Background

Alzheimer's disease (AD) is the most common cause of dementia; approximately 5.8 million Americans suffered from AD in 2019 and, by 2050, it is projected that 14 million individuals in the United States will be affected by AD [1]. AD is characterized by pathological changes in the brain: accumulation of amyloid-beta plaques (extracellular deposits of amyloid-beta peptides) and neurofibrillary tangles (NFTs, intraneuronal fibrillar aggregates of hyperphosphorylated tau). Clinically, AD is defined by gradual and progressive memory loss [2]. AD can be categorized as sporadic AD or Autosomal Dominant AD (ADAD) [3]. ADAD is caused by mutations or duplications of the amyloid precursor protein (APP), mutations in presenilin 1 (PSEN1), or mutations in presenilin 2 (PSEN2), an autosomal dominant inheritance within family members for more than two generations, and an onset earlier than 65 years old [4, 5]. More than 400 mutations have been reported on these three genes, but PSEN1 harbors the most mutations, which are also associated with the youngest age at onset with affected individuals typically being 30–50 years old [4, 6,7,8]. In fact, PSEN1 mutations have also been reported in late-onset AD [9]. Even though ADAD is rare (< 0.5% of all AD cases) [10], these cases have provided unique insights into the pathobiology of the disease, especially the formulation of the amyloid hypothesis: that accumulation of amyloid-beta aggregates initiates the pathologic process of AD. All three ADAD-causing genes are part of the amyloid-beta processing pathway. However, neuropathological studies have shown that there are common and distinct pathological characteristics [11]. Both ADAD and AD present neuronal loss, neurofibrillary tangles, amyloid plaques and cerebral amyloid angiopathy among others, but ADAD show, for example, cottonwool plaques, more severe cerebral amyloid angiopathy, more common intracerebral hemorrhage or higher abundance of Lewy bodies [5, 12].

Recently, studies screening the whole genome, or the brain transcriptome have been instrumental in elucidating downstream genes and pathways implicated in disease, highlighting the importance of studying AD beyond the amyloid pathway [13,14,15,16,17,18]. However, most of these studies have been focused on sporadic AD, so their findings cannot always be extrapolated to Mendelian forms of AD; differences between these two forms of the disease are well known [5, 12, 19]. Several studies focused on ADAD have been limited to genetic studies focused on families [6, 20, 21] and animal studies aiming to understand the amyloid cascade hypothesis [22]. Studies involving large diverse ADAD cohorts are limited.

Circular RNAs are a family of non-coding RNAs that result from backsplicing events (the 3’ end of the transcript links covalently to the 5’ forming a loop) [23, 24]. The knowledge of circular RNAs is still limited, but it is thought that they are implicated in the regulation of microRNAs via sequestration, leading to a loss of function of the microRNA [20, 23,24,25]. Circular RNAs are highly expressed in the nervous system and especially in synapses [20]. Dysregulation of circular RNAs has already been shown for several central nervous diseases, including AD, Parkinson’s disease, and traumatic brain injury [20, 25,26,27]. CircRNA were systematically screened in brain samples from AD compared to controls [20]. They successfully identified more than 100 circRNAs associated with AD status and disease severity measured by Braak neurofibrillary tangles (NFT) and Clinical Dementia Rating (CDR®) [28]. When subsetting the analyses to ADAD, 236 circRNAs were found to be dysregulated; 56 of them independently of the severity measured by Braak NFT The circRNAs associated with both ADAD and AD showed larger effect sizes in ADAD than in AD. However, no specific analyses regarding the circular forms of the ADAD genes were performed. In this study, we used bulk RNA-seq to postmortem parietal cortex samples from controls, sporadic AD, and ADAD, we investigated the gene expression profiles of linear and circular transcripts of the ADAD genes to determine their possible involvement in the pathobiology of ADAD.

