Whole transcriptome RNA-Seq reveals drivers of pathological dysfunction in a transgenic model of Alzheimer’s disease

doi:10.21203/rs.3.rs-3617553/v1

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Whole transcriptome RNA-Seq reveals drivers of pathological dysfunction in a transgenic model of Alzheimer’s disease

https://doi.org/10.21203/rs.3.rs-3617553/v1

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Alzheimer’s disease (AD) affects more than 55 million people worldwide, yet current theories cannot fully explain its aetiology. Accordingly, gene expression profiling has been used to provide a holistic view of the biology underpinning AD. Focusing primarily on protein-coding genes, such approaches have highlighted a critical involvement of microglia-related inflammatory processes. Simultaneous investigation of transcriptional regulators and noncoding RNA (ncRNA) can offer further insight into AD biology and inform development of disease modifying therapies. We previously described a method for whole transcriptome sampling to simultaneously investigate protein-coding genes and ncRNA. Here, we use this technique to explore transcriptional changes in a murine model of AD (15-month-old APP/PS1 mice). We confirmed extensive involvement of microglia-associated genes and gene networks, consistent with literature. We also report a wealth of differentially-expressed non-coding RNA – including microRNA, long non-coding RNA, small nuclear and small nucleolar RNA, and pseudogenes – many of which have been overlooked previously. Transcription factor analysis determined that six transcription factors likely regulate gene expression changes in this model (Irf8, Junb, c-Fos, Lmo2, Runx1, and Nfe2l2). We then utilised validated miRNA-target interactions, finding 60 interactions between 15 miRNA and 42 mRNA (messenger RNA) with largely consistent directionality. Furthermore, we found that eight transcription factors (Clock, Lmo2, Runx1, Nfe2l2, Egr2, c-Fos, Junb, and Nr4a1) are likely responsible for the regulation of miRNA expression. Taken together, these data indicate a complex interplay of coding and non-coding RNA, driven by a small number of specific transcription factors, contributing to transcriptional changes in 15-month-old APP/PS1 mice.

Alzheimer's disease

RNA-Seq

noncoding-RNA

APP/PS1

bioinformatics

Alzheimer’s disease (AD) is a progressive degenerative disorder affecting an estimated 55 million people worldwide [1], and results in severe cognitive and memory deficits. Its characteristic pathology includes widespread cell death [2], extracellular aggregates of Amyloid-β peptide [3], and intracellular aggregates of hyperphosphorylated tau protein [4]. While significant strides have been made in understanding the aetiology and pathogenesis of AD, current theories cannot yet fully explain how or why the sporadic form of AD develops.

Important insights may therefore be gained by moving from a direct focus on amyloid- β and tau to a holistic view of the disease based on whole transcriptome sequencing and understanding the relationships between different RNA biotypes. Indeed, there is a growing recognition of the importance of non-coding RNA (ncRNA), which outnumber protein-coding genes in both number and expression, in both health and disease [5, 6]. Already, many microRNA (miRNA), long non-coding RNA (lncRNA), and other RNA biotypes have been found to contribute to disease processes in AD. For example, several miRNA influence the expression and downstream metabolism of the Amyloid Precursor Protein (APP) [7–10], the expression and processing of tau protein [11–13], and processes of neuroinflammation [14–16]. Indeed, miRNA signatures are mooted to be useful diagnostic biomarkers of disease progression [17]. Further, long non-coding RNA (lncRNA) have also been found to be differentially expressed in people with AD and in animal models. These include β-secretase modifying lncRNA BACE1-AS [18], inflammation associated SNHG1 and NDM29 [19, 20] and the promiscuous lncRNA NEAT1, which acts as a miRNA sponge [21] and an indirect regulator of gene expression through controlling nuclear retention [22] or DNA methylation [23]. Very little is currently known about other forms of ncRNA in AD as they are rarely studied, however some small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), and PIWI-interacting RNA (piRNA) play roles in processes that are disrupted in AD [24–26].

