% (rs429358), and 100.0% (rs7412). The minor allele frequency of rs1057258 was 0.28. No deviation from the Hardy-Weinberg equilibrium was detected for rs1057258 (AD subjects: p = 0.75, control subjects: p = 0.46), rs429358 (AD subjects: p = 0.59, control subjects: p = 0.59), or rs7412 (AD subjects: p = 0.77, control subjects: p = 0.38). The APOE isotype-related genotypes are combinations of the APOE ɛ2, ɛ3, and ɛ4 alleles derived from the two genotypes of rs429358 (T334C) and rs7412 (C472T): ɛ2, 334T/472T; ɛ3, 334T/472C; and ɛ4, 334C/472C. The ɛ4 genotype is a risk factor for AD [42].
Bisulfite conversion and pyrosequencing
We used JASPAR (http://jaspar.binf.ku.dk/) to select four CpG sites predicted to bind major transcription factors (Supplementary Figure 1). First, we analyzed the number of potential transcription factor binding sites in the promotor (from exon 1 to –200 bp) of INPP5D. Then, we focused on four CpG sites (from –54 to –116 bp) where the largest number of transcription factors was predicted to bind with a high score (predictive value >8). The gDNA extracted from leukocytes (1000 ng/sample) was converted with bisulfate using the EpiTect Plus DNA Bisulfite Kit (Qiagen, Valencia, CA, USA). Converted gDNA was then used as a template for PCR amplification with a forward primer (5′- GTTGAAAAGTTTTGAGGGAGAGTA-3′) and reverse primer (5′-[Biotin]- ACAACTAAAATCCCCCTAAATAAAA-3′). The final volume of each PCR was 39.0 μl, and each reaction contained 3.2 μl gDNAs, 0.2 μM each forward and reverse primers, AmpliTaq gold (Applied Biosystems), 10×PCR buffer with 15 mM MgCl2, and 2 mM dNTP. Cycling conditions were as follows: denaturation for 10 min at 94°C; followed by 45 cycles, each comprising 94°C for 30 s, 56°C for 30 s, and 72°C for 1 min; and a final extension of 10 min at 72°C. The PCR products were sequenced with PyroMark Q24 Advanced (Qiagen) and the sequencing primer, 5′- TTTTGAGGGAGAGTAGA-3′. We used non-converted DNA as an additional control for the specificity of PCR amplification of bisulfite-treated DNA. Methylation rates at each CpG site were quantified in duplicate using PyroMark Q24 Advanced Software (Qiagen).
Statistical analysis
We performed statistical analyses with SPSS 22.0 software (IBM Japan, Tokyo, Japan). Sex and APOE genotype were analyzed with Fisher’s exact test. We conducted a test of normality with the Shapiro-Wilk test. Comparisons of INPP5D mRNA expression and each CpG methylation rate between AD and control subjects were conducted with the Mann-Whitney U test. Comparisons of INPP5D mRNA expression with sex, APOE genotypes, HT, HL, and use of acetylcholine esterase inhibitor medications were conducted with the Mann-Whitney U test. Correlations of INPP5D mRNA expression with age at blood sampling, onset, duration of illness, MMSE, total score of ADAS, NPI, MADRS, and methylation rates at each CpG site were conducted with the Spearman’s rank correlation with post-hoc Bonferroni correction (p = 0.0046). Comparisons of the INPP5D mRNA expression with rs1057258, CDR, and recall in MMSE were performed with one-way analyses of variance (ANOVA) with the post-hoc Dunnett’s t test and Dunnett’s T3 test if those data followed normal distribution, or the Kruskal-Wallis test with the post-hoc Steel-Dwass test (EZR version 1.2643) if the data did not follow normal distribution. Relationships between INPP5D mRNA expression levels and each psychological symptom in NPI (presence of the symptom = 1, absence = 0) were analyzed with the multiple linear regression analysis stepwise method. Statistical significance was defined at the 95% level (p = 0.05).
RESULTS
Participant characteristics
AD and control subjects did not differ in sex (p = 1.0) or age (p = 0.235). However, a significant difference in APOE genotype between AD and control subjects was observed (Fisher’s exact test; p < 0.001). Clinical characteristics and results of psychological tests in AD subjects are shown in Table 1.
