Circulating miRNA Signatures in Early-Stage Huntington’s Disease

doi:10.21203/rs.3.rs-2440808/v1

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Circulating miRNA Signatures in Early-Stage Huntington’s Disease

https://doi.org/10.21203/rs.3.rs-2440808/v1

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Huntington’s Disease (HD) is a neurodegenerative disorder caused by the expansion of CAG repeats on exon 1 of the huntingtin (htt) gene. This mutation results in the expression of an aberrant protein, mutant HTT, which sets in place a cascade of events that eventually leads to neuronal death within the basal ganglia and cerebral cortex. MicroRNAs (miRNAs) are a class of small non-coding RNAs of 18 - 22 nucleotides long that play important roles in post-transcriptional regulation due to their abilities to interact with the 3'-UTR regions of mRNAs. Though generated in the nucleus, a significant portion of miRNAs are secreted into the plasma as free molecules or in vesicles for intercellular signaling. Those circulating miRNAs may provide a unique opportunity to study important pathophysiological mechanisms in HD in a non-invasive manner due to their resistance to degradation, ease of detection, and their known regulatory roles in response to inflammation and neurodevelopmental disorders. More recent studies have suggested that miRNA could be used in therapeutic applications. In this study, we sought to identify the aberrant expression of specific miRNAs extracted from the plasma of early-stage HD patients.

Clinical Trial Registration number: NCT01937923

circulating miRNA

Huntington’s disease

mutant huntingtin

plasma miRNAs

PCR array

Huntington’s Disease (HD) is an autosomally and dominantly inherited neurodegenerative disorder caused by the expansion of CAG repeats in the huntingtin (htt) gene on chromosome 4 [1]. This mutation causes the expression of mutant HTT (mtHTT) proteins, which are prone to self-aggregating and disrupting cellular processes such as gene regulation, protein degradation, and vesicle transportation, and which eventually leads to neuronal death most prominently within the basal ganglia and cortex [2, 3]. Several studies have focused on developing markers to understand disease mechanisms salient to HD, including onset or progression. That is, although HD is a monogenetic disorder, it displays highly variable clinical expression: onset can vary from 4 to 80 years of age which is explained only in part to the to the length of CAG repeat [4] and progression can be quite variable, lasting anywhere from several years to decades [5]. This suggests that clinical heterogeneity in HD is influenced by factors other than genetic predispositions. The cadre of potential HD biomarkers that have been explored includes inflammatory cytokines [6], mutant HTT proteins [7], and transcriptomic markers such as ANXA1 and ROCK1 [8]; however, all have thus far demonstrated limited utility due in part of their rapid turn-over and high variability [9].

miRNAs are a class of small non-coding RNAs ranging in size between 18–22 nucleotides in length. A major function of miRNAs is the regulation of gene expression, usually via interactions with 3’-UTR regions of other mRNAs. This interaction either inhibits translation or results in the degradation of the target transcript [10]. miRNAs also play important roles in the differentiation, development, and senescence of neurons [11]. Circulating miRNA’s have been widely studied in other human diseases, such as Alzheimer’s Disease [12], Parkinson’s Disease [13] and cancers [14]. Given their availability in blood, stability, resistance to degradation and RNase activities, and particular relevance to gene expression, they hold great promises as non-invasive diagnostic, prognostic, and/or surveillance biomarkers [15][16].

The relevance of altered miRNAs in HD has been suggested previously. For example, miR-10b-5p [17], miR-101 [18], and miR-9 [19] have been reported as altered. However, these studies were performed in post-mortem tissues obtained from patients typically in more advanced stages of disease, making it difficult to decipher if these alterations reflected contributing pathological factors or non-specific responses to neurodegeneration. In order to better understand the potential role of miRNAs to the pathology of HD and to potentially develop them as biomarkers of phenoconversion, disease progression or response to therapy, peripheral readouts are of paramount importance. With this perspective, we measured circulating miRNAs from plasma obtained from early-stage HD patients. Our findings provide potential insights into potential salient mechanisms relevant to HD as well as support for the future development of circulating miRNAs as HD biomarkers.