Key to understanding the role of ncRNA is understanding the relationships between RNA biotypes. However, to date, our knowledge of how the protein-coding and non-coding transcriptome interact is limited and derived from comparison of studies of protein-coding or non-coding transcriptomes in isolation. Accordingly, to examine the role of ncRNA and mRNA in AD, we developed a modified RNA-Seq protocol for identifying and quantifying both protein-coding and non-coding RNA from the same sample simultaneously. This method avoids biases introduced by separate isolation of long and short RNA and construction and sequencing of separate libraries for different RNA biotypes. Using this method, in the current study we examined the entire transcriptome of the APP_swe/PSEN1dE9 mouse model of AD, which includes a humanised transgene of APP bearing the Swedish mutation and a deletion of exon 9 on the human presenilin-1 gene. This model is widely used to imitate the early stages of the disease, including synaptic dysfunction, synaptic and neuronal loss, as well as plaque formation [27]. This allowed us to establish whole transcriptome profiles and examine expression profiles of both the miRNA and transcription factors and their targets in the same sample at the same time. This permitted simultaneous identification of functional relationships between RNA and the regulatory factors driving gene expression changes contributing to the pathological phenotype of the APP_swe/PSEN1dE9 model mice.

Animal Studies

All animal use was compliant with the New Zealand Animal Welfare Act 1991 and performed under guidelines and approval of the University of Otago Animal Ethics Committee (approval number DET09/15). The reporting in this manuscript follows the recommendations in the Animal Research: Reporting on In Vivo Experiments (ARRIVE) guidelines [28]. In this study a double transgenic model of Alzheimer’s disease was used (APPswe/PS1dE9, B6C3 background, hereafter referred to as APP/PS1). These mice were originally sourced from The Jackson Laboratory (https://www.jax.org/strain/004462) and maintained as a colony at the University of Otago breeding facility. Animals underwent no additional procedures prior to their stated use. All mice were genotyped for the presence of human exon-9-deleted variant PSEN1, which co-segregates with the APPswe gene, as previously described [29]. For RNA-Seq, male transgenic (tg) and wild-type (wt) littermates at 15 months old (n = 4 per group) were anaesthetised with sodium pentobarbitol and the brains were removed into ice cold artificial cerebrospinal fluid solution (aCSF; in mM: 124 NaCl, 3.2 KCl, 1.25 NaH₂PO₄, 26 NaHCO₃, 2.5 CaCl₂, 1.3 MgCl₂, 10 d-glucose). The left hippocampus was dissected and snap-frozen on dry ice. All samples were stored at -80°C until used.

RNA sequencing

Sequencing was performed as previously described [30]. Briefly, total RNA was extracted from the hippocampi of male 15-month-old APP/PS1 tg mice and wt littermates (n = 4 pairs) using the mirVana™ PARIS™ RNA isolation kit (Invitrogen; Cat #AM1556). Libraries were prepared using slight alterations to the Ion Total RNA-Seq kit v2 (Life Technologies; Cat #4479789), with ribosomal RNA removal by QIASeq FastSelect (Qiagen; Cat #334386). Pooled equimolar libraries underwent emulsion Polymerase Chain Reaction (PCR) with the Ion OneTouch™ 2 system using the Ion PI™ Hi-Q OT2 200 kit (Invitrogen; Cat #A26434). Two pools of mixed barcoded libraries were sequenced on two Ion PI™ v3 chips (Invotrogen; Cat #A26772), with two mixed pairs per chip, avoiding the use of sequential barcodes on the same chip.

Reverse Transcription Quantitative real-time PCR (RT-qPCR)

RNA was isolated from the hippocampi of 15-month-old wt and tg mice (n = 6 pairs), using the mirVana™ PARIS™ RNA isolation kit (Invitrogen; Cat #AM1556). RNA sample concentrations and integrity were determined using spectrophotometry (Nanodrop 1000; Thermo Scientific, USA) and a Bioanalyzer, using an RNA 6000 NanoLabchip (Bioanalyzer 2100; Agilent Techologies, USA). Only samples with an average RNA integrity number > 9 were used. Gene-specific complementary DNA (cDNA) libraries were prepared using the TaqMan mRNA reverse transcription kit (Applied Biosystems), primed using random hexamers, according to manufacturer’s instructions. Amplification was performed in triplicate using 20x TaqMan Gene Expression Assays, with Hprt as housekeeping (primers used; Trem2–04209424_m1, Tyrobp – 00449152_m1, Cst7–00438351_m1, Hprt – 03024075_m1). These reactions were amplified using a ViiA7 Real-Time PCR System (Applied Biosystems). Data were normalised against Hprt expression, and the 2^−ΔΔCT method used to calculate fold-change. A two-tailed paired Students T-test was then used to determine statistically significant changes with a threshold of p < 0.05.

