Abstract
Objective
To determine whether exosomal microRNAs (miRNAs) in CSF of patients with FTD can serve as diagnostic biomarkers, we assessed miRNA expression in the Genetic FTD Initiative (GENFI) cohort and in sporadic FTD.
Methods
GENFI participants were either carriers of a pathogenic mutation in progranulin (GRN), chromosome 9 open reading frame 72 (C9orf72) or microtubule-associated protein tau (MAPT), or were at risk of carrying a mutation because a first-degree relative was a known symptomatic mutation carrier. Exosomes were isolated from CSF of 23 pre-symptomatic and 15 symptomatic mutation carriers, and 11 healthy non-mutation carriers. Expression of 752 miRNAs was measured using qPCR arrays and validated by qPCR using individual primers. MiRNAs found differentially expressed in symptomatic compared to pre-symptomatic mutation carriers were further evaluated in a cohort of 17 patients with sporadic FTD, 13 patients with sporadic Alzheimer’s disease (AD), and 10 healthy controls (HCs) of similar age.
Results
In the GENFI cohort, miR-204-5p and miR-632 were significantly decreased in symptomatic compared to pre-symptomatic mutation carriers. Decrease of miR-204-5p and miR-632 revealed receiver operator characteristics with an area of 0.89 [90% CI: 0.79–0.98] and 0.81 [90% CI: 0.68–0.93], respectively, and when combined an area of 0.93 [90% CI: 0.87–0.99]. In sporadic FTD, only miR-632 was significantly decreased compared to AD and HCs. Decrease of miR-632 revealed an area of 0.90 [90% CI: 0.81–0.98].
Conclusions
Exosomal miR-204-5p and miR-632 have potential as diagnostic biomarkers for genetic FTD and miR-632 also for sporadic FTD.
Introduction
Frontotemporal dementia (FTD) is now recognized as the most common cause of early onset dementia in people under the age of 60 years [1]. FTD usually presents with either behavioural or language impairment. The pathogenic mechanisms resulting in FTD remain largely unknown but current knowledge suggests that genetic, epigenetic, and environmental factors contribute to disease development [2]. Approximately 40% of FTD patients have a positive family history of dementia [3] and about 25% of FTD patients have an identified genetic form of the disease [1]. The vast majority of genetic FTD is inherited in an autosomal dominant pattern caused by mutations in one of three genes: chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN), or microtubule-associated protein tau (MAPT). These genes not only provide an opportunity to study the disease in its pre-symptomatic phase but also offer great hope for elucidating the pathogenic mechanisms that cause FTD. There is some mounting evidence that alterations in microRNA (miRNAs) may occur in FTD [4–6]. MiRNAs are small, noncoding RNAs that regulate gene expression through post-transcriptional silencing of target mRNAs [7]. The same miRNA may regulate hundreds of target mRNAs affecting complex disease pathways [8]. MiRNAs are stable in body fluids and can be enriched in extracellular vesicles termed exosomes. These vesicles were thought to be a means for cells to discard unnecessary molecules into the extracellular space [9], but more recent studies have shown that cells can transfer proteins, lipids, DNA, RNA and miRNA to other cells via exosomes [10]. Exosomes display different miRNA profiles compared to serum and cells, suggesting that a specific selection of exosomal miRNAs provide signals to regulate pathways in recipient cells [11]. This inter-cellular transfer can influence a multitude of biological processes relevant to the nervous system such as neuronal survival, neurite outgrowth, and synaptic plasticity [12–15]. Disease-relevant miRNAs may be enriched within exosomes [16] and since miRNA expression can vary in different disease states, exosomal miRNAs are attractive targets for biomarker profiling [17 18].
Genetic FTD is a rare condition and single groups have only been able to study small numbers of patients. Through the Genetic Frontotemporal dementia Initiative (GENFI), we obtained CSF from individuals who were either symptomatic or pre-symptomatic carriers of a known pathogenic mutation in GRN, MAPT, or C9orf72, or who were non-affected first-degree relatives of a known symptomatic carrier (healthy non-mutation carriers). We characterized miRNA expression profiles and found miR-204-5p and miR-632 significantly decreased in symptomatic compared to pre-symptomatic mutation carriers, suggesting low miR-204-5p and miR-632 as potential diagnostic biomarkers. In a separate cohort, we found miR-632 significantly decreased in sporadic FTD compared to sporadic Alzheimer’s disease (AD) and healthy controls (HCs), highlighting its potential as a diagnostic biomarker for sporadic FTD.
Methods
Ethics statements, sample collection and clinical data
Written informed consent and local research ethics boards’ approval was obtained. Six GENFI centres contributed CSF (Karolinska Institute, Department of Neurobiology, Stockholm, Sweden; Erasmus Medical Center, Department of Neurology, Rotterdam, the Netherlands; University College London, Dementia Research Centre, London, England; Université Laval, Département des Sciences Neurologiques, Quebec City, Canada; University of Milan, Centro Dino Ferrari, Fondazione Ca’ Granda IRCCS Ospedale Policlinico, Milan, Italy; University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Canada). The GENFI cohort consisted of 49 subjects: 38 mutation carriers (22 GRN, 11 C9orf72, 5 MAPT) and 11 first-degree relatives who tested negative for a mutation in the gene that had been found mutated in their affected first-degree relative (healthy non-mutation carriers). 23 mutation carriers were pre-symptomatic and 15 mutation carriers were symptomatic. The clinical presentation was bvFTD (n = 12), non-fluent variant PPA (nfvPPA) (n=1), semantic variant svPPA (n=1), or dementia not otherwise specified (D-NOS) (n=1) (Supplementary table 1). Mini-Mental State Examination (MMSE® [19]) was carried out in all individuals. A cohort of sporadic FTD, sporadic AD, and healthy controls (HCs) was recruited at the University Health Network Memory Clinic, Toronto and the University of California San Francisco Memory and Aging Center. This sporadic disease cohort consisted of bvFTD (n=7), bvFTD/ALS (n=4), svPPA (n=3), nfvPPA/ALS (n=1), svPPA/ALS (n=1), and nfvPPA (n=1), sporadic AD (n=13), and HCs (n=10) (Supplementary table 2). BvFTD met the Rascovsky diagnostic criteria [20], PPA met the Gorno-Tempini diagnostic criteria [21], ALS met the El Escorial diagnostic criteria [22], and AD met the McKhann diagnostic criteria [23].
