Novel Plasma miRNAs as Biomarkers and Therapeutic Targets of Alzheimer’s Disease at the Prodromal Stage

Abstract

Background:

Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer’s disease (AD) involving imbalanced beta-site amyloid precursor protein cleaving enzyme 1 (BACE1). MicroRNAs (miRNAs) are associated with AD.

Objective:

This study aimed to investigated whether plasma miRNAs can predict prodromal AD or are associated with AD pathology.

Methods:

Participants in the discovery set (n = 10), analysis set (n = 30), and validation set (n = 80) were screened from the China Longitudinal Aging Study. RNA was extracted from the participants’ plasma. Microarray sequencing provided miRNA profiles and differentially expressed miRNAs (DEmiRNAs) in the discovery set included patients with ¹⁸F-Flutemetamol positron emission tomography scan-confirmed aMCI. Potential biomarkers were screened in the analysis set. The predict capability of candidate miRNAs was assessed in the validation set. Candidate miRNAs modulation of BACE1 expression was explored in rat and human hippocampal neurons in vitro.

Results:

We verified 46 significant DEmiRNAs between the aMCI and NC groups (p < 0.05), among which 33 were downregulated. In the analysis set, miR-1185-2-3p, miR-1909-3p, miR-22-5p, and miR-134-3p levels decreased significantly in the aMCI group. These miRNAs and previously identified miR-107 were selected as potential biomarkers. A prediction model comprising these five miRNAs showed outstanding accuracy (81.25%) to discriminate aMCI at cut-off value of 0.174. Except for miR-134-3p, the other four miRNAs significantly suppressed Bace1 expression in rat hippocampal neurons in vitro. BACE1 modulation of miR-1185-2-3p, miR-1909-3p, and miR-134-3p was confirmed in human hippocampal neurons in vitro.

Conclusion:

A predictive model consisting of five BACE1-related plasma miRNAs could be a novel biomarker for aMCI.

Keywords

Alzheimer’s disease biomarkers microRNAs mild cognitive impairment

INTRODUCTION

Alzheimer’s disease (AD) is a progressive and irreversible disorder and is the most common form of dementia. As the world’s aging population has grown, AD has become a major health concern. The main pathological features of AD are extracellular deposits of amyloid-β (Aβ) peptides as senile plaques, intraneuronal neurofibrillary tangles, and large-scale neuronal loss [1 –3]. Deposition of Aβ is reported to start 15–20 years before the onset of clinical symptoms [4]. Thus, Aβ peptides have been viewed as potential targets in the past two decades. However, all high-profile drugs targeting Aβ peptides or deposited amyloid plaques have failed [5 –7]. It has been proposed that agents binding to Aβ would have only marginal effects on preexisting amyloid [8]. Thus, the early identification and prevention of AD at the prodromal stage is urgent and important. Mild cognitive impairment (MCI) is defined as the prodromal stage of dementia [9]. Amnestic MCI (aMCI) is considered a prodromal stage of AD with an annual conversion rate to AD of 10–15%[10]. Clinical characteristics, neurocognitive tests, positron emission tomography (PET), and magnetic resonance imaging (MRI) can be used to diagnose aMCI [11, 12]. However, neurocognitive tests are time consuming. Amyloid PET using different probes (e.g., ¹⁸F-flutemetamol and ¹⁸F-florbetapir) is available to detect and monitor brain amyloid deposition in patients with MCI precisely [13, 14]. Longitudinal studies showed that subjects with amyloid-positive MCI have greater cognitive deterioration than amyloid-negative subjects [15, 16]. But brain imaging scan, especially amyloid PET scan, are very expensive and need sophisticated equipment, making their universal utility impractical. In addition to the above-mentioned technologies, body fluids biomarkers could also be used to diagnose MCI. However, a cerebrospinal fluid (CSF) assay requires a lumbar puncture, which is an invasive procedure. Blood biomarkers could be an attractive option, because obtaining a blood sample is an economical, non-invasive and simple procedure. However, currently, there are no effective peripheral blood biomarker for aMCI diagnosis.