Study Population

Plasma samples were obtained from 17 patients with early HD (stage I and II Unified Huntington’s Disease Rating Scale Total Functional Scores 9–13 [20], age 23–66, 64.7% male) enrolled in the Pre-cell therapy observational study in early Huntington’s disease (PRE-CELL; UC Davis, California, NCT01937923. All study protocols were approved by the University of California, Davis (UCD) and the Partners Human Research Committees. Plasma samples from age and gender-matched controls (N = 13, age 30–67, 58.3% male) were purchased from Precision for Medicine (Bethesda, Maryland).

Blood Sample Collection and processing

Samples were collected using EDTA containing vacutainers (BD, New Jersey) and centrifuged (2000 rpm) at 4 ºC for 20 minutes. The supernatant was transferred and aliquoted into Nunc Cryogenic tubes and immediately frozen at -80 ºC until the time of processing.

miRNA Isolation

Frozen plasma samples were thawed and centrifuged at 3000 xg for 5 min. RNA isolation was performed using 200 µL of the plasma supernatant, using the miRNeasy Serum/Plasma Advanced Kit (QIAGEN, German) according to the manufacturer's directions, and eluted in 18 µL of nuclease-free water to concentrate the RNA content.

miRNA Profiling with the miScript qPCR System

Extracted miRNA samples were reversely transcribed, pre-amplified using the miScript Single Cell qPCR Kit (QIAGEN, German) and subjected to miScript PCR arrays (QIAGEN, German). A first profiling was performed with a pool of a randomly selected four plasma samples (two samples from each group, respectively) on ~ 2400 miRNA targets using the Human miRNome miScript miRNA PCR Array (QIAGEN, German) to identify detectable circulating miRNA targets. A second profiling was performed with all available plasma samples on detected miRNAs with miScript customized PCR arrays. For this analysis, a cycle threshold (Ct) value of 30 was used to determine detection; samples with fewer than 50% miRNAs detected were removed from the downstream analysis. Quality Control was performed according to manufacturer’s instructions; a triplicate of cel-miR-39-3p was used to adjust for batch effects. RefFinder [21] was used to identify all detectable targets from the PCR array data along with the constants calculated from the global mean [22], which was used for normalization. Fold change (FC) values were calculated with the 2^−∆∆Ct method [23] for the identification of up- and down-regulations, as defined as FC > 1 and FC < 1, respectively.

Statistical Analyses

Descriptive summaries were computed by patient cohort (control and HD). Continuous variables were summarized as the median and interquartile range (IQR; 25–75 percentiles) and categorical variables as frequencies (percentages). Differences in the distributions of categorical and continuous variables were assessed using either the chi-square or the Mann-Whitney test, respectively.

A linear regression model was constructed to quantify the association between each normalized miRNA expression level (2^− x where x denotes the difference between the expression level and the global mean) and diagnostic group (Control, HD). As a sensitivity analysis, similar models were constructed while simultaneously accounting for age and sex. The coefficient corresponding to the diagnostic group, and its associated 95% confidence interval and p-value, was used to summarize each model and denotes the expected difference in expression levels between HD and control subjects. False-discovery rate-adjusted p-values were computed to account for the testing of multiple miRNAs [24]. All analyses were computed using R (4.2.2) and the following add-on libraries (multiMiR [25], clusterProfiler [26], and ggplot2 [27]).

Demographics

The median (interquartile range, IQR) age of the controls and HD subjects were 43 (33–56) and 55 (38–56), respectively. Similarly, 42% (5/12) and 35% (6/17) were female. We were unable to detect age and sex differences by the diagnostic group [Table 1].

Table 1

**Demographics of enrolled subjects.** Categorical variables are summarized as frequencies (proportions) and continuous variables are summarized as median [IQR: 25th, 75th quantile]. P-values are obtained from Fisher Exact tests for categorical variables and Wilcoxon ran sum test for continuous variables.
Variable	Level	Control N = 12	HD N = 17	P
Age		42.50 [32.75,56.25]	55.00 [38.00,56.00]	0.706
TFC Score		- [-,-]*	11.00 [10.00,12.00]
Sex	Male	7 (58.3)	11 (64.7)	1.000
	Female	5 (41.7)	6 (35.3)	1.000
*TFC scores were not collected among the control cohort.