Samples for miRNA detection
Lumbar puncture was performed with a 20- or 24-gauge spinal needle and fluid was collected in polypropylene tubes according to local standards. Most sites follow ADNI procedures manual (http://www.adni-info.org/). CSF was stored in aliquots at −80°C until use.
Real-time polymerase chain reaction
For the genetic cohort (n=49), 500 μl of each CSF sample was thawed and centrifuged at 10,000×g for 5 min to pellet any debris. To isolate exosomes, the supernatant was transferred to a new reaction vial and 200 μl precipitation buffer (miRCURY™ Exosome Isolation Kit, Exiqon, Copenhagen, Denmark) was mixed with the supernatant. The mix was incubated at 4°C for 60 min and spun for 30 min at 10,000×g at 20°C. The supernatant was discarded and lysis buffer containing synthetic spike-ins (UniSp2, UniSp4, and UniSp5) was added to the pellet. RNA was extracted using spin column chromatography (miRCURY™ RNA Isolation Kit, Exiqon). To obtain cDNA, each RNA sample was incubated for 60 min at 42°C in the presence of Reaction Buffer, nuclease-free water, Enzyme Mix, and synthesis RNA spike-in mix (cel-miR-39-3p and UniSp6) (miRCURY™ RNA Isolation Kit, Exiqon). Reverse transcriptase (RT) was heat-inactivated for 5 min at 95°C and the cDNA samples were immediately stored at −80°C. Immediately prior to real-time polymerase chain reaction (RT-PCR), each cDNA sample was thawed and added to a Master Mix working-solution containing SYBR® Green (Exiqon). 10 μl of this mix was added to each of the 768 wells of the ready-to-use Human microRNA panel I+II, V4.M (Exiqon). Panel I+II contained a total of 752 individual miRNA primer sets plus control assays. Plates were spun at 1500×g for 1 min. Plates were run on the Applied Biosystems 7900HT Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA USA). Only miRNAs detected with Ct < 40 were included in the analysis. After normalization to cel-mir-39-3p, as previously described by Freischmidt et al. [24], Ct values were converted to linear scale relative to the control group (healthy non-mutation carriers) and Log2 conversion was applied (Exiqon Data Analysis Guide for miRCURY™ GenEx software v 3). For the sporadic disease cohort (n=40), CSF was thawed and cDNA was obtained from the exosomal miRNA content as described above. For technical validation of the results obtained in the GENFI cohort and for the sporadic disease cohort, a Master Mix working-solution containing either hsa-204-5p or hsa-miR-632 PCR primer set (both Exiqon) and SYBR® Green (Exiqon) was prepared. Master mix and samples were added to 96-well plates and run on the Applied Biosystems Step One Plus Real-Time PCR System (Thermo Fisher Scientific). MiRNA expression changes were calculated relative to healthy controls using the 2−ΔΔCt method [25] with ΔCt = Ct miRNA– Ct reference and ΔΔCt = ΔCt patient or mutation carrier - ΔCt control pool. UniSp6 spike-in was used as a reference for normalization. RNA and DNA spike-ins showed steady levels across samples indicating accurate RT reaction and PCR. Applied Biosystems SDS 2.2.2. software (Thermo Fisher Scientific) and GenEx 6 (MultiD Analyses, Göteborg, Sweden) were used for miRNA expression processing prior to statistical analysis.
Statistical analysis
Welch's t-test was performed and corrected for multiple comparisons using the Holm-Sidak method when relative miRNAs expression changes passed D'Agostino & Pearson normality test. When relative miRNA expression changes calculated as 2−ΔΔCt were not normally distributed, Mann-Whitney U test was performed. Fisher's exact test was used to detect differences in miRNA detection frequency. Correlations between clinical data and miRNA expression were calculated using Spearmans’s rank order correlation. Receiver operating characteristics (ROC) curves and the area under the curve (AUC) were established to evaluate the diagnostic value of miRNA expression changes. For cross-validation, we used 50% of the dataset to train linear models and 50% to validate the results. We then calculated Pearson’s bivariate correlation. Statistical analysis was performed using Graph Pad Prism v. 7.01 (La Jolla, CA, USA). IBM SPSS v 24.0 (Armonk, New York, U.S.) was used for logistic regression, ROC calculations, and cross-validation. P values < 0.05 were considered significant. When the 90% confidence interval (CI) included 1, p values were reported as p trend.