microRNAs (miRNAs) are a class of noncoding RNA, which fine-tune the translation of protein encoding mRNAs at the post-transcriptional level [17, 18]. The important roles of aberrantly expressed miRNAs have been highlighted in various human diseases, including neurodegenerative disease [19]. Aberrant expression of miRNAs might predict the onset of cognitive dysfunctions [20]. Recently, the role of miRNAs in the diagnosis for AD or MCI has been studied extensively. Several miRNAs have been shown to regulate the expression of AD related genes, such as those encoding amyloid-β protein precursor (AβPP), beta-site APP cleaving enzyme 1 (BACE1), and brain-derived neurotrophic factor (BDNF) [21 –23]. This ability of selected miRNAs to target mRNAs that are altered in disease conditions makes them potential candidates as therapeutics or as targets of therapeutics. Not every MCI-affected subject will develop AD; therefore, it is important to identify aMCI early, as it is the most likely subtype to develop into AD. Furthermore, miRNAs associated with the pathology of AD might be better predictors of progression of patients with aMCI. In this study, we aimed to find an effective predict model composed of plasma miRNAs for the diagnosis of aMCI at an earlier stage than is possible using biomarkers of Aβ and highly phosphorylated tau protein, and to investigate the mechanism by which miRNAs modulate BACE1 expression in vitro.

MATERIALS AND METHODS

Study population and sample collection

The workflow of this study is shown in Fig. 1a. All plasma samples were collected from subjects enrolled in the China longitudinal aging study (CLAS, ClinicalTrials.gov Identifier: NCT03672448) [24]. The CLAS study was approved by Institutional Ethical Committee of the Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine. Written informed consent was obtained from each study participant or their legal guardians.

Fig. 1

Identification of significantly downregulated miRNAs in aMCI. (a) Workflow of the present study. (b) Volcano plots of all miRNAs detected using the microarray in the discovery set. Red and green indicate significantly up- and downregulated miRNAs, respectively. Blue triangle on the plot are the selected miRNAs validated in the analysis set. (c) Heatmap of 31 significantly downregulated human miRNAs detected using the microarray. ^*miRNA that has targets sites on the BACE1 mRNA sequence, as predicted using the Targetscan7.2 database or miRDB database. aMCI, amnestic mild cognitive impairment.

Petersen’s criteria [25] was used to diagnose aMCI: 1) memory complaint, preferably corroborated by a spouse or relative; 2) objective memory impairment; 3) normal general cognitive function; 4) intact activities of daily living; and 5) absence of dementia. The core clinical criteria of the NIA-AA for the diagnosis of MCI were used as inclusion criteria together [9]. In the discovery set, subjects with aMCI were confirmed to have positive amyloid deposition in the brain using ¹⁸F-Flutemetamol PET scans. Normal controls were age-, sex-, and educational level-matched and had normal cognitive function. Subjects with other mental disorders, nervous system diseases, and history of psychotropic medicines were excluded. The subjects in the discovery set, analysis set, and validation set were absolutely independent, respectively.

All participants underwent a screening process that included a review of their medical history, physical and neurological examinations, laboratory tests, and MRI scans. Several psychological and psychosocial assessments were performed by a psychologist: the Mini-Mental State Examination (MMSE) [26], the Montreal Cognitive Assessment (MoCA) [27], the Neuropsychological Test Battery [28] (include Wechsler Memory Scale, Verbal Fluency Test-fruits and idioms, Hopkins Verbal Learning and 30-minute Delayed Test, Visual Recognition Function Test, etc.), and the Geriatric Depression Scale [29]. Attending psychiatrists collected information on the current and past history of diseases from each participant, conducted physical examinations, evaluated functioning, and determined diagnoses using: the Activities of Daily Living scale [30], the Clinical Dementia Rating scale [31], the Global Deterioration Scale [32], and the Hachinski Ischemia Scale [33].