Bioinformatics

Data were initially analysed as described previously [30]. Briefly, data from each barcoded library were separated into data files on the Ion Torrent Suite version 5.4 (Life Technologies, USA). Adapter sequences were trimmed using AdapterRemoval v2.1.7 and reads were trimmed for quality using Trimmomatic v0.38. Reads were aligned to the Mus musculus GRCm38.95 reference genome using STAR v2.5.4b. Additionally, miRNA reads were extracted and analysed separately using miRDeep2 v0.1.2 and piRNA data were obtained from piRNABank. Data were analysed using R version 4.0.2 in RStudio v1.3.959. The function featureCounts from the package Rsubread generated raw counts data. Differential expression analysis was performed using the exactTest function from the package edgeR. Heatmaps and agglomerative hierarchical clustering was achieved using the package pheatmap.

For mRNA, differentially expressed genes were entered into the web interface of the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) [31–41], which is a curated database of experimentally validated protein-protein interactions and co-expression. STRING-db was also used to perform Markov clustering (MCL; a form of unsupervised graph clustering that shows robust behaviour in analysis of protein-protein interactions), using an inflation parameter of 1.8, a value shown to be optimal in these kinds of analyses [42, 43]. The purpose of this was to discover related clusters and nodes within the protein network. Clusters grouped by STRING were entered into Enrichr [44, 45], a web interface for functional enrichment of gene lists, using the database Wikipathways. Transcription factor enrichment analysis was performed using the webserver BART (Binding Analysis for Regulation of Transcription) [46, 47] for mRNA.

MiRNA targets were explored using DIANA Tarbase v.8 [48], which lists experimentally validated microRNA-target interactions from a variety of sources. Additionally, transcription factors targeting miRNA loci were found using TransmiR [49, 50].

Data availability

The data discussed here have been deposited in NCBI’s Gene Expression Omnibus [51] and are accessible through GEO Series accession number GSE163878 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163878).

We aimed to understand the extent of transcriptomic change between 15-month APP/PS1 mice and wt, an age where extensive plaque burden is routinely observed [29]. To achieve this, we identified the RNA biotypes represented within differentially expressed gene set. To explore the biological impact of the differentially expressed genes we used a bioinformatics approach and we explored possible interactions between different RNA species within the dataset, drawing on the strength of the simultaneous whole-transcriptome analysis.

RNA-Seq reveals genes differentially expressed between APP/PS1 and wild-type control mice at 15 months.

Here, we provide a holistic understanding of transcriptomic changes in the APP/PS1 model of AD by analysis of differentially expressed genes using our newly developed protocol [30] which allowed access to all RNA biotypes. Following False Discovery Rate correction (FDR < 0.05; Table 1), 20 genes were significantly changed in the APP/PS1 mice including both App and Psen1 (log fold change 1.22 and 0.85 respectively; edgeR package for R) as expected. We also found a number of other genes commonly found differentially expressed in AD and involved in disrupted processes, such as Trem2 (log fold change 1.35), Tyrobp (log FC 2.21), and Gfap (log FC 1.66).

Lower stringency in data analysis can discover important genes for further investigation that would otherwise be filtered out by more stringent settings. Therefore, we made use of unadjusted p-values for further analyses of these data. At unadjusted p < 0.05, the list of differentially expressed genes expanded to 610 genes, allowing for a much more in-depth analysis of gene expression. The full list of differentially expressed genes is shown in Online Resource 1. Of these 610 genes, 448 are upregulated and 162 downregulated (log2 fold-change > 0.359 and < -0.359; Figure 1a).

Next, to analyse relationships and similarities between samples and genes, hierarchical clustering was performed. Wt and tg samples clustered to type, indicating that their gene expression profiles are more similar within-group than between- groups (Figure 1b). This suggests that differentially expressed genes can clearly distinguish tg from wt animals; supporting the validity of the results.