Target prediction and Gene Ontology analysis
Targets of each significantly different miRNA were predicted using miRWalk 2.0 which combines information from 12 existing miRNA-target prediction programs (DIANA-microTv4.0, DIANA-microT-CDS, miRanda-rel2010, mirBridge, miRDB4.0, miRmap, miRNAMap, doRiNA/PicTar2, PITA, RNA22v2, RNAhybrid2.1 and Targetscan6.2) [26] (Supplementary table 3). Only experimentally validated mRNAs were included in further analyses. The KEGG database was used to identify target mRNAs in biological pathways (c2.cp.kegg.v5.1.symbols.gmt) [27]. To assess whether target mRNAs were previously found highly expressed in the human frontal and temporal lobes relative to the entire human brain, we searched the Allen Brain Atlas (http://www.brain-map.org) [28]. The original search can be reproduced at http://human.brain-map.org/microarray/search/show?domain1=4005&domain2=4009,4132&selected_donors=9861,10021,12876,14380,15496,15697&search_type=differential. FunRich version 3.0 was used to generate Venn diagrams of validated targets found with miRWalk 2.0, the KEGG pathway database, and the Allen Brain Atlas.
Results
Exosomal miR-204-5p and miR-632 expression is low in genetic FTD
We reasoned that a clinically useful diagnostic biomarker would be detectable in healthy individuals and altered in disease. We found two miRNAs (miR-204-5p and miR-632) in all exosomal CSF samples of healthy non-mutation carriers and an additional six miRNAs in at least 70% (miR-605-5p, let-7a-5p, miR-548a-3p, miR-23b-3p, miR-125b-5p, and miR-937-3p) (Supplementary figure 1). MiRNA expression in exosomal CSF samples from healthy non-mutation carriers was used to obtain baseline values for each miRNA. MiRNA expression relative to this baseline was compared between pre-symptomatic and symptomatic mutation carriers. No significant expression changes were found between healthy non-mutation carriers and pre-symptomatic mutation carriers. Relative expression of both miR-204-5p and miR-632 was significantly lower in symptomatic compared to pre-symptomatic mutation carriers (p<0.005 and p<0.05) (Figure 1 A). Relative expression of miR-204-5p was significantly lower in symptomatic mutation carriers with either GRN or C9orf72 mutations (p<0.05 and p<0.05) (Figure 1 B and C). Relative expression of miR-632 was significantly lower in symptomatic compared to pre-symptomatic mutation carriers in the GRN group (p<0.05), but not in the C9orf72 group (Figure 1 B and C). With only one symptomatic mutation carrier in the MAPT group, statistical analysis was not possible. Most symptomatic mutation carriers had been diagnosed with bvFTD (80%) (Supplementary table 1). Relative expression of both miR-204-5p and miR-632 was still significantly lower when bvFTD only was compared to pre-symptomatic mutation carriers (p<0.005 and p<0.05) (Figure 1 D). Technical validation using individual primer sets showed decrease of miR-204-5p and miR-632 similar to the results obtained with the miRNA panels, when relative transcript number was compared to the pooled sample of healthy non-mutation carriers (Supplementary figure 2A-D) Raw Ct values of both miRNAs were significantly higher in symptomatic mutation carriers, indicating decreased expression in symptomatic individuals, independent of normalization (Supplementary figure 2 E). Only one individual was diagnosed with either svPPA, nfvPPA, or D-NOS, therefore, statistical analysis of these clinical phenotypes was not possible. Age was significantly different between groups with symptomatic mutations carriers being older than pre-symptomatic mutation carriers (all mutation carriers p<0.0001, GRN mutation carriers p<0.005, C9orf72 mutation carriers p<0.05, and bvFTD p<0.0001). Notably, there was no correlation between miR-204-5p expression and age in healthy-non mutation carriers and healthy controls (HCs) and there was a modest increase of miR-632 expression with age in these healthy individuals (p<0.05) (Supplementary figure 3). When we analyzed females and males separately, we found a decrease of 204-5p and miR-632 in symptomatic compared to pre-symptomatic female mutation carriers (n=25), before correcting for multiple comparisons (p<0.005 and p<0.05). The numbers of male mutation carriers was smaller (n=13) and comparing miR-204-5p and miR-632 between symptomatic and pre-symptomatic male mutation carriers only revealed a trend towards significances, before correction for multiple comparisons (p<0.06 and p<0.07) (data not shown). We did not observe a significant change of miR-204-5p or miR-632 relative to disease duration or MMSE ® results in healthy non-mutation carriers, pre-symptomatic, or symptomatic individuals.
Figure 1. Relative expression of miR-204-5p and miR-632 is lower in symptomatic compared to pre-symptomatic mutation carriers.
MiRNA expression was calculated relative to that of healthy non-mutation carriers. Data from individuals with any of the three mutations were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (A). Only data from individuals with a GRN mutation were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (B). Only data from individuals with a C9orf72 mutation were grouped and expression of miR-204-5p was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (C). Only data from individuals with any of the three mutations and the bvFTD phenotype were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (D). Welch’s t-tests were corrected for multiple comparisons using the Holm-Sidak method, *p<0.05, *** p<0.005. Mean and standard deviation of mean are shown.