Methods

Study population

The discovery phase included bulk RNA-seq data from parietal cortex samples from non-Hispanic white (NHW) participants: 17 ADAD participants from the Dominantly Inherited Alzheimer Network (DIAN) (14 PSEN1 and three APP mutation carriers), 59 sporadic AD participants and ten control participants from the Knight-ADRC at Washington University in Saint Louis. The replication phase included four ADAD cases from DIAN (two PSEN1, one PSEN2, and one APP mutation carriers), and 194 sporadic AD cases and 13 controls from the Knight-ADRC. Demographic information can be found in Table 1, and neuropathological characteristics of the included brains is specified in Additional file 1: Table S1. Finally, we leveraged the Mount Sinai Brain Bank (MSBB) dataset (syn3157743) for replication of the sporadic AD findings (Additional file 1: Table S2). MSBB contains brain RNA-seq data from different brain regions. From Brodmann area (BM) 10 (frontal pole): 143 AD, 29 Controls; from BM22 (superior temporal gyrus): 134 AD, 26 Controls; from BM36 (parahippocampal gyrus): 123 AD, 24 Controls; from (BM) 44 (inferior frontal gyrus): 132 AD, 25 Controls.

Table 1 Demographic characteristics of the discovery and replication datasets
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Library preparation and sequencing

The data generation for the discovery dataset has been previously published [20, 21]. We followed the same protocol to generate the replication dataset. Briefly, total RNA was obtained from frozen parietal cortex tissue using the Tissue Lyser LT and RNeasy Mini Kit (Qiagen). After quality control, libraries were generated using TruSeq Stranded Total RNA Sample Prep with Ribo-Zero Gold kit (Illumina). Eighty million 2 × 150 bp reads were generated on average for each sample using an Illumina HiSeq 4000. For the replication dataset, we obtained the RNA from frozen brain tissue with the Maxwell RSC simplyRNA tissue kit (Promega). After quality control, TruSeq Stranded Total RNA Sample Prep with Ribo-Zero Gold kit (Illumina) was used to generate the libraries. Thirty-five million 2 × 150 bp reads were generated on average for each sample using an Illumina NovaSeq 6000.

RNA-seq quality control, alignment, and circular RNAs detection

Both datasets were processed and aligned separately following similar pipelines to the ones previously published by our group [20, 21]. Genome reference and gene models were selected following the TOPMed pipeline (https://github.com/broadinstitute/gtex-pipeline/blob/master/TOPMed_RNAseq_pipeline.md). Reference genome GRCh38 and GENCODE 33 annotation, including the addition of ERCC spike-in annotations were used. We excluded ALT, HLA, and Decoy contigs from the reference genome due to the lack of RNA-seq tools that allow proper handling of these regions. To obtain the linear counts we followed standard guidelines. Briefly, the raw reads were aligned to the human reference genome (GRCh38) using STAR (v.2.7.1a) [29]. We evaluated the quality of the alignment using sequencing metrics such as reads distribution, ribosomal content or alignment quality provided by STAR [29] using Picard tools (v.2.8.2) [30]. Gene expression was quantified using Salmon (v.1.2.0) [31] and the GENCODE reference genome (GRCh38). All transcripts or genes with less than ten reads in more than 90% of the individuals were removed.

To obtain the circular RNA counts, all raw reads were first aligned to the human reference genome (GRCh38) using STAR [29] in chimeric alignment mode. The remaining alignment parameters were selected specifically for circular RNA detection as suggested by the developers of the circular RNA calling software DCC [32]. Circular RNA detection, annotation and quantification was performed using DCC (v.0.4.8). We excluded any circRNA that had missing counts in more than 25% of the samples. As part of the general quality control, circRNAs that were not present in at least three samples, with a minimum of three counts in at least one of them, were removed. Additionally, we removed any circRNA with missing counts in more than 75% of the samples. Then we proceeded to extract the linear and circular forms corresponding to the three ADAD genes (APP, PSEN1 and PSEN2) from each dataset. For all three datasets, only circPSEN1 could be detected; circAPP and circPSEN2 were not detected. Consequently, the analyses were focused on linear PSEN1 and circPSEN1. The species of circPSEN1 varied depending on the dataset and the brain region for the MSBB. Overall, seven circPSEN1 species were detected by DCC; but four of them (hsa_circ_0008521, hsa_circ_0003848, hsa_circ_0007013, hsa_circ_0002564) were commonly detected in all three datasets and most of the brain areas (except hsa_circ_0008521 for BM10, hsa_circ_0007013 for BM22 and BM44).