Peripheral blood was collected from every subject after fasting for 12 h. Then plasma was separated by centrifugation at 3,000 rpm for 20 min at 4°C and stored at –80°C until required.

miRNA microarray sequencing

RNA was isolated using the TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) and purified using an RNeasy mini kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. RNA quality and quantity were measured by using a nanodrop spectrophotometer (Nanodrop ND-1000, Nanodrop Technologies, Wilmington, DE, USA). After quality control, a miRCURY™ Hy3™/Hy5™ Power labeling kit (Exiqon, Vedbaek, Denmark) was used according to the manufacturer’s guidelines for miRNA labelling. After stopping the labeling procedure, the Hy3™-labeled samples were hybridized on the miRCURY™ LNA Array (v.19.0) (Exiqon) according to the array manual. The slides were then scanned using an Axon GenePix 4000B microarray scanner (Axon Instruments, Foster City, CA, USA). The scanned images were then imported into GenePix Pro 6.0 software (Axon Instruments) for grid alignment and data extraction. Replicated miRNAs were averaged and miRNAs with intensities≥30 in all samples were chosen to calculate the normalization factor. Expressed data were normalized using Median normalization. After normalization, significantly differentially expressed miRNAs (DEmiRNAs) between the two groups were identified via their fold-change and p-value of a two-sample independent t-test.

Replication of the results in an analysis set

The expression levels of miRNAs were confirmed using quantitative real-time reverse transcription PCR (qRT-PCR) in an analysis set. RNA was extracted using the TRIzol LS Reagent (Invitrogen, Waltham, MA, USA). miRNA quality and quantity were determined by 260/280 nm absorbance using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies). A total of 300 ng RNA was used to prepare cDNA samples on a Gene Amp PCR System 9700 (Applied Biosystems, Foster City, CA, USA). Then, qPCR using the prepared cDNA, was performed in a QuantStudio5 Real-time PCR System (Applied Biosystems). Primers were designed using Primer 5.0 (Premier Biosoft, Palo Alto, CA, USA; Supplementary Table 1). The relative levels of miRNAs were determined in terms of their fold change, using the formula (2^–ΔΔ CT) [34]. Hsa-miR-93 was used as an endogenous control. The qRT-PCR assays were performed in triplicate.

Construction of the predictive model

To identify independent predictive parameters of aMCI, univariable and multivariable logistic regression analyses were performed for the plasma levels of each miRNA. A p < 0.05 was considered statistically significant. Parameters with a p < 0.05 based on the univariate analysis were further included in the multivariable logistic regression analysis. According to the multivariable logistic regression results, we constructed a predictive model. The accuracy of this model was examined using receiver operating characteristic (ROC) curve analysis. The best cut-off value was selected according to the Youden-index.

Cell culture and treatment

Rat primary hippocampal neurons were isolated from E18 embryos according to the following protocol. The protocol has received approval by the Institutional Ethical Committee of the Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine. The pregnant rat was euthanized via inhalation of CO₂. The uterus was removed and placed rapidly in cold phosphate buffer saline (PBS). The embryos were removed from the uterine segments and placed in a new petri dish containing cold PBS. The embryos were decapitated, the brain was removed from the head, and the embryonic rat hippocampal tissues were dissected under a surgical microscope. The hippocampal tissues were digested using 0.15%trypsin for 20 min at 37°. The hippocampal neuronal suspensions were then filtered through 70-μm cell strainers. Neurons were plated on poly-L-lysine coated 6-well plates at ∼100,000 cells/cm², and then cultured in neurobasal media supplemented with 2%B27, 0.25%Glutamax, 0.25%Glutamate, and 0.5%Penicillin/Streptomycin. Next day, the medium was replaced with another neurobasal medium supplemented with 2%B27, 0.25%Glutamax, and 0.5%Penicillin/Streptomycin.