Low exosomal miR-204-5p and miR-632 expression distinguishes symptomatic from pre-symptomatic individuals
To assess whether the changes in miR-204-5p and miR-632 expression can distinguish symptomatic from pre-symptomatic individuals, we calculated receiver operating characteristics (ROC). We found that a decrease of miR-204-5p and miR-632 discriminated well between pre-symptomatic and symptomatic individuals. The area under the curve (AUC) for miR-204-5p was 0.89 [90% confidence interval (CI): 0.79–0.98] (p<0.005) and the AUC for miR-632 was 0.81 [90% CI: 0.68–0.93] (p<0.005). Combination of miR-204-5p and miR-632 narrowed the CI and increased the AUC to 0.93 [90% CI of 0.87–0.99] (p <0.05) (Figure 2 A). In the GRN group, miR-632 discriminated well between pre-symptomatic and symptomatic individuals with an AUC of 0.85 [90% CI of 0.71–0.99] (p<0.01) and there was a trend for miR-204-5p and the combination of miR-204-5p and miR-632 (Figure 2 B). In the C9orf72 group, only three individuals were symptomatic and ROC analysis did not yield significant results. For patients with bvFTD, miR-204-5p and miR-632 discriminated well between pre-symptomatic and symptomatic individuals with AUCs of 0.91 [90% confidence interval (CI): 0.82–0.99] and 0.83 [90% confidence interval (CI): 0.71–0.95] (both p <0.005) and there was a trend for the combination of miR-204-5p and miR-632 (Figure 2 C). In a cross-validation analysis, low miR-204-5p correlated significantly with symptomatic status (Pearson correlation r = 0.636 and p < 0.05 in both the training and validation dataset), while low miR-632 did not significantly correlate with symptomatic status in our model.
Figure 2. ROC curve analysis discriminates symptomatic from pre-symptomatic mutation carriers.
MiR-204-5p and miR-632 expression can discriminate between pre-symptomatic and symptomatic individuals based on ROC. 90% CI reported in brackets. Dashed grey lines represent miR-204-5p, dotted black lines represent miR-632, and solid black lines represent the combination of miR-204-5p and miR-632 determined by logistic regression. All mutation carriers (A), GRN mutation carriers (B), and bvFTD phenotype (C) were analyzed separately, *p<0.05, ** p<0.01, *** p<0.005, # p trend CI includes 1.0.
Specific exosomal miRNAs are detected less or more commonly in FTD
We hypothesized that certain miRNAs would not be decreased or increased but undetectable in either health or disease. We compared symptomatic mutation carriers (symptomatic) with healthy non-mutation carriers and pre-symptomatic mutation carriers (healthy) to assess for differences between disease and health, regardless of mutation status. Comparing the frequency of detected miRNAs between symptomatic and healthy participants, we found miR-23b-3p, miR-326, miR-877-5p, miR-892a less commonly (p<0.05) and miR-708-3p more commonly (p<0.01) in symptomatic compared to healthy participants (Table 1). When we compared pre-symptomatic to symptomatic mutation carriers, we found miR-30b-5p and miR-373-3p less commonly in the GRN group (p<0.05) (Table 1). No significant differences were found between pre-symptomatic and symptomatic carriers of C9orf72 or MAPT mutations.
Table 1. Certain miRNAs were less frequently detected in a subgroup of study participants.
The detection frequency of miRNAs was compared between healthy non-mutation carriers and pre-symptomatic mutation carriers (healthy) and symptomatic mutation carriers (symptomatic) using Fisher’s exact test.
| miRNA | group | healthy | symptomatic | p-value | comment | ||
|---|---|---|---|---|---|---|---|
| detectable | undetectable | detectable | undetectable | ||||
|
| |||||||
| miR-23b-3p | all participants (n=49) | 20 | 14 | 3 | 12 | p<0.05 | less common in symptomatic |
|
| |||||||
| miR-326 | all participants (n=49) | 8 | 26 | 0 | 15 | p<0.05 | less common in symptomatic |
|
| |||||||
| miR-877-5p | all participants (n=49) | 14 | 20 | 1 | 14 | p<0.05 | less common in symptomatic |
|
| |||||||
| miR-892a | all participants (n=49) | 8 | 26 | 0 | 15 | p<0.05 | less common in symptomatic |
|
| |||||||
| miR-708-3p | all participants (n=49) | 21 | 13 | 15 | 0 | p<0.01 | more common in symptomatic |
|
| |||||||
| miR-30b-5p | GRN mutation carriers (n=22) | 7 | 4 | 1 | 10 | p<0.05 | less common in symptomatic |
|
| |||||||
| miR-373-3p | GRN mutation carriers (n=22) | 8 | 3 | 2 | 9 | p<0.05 | less common in symptomatic |
MiR-23b-3p, miR-326, miR-877-5p, miR-892a were detected less commonly and miR-708-3p more commonly in symptomatic compared to pre-symptomatic mutation carriers and healthy non-mutation carriers. The detection frequency of miRNAs was compared between pre-symptomatic mutation carriers and symptomatic mutation carriers using Fisher’s exact test. MiR-30b-5p and miR-373-3p were detected less commonly in symptomatic compared to pre-symptomatic GRN mutation carriers, * p<0.05, ** p<0.01.
Expression of exosomal miR-632 is lower in sporadic FTD compared to sporadic AD and HCs
We next sought to validate miR-204-5p and miR-632 as biomarker candidates in a cohort of sporadic FTD. We observed no significant decrease of miR-204-5p expression in sporadic FTD compared to sporadic AD or HCs of similar age (Figure 3 A, left); however, mir-632 was significantly decreased in sporadic FTD compared to HCs or AD patients (p<0.005) (Figure 3 A, right). There was no significant difference between FTD phenotypes (bvFTD, bvFTD/ALS, svPPA, nfvPPA/ALS, svPPA/ALS, nfvPPA). To evaluate the diagnostic value of miR-632 in differentiating sporadic FTD from AD and HCs, we constructed ROC curves (Figure 3 B). When FTD was compared to all non-FTD (HC & AD), the AUC was 0.90 [90% CI: 0.81–0.98] (p< 0.005). There was a trend for AUC to distinguish FTD from HC or AD separately (Figure 3 B). In a cross-validation analysis, low miR-632 correlated significantly with a diagnosis of FTD (Pearson correlation r = 0.578 and p < 0.05 in both the training and validation dataset).