Statistical analyses

We tested if the levels of circular PSEN1 (circPSEN1) and linear PSEN1 were different among groups by comparing AD and control participants, ADAD and AD participants, and ADAD and control participants in both the discovery and the replication datasets. The same analysis of circPSEN1 was also performed between sporadic AD participants and control subjects in the MSBB dataset (no ADAD participants were available in the MSBB dataset). We also investigated which specific circPSEN1 transcripts were predominant in each group and dataset. After normalization of the counts, differential expression (DE) analyses were performed specifically for circPSEN1 and linear PSEN1 using DEseq2 version 1.22.2 [33] to determine significance. Any association with p value < 0.05 was considered significant. All DE analyses were adjusted for postmortem interval (PMI), RNA quality as measured by median transcript integrity number (TIN) [34] and sex. We also tested if circPSEN1 was associated to Braak NFT or age at death to investigate if our findings were driven by disease severity as previously described [20]. Briefly, the variable of interest was added to the model to evaluate the effect on the p-value, effect size and direction of the circPSEN1 association.

Due to the moderate size of our sample, we combined the discovery and replication datasets, since they were processed using the same pipeline, and performed a joint analysis adding dataset to the model to adjust for possible differences in the DE analysis.

In-silico functional study

To investigate the biological function of circPSEN1, we accessed the Circular RNA Interactome website [35] to predict which miRNAs have the potential to target any of the circPSEN1 species identified in our datasets. Then, we used the DIANA mirPath software version 3 [36] to identify which genes and pathways were regulated by the identified miRNA using the microT-CDS algorithm and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we investigated if any of the common genes listed in the pathways identified by the DIANA mirPath software version 3 were differentially expressed in the ADAD cases compared to controls or sporadic AD cases in the discovery dataset.

Discriminative ability of circPSEN1

To evaluate if circPSEN1 can discriminate ADAD from the other brains, we used three binomial regression models built using three different circPSEN1 normalized counts: aggregate counts, hsa_circ_0008521, and hsa_circ_0003848 counts to classify ADAD vs. controls, ADAD vs. AD, and AD vs. controls in both the discovery and the replication datasets separately. We used the discovery dataset to train the models, and the replication dataset to validate them. We then evaluated the model performance through receiver operating characteristic curves (ROCs) and areas under the ROC curve (AUCs). The binomial regression models and the receiver operating characteristic curve analyses were performed using R packages stats version 3.5.2 and ROCR version 1.0-7.

Results

circPSEN1 is more abundant in ADAD than sporadic AD and controls

The circPSEN1 normalized counts were significantly higher in ADAD cases (N = 17) compared to controls (N = 10; p = 3.93 × 10–04, log2FC = 0.80) and sporadic AD cases (N = 59; p = 6.17 × 10–03, log2FC = 0.52), but did not differ between AD cases and controls (p = 0.21, log2FC = 0.29) (Table 2 and Fig. 1A) in the discovery dataset. The trend was also observed in the replication dataset in all three comparisons (log2FCADADvsCO = 0.72; log2FCADADvsAD = 0.74; log2FCADvsCO = 0.17) (Table 2 and Fig. 1B). Due to the limited sample size of the ADAD group, we do not have enough statistical power to detect differences in the replication dataset (24 ADAD samples are required to have 80% power with a probability of type I error of 0.05). Consequently, we performed a joint analysis of the two datasets. A more significant association was found in the join analyses than in the discovery dataset, indicating that the higher expression level of circPSEN1 specific to ADAD than sporadic AD cases and controls (pADADvsCO = 1.63 × 10–04, log2FC = 0.79; pADADvsAD = 8.22 × 10–04, log2FC = 0.60; pADvsCO = 0.30, log2FC = 0.19; Table 2).

Table 2 Differential expression results
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Fig. 1
figure 1

Comparison of the circular PSEN1 normalized counts in the discovery (A) and replication (B) datasets and the normalized counts for the linear forms of PSEN1 in the discovery (C) and replication (D) datasets for Controls (grey), AD (Alzheimer's disease—ocher) and ADAD (autosomal dominant Alzheimer's disease—blue)

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We leveraged the MSBB dataset to confirm that there were no differences between the normalized counts of circPSEN1 between AD cases and controls. circPSEN1 is not differentially expressed in any of the four brain regions (BM10, BM22, BM36, BM44) available in the MSBB dataset (Additional file 2: Fig. S1 and Additional file 1: Table S3), which is consistent with our finding that higher circPSEN1 expression is specific to ADAD cases.