Human hippocampal neurons were purchased from Sciencell (Carlsbad, CA, USA), and plated on poly-L-lysine (2μg/cm2, Sigma-Aldrich, St. Louis, MO, USA) coated 6-well plates at ∼50,000 cells/ cm². The culture medium, comprising Neuronal Medium (Sciencell), 1%Neuronal Growth Supplement (Sciencell), and 1%Penicillin/Streptomycin, was replaced every two days. All cells were cultured at 37°C in a humidified incubator with 5%CO₂ environment. Lentiviral particles containing expression vectors encoding miRNA mimics, miRNA inhibitor, or flanking control sequences were purchased from GenePharma (Shanghai, China). Rat hippocampal neurons were transfected with control or miRNA mimics or miRNA inhibitor lentiviruses at optimal concentrations (miR-107 and miR-134-3p: multiplicity of infection (MOI) = 5; miR-1185-2-3p, miR-1909-3p, and miR-22-5p: MOI = 10, Supplementary Figure 1) on the first day in vitro (DIV1) and harvested on DIV7. Human primary hippocampal neurons were transfected at anoptimal concentration (MOI = 10, Supplementary Figure 2) on DIV4 and harvested on DIV10.

qRT-PCR

For cultured cells and neurons, total RNAs were extracted using the TRIzol LS Reagent (Invitrogen). RNA quality and quantity were measured using a Nanodrop-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The cDNAs were synthesized using a HiScript II 1st Strand cDNA Synthesis Kit (Vazyme, Jiangsu, China) according to manufacturer’s instructions. qPCR reactions performed in a Fast-7000 Real-time PCR system (Thermo Fisher Scientific). Threshold cycles (C_T value) were generated automatically, and the relative expressions were shown as (2^–ΔΔ CT) [34]. qRT-PCR was performed in triplicate. The relative mRNA levels of target genes were normalized to expression levels of the indicated reference genes. Primers were synthesized by Sangon Biotech (Shanghai, China; Supplementary Table 1).

Western blotting analysis

Cells or neurons were lysed in radioimmunoprecipitation assay (RIPA) Lysis Buffer (Beyotime, Shanghai, China) which contained 1 mM of Phenylmethanesulfonyl fluoride (Beyotime) according to the manufacturer’s instructions. Cell lysate samples were electrophoresed on SDS-polyacrylamide gels in Tris-glycine buffer containing SDS. Proteins were transferred to 0.22μm nitrocellulose membranes. The membranes were then blocked in 3%Bovine Serum Albumin (BSA) diluted in Tris-buffered saline-Tween 20 (TBST) buffer at room temperature, and then incubated overnight at 4° with primary antibodies (BACE1 [Abcam, Cambridge, UK, 1:2000], β-actin [Cell Signaling Technology, Danvers, MA, USA, 1:2000], APP [Thermo Fisher Scientific, 1:500]). The membranes were then washed in TBST and incubated with secondary antibodies for 1 h at room temperature. The immunoreactivity of the proteins was detected using a chemiluminescent substrate. All experiments were performed in triplicate.

RESULTS

Baseline characteristics of the study population

The clinical characteristics of the participants are listed in Table 1. The discovery set was used to screen the DEmiRNAs) and comprised five subjects with aMCI and five normal control (NC) subjects. All subjects with aMCI in this set were confirmed using ¹⁸F-Flumetamol PET (amyloid positive). The analysis set, comprising 20 subjects with aMCI and 10 NC subjects, was utilized to quantify the DEmiRNAs found using the microarray and to screen potential biomarkers. The validation set, comprising 40 subjects with aMCI and 40 NC subjects, was used to evaluate the clinical value of the selected miRNAs. There were no significant differences between the aMCI groups and NC groups in terms of age, sex, and educational years (Table 1, all had a p-value > 0.05). Cognitive function was evaluated using MoCA scores, and the average MoCA scores of the aMCI groups were all significantly lower than those of the NC groups in all three datasets (Table 1, all had a p-value < 0.05).