Figure 3. Relative expression of miR-632 is lower in sporadic FTD compared to sporadic AD and HCs.
MiRNA expression was calculated relative to that of HCs using the 2−ΔΔCt method. Expression values of HCs, sporadic AD, and sporadic FTD were compared. Expression of miR-204-5p was similar between groups (A, left) and expression of miR-632 was found significantly lower in FTD using Welch’s t-tests (A, right). MiR-632 expression can discriminate between FTD and non-FTD (HC & AD) based on ROC. 90% CI reported in brackets. Dashed grey line represents HC vs FTD, dotted black line represents AD vs. FTD, and solid black line represents the combination of HC & AD determined by logistic regression (B), ** p<0.01, *** p<0.005, # p trend CI includes 1.0. Mean and standard deviation of mean are shown.
Decrease or loss of miRNA may result in disease-relevant pathway activation
Since the main function of miRNAs is silencing of mRNA, we identified mRNA targeted by miRNAs we found decreased (miR-204-5p and miR-632), less commonly in FTD (miR-23b-3p, miR-326, miR-877-5p, miR-892a, miR-30b-5p, and miR-373-3p), or more commonly in FTD (miR-708-3p) (Supplementary table 3). We found 375 mRNAs targeted by miR-204-5p and 38 mRNAs targeted by miR-632, including three mRNAs targeted by both miRNAs (HRK, KNTC1, and POU2F1) (Figure 4 A). When we compared this group of target mRNAs with mRNAs enriched in the human frontal and temporal lobes (Allen Institute, http://www.brain-map.org) [28], we found HRK, a central mediator of apoptosis [29], to be a potential target of both miR-204-5p and miR-632 (Figure 4 A). Wnt signalling has been implicated as a central disease pathway in FTD with GRN mutations [30 31]. One of the mRNAs targeted by miR-204-5p (FZD8) in the wnt signalling pathway was highly expressed in the human frontal and temporal lobes. Each of the miRNAs we found decreased (miR-204-5p and miR-632) or less frequently in FTD (miR-23b-3p, miR-326, miR-877-5p, miR-892a, miR-30b-5p, and miR-373-3p) targets several mRNA enriched in the human frontal and temporal lobes (Figure 4 B). Interestingly, targets of miR-204-5p and of the two miRNAs less frequent detected in symptomatic GRN mutation carriers (miR-30b-5p and miR-373-3p) were relatively enriched in the frontal and temporal lobes (27, 13, and 9 targets). In addition to wnt signalling, RNA targets of exosomal miRNAs were found in apoptosis, MAPK signalling, endocytosis, notch signalling, and neurotrophin signalling (Figure 4 B).
Figure 4. Venn diagrams of miRNA targets.
Overlap of mRNA targeted by miRNAs found decreased in symptomatic compared to non-symptomatic mutation carriers (miR-204-5p and miR-632), mRNAs enriched in the human frontal and temporal lobes (frontotemporal), and mRNAs implicated in wnt signalling (A). Overlap of mRNA targeted by miRNAs less frequently detectable in FTD (miR-23b-3p, miR-326, miR-877-5p, miR-892a, miR-30b-5p, and miR-373-3p), more frequently detected in FTD (miR-708-3p), mRNAs enriched in the human frontal and temporal lobes, and mRNAs implicated in wnt signalling, apoptosis, MAPK signalling, endocytosis, notch signalling, or neurotrophin signalling (B). Number of validated targets within a pathway are shown in grey cells and % of targets found in each pathway are shown in brackets below.
Discussion
Discovery of biomarkers for FTD would result in more accurate diagnoses and facilitate early and specific treatment efforts. Previous studies indicate altered expression of specific miRNAs in the brains of patients affected by neurodegenerative diseases including FTD, AD, Parkinson's disease, and Huntington’s disease [5 32 33]. Galimberti et al. measured miRNAs in both serum and CSF and found both miR-125b and miR-26b significantly decreased in AD [34]. More recently, Sørensen et al. found let-7i-5p and miR-15a-5p increased and miR-29c-3p decreased in CSF samples from patients with AD [35]. Since disease-relevant miRNAs may be enriched within exosomes [17 18], we opted to evaluate exosomal miRNA. We found significantly lower expression of miR-204-5p and miR-632 in symptomatic compared to pre-symptomatic mutation carriers in the genetic FTD cohort. While the C9orf72 group followed this trend, most data supporting our conclusions come from symptomatic GRN mutation carriers and individuals diagnosed with bvFTD.
In the sporadic disease cohort, miR-204-5p expression was not significantly different in FTD compared to AD and HCs, suggesting that genetic factors influence miR-204-5p expression. On the other hand, miR-632 was significantly decreased in sporadic FTD, underlining its potential as a diagnostic biomarker candidate for both genetic and sporadic FTD. ROC discriminated well between FTD and non-FTD (HC & AD). We appreciate that the frequency distribution of FTD, AD, and healthy controls in our sample was not necessarily representative and that the true sensitivity and specificity of the test may be lower in a typical clinical setting.