Table 1: Differentially expressed genes between APP/PS1 mice and wild-type controls (15 month), at FDR < 0.05. Note Lamr1-ps1 (Laminin receptor 1 pseudogene 1) is believed to be an artifact of transgene insertion in this model and was therefore excluded from further analyses. Asterisks indicate genes associated with microglial function by literature search.

Ensembl ID	Gene Symbol	log2 FC	log2 CPM	p-Value	FDR
ENSMUSG00000081229	Lamr1-ps1	6.729103	1.044629	3.38E-19	6.69E-15
ENSMUSG00000046805	Mpeg1 *	1.990667	4.700738	2.06E-17	2.04E-13
ENSMUSG00000030789	Itgax *	3.608	1.839671	8.00E-13	5.28E-09
ENSMUSG00000068129	Cst7 *	3.439047	1.547066	7.28E-12	3.60E-08
ENSMUSG00000079037	Prnp	1.549274	8.957976	7.77E-11	3.08E-07
ENSMUSG00000079293	Clec7a *	3.198116	0.821581	4.42E-10	1.46E-06
ENSMUSG00000018927	Ccl6 *	3.39885	0.393266	1.02E-08	2.89E-05
ENSMUSG00000023992	Trem2 *	1.354341	3.074181	2.30E-07	0.000569
ENSMUSG00000030579	Tyrobp *	2.208173	2.011728	1.55E-06	0.003405
ENSMUSG00000022892	App	1.225	10.43842	1.83E-06	0.00363
ENSMUSG00000069516	Lyz2 *	2.174822	3.03749	2.57E-06	0.004391
ENSMUSG00000004707	Ly9 *	2.23793	0.262328	2.66E-06	0.004391
ENSMUSG00000036896	C1qc *	1.999691	4.110238	2.99E-06	0.004544
ENSMUSG00000040552	C3ar1 *	1.680807	1.26491	3.48E-06	0.004915
ENSMUSG00000069515	Lyz1 *	2.629014	1.000814	4.05E-06	0.005342
ENSMUSG00000073418	C4b *	1.42005	7.102349	1.07E-05	0.013281
ENSMUSG00000019969	Psen1	0.848874	5.434479	1.20E-05	0.013978
ENSMUSG00000015451	C4a*	1.441767	5.741604	2.20E-05	0.024169
ENSMUSG00000027015	Cybrd1	1.999986	1.791556	2.69E-05	0.027984
ENSMUSG00000020932	Gfap	1.658875	8.593936	4.30E-05	0.042537

Validation of RNA-Seq data by RT-qPCR

In order to further validate the data obtained from the modified RNA-Seq protocol further, the expression of selected genes as measured by RNA-Seq was compared to RT-qPCR measurement. For this analysis, we chose the Cst7 as the gene with one of the highest fold-change values, and Trem2 and Tyrobp as genes commonly identified in literature. All three of these genes were also found differentially expressed by RT-qPCR (Online Resource 2).

Functional enrichment analysis highlights key gene clusters and pathways changed in the APP/PS1 mouse model of AD.

To better understand enriched processes and pathways among differentially expressed protein coding genes, Markov clustering of differentially expressed mRNA in STRING was used to analyse protein-protein interactions. An inflation parameter of 1.8 was used, and only experimentally validated relationships, curated databases, and conserved protein co-expression were considered. This method grouped differentially expressed protein coding genes into four major clusters based on their protein interactions (Figure 2a). Each cluster had unique strongly enriched pathways, as shown in Figure 6b, with an overriding theme of microglia involvement and complement activation. Cluster 1 was enriched for the complement and coagulation cascade pathway, Cluster 2 for the TYROBP causal network (a pathway regulating signal transduction across microglial plasma membranes in disease-associated microglia [DAM]), Cluster 3 for Macrophage markers, and Cluster 4 for Classical complement activation. Indeed, 14 of the top 20 differentially expressed mRNA are associated with microglia indicating widespread changes in microglial function are occurring in the APP/PS1 mouse by 15 months (identified by asterisks in Table 1).