INPP5D mRNA expression levels
INPP5D mRNA expression in peripheral leukocytes from AD subjects was higher than that of control subjects (1.16±0.39 versus 1.0±0.23, p = 0.049, Fig. 1). The power was 0.89 (50 patients and 50 controls, β/α ratio = 1.0 and effect size = 0.05, by G*Power 3, http://www.gpower.hhu.de/). The power value is sufficient to conclude a difference (power value >0.8). INPP5D mRNA expression levels were not correlated with gender (AD subjects, p = 0.550; control subjects, p = 0.343), age at blood sampling (AD, p = 0.968, r = 0.006; control, p = 0.906, r = –0.018), APOE genotype (AD, p = 0.884; control, p = 0.661), onset age of AD (p = 0.694, r = 0.058), rs1057258 genotype (AD subjects, p = 0.999; control subjects, p = 0.398), HT (p = 0.372), HL (p = 0.960), or use of an acetylcholine esterase inhibitor (p = 0.054). The correlation between INPP5D mRNA expression and duration of illness in AD subjects (p = 0.018, r = –0.336) also did not reach statistical significance after Bonferroni correction (p = 0.0046).
INPP5D mRNA expression was higher in Alzheimer’s disease (AD) subjects than in control (Ct) subjects. Each symbol represents one individual. Comparisons between AD and control subjects were conducted with the Mann–Whitney U test. The horizontal bars represent the mean±standard error.
INPP5D mRNA expression and clinical parameters
INPP5D mRNA expression levels were correlated with MMSE (p = 0.002, r = 0.426, Fig. 2A), total score of ADAS (p < 0.001, r = –0.697, Fig. 2B), and the word recall subcategory of ADAS (p = 0.003, r = –0.499, Supplementary Figure 2). INPP5D mRNA expression levels were significantly associated with the recall subcategory of MMSE (p < 0.001, Supplementary Figure 3) and CDR (p = 0.01, Fig. 3). We found no correlation between INPP5D mRNA expression and MADRS (p = 0.155) or the NPI total score (p = 0.242). However, INPP5D mRNA expression was significantly associated with the hallucination subcategory of NPI with the multiple linear regression analysis stepwise method (R2 = 0.092, p < 0.001; hallucination: p = 0.016, Supplementary Figure 4).
Correlation analysis between INPP5D mRNA expression in AD subjects and A) Mini-Mental State Examination (MMSE) and B) Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS). Correlation analysis was conducted with the Spearman’s rank correlation coefficient.
INPP5D mRNA expression levels were compared with the Clinical Dementia Rating (CDR) with the Kruskal-Wallis test and the post-hoc Steel-Dwass test (*p<0.05). The horizontal bars represent the mean±standard error.
Methylation status
The sequence and position of the four CpG sites in the upstream region of INPP5D exon 1 are depicted in Supplementary Figure 1. We found no significant difference between the AD group and control subjects (CpG 1: average±SD = 5.5±4.3 versus 4.5±1.8, p = 0.677; CpG 2:1.9±1.8 versus 1.5±0.8, p = 0.759; CpG 3:2.3±2.0 versus1.8±0.9, p = 0.517; CpG 4:4.4±4.0 versus 3.4±1.6, p = 0.484; average rate: 3.5±3.0 versus 2.8±1.3, p = 0.551, Supplementary Figure 5). INPP5D mRNA expression was not correlated with CpG 1 (p = 0.775, r = 0.029), CpG 2 (p = 0.733, r = 0.035), CpG 3 (p = 0.733, r = 0.035), CpG 4 (p = 0.530, r = 0.064), or the average rate (p = 0.720, r = 0.036).