Using in silico analysis, we found HRK to be a potential target of both miR-204-5p and miR-632 in the human frontal and temporal lobes. HRK encodes for the apoptosis activator, HARAKIRI [29]. Since the main function of miRNAs is silencing of mRNA, low miR-204-5p and miR-632 could result in pathologically increased HRK and apoptosis leading to degenerative changes within the frontal and temporal lobes of FTD patients. Wnt signalling has been implicated in FTD with GRN mutations [30 31] and targeting the wnt signalling pathway may emerge as a future therapeutic [36]. In addition to apoptosis and wnt signalling, mRNA targets were found in other biological pathways that have been linked with neurodegeneration and/or FTD such as MAPK signalling [37], endocytosis [38 39], notch signalling [40], and neurotrophin signalling [41 42].
In summary, we showed exosomal miR-204-5p and miR-632 to have potential as diagnostic biomarkers for genetic FTD and miR-632 also for sporadic FTD. Through in silico target prediction and disease pathway analysis, we found some of these miRNAs to target mRNAs involved in pathways previously linked to FTD. To our knowledge, none of the miRNAs we found significantly altered in CSF exosomes have previously been reported in FTD or been implicated in its pathology [5]. Since miRNAs are still in their infancy, this is not unexpected. We must consider some limitations of the current study. We appreciate that sex was not matched in all groups. For example, while pre-symptomatic and symptomatic GRN groups contained equal numbers of females and males in the respective groups, all symptomatic C9orf72 mutation carriers were male, which may have introduced bias. Furthermore, most pre-symptomatic and symptomatic mutation carriers tested positive for a mutation in GRN, so our results will have to be confirmed in larger cohorts including more patients with C9orf72 and MAPT mutations. Ideally, our results would be confirmed in prospective studies including cohorts of genetic and sporadic FTD before the miRNA expression changes described here can be used in clinical practice. For the time being, our findings highlight that exosomal miRNAs have potential as diagnostic biomarkers for genetic and sporadic FTD.
Supplementary Material
MiRNA expression calculated with the 2−ΔΔCt method for healthy non-mutation carriers and HCs was correlated with age using Spearman’s test. Data from healthy non-mutation carriers and from HCs are shown separately (left and middle) and combined (right), *p<0.05, not significant (n.s.).
Cases 50–89. Abbreviations: HC, healthy controls; AD, Alzheimer’s disease; bvFTD, behavioural variant FTD; nfvPPA, non-fluent variant PPA; svPPA, semantic variant PPA; ALS, amyotrophic lateral sclerosis.
MiRNA expression was calculated using the 2−ΔΔCt method relative to pooled data from healthy non-mutation carriers. Data from individuals with any of the three mutations were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (A). Only data from individuals with a GRN mutation were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (B). Only data from individuals with a C9orf72 mutation were grouped and expression of miR-204-5p was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (C). Only data from individuals with any of the three mutations and the bvFTD phenotype were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (D). Raw Ct values of both miRNAs were significantly higher in symptomatic mutation carriers (E). Mir-204-5p expression was normally distributed and Welch’s t-test was used to compare groups, *p<0.05, ** p<0.01, *** p<0.005. Mir-632 expression was not normally distributed and Mann-Whitney U test was used to compare groups, *p<0.05, *** p<0.005. Mean and standard deviation of mean are shown.
MRNAs enriched in the human frontal and temporal lobes (Allen Brain Atlas, http://www.brain-map.org), wnt signalling, apoptosis, MAPK signalling, endocytosis, notch signalling, and neurotrophin signalling (KEGG v5.1). MRNA targets of miR-204-5p, miR-632, miR-23b-3p, miR-326, miR-877-5p, miR-892a, miR-30b-5p, and miR-373-3p, and miR-708-3p (MiRWalk 2.0).
Cases 1–49. Abbreviations: bvFTD, behavioural variant FTD; D-NOS, dementia not otherwise specified, nfvPPA, non-fluent variant PPA; svPPA; semantic variant PPA; GRN, progranulin; C9orf72, chromosome 9 open reading frame 72; MAPT, microtubule-associated protein tau.
Bar graph showing the eight exosomal miRNAs present in at least 70% of healthy non-mutation carriers (miR-204-5p, MiR-632, let-7a-5p, miR-605-5p, miR-23b-3p, miR-125b-5p, miR-548a-3p, and miR-937-3p).