Table 1

Demographic characteristics of study participants

	aMCI group	NC group	p
Discovery set
n	5	5
Age (y)^a	68.00±2.83	70±2.12	0.24
Sex (female, %)^b	2 (40%)	2 (40%)	1.00
Education (y)^c	11.0±3.08	11.6±2.88	0.76
MoCA^c	24.40±2.19	26.4±0.55	0.07
Brain Aβ deposition^d	Positive	–
Analysis set
n	20	10
Age (y) ^a	71.8±7.38	70±6.65	0.54
Sex (female, %) ^b	14 (70%)	6 (60%)	0.69
Education (y) ^c	10.65±2.55	10.2±2.64	0.16
MoCA^c	20.95±2.42	24.9±1.97	< 0.01^**
Validation set
n	40	40
Age (y) ^a	78.25±5.48	78.23±5.62	0.98
Sex (female, %) ^b	34 (85%)	34 (85%)	1.00
Education (y) ^c	2.65±2.94	3.65±3.36	0.16
MoCA^c	11.98±3.35	21.22±4.40	< 0.01^**

^aData were analyzed using two sample independent t-test; ^bData were analyzed using the χ² test; ^cData were analyzed using the Mann-Whitney U test; ^d Brain Aβ position was detected using ¹⁸F-Flumetomal PET. ^*p < 0.05, ^**p < 0.01. aMCI, amnestic mild cognitive impairment; NC, normal controls.

Discovery phase: miRNA profiles of aMCI and control plasma

According to results of the miRNA microarray, we identified 46 DEmiRNAs between the aMCI group and the NC group in the discovery set (p < 0.05, Fig. 1b). Compared with those in the normal controls, 13 miRNAs were upregulated and 33 were downregulated in the aMCI group (Fig. 1b). Among the downregulated miRNAs, 31 miRNAs were human miRNAs. In addition to the miRNAs identified above, we were also interested in miR-134-3p, the mature sequence of miR-134. The reasons were as follows: 1) An outlier value in miR-134-3p was found in the microarray sequencing data (Data are openly available in GEO database, reference number [GSE147232]), which might cause a low fold-change value with a high p-value (Fold-change = 0.099, p = 0.362); 2) miR-134 is abundantly expressed in the brain and was demonstrated to regulate synaptic plasticity and memory formation [35, 36]; and 3) few previous studies have focused on miR-134-3p. Thus, miR-134-3p together with the 31 significantly decreased miRNAs were selected for secondary screening.

Selection of BACE1-related miRNAs for further validation

The target genes of the 32 miRNAs were predicted using the Targetscan7.2 database [37] and miRDB database [38, 39]. Combining the results from both databases showed that 13 miRNAs (Supplementary Table 2) and miR-134-3p were deemed to have target sites on BACE1 mRNA 3’UTR. Among them, five miRNAs (miR-1185-2-3p, miR-1909-3p, miR-22-5p, miR-134-3p, and miR-5691) were selected for advanced validation, and their potential target sites on the BACE1 mRNA 3’UTR are shown in Supplementary Figure 3. The mature sequence of miR-5691 has four putative binding sites on the BACE1 mRNA 3’UTR with matches to the complementary seed sequence, while miR-1185-2-3p has three putative binding sites. The remaining three miRNAs only have one target site on BACE1 mRNA 3’UTR, respectively.

To validate the microarray results, qRT-PCR was utilized to quantify the selected miRNAs in the analysis set. Except for miR-5691, the expression levels of the remaining four miRNAs were decreased in subjects with aMCI, which was consistent with the microarray results (Fig. 2a). Our previous study demonstrated a high capability of plasma miR-107 to discriminate subjects with aMCI from normal controls [40]. Furthermore, miR-107 has a potential target site in the BACE1 mRNA [21 , 41]. Hence, combining the results above, miR-107 together with miR-1185-2-3p, miR-1909-3p, miR-22-5p, and miR-134-3p were selected as potential biomarkers.