Further exploration of these clusters was performed by functional enrichment and pathway analysis using Enrichr. Interestingly, Cluster 1 contained the genes encoding TGF-β1 and its two major receptors (Tgfb1, Tgfbr1 and Tgfbr2). All three genes were upregulated in APP/PS1 mice (log FC 0.86, 0.51, 0.65; p = 0.004, 0.01, 0.004 respectively; Figures 2a, 2b, 3). TGF-β1 is a cytokine preferentially secreted by astrocytes in response to inflammatory conditions, acting to modulate microglial reactivity [52]. Human AD patients show an increased expression of TGF-β1, but a reduction in its anti-inflammatory efficacy through a reduction in the expression of its receptors [53, 54]. Such an increase has not previously been reported in APP/PS1 mice, though the concomitant increase in the expression of Tgfbr1 and Tgfbr2 points to this being a response to, and attempt to mediate, microglial reactivity and rampant neuroinflammation.

Analysis of enriched Transcription Factors

Next, we aimed to understand the underlying mechanisms influencing the differentially expressed genes. Utilizing the webserver BART (Binding Analysis for Regulation of Transcription) [46, 47], which draws on ChIP-Seq (Chromatin Immunoprecipitation sequencing) data, we identified key regulators of gene expression through detection of shared and conserved regulatory elements within our 610-gene input list and cross-referenced these with the differentially expressed mRNA.

Using this approach, we found six differentially expressed transcriptional regulators (Table 2; refer to Online Resource 3 for full list of predicted transcription factors). All six of these transcription factors have been previously tied to microglial function, apoptosis, or autophagy [55–60]. These results suggest that these six regulatory factors may act as major regulatory hubs and underlie many of the changed pathways seen in this gene set.

Table 2: Differentially expressed transcriptional regulators that target differentially expressed genes, as predicted by BART, and their biological relevance.

Transcription Factor	Wilcoxon statistic	Wilcoxon p-value	Z-score	Max AUC	Biological relevance
IRF8	7.147	4.43E-13	2.328	0.843	Activates microglia [58]
JUNB	6.328	1.24E-10	2.728	0.874	Inhibits apoptosis [56]
c-FOS	4.466	3.98E-06	2.702	0.737	Promotes apoptosis [57]
LMO2	2.712	3.34E-03	3.736	0.654	Activates microglia [55]
RUNX1	2.293	1.09E-02	0.416	0.656	Toll-like receptor signalling in microglia [60]
NFE2L2	2.122	1.69E-02	1.822	0.702	Regulates autophagy [59]

Biotype analysis of RNA-Seq data

To understand the extent of transcriptomic change within the APP/PS1 mouse model, we identified the RNA biotypes represented within the differentially expressed gene set. This analysis showed that 328 of the differentially expressed species were derived from protein-coding genes, 89 pseudogenes, 37 snoRNA, 26 miRNA, 12 long intergenic non-coding RNA (lincRNA), seven snRNA, seven non-classified RNA (miscRNA), six piRNA, and three lncRNA (GRCm38.100 reference genome; mirDeep2.0; piRNABank; Figure 1c; Figures 3-6). The proportions of protein-coding to non-coding gene biotypes were consistent with reported cellular RNA contents [61], and these were higher and more diverse than other data obtained from more conventional RNA-Seq library construction techniques using similar samples [62, 63, 30].

Functional enrichment analysis of non-coding RNA

miRNA: We found that changes in miRNA expression echo functional enrichment seen in mRNA expression. Of the 26 miRNA differentially expressed in APP/PS1 mice, 13 were found to be upregulated, and 13 downregulated (Figure 4b). Exploring the functions of these miRNA by literature search reveals 19 miRNA tied to microglial function (Table 3). MicroRNA involved in inhibiting microglial activation were significantly downregulated – for example miR-204-5p has been shown to suppress microglial activation (log2 FC = -1.79, p < 0.0001 [64]), miR-7116-5p similarly inhibits microglial activation by targeting TNF-α, which is necessary for pro-inflammatory microglial responses (log2 FC = -1.78, p < 0.0001 [65]), and miR-7a-5p suppresses microglial inflammatory activation (log2 FC = -0.53, p < 0.05 [66]). Conversely, several miRNA were upregulated that have been shown to have pro-inflammatory or pro-apoptotic functions, and are expressed in or associated with microglia, including miR-146a-5p (log2 FC = 0.98, p < 0.01) and miR-29b-3p (log2 FC = 0.52, p < 0.05) [67, 68]. Overall, we see upregulation of pro-inflammatory and pro-apoptotic miRNA, and downregulation of anti-inflammatory and anti-apoptotic miRNA, mirroring the functions of upregulated protein-coding genes.