Acknowledgments
Funding Statement
Raphael Schneider has received funding from the ALS Society of Canada (Clinical Research Fellowship)
Paul McKeever has received funding from the Alzheimer Society of Canada (Doctoral Award)
John van Swieten has received funding from the European Joint Program - Neurodegenerative Disease Research, the Netherlands Alzheimer Foundation (70-73305-98-105), and the Netherlands Organization for Health Research and Development
Daniela Galimberti has received funding from the Italian Ministry of Health
Jonathan Daniel Rohrer has received funding from the UK Medical Research Council through a Clinician Scientist Fellowship (MR/M008525/1) and the National Institute for Health Research - Rare Disease Translational Research Collaboration
Adam Boxer has received funding from the U.S. Department of Health and Human Services, National Institutes of Health NIH Clinical Center (R01AG038791 and U54NS092089) and the TAU consortium
Mario Masellis and Carmela Tartaglia have received funding from the Canadian Institutes of Health Research, Centres of Excellence in Neurodegeneration (Institute of Neurosciences, Mental Health and Addiction)
Janice Robertson has received funding from the Government of Canada: Canadian Institutes of Health Research Centres of Excellence in Neurodegeneration grant (The TAR DNA-Binding Protein (TDP-43) and ALS) and the James Hunter Initiative
GENFI consortium members
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Christin Andersson - Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; [ christin.andersson@karolinska.se ]
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Silvana Archetti – Biotechnology Laboratory, Department of Diagnostics, Civic Hospital of Brescia, Brescia, Italy; [ archetti.s@tiscali.it ]
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Andrea Arighi - Neurology Unit, Department of Physiopathology and Transplantation, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy; [ andrea.arighi@yahoo.it ]
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Luisa Benussi - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; [ lbenussi@fatebenefratelli.eu ]
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Giuliano Binetti - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; [ gbinetti@fatebenefratelli.eu ]
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Sandra Black - LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, Canada; [ sandra.black@sunnybrook.ca ]
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Martina Bocchetta - Dementia Research Centre, UCL Institute of Neurology, UK; [ m.bocchetta@ucl.ac.uk ]
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David Cash - Dementia Research Centre, UCL Institute of Neurology, UK; [ d.cash@ucl.ac.uk ]
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Maura Cosseddu - Centre of Brain Aging, University of Brescia, Brescia, Italy; [ maura.cosseddu@gmail.com ]
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Katrina Dick - Dementia Research Centre, UCL Institute of Neurology, UK; [ k.dick@ucl.ac.uk ]
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Marie Fallström - Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden; [ marie.fallstrom@karolinska.se ]
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Carlos Ferreira - Instituto Ciências Nucleares Aplicadas à Saúde, Universidade de Coimbra, Coimbra, Portugal [ c_dferreira@yahoo.com ]
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Chiara Fenoglio - Department of Pathophysiology and Transplantation, "Dino Ferrari" Center, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy; [ chiara.fenoglio@unimi.it ]
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Nick Fox - Dementia Research Centre, UCL Institute of Neurology, UK; [ n.fox@ucl.ac.uk ]
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Morris Freedman - Division of Neurology, Baycrest Centre for Geriatric Care, University of Toronto, Canada; [ mfreedman@baycrest.org ]
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Giovanni Frisoni – Geneva Neuroscience Center, University Hospitals and University of Geneva, Geneva, Switzerland; IRCCS Fatebenefratelli, Brescia, Italy; [ gfrisoni@fatebenefratelli.eu ]
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Giorgio Fumagalli - Neurology Unit, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; [ giorgiofumagalli@hotmail.com ]
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Stefano Gazzina - Centre of Brain Aging, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; [ stefanogazzina@alice.it ]
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Roberta Ghidoni - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; [ rghidoni@fatebenefratelli.eu ]
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Marina Grisoli - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy; [ Marina.Grisoli@istituto-besta.it ]
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Vesna Jelic - Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden; [ vesna.jelic@ki.se ]
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Lize Jiskoot – Department of Neurology, Erasmus Medical Center, Rotterdam; [ l.c.jiskoot@erasmusmc.nl ]
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Ron Keren - University Health Network Memory Clinic, Toronto Western Hospital, Toronto, Canada; [ Ron.Keren@uhn.ca ]
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Gemma Lombardi - Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; [ gemmalomb@gmail.com ]
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Carolina Maruta - Lisbon Faculty of Medicine, Language Research Laboratory, Lisbon, Portugal; [ carolmaruta@gmail.com ]
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Lieke Meeter - Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands; [ h.meeter@erasmusmc.nl ]
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Mendonça A - Dementia Clinics, Neurology Department, Institute of Molecular Medicine and Faculty of Medicine of Lisbon, Portugal; [ mendonca@neurociencias.pt ]
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Rick van Minkelen - Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands; [ r.vanminkelen@erasmusmc.nl ]
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Benedetta Nacmias - Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; [ benedetta.nacmias@unifi.it ]
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Linn Öijerstedt - Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden; [ linn.oijerstedt@ki.se ]
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Sebastien Ourselin - Centre for Medical Image Computing, University College London, UK; [ s.ourselin@ucl.ac.uk ]
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Alessandro Padovani - Neurology Unit, Department of Medical and Experimental Sciences, University of Brescia, Brescia, Italy; [ alessandro.padovani@unibs.it ]
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Jessica Panman – Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands; [ j.panman@erasmusmc.nl ]
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Michela Pievani - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; [ mpievani@fatebenefratelli.eu ]
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Cristina Polito - Department of Clinical Pathophysiology, University of Florence, Florence, Italy; [ cristina.polito@unifi.it ]
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Enrico Premi - Centre for Ageing Brain and Neurodegenerative Disorders, Neurology Unit, University of Brescia, Brescia, Italy; [ zedtower@gmail.com ]
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Sara Prioni - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy; [ Sara.Prioni@istituto-besta.it ]
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Rosa Rademakers [as London Ontario geneticist] - Department of Neurosciences, Mayo Clinic, Jacksonville, Florida; [ Rademakers.Rosa@mayo.edu ]
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Veronica Redaelli - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy; [ Veronica.Redaelli@istituto-besta.it ]
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Ekaterina Rogaeva - Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Canada; [ ekaterina.rogaeva@utoronto.ca ]
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Giacomina Rossi - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy; [ Giacomina.Rossi@istituto-besta.it ]
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Martin Rossor – Dementia Research Centre, UCL Institute of Neurology, UK; [ m.rossor@ucl.ac.uk ]
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James Rowe - Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK; [ james.rowe@mrc-cbu.cam.ac.uk ]
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Elio Scarpini - Neurology Unit, Department of Physiopathology and Transplantation, Fondazione Cà Granda, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico, Milan, Italy; [ elio.scarpini@unimi.it ]
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Fabrizio Tagliavini - Division of Neurology V and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy; [ fabrizio.tagliavini@istituto-besta.it ]
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Sandro Sorbi - Dipartimento di Neuroscienze, Area del Farmaco e Salute del Bambino, Firenze, Italy; [ sandro.sorbi@unifi.it ]
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David Tang-Wai - University Health Network Memory Clinic, Toronto Western Hospital, Toronto, Canada; [ David.Tang-Wai@uhn.ca ]
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David Thomas - Neuroradiological Academic Unit, UCL Institute of Neurology, UK; [ d.thomas@ucl.ac.uk ]
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Hakan Thonberg - Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden; [ hakan.thonberg@karolinska.se ]
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Pietro Tiraboschi - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy; [ Pietro.Tiraboschi@istituto-besta.it ]
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Ana Verdelho - Department of Neurosciences, Santa Maria Hospital, University of Lisbon, Portugal; [ averdelho@medicina.ulisboa.pt ]
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Jason Warren - Dementia Research Centre, UCL Institute of Neurology, UK; [ jason.warren@ucl.ac.uk ]
Footnotes
Contributorship
Raphael Schneider wrote and revised the manuscript and contributed to study design, acquisition of data, analysis, and interpretation of data. The author approved the final version of the manuscript.