Fig. 2

Identification of candidate miRNAs as potential biomarkers. (a) The relative expression levels of miR-1185-2-3p, miR-1909-3p, miR-22-5p, miR-134-3p, and miR-5691 in plasma samples in the analysis set, which were calculated using the 2^–ΔΔ CT method. Significant difference between two groups were assessed using a two sample independent t test. (b) Correlation of candidate biomarkers (miR-1185-2-3p, miR-1909-3p, miR-22-5p, miR-107, and miR-134-3p) with MMSE, MoCA, and ADL scores in the validation set. (c) Receiver operating characteristic (ROC) curve results of each candidate biomarker (miR-1185-2-3p, miR-1909-3p, miR-22- 5p, miR-107, and miR-134-3p) to discriminate subjects with aMCI from normal controls in the validation set. (d) ROC curve results of the predictive model for aMCI diagnosis. aMCI, amnestic mild cognitive impairment; NC, normal controls; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; ADL: Activities of Daily Living. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

Validation phase: Clinical value of the five selected miRNAs as biomarkers

Several measurements were used to describe the clinical characteristics of the participants, including MMSE, MoCA, and Activities of Daily Living scale (ADL). The levels of five miRNAs correlated significantly and positively with the MMSE and MoCA scores, while the level of miR-1909 correlated negatively with the ADL scores (Fig. 2b). The results showed that the expression levels of these miRNAs were related to cognitive function. We then used ROC curve analysis to investigate the utility of each miRNA as a diagnostic biomarker, separately. miR-1909-3p had the highest discrimination accuracy (area under the receiver operating characteristic curve (ROC) (AUC) = 0.872), followed by miR-107 (AUC = 0.773). miR-134-3p had the weakest discrimination accuracy (AUC = 0.631, Fig. 2c).

The predictive effects of the five altered miRNAs were assessed using logistic regression. The lower levels of each miRNA increased the risk of aMCI according to univariate logistic regression (all had a p-value < 0.05, Table 2). All factors were subsequently included in multivariable logistic regression analysis. The results showed that miR-1909-3p (p = 0.001) and miR-107 (p = 0.023) might be independent factors to diagnose aMCI (Table 2). According to the multivariable logistic regression results, we constructed a predictive model: 7.341-0.029×(expression level of miR-134-3p) –0.150×(expression level of miR-22-5p) - 0.604×(expression level of miR-1185-2-3p) –5.321×(expression level of miR-1909-3p) –1.372×(expression level of miR-107). This combined model improved the individual performances of the altered miRNAs and performed well in discriminating subjects with aMCI from normal controls (AUC: 0.901, Fig. 2d). According to the Youden-index, the optimal cut-off value for this model was 0.174, and the sensitivity and specificity were 80.0%and 82.5%, respectively. When using this model to discriminate aMCI and normal controls, the accuracy was 81.25%.

Table 2

The results of logistic regression analyses of five miRNAs in predicting aMCI in the validation set

miRNAs	Univariable Logistic Regression			Multivariable Logistic Regression
	OR	95%CI	p	OR	95%CI	p
miR-1185-2-3p	0.334	0.176–0.634	< 0.01	0.547	0.220–1.360	0.19
miR-1909-3p	0.002	0.001–0.033	< 0.01	0.005	0.001–0.127	< 0.01
miR-22-5p	0.054	0.010–0.301	< 0.01	0.861	0.066–11.257	0.91
miR-107	0.175	0.065–0.470	< 0.01	0.254	0.078–0.827	0.02
miR-134-3p	0.447	0.208–0.958	0.04	0.972	0.304–3.105	0.96

OR, odds ratio; CI, confidence interval.