miRNA Profiling with miScript miRNA PCR Array

From the initial screening of ~ 2400 miRNAs available in the miRNome miScript miRNA PCR Array, we were able to detect 525 miRNAs, of these 153 were selected for differential expression analysis based on previous post-mortem brain studies [17, 18]. From these 153 miRNAs, we identified 42 miRNAs that were significantly altered (FDR corrected p < 0.05) in HD plasma samples as compared to control samples, using an unadjusted regression model [Table 2]. When we accounted for age and sex, we identified 46 miRNAs, 29 miRNAs were common between the two models (data not presented, but available from the authors). A majority of circulating miRNA targets (29 miRNAs of the 42, 69.0%) were found to be downregulated in the HD cohort as compared to the control cohort. Due to the lack of demographic differences identified in Table 1, and the desire to not overfit the regression model, miRNAs identified in the unadjusted analysis were used for the downstream analysis.

Table 2

**Significantly altered circulating miRNAs in early-stage HD plasma samples.** Out of 153 miRNAs detected in plasma, 42 were found to be significantly (**FDR adjusted p-value < 0.05**) altered between patients with Huntington’s Disease (HD) and healthy controls. Approximately 69.0% were down-regulated (fold change < 1).
miRNA	-dCt mean (sd)		FDR p-value	Regulation in Plasma
miRNA	HD	Control	FDR p-value	Regulation in Plasma
hsa-miR-199a-5p	-1.77 (1.04)	0.5 (1.03)	< 0.001	DOWN
hsa-miR-323a-3p	-5.3 (0.79)	-2.99 (0.98)	< 0.001	DOWN
hsa-miR-151a-5p	-1.42 (0.97)	0.59 (1.06)	0.001	DOWN
hsa-miR-195-5p	7.43 (1.15)	5.01 (1.56)	0.002	UP
hsa-miR-425-3p	-1.96 (0.64)	-0.59 (0.82)	0.002	DOWN
hsa-miR-486-3p	-1.22 (0.99)	-2.75 (0.55)	0.002	UP
hsa-miR-144-3p	2.13 (1.11)	-0.26 (1.78)	0.004	UP
hsa-miR-363-3p	2.93 (1.07)	1.09 (1.17)	0.004	UP
hsa-miR-374b-5p	-0.29 (1.07)	1.47 (1.03)	0.004	DOWN
hsa-miR-99b-5p	-3.48 (0.85)	-2.12 (0.75)	0.004	DOWN
hsa-miR-107	0.43 (1.4)	-1.63 (1.1)	0.005	UP
hsa-miR-652-3p	-0.03 (0.55)	1 (0.73)	0.005	DOWN
hsa-miR-16-5p	8.34 (1.36)	6.14 (1.64)	0.007	UP
hsa-miR-28-5p	-4.61 (0.99)	-3.1 (1.04)	0.007	DOWN
hsa-miR-369-3p	-4.25 (1.17)	-2.1 (1.67)	0.007	DOWN
hsa-miR-431-5p	-3.49 (0.9)	-1.99 (1.12)	0.007	DOWN
hsa-miR-451a	9.33 (1.36)	7.12 (1.63)	0.007	UP
hsa-miR-194-5p	0.39 (0.94)	-0.85 (0.74)	0.009	UP
hsa-miR-409-3p	-2.88 (1.35)	-1 (1.29)	0.009	DOWN
hsa-miR-543	-3.95 (1.45)	-2.05 (1.12)	0.009	DOWN
hsa-miR-224-5p	-3.01 (1.14)	-1.4 (1.17)	0.011	DOWN
hsa-miR-330-3p	-5.02 (0.94)	-3.86 (0.54)	0.012	DOWN
hsa-miR-25-3p	5.41 (1.34)	3.52 (1.45)	0.013	UP
hsa-miR-148b-3p	-0.77 (0.86)	0.51 (1.04)	0.015	DOWN
hsa-miR-744-5p	-3.93 (1.52)	-1.73 (1.8)	0.015	DOWN
hsa-miR-152-3p	-2.74 (0.88)	-1.55 (0.9)	0.016	DOWN
hsa-miR-625-3p	-2.93 (0.97)	-1.54 (1.27)	0.021	DOWN
hsa-miR-338-3p	-3.31 (0.88)	-2.26 (0.83)	0.024	DOWN
hsa-miR-93-5p	4.46 (1.25)	2.66 (1.77)	0.025	UP
hsa-miR-185-5p	1.61 (1.24)	0.01 (1.48)	0.026	UP
hsa-miR-485-3p	-4 (1.6)	-2.37 (0.89)	0.026	DOWN
hsa-miR-584-5p	-1.85 (1.07)	-0.63 (0.9)	0.026	DOWN
hsa-miR-628-5p	-5.5 (0.56)	-4.61 (0.87)	0.026	DOWN
hsa-miR-106b-5p	3.29 (0.94)	1.81 (1.68)	0.031	UP
hsa-miR-142-3p	0.27 (1.09)	1.62 (1.25)	0.031	DOWN
hsa-miR-486-5p	3.46 (1.13)	1.83 (1.77)	0.032	UP
hsa-miR-101-3p	2.49 (0.85)	1.44 (1.03)	0.033	UP
hsa-miR-221-3p	1.91 (1.37)	3.48 (1.34)	0.033	DOWN
hsa-miR-146a-5p	1.97 (1.83)	3.8 (1.27)	0.037	DOWN
hsa-miR-127-3p	-1.24 (1.1)	-0.1 (0.95)	0.038	DOWN
hsa-miR-1260a	-1.38 (1.36)	-0.01 (0.99)	0.039	DOWN
hsa-miR-19b-3p	4.57 (1.2)	3.34 (1)	0.039	UP
hsa-miR-30b-5p	0.65 (0.86)	1.75 (1.17)	0.043	DOWN
hsa-miR-340-5p	-1.96 (1.05)	-0.5 (1.69)	0.045	DOWN
hsa-miR-134-5p	-4.84 (1.28)	-2.39 (3.19)	0.046	DOWN
hsa-miR-193a-5p	-1.95 (1.26)	-3.17 (0.94)	0.046	UP
hsa-miR-20a-5p	5.6 (1.2)	4.07 (1.74)	0.046	UP
hsa-miR-92a-3p	5.83 (1.15)	4.41 (1.6)	0.048	UP