Pseudogenes: Intriguingly, pseudogenes make up the second largest group of differentially expressed RNA, with 88 annotated genes (54 upregulated, 34 downregulated). Unfortunately, little information is available as to the functional relevance of these transcripts (Figure 5), though this is likely due to the paucity of information about functional pseudogenes in general. Only very recently has research into pseudogenes begun to unearth what roles they may play. Various pseudogenes have been identified as differentially expressed in human and animal models of AD [69–71], and as our understanding of pseudogene functionality increases, it would be useful to revisit the pseudogenes identified in this analysis.

snoRNA: Besides pseudogenes, snoRNA represent the largest proportion of differentially expressed ncRNA in this dataset (Figure 4a). Of these, 8 were downregulated and 29 upregulated, although only 4 of these have distinct snoRNA classifications – Snora78 (log2 FC = 0.83; p < 0.01), Snora34 (log2 FC = 1.68; p < 0.05), Snora2b (log2 FC = 1.65; p < 0.05), and Snord71 (log2 FC = 2.65; p < 0.05). Snora34 (ACA34) and Snora2b (ACA2b), are predicted to guide pseudouridylation of 28S rRNA, Snora78 (ACA64) is predicted to be involved in pseudouridylation of 5.8S rRNA, while Snord71 (MBII-239) guides methylation of 5.8S rRNA. Curiously, some evidence suggests that 28S rRNA is affected in APP/PS1 mice [72], and that there may be an excess of pseudouridine in AD patients [73]. The effect of changes in snoRNA expression and resulting rRNA modifications is still unclear. However, given the crucial role rRNA modifications play in downstream gene expression through mRNA selection, these changes may have far-reaching downstream influences on AD-like pathology.

Table 3: Differentially expressed miRNA related to microglial function and their functional significance.

MicroRNA	log2 FC	P-value	Function	Reference
mmu-miR-335-5p	1.68	< 0.0001	Expressed in microglia; anti-inflammatory.	[74]
mmu-miR-381-3p	1.32	< 0.005	Expressed in microglia; modulates NF-κB signalling.	[75]
mmu-miR-330-3p	0.99	< 0.01	Expressed in microglia; modulates NF-κB signalling.	[76]
mmu-miR-146a-5p	0.98	< 0.01	Expressed in microglia; Primes microglia for activation.	[67]
mmu-miR-19b-3p	0.96	< 0.005	Expressed in microglia; increases production of inflammatory cytokines like TNF-α.	[77]
mmu-miR-101b-3p	0.79	< 0.005	Expressed in microglia; pro-inflammatory, pro-pyroptotic.	[78]
mmu-miR-26b-5p	0.79	< 0.05	Expressed in microglia; anti-apoptotic through targeting IL-6.	[79]
mmu-miR-29c-3p	0.68	< 0.05	Expressed in microglia; anti-inflammatory effects.	[80]
mmu-miR-26a-5p	0.58	< 0.01	Expressed in microglia; anti-inflammatory, modulates TNF-α.	[81]
mmu-miR-222-3p	0.55	< 0.05	Expressed in microglia; pro-inflammatory, pro-apoptotic.	[82]
mmu-miR-29b-3p	0.52	< 0.05	Expressed in microglia; Promotes expression of pro-inflammatory cytokines.	[68]
mmu-miR-7a-5p	-0.53	< 0.05	Suppresses microglial activation.	[20]
mmu-miR-138-5p	-0.73	< 0.0001	Expressed in microglia; anti-inflammatory, anti-apoptotic, Caspase-1.	[83]
mmu-miR-29a-3p	-0.73	< 0.0001	Suppresses microglial activation.	[84]
mmu-miR-128-3p	-0.74	< 0.0001	Suppresses microglial activation.	[85]
mmu-miR-5121	-0.89	< 0.0001	Released from microglia in exosomes – promotes neuronal repair.	[86]
mmu-miR-329-3p	-1.66	< 0.05	Promotes microglial activation; pro-apoptotic.	[87]
mmu-miR-7116-5p	-1.78	< 0.0001	Inhibits TNF-α expression; suppresses microglial activation.	[65]
mmu-miR-204-5p	-1.79	< 0.0001	Suppresses microglial activation.	[64]