Modulation on BACE1 expression in hippocampal neurons in vitro

In rat hippocampal neurons, miR-1185-2-3p, miR-1909-3p, miR-22-5p, and miR-107 suppressed Bace1 expression as compared with the control (Fig. 3b, c). Subsequent downregulation of APP C-terminal fragment beta (CTF-β) production was observed in parallel with lower BACE1 protein levels (Fig. 3b-d). Conversely, inhibition of these four miRNAs significantly increased BACE1 protein levels (Fig. 3f, g), indicating that BACE1 protein levels are regulated by these miRNAs in hippocampal neurons. Upregulation of BACE1 protein levels led to significantly increased production of CTF-β (Fig. 3f-h).

Fig. 3

Five selected miRNAs modulate proteins that participate in the Aβ cascade hypothesis in hippocampal neurons. (a) Bace1 mRNA levels, (b) represent western blotting image, (c) relative BACE1 protein levels, and (d) relative CTF-β protein levels at 7 days post-transfection with control or miRNA mimics lentiviruses, separately, in rat hippocampal neurons. (e) Bace1 mRNA levels, (f) representative western blotting images, (g) relative BACE1 protein levels, (h) CTF-β protein levels at 7 days post-transfection with control or miRNA inhibitor lentiviruses, separately, in rat hippocampal neurons. (i) Representative western blotting image, (j) relative BACE1 protein levels, and (k) relative CTF-β protein levels in human hippocampal neurons after transfection with miRNA inhibitor lentiviruses, separately. Error bar represents the standard error. (^*p < 0.05, ^**p < 0.01, student’s t test as compared with the control).

In human hippocampal neurons, inhibition of miR-1185-2-3p, or miR-1909-3p increased the BACE1 protein levels significantly and promoted subsequent CTF-β production (Fig. 3i-k), in line with the results found in rat hippocampal neurons. Surprisingly, inhibition of miR-134-3p also increased the BACE1 protein levels (Fig. 3i-k) in human hippocampal neurons.

DISCUSSION

The challenge of achieving early diagnosis of AD through biomarkers from peripheral blood has not been completely solved. In 2020, high phosphorylation Tau protein 181 and 217 from peripheral blood were reported to discriminate AD from young adults and cognitively unimpaired older adults, but not MCI [42 –44]. Although existing neuroimaging techniques are available to detect very early Aβ deposition, the high costs make it difficult to use this method widely in elderly subjects at risk of AD. Moreover, CSF would be an ideal source for specific biomarkers for central nervous system disorders; however, the difficulty, invasiveness, morbidity, and risk related to CSF-collection have prevented its wide use. Circulating miRNAs offer a rapid, convenient, non-invasive, and cost-effective method for early diagnosis. Currently, efforts have been made to discover altered circulating miRNAs in patients with AD, with the aim of providing potential biomarkers. However, few studies have focused on subjects with aMCI. In this study, we used microarray sequencing to identify altered miRNA profiles in the plasma of subjects with aMCI and identified potential peripheral biomarkers to discriminate subjects with aMCI from cognitively unimpaired older adults.

Alzheimer’s disease is a complex and multifactorial disease. The imbalance of miRNAs expression is involved in the pathological initiation and progress of the disease [45]. In the current study, we revealed the miRNA profiles using a miRNAs microarray for the first time in subjects with aMCI compared with controls. We detected 46 DEmiRNAs between subjects with aMCI and normal controls, of which 13 were associated with BACE1. In the analysis set, bioinformatic methods and qRT-PCR validated four of the original 13 BACE1-associated and downregulated human miRNAs: miR-1185-2-3p, miR-1909-3p, miR-22-5p, and miR-134-3p. The current study is the first to report dysregulation of miR-1185-2-3p, miR-1909-3p, miR-22-5p, and miR-134-3p in the plasma of subjects with aMCI. Several studies have revealed that miR-107 levels are decreased in the brain and biofluids in patients with AD and indicated that miR-107 might be involved in AD pathogenesis through regulating BACE1 and cyclin dependent kinase 5 (CDK5) activity [21 , 46]. We hypothesized that these four miRNAs and miR-107 could be further validated as candidate biomarkers for aMCI. In the validation set, multivariate analysis found that only two variables, miR-1909-3p and miR-107, had statistical significance after adjusting for confounding factors, which indicated that these two variables were independent risk factors of aMCI. But miR-1185-2-3p, miR-22-5p, and miR-134-3p were confirmed to be closely associated with the pathology of AD in the present study; therefore, they were also included in the predictive model at last. The high accuracy of miR-107 observed here was consistent with the results of our previous study [40]. The consistency in different tissues and different stages of cognitive decline of miR-107 makes it a stable biomarker. We combined all five altered miRNAs in a pedictive model: 7.341-0.029×(expression level of miR-134-3p) - 0.150×(expression level of miR-22-5p) –0.604×(expression level of miR-1185-2-3p) - 5.321×(expression level of miR-1909-3p) - 1.372×(expression level of miR-107), which showed high accuracy (81.25%) to discriminate subjects with aMCI from controls; thus, we considered these miRNAs as promising biomarkers.