KEGG Pathway Enrichment Analysis

We identified four miRNAs (hsa-miR-16-5p, -106b-5p, -107, -195-5p) that could directly interact with HTT mRNA, and seven miRNAs associated (p < 0.05) with the Huntington’s Disease pathway (hsa-miR-16-5p, -106b-5p, -93-5p, -338-3p, -652-3p, -28-5p, -744-5p). Thirty-six miRNAs were associated with at least one of the KEGG pathways relevant to HD [28–33], [Fig. 1].

Notably, the majority of identified miRNAs were associated with pathways involved in cellular metabolism, proliferation and survival, such as cellular senescence (29 miRNAs, 69%), MAPK signaling (27 miRNAs, 64.3% miRNAs), PI3K-Akt signaling (26 miRNAs, 61.9%), cell cycle (25 miRNAs, 59.5%), hippo signaling (24 miRNAs, 57.1%), AMPK signaling (24 miRNAs, 57.1%), p53 signaling (23 miRNAs, 54.8%), neurotrophin signaling (23 miRNAs, 54.8%), mTOR signaling (23 miRNAs, 54.8%) and ErBb signaling pathways (22 miRNAs, 52.4%).

We conducted a targeted approach to determine the expression of circulating miRNAs collected from plasma from patients in Stage I or II Huntington’s Disease, focusing on miRNA’s previously reported as demonstrating altered in expression in post-mortem studies. We found that at least a third of the miRNAs identified are known to be involved in mechanisms relevant to HD including: apoptosis, cellular senescence, ubiquitin mediated proteolysis and signaling pathways such as Neurotrophin, p53, ErbB, Notch, MAPK, AMPK, PI3K-Akt, mTOR, Wnt and Hippo [28–35]. In addition, several miRNAs identified are known to interact directly with the htt gene, including miR-16-5p and miR-107 [36, 37]. Particularly salient to HD, miR-16-5p is also known to directly interact with brain-derived neurotrophic factor (BDNF) [38, 39]. BDNF is regulated by HTT, and the altered structure of the mutant HTT protein in HD patients impairs the expression of BDNF, which may further contribute leads to neuronal cell death [40]. miR-16-5 has also been identified as a tumor suppressor, given its ability to inhibit cell proliferation [41]; its overexpression in neural cells has been found to lead to decreased neurite length and branching and to induce neuronal apoptosis by targeting BCL-2 and BACE1 [42, 43]. Abnormalities in the growth and plasticity of dendrites have been reported in HD [44].