lncRNA: When exploring the functionality of the 15 combined lncRNA and lincRNA (Figure 6a), two specific lncRNA stand out – AU020206 (log2 FC = 0.96; p < 0.05) and 2410018L13Rik (log2 FC = 1.00; p < 0.05). AU020206 has been implicated in cholesterol homeostasis, and is differentially expressed in atherosclerosis [88, 89]. Cholesterol homeostasis has a complex relationship with AD, with interactions shown between ApoE4, Aβ, and cholesterol [90, 91]. 2410018L13Rik, meanwhile, is a lncRNA associated with ageing [92]. The other differentially expressed lncRNA in our dataset are poorly characterised or understood.

snRNA: Of the 7 reported differentially expressed snRNA, all of which were found to be upregulated (Figure 6b), only 1 has specific classification – Rnu5g, coding for the U5 snRNA (log2 FC = 0.86; p < 0.05). U5 snRNA is unique in that it is a component of both the major and minor spliceosome. Spliceosomal components have been found to aggregate in close proximity to Aβ plaques in AD brains, suggesting some kind of relationship between AD pathology and the spliceosome [93]. Alternative splicing can play a major role in the development of AD phenotypes through alterations in the exon combinations for a number of key genes [25, 94]. Additionally, the minor spliceosome has recently been implicated in neurodegenerative disorders – notably Spinal Muscular Atrophy (SMA) and Amyotrophic Lateral Sclerosis (ALS) [95]. The functional significance of the minor spliceosome is relatively poorly understood, and its potential roles in AD have not been fully investigated.

A synthesis of RNA expression – investigating RNA-RNA interactions

While much can be learned from individual analysis of differentially expressed gene biotypes, one major strength of simultaneous whole-transcriptome analysis comes from the ability to identify possible interactions between different RNA species within the same dataset.

miRNA and their mRNA targets: The primary example of RNA-RNA interactions is arguably between miRNA and their mRNA targets – a relationship that is among the more well-understood and well-characterised. Information about miRNA:mRNA relationships within our differentially expressed genesets were obtained using DIANA-Tarbase v.8 [48], which contains over 300,000 mouse miRNA-mRNA interactions, and miRWalk 2.0, which contains ~2000 mouse miRNA and 29,000 mRNA.

Analysis of the top differentially expressed protein-coding genes from the RNA-Seq dataset revealed a total of 60 interactions between 15 miRNA and 42 mRNA (Table 4). Most of these interactions show directionality consistent with the transcriptional repression effect of miRNA – 11 of 21 mRNA targets of upregulated miRNA are themselves downregulated, and 35 of 42 mRNA targets of downregulated miRNA are upregulated.

Transcription factors and miRNA targets: These data also allow for the investigation of how transcription factors may affect the expression of non-coding RNA. This kind of analysis is most accessible for miRNA, whose expression, genetic location, and promoter activity are more well-characterised than other ncRNA species. To explore the relationship between transcription factors and miRNA, genes associated with the Gene Ontology keyword “transcription factor activity” were entered into TransmiR, a curated database of transcription factor-miRNA interactions [49, 50]. This analysis resulted in finding 58 total interactions of 8 unique transcription factors (differentially expressed in these APP/PS1 samples) and 18 miRNA (Table 5). Of these 58 interactions, in 29 the miRNA expression is changed in the same direction as the transcription factor.

Echoing their importance to transcriptional regulation of protein-coding genes, the transcription factors c-Fos, Junb, Lmo2, Nfe2l2, and Runx1 are the key regulatory factors controlling miRNA expression in this model (Table 5). The other transcription factors seen in this analysis are Clock (Circadian Locomotor Output Cycles Kaput), Egr2 (Early growth response protein 2), and Nr4a1 (Nuclear receptor 4A1).

Altogether, these links reinforce the changes in gene expression seen in APP/PS1 mice at 15 months. The transcription factors Runx1, c-Fos, Junb, Nfe2l2, and Lmo2 seem to be hubs of transcriptional regulation, while