BACE1 is one of the promising targets of AD therapy; therefore, a better understanding the mechanism by which BACE1 is regulated might help to guide the proper use of BACE1 inhibitors in AD therapy. All five identified miRNAs modulate BACE1 expression and the downstream production of CTF-β in rat hippocampal neurons and human hippocampal neurons, respectively, which might be important in the initiation of AD pathology. These BACE1-associated miRNAs, in addition to being efficient biomarkers for early aMCI prediction, might also be used to predict the progression of aMCI to AD. Further longitudinal studies are needed to confirm their predictive effect. Our study demonstrated that overexpression of five altered miRNAs decrease BACE1 expression by approximately 20–50%. These miRNAs might be potential treatment target to modulate BACE1. Several miRNAs have been identified as regulators of BACE1, suggesting that an extensive network of multiple miRNAs might regulate BACE1 expression in a coordinated manner. The ability of the five selected miRNAs to target BACE1, which is associated with sporadic AD makes these molecules interesting candidates as therapeutics (in the form of miRNA mimics) to reduce brain Aβ levels in vivo. Analysis of sequencing data enabled us to understand the miRNA-target networks and to identify key miRNAs involved in cognitive decline processes.

We believe that the results of the current study provide preliminary clinical and pathological evidence for the inclusion of a panel of plasma-based miRNA biomarkers toward aMCI. However, this plasma miRNA panel might have some limitations. Further research is needed to optimize the assay, validate the findings in unselected and diverse populations, and determine its potential role in the aMCI clinical prognosis. Secondly, the effects of miRNAs on modulation of BACE1 were only explored in vitro. More extensive research is required to validate these results in vivo and to further reveal their particular regulatory mechanism. Last, the univariate and multivariate logistic regression results were not consistent, some miRNAs became insignificant in the multivariate model. We think that the network regulation process of miRNAs is complex, and their regulation intensity is different. In the future, we will conduct in-depth research on the relationship between miRNA regulation and the fine discrimination among different aMCI patients, to guide the follow-up after possible treatment intervention.

Overall, we uncovered a differential expression profile of both previously identified and novel miRNAs to predict AD at the prodromal stage. The plasma levels of all five miRNAs effectively discriminate the prodromal stage of AD from normal controls, suggesting the potential clinical value of combined miRNA analysis. Furthermore, revealing the regulation exerted by these miRNAs might deepen our understanding of the fundamental cause of the imbalance of BACE1 in AD.

Footnotes

ACKNOWLEDGMENTS

This work was supported by the Clinical Research Center, Shanghai Mental Health Center (grant number: CRC2019ZD03, CRC2017ZD02), the National Natural Science Foundation of China (grant numbers: 81571298), the Shanghai Clinical Research Center for Mental Health (grant number: 19MC1911100), the Science and Technology Commission of Shanghai Municipality (grant number: 18411961500), and the Shanghai Health System Excellent Talent Training Program (grant number: 2017BR054).

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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