Paul McKeever provided meaningful input to the manuscript and contributed to analysis and interpretation of data. The author approved the final version of the manuscript.
TaeHyung Kim provided meaningful input to the manuscript and contributed to analysis and interpretation of data. The author approved the final version of the manuscript.
Caroline Graff provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
John van Swieten provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Anna Karydas provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Adam Boxer provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Howie Rosen provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Bruce Miller provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Robert Laforce Jr provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Daniela Galimberti provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Mario Masellis provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Barbara Borroni provided meaningful input to the manuscript and contributed to acquisition of data. The author approved the final version of the manuscript.
Zhaolei Zhang provided meaningful input to the manuscript and contributed to analysis and interpretation of data. The author approved the final version of the manuscript.
Lorne Zinman provided meaningful input to the manuscript and contributed to analysis and interpretation of data. The author approved the final version of the manuscript.
Jonathan Daniel Rohrer provided meaningful input to the manuscript and contributed to study design, acquisition of data, and interpretation of data. The author approved the final version of the manuscript.
Maria Carmela Tartaglia revised the manuscript and contributed to study design, acquisition of data, analysis, and interpretation of data. The author approved the final version of the manuscript.
Janice Robertson revised the manuscript and contributed to study design, acquisition of data, analysis, and interpretation of data. The author approved the final version of the manuscript.
The corresponding authors Janice Robertson and Maria Carmela Tartaglia agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Competing Interests
There are no competing interests to report
Ethics approval
Written informed consent and local research ethics boards’ approval was obtained at all participating centres (Six GENFI centres contributed CSF: Karolinska Institute, Department of Neurobiology, Stockholm, Sweden; Erasmus Medical Center, Department of Neurology, Rotterdam, the Netherlands; University College London, Dementia Research Centre, London, England; Université Laval, Département des Sciences Neurologiques, Quebec City, Canada; University of Milan, Centro Dino Ferrari, Fondazione Ca’ Granda IRCCS Ospedale Policlinico, Milan, Italy; University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Canada)
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
MiRNA expression calculated with the 2−ΔΔCt method for healthy non-mutation carriers and HCs was correlated with age using Spearman’s test. Data from healthy non-mutation carriers and from HCs are shown separately (left and middle) and combined (right), *p<0.05, not significant (n.s.).
Cases 50–89. Abbreviations: HC, healthy controls; AD, Alzheimer’s disease; bvFTD, behavioural variant FTD; nfvPPA, non-fluent variant PPA; svPPA, semantic variant PPA; ALS, amyotrophic lateral sclerosis.
MiRNA expression was calculated using the 2−ΔΔCt method relative to pooled data from healthy non-mutation carriers. Data from individuals with any of the three mutations were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (A). Only data from individuals with a GRN mutation were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (B). Only data from individuals with a C9orf72 mutation were grouped and expression of miR-204-5p was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (C). Only data from individuals with any of the three mutations and the bvFTD phenotype were grouped and expression of both miR-204-5p and miR-632 was found to be significantly lower in symptomatic compared to pre-symptomatic individuals (D). Raw Ct values of both miRNAs were significantly higher in symptomatic mutation carriers (E). Mir-204-5p expression was normally distributed and Welch’s t-test was used to compare groups, *p<0.05, ** p<0.01, *** p<0.005. Mir-632 expression was not normally distributed and Mann-Whitney U test was used to compare groups, *p<0.05, *** p<0.005. Mean and standard deviation of mean are shown.
MRNAs enriched in the human frontal and temporal lobes (Allen Brain Atlas, http://www.brain-map.org), wnt signalling, apoptosis, MAPK signalling, endocytosis, notch signalling, and neurotrophin signalling (KEGG v5.1). MRNA targets of miR-204-5p, miR-632, miR-23b-3p, miR-326, miR-877-5p, miR-892a, miR-30b-5p, and miR-373-3p, and miR-708-3p (MiRWalk 2.0).
Cases 1–49. Abbreviations: bvFTD, behavioural variant FTD; D-NOS, dementia not otherwise specified, nfvPPA, non-fluent variant PPA; svPPA; semantic variant PPA; GRN, progranulin; C9orf72, chromosome 9 open reading frame 72; MAPT, microtubule-associated protein tau.
Bar graph showing the eight exosomal miRNAs present in at least 70% of healthy non-mutation carriers (miR-204-5p, MiR-632, let-7a-5p, miR-605-5p, miR-23b-3p, miR-125b-5p, miR-548a-3p, and miR-937-3p).