DISCUSSION
Our study revealed two major findings. First, INPP5D mRNA expression was higher in AD subjects than in control subjects. None of the background characteristics such as age at sampling, gender, duration of illness, rs1057258 of INPP5D, APOE genotype, HT, HL, use of acetylcholine esterase inhibitor medications, or onset age of AD were correlated with INPP5D mRNA expression. To our knowledge, no report has investigated INPP5D mRNA expression and DNA methylation using leukocytes of AD subjects. INPP5D functions broadly as a negative regulator of immune signaling [16, 17]. Regulating microglial activation and neuroinflammation may be a therapeutic strategy for AD in both humans [44, 45] and mouse models [46, 47]. Several genes including TREM2 and ABCA7 are related to AD pathogenesis via impacts on neuroinflammation [8]. DAP12 is an adaptor molecule for TREM1/2 and an INPP5D binding partner [48]. INPP5D also may affect the microglial function through the DAP12. Thus, IINPP5D may be directly and indirectly related to AD pathogenesis through microglial activation, neuroinflammation, and the immune response. Elevated INPP5D mRNA expression may be associated with neuroprotective effects by suppressing microglial activation and inflammation in the early stage of AD. Both positive and negative correlations between INPP5D mRNA expression and MMSE and total score of ADAS may support this hypothesis. Lower MMSE scores and its sub-scores and higher ADAS scores and its sub-scores are related to worse cognitive performance. In addition, the CDR score tended to decrease with decreasing INPP5D mRNA expression. Disruption of the episodic memory system is the earliest sign and a core feature of AD, and seems to be a strong predictor of progression from mild cognitive impairment to major cognitive disorders [49]. The recall subcategory of MMSE and the word recall subcategory of ADAS are assessments of delayed recall and are part of episodic memory. Interestingly, the recall subcategory of MMSE and the word recall subcategory of ADAS worsened with decreasing INPP5D mRNA expression. From these results, lower INPP5D mRNA expression is related to cognitive decline. In addition, we detected nominal significance of the negative correlation between INPP5D mRNA expression and duration of illness. Thus, elevated INPP5D mRNA expression in leukocytes may be a useful biomarker for neuroprotective effects in the early stage of AD. Interestingly, AD subjects with hallucination, a subcategory of NPI, showed lower INPP5D mRNA expression than those without hallucination in liner regression analysis. Neuropsychiatric symptoms including hallucination are highly prevalent along with disease progression in AD subjects [50].
Second, we found no significant change in the methylation ratio in the upstream region of INPP5D exon 1 in leukocytes in AD patients and no correlation between the methylation ratio and INPP5D mRNA expression. Generally, an inverse relationship is present between CpG methylation and transcriptional activity [51, 52]. In the promoter region, hypermethylation is usually related to transcriptional silencing [53, 54]. Although we selected target CpG sites that may theoretically bind major transcription factors in silico, an in vitro verification study such as a luciferase-reported assay is needed to confirm that these target CpG sites are actually bound by transcription factors and regulate geneexpression.
Our study has several limitations. First, the sample size was relatively small, which may lead to a type II error. Although INPP5D mRNA expression was statistically higher in AD subjects than in control subjects, the difference was not so significant (1.16±0.39 versus 1.0±0.23). Because of the large overlapping, it is difficult to discriminate AD from healthy subjects using mRNA expression level as a single marker. In the future, we should conduct a replication study with a larger sample size. Second, this study was cross-sectional. We recruited AD patients in various stages. We should examine INPP5D mRNA prospectively from mild cognitive impairment through late stages of AD to confirm the stage-dependent expression changes. Lastly, we have no information about major comorbidities (HT, HL, and DM) in control subjects. We did not investigate the correlation between INPP5D mRNA expression and major comorbidities in control subjects.
In conclusion, we revealed that INPP5D mRNA expression was higher in AD subjects than in control subjects and that expression was correlated with cognitive decline. Elevated INPP5D mRNA expression may be a useful biomarker for the early stageof AD.
Footnotes
ACKNOWLEDGMENTS
We wish to thank Zaidan Niihama Hospital and Kuroda Hospital for collecting blood samples and Ms. Chiemi Onishi for her technical assistance. This work was partially supported by a Health and Labor Science Research Grant from the Japanese Ministry of Health, Labour and Welfare and a Grant-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology, JSPS KAKENHI Grant Numbers 15K09808 and 16K21207.