Similar to RNA-seq, circPSEN1 total count is the result of the addition of all the counts from the different species of circPSEN1. We investigated if the differences observed between ADAD and AD or controls were the result of an overall increase or the increase of a specific species. Seven circPSEN1 species were found in the discovery and replication dataset (Table 3). All of them are exon derived except one that is intron–exon derived (circPSEN1- 73147795–73165413). Considering the four species commonly detected in both datasets, the most abundant are hsa_circ_0008521, hsa_circ_0003848, and hsa_circ_0002564; all three abundant species are significantly different between ADAD and CO (p = 2.57 × 10–04, 1.65 × 10–03, 0.036, log2FC = 0.96, 0.87, 0.70) and ADAD and AD (p = 2.20 × 10–03, 6.97 × 10–03, 8.00 × 10–03, log2FC = 0.72, 0.62, 0.64). No significant differences were found between disease status for hsa_circ_0007013 (Table 3 and Additional file 3: Fig. S2), suggesting that all three circRNA species are driving the association. No significant differences were found between AD and controls for the two species detected in the MSBB dataset in any brain region (Additional file 1: Table S3). We also investigated the relationship among these three species using correlation tests. We observed high correlation between hsa_circ_0008521 and hsa_circ_0003848 in the discovery (r2 = 0.80, p < 2.20 × 10–16), replication (r2 = 0.31, p = 4.22 × 10–06), and MSBB (BM22—r2 = 0.42, p = 3.44 × 10–08|BM36—r2 = 0.58, p = 7.79 × 10–15|BM44—r2 = 0.71, p = 2.2 × 10–16) datasets. Hsa_circ_0002564 shows weak correlations with the other two circPSEN1 species even though some of them are nominally significant (with hsa_circ_0008521 only in the discovery dataset—r2 = 0.25, p = 0.02, and with hsa_circ_0003848 in the discovery—r2 = 0.26, p = 0.01 and in the BM22 of the MSBB dataset—r2 = 0.31, p = 8.15 × 10–05). This results suggest that hsa_circ_0008521 and hsa_circ_0003848 seem to share the same mechanism of dysregulation, which might be different from hsa_circ_0002564.

Table 3 Species of circPSEN1 characterization and differences across groups
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To ensure that the association was not driven by disease severity or age at death, we tested if Braak NFT score or age at death were influencing the association of circPSEN1 with ADAD. We observed no significant changes in the results (Additional file 1: Table S4), suggesting that these findings are not due to pathology severity or the age of the individual.

Circular PSEN1 is independent from linear PSEN1

We then investigated if the association between ADAD and circPSEN1 was also observed in the linear form of PSEN1. No significant differences were found (Table 2, Fig. 1C, D).

We then tested the independence of the normalized counts for the linear and circular forms of PSEN1. Significant correlation was observed for the ADAD individuals or the controls (Additional file 4: Fig. S3). The correlation between the linear PSEN1 and the circPSEN1 was weak (RDiscovery = 0.42; RReplication = 0.20), even though it was nominally significant (pDiscovery = 8.20 × 10–04, pReplication = 3.90 × 10–03) in the AD group. To assess if this correlation was affecting our results in the AD group, linear PSEN1 was added to the differential expression analysis as previously described [37]. The changes of circPSEN1 were still significant (p = 1.71 × 10–04), even when adjusting for linear PSEN1. This result suggests that even though the correlation of linear and circular PSEN1 was nominally significant, the linear and circular forms are independent. Similar results were observed for correlation analyses using for PSEN1 species against linear counts (Additional file 5: Fig. S4).

ADAD individuals have higher circPSEN1 counts independently of the mutation

ADAD mutations are most prevalent in PSEN1 among individuals in both the discovery and the replication datasets. Thus, we evaluated if the high expression levels of circPSEN1 were unique to PSEN1 mutation carriers. In the joint dataset (Fig. 2A), the normalized counts of circPSEN1 between PSEN1 mutation carriers (N = 16) and APP mutation carriers (N = 4) are not significantly different (log2FC = 0.332, p = 0.354). When compared to AD cases (N = 253) or controls (N = 23), PSEN1 mutation carriers showed increased levels of circPSEN1 (log2FC = 0.634, p = 1.730 × 10–03, and log2FC = 0.787, p = 9.010 × 10–04). APP mutation carriers also showed higher counts of circPSEN1 when compared to controls (log2FC = 0.597, p = 0.049). Due to the limited sample size of PSEN2 mutation carriers (N = 1), no statistical test was performed. No differences were found among mutations carriers regarding linear PSEN1 normalized counts (Fig. 2B).