Although its role in HD remains unclear, miR-107 is a post-transcriptional regulator of the Beta-Secretase 1 (BACE1) in Alzheimer’s Disease (AD) [45]. This miRNA has been associated with oxidative stress [46], an important pathophysiological mechanism both in HD as well as in other neurodegenerative disorder, suggesting that this may be shared mechanism.

Other miRNAs that were differentially expressed in HD samples are known to play important roles in metabolism. For example, miR-144-3p demonstrated a five-fold higher expression in the HD cohort. This miRNA is known for its role in regulating mitochondrial function [47]; its over-expression may represent a response to elevated ROS levels in an attempt to restore and maintain the viability of mitochondria [48]. It may be especially relevant as mitochondrial dysfunction occurs early in HD, including during the motor pre-symptomatic period, and may contribute to early neuronal dysfunction in HD [49].

The expression of some of the miRNAs in plasma differed from what has been published previously using post-mortem brain tissue [17, 18]. For example, miR-199a-5p, miR-151a-5p, and miR-425-3p. This is not entirely unexpected as the expression of miRNA is expected to be dynamic and likely reflect disease stage-dependency. Nevertheless, the alterations in miRNAs that reflect known mechanisms salient in HD, even in a relatively small study, support the relevance and significance of our work. Our findings support the need for future studies to better understand how dynamic changes may be related to disease severity, disease onset, and to disease progression.

It is also worth noting some of the technical limitations For example, the normalization of miRNA expressions in biofluids can be somewhat limited by the difficulties finding good housekeeping reference genes [50]. Similarly, KEGG analyses provide only indirect evidence to infer functionality or potential effects on differential gene expression; that is, all coding-RNA targets in the analysis are selected based on previous validation studies, and these results can largely depend on how well-studied a miRNA is. Therefore, profiling of the downstream coding-RNA targets may be imperative to further illustrate the functionalities of those identified miRNAs.

In summary, our findings support the presence of miRNA targets in plasma that reflect both known and potential new pathological mechanisms relevant to HD and that circulating miRNAs hold great potential for development as non-invasive HD biomarkers.

Ethics approval:

All study protocols were approved by the University of California, Davis (UCD) and the Partners Human Research Committees. This study was performed in line with the principles of the Declaration of Helskinski.

Consent to participate:

Informed consent was obtained from all individual participants included in the study.

Consent for publication:

No identifying information is included in this manuscript. Participants provided informed consent for publication of findings resulting from their participation/this work.

Data Availability:

The datasets generated for the current study are available from the corresponding author on reasonable request.

Competing interests:

The authors have no relevant financial or non-financial interests to disclose.

Funding: This work was supported by the National Institutes of Health (NS106384 and NS114562) and a grant from the California Institute for Regenerative Medicine (DR2-05415).

Author Contributions:

Yiran Tao: sample and data analysis, writing and editing drafts, study conception and design, approval of final version of draft

Nathaniel Mercaldo: statistical analysis, editing drafts, study conception and design, approval of final version of draft

Vicki Wheelock: material acquisition, data collection, critical review, editing drafts, study conception and design, approval of final version of draft

Alexandra Duffy: material acquisition, data collection, critical review, editing drafts, study conception and design, approval of final version of draft

Joshua Ashok: material acquisition, data collection, critical review, editing drafts, study conception and design, approval of final version of draft

H. Diana Rosas: writing and editing drafts, study conception and design, critical review, funding, approval of final version of draft

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Circulating miRNA Signatures in Early-Stage Huntington’s Disease

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Version 1

Abstract

Figures

Introduction

Materials And Methods

Study Population

Blood Sample Collection and processing

miRNA Isolation

miRNA Profiling with the miScript qPCR System

Statistical Analyses

Results

Demographics

miRNA Profiling with miScript miRNA PCR Array

KEGG Pathway Enrichment Analysis

Discussion

Conclusion

Declarations

References

Status:

Version 1