Fig. 2
figure 2

Comparison of the normalized counts of circular PSEN1 (A)/linear PSEN1 (B) between controls, AD, and different mutation carriers in the joint dataset—Controls (grey), ADs (Alzheimer's disease—ocher), PSEN1 mutation carriers (blue), and APP mutation carriers (green)

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In-silico analyses functional annotation

We found the three most abundant species of circPSEN1 in our samples to be hsa_circ_0008521, hsa_circ_0003848, and hsa_circ_0002564, with the first two most likely driving the overall signal. CircRNAs have been reported to regulate gene expression by sequestering miRNA. Because the biological function of these circPSEN1 species has not been described, we explored if the biological function of circPSEN1 might be elucidated by those miRNAs targeted to them. Using the Targetscan prediction tool [38] from the CircInteractome database [35], we identified 26 miRNAs that could potentially target to the three most abundant species of circPSEN1. We used them as input in the microT-CDS tool of the DIANA mirPath v.3 software to elucidate which pathways are potentially regulated by the identified miRNA. Several pathways were found significantly associated with these 26 miRNAs: axon guidance (p = 3.39 × 10–07); hippo signaling pathway (p = 7.38 × 10–07); lysine degradation (p = 2.48 × 10–05); and Wnt signaling pathway (p = 5.58 × 10–04) among other KEGG pathways (Additional file 1: Table S5). We identified 31 genes that were common in the top ten KEGG pathways (Additional file 1: Table S6); the counts of two of them were found significantly lower in ADAD brains compared to AD brains. FZD4 and RAF1 were nominally significant in the discovery (p = 0.045; p = 1.07 × 10–03) and replication (p = 1.55 × 10–03; p = 0.049) datasets. Both genes are related to the transmission of chemical signals between the cell surface and the nucleus.

Circular PSEN1 normalized counts can discriminate ADAD

The predictive ability of circPSEN1 counts (aggregate, hsa_circ_0008521, and hsa_circ_0003848) was evaluated using ROCs and AUCs (Additional file 6: Fig. S5). Aggregated and individual normalized counts of circPSEN1 show no predictive ability for AD vs. CO. However, circPSEN1 showed good predictive power for ADAD vs. AD. The AUC for the aggregated counts in the discovery dataset was 0.71; that in the replication dataset was 0.81. When we evaluated the predictive power of the two circPSEN1 species separately, hsa_circ_0008521 seemed to have slightly better predictive power than hsa_circ_0003848. Yet, the aggregate counts of circPSEN1 seemed to show a more robust discriminative power, which generated similar AUCs across datasets.

The discriminative power of circPSEN1 increased when we attempted to classify ADAD vs. CO, with an AUC of 0.81 in the discovery dataset and an AUC of 0.85 in the replication dataset for the aggregated counts of circPSEN1. The trends were very consistent for both datasets for all three predictions despite the differences in sample size. This result suggests that, even though the most abundant circPSEN1 species seem to have more discriminative power, the less abundant ones (hsa_circ_0007013, hsa_circ_0002564, hsa_circ_0008218, hsa_circ_0032509) are also contributing since the discriminative power of the aggregate counts is more stable in the two datasets.

Discussion

In this study, we provide evidence that the transcriptional signatures differ between ADAD and AD brains. By analyzing the largest dataset to date of ADAD brains, we have found that circPSEN1 expression is increased in ADAD brains but not in AD cases or controls, a result which is independent of age and disease severity (as measured by Braak NFT score). Our results show that this increased expression is not specific to PSEN1 mutation carriers, as similar results were observed in APP mutation carriers, or due to linear PSEN1, suggesting a biological mechanism specific to the circPSEN1.

A previous study [20], demonstrated that expression changes of circRNAs in pre-symptomatic AD, sporadic AD, and ADAD are different and not always related to severity of the disease. They found more than 100 circRNAs dysregulated in the context of AD, demonstrating the involvement of circRNA in the pathobiology of AD. In fact, they found even more circRNA dysregulated in their comparison of ADAD participants versus controls. In the present study we add evidence to the importance of the dysregulation of circRNAs. Dube et al., identified that circRNAs that were dysregulated in both AD and ADAD, presented with larger effect sizes in ADAD. In here, we found that circPSEN1 that is related to the amyloid pathway is uniquely dysregulated in ADAD participants. This emphasizes the importance of studying not only the molecular similarities between AD and ADAD, but also the differences.

It has been demonstrated that circular RNAs are generated through the spliceosome, suggesting that additional to the miRNA regulation through their sponge function, circular RNA generation is one of the mechanisms that regulates the production of linear RNA [39]. On top of that, spliceosomal proteins have been reported to aggregate with tau tangles [40] and to be down-regulated in the presence of amyloid-beta42 [41]. The production of circPSEN1 could be due to the mutations present in PSEN1 via spliceosome alterations. Among the PSEN1 mutations in this study, p.H163R is located at the boundaries of hsa_circ_0002564. This might explain the dysregulation of this particular circular RNA. However, this mutation was present in only four of the ADAD brain samples, suggesting that other regulatory events are taking place in the ADAD individuals unrelated to this mutation. In fact, previous studies have demonstrated that introns regulate the biogenesis of circular RNA. Given that most of the PSEN1 mutations are located within the exons, it is likely that this might not be the biological explanation.

Our in-silico functional analysis predicted 26 miRNAs that could bind the three abundant circPSEN1 species. These 26 miRNAs are significantly associated with several pathways, including wnt, hippo and axon guidance pathways that have been previously related to the development of AD and to neuroinflammation [42,43,44,45]. Among the 26 identified miRNAs, miR-144 has previously been associated with AD [46]; in fact, miR-144-3p targets APP, significantly inhibiting protein expression [47]. The overexpression of miR-433 targets JAK2 (janus kinase 2) which contributes to the progression of AD by inhibition of amyloid-beta-induced neuronal viability [48]. Additionally, miR-566 [49] and miR-885-5p [50] were also found to be dysregulated in AD. Finally, miR-655 inhibited the inflammatory response of microglia by targeting TREM2 (triggering receptor expressed on myeloid 2) [51], which is known to affect amyloid and tau pathologies. Our finding adds evidence to the fact that AD is not restricted to neurons but involves several mechanisms including inflammation [52]. Given the involvement of microglia in the inflammatory process, axon guidance [53], and the role of wnt pathway [45], the presence of circPSEN1 might be originating from the microglia. Analyses of circPSEN1 using publicly available RNAseq data from IPSCs [54] (Additional file 7: Fig. S6) showed no differences between mutation carriers and isogenic corrected cells. Additionally, previous studies have shown that the neuronal proportion in ADAD brains seems to be lower compared to AD [21]. Together, this suggests that the circPSEN1 association described in here might not have a neuronal origin. To date, there is no reference panel or single-cell circular RNA-seq data to test this hypothesis via digital deconvolution or differential expression analyses. Future studies using these and other tools can not only inform of the cell of origin but also whether the pathways are involved in vascular clearance, angiogenesis, or blood–brain barrier regulation, among others.

circPSEN1 might be a regulatory factor located at the top hierarchical levels of the dysregulation of the amyloid-beta pathway and leading to the neuroinflammatory status. Our results show that circPSEN1 is dysregulated in all ADAD cases, independent of the specific mutation. However, due to the limited sample size of PSEN2 and APP carriers, we cannot disregard the possibility that this alteration is unique to PSEN1 mutation carriers.

Additional analyses to understand the biological function of circPSEN1 and its relationship with PSEN1 are needed, along with the study of circular and linear forms of PSEN2 and APP in mutation carriers to elucidate the biological consequences of circular RNAs in ADAD in comparison to AD. If further replicated, circPSEN1 might be targeted to diminish neuroinflammation in ADAD individuals to delay the onset of the disease or slow down its progression.

This study includes the largest sample of ADAD brains analyzed to date. However, it is still a study with limited sample size, therefore limiting the statistical power of this analysis. Although our findings are novel and possibly biologically relevant, due to the limited knowledge about circular RNAs and their biological function, along with the relationship between linear and circular forms of the same gene, we cannot claim any causal involvement of circPSEN1 with ADAD or AD.

Conclusions

In conclusion, our circPSEN1 differential expression analysis has shown significant differences in the expression of circPSEN1 that are unique to ADAD, and independent of gene mutation. Due to the biological function previously ascribed to circular RNAs and our in-silico analyses, we hypothesize that this finding might be related to neuroinflammatory events that lead or that are caused by the accumulation of amyloid-beta. Future studies aimed at understanding the biological function of circPSEN1 might lead to a better understanding of its pathological involvement with ADAD and its potential as drug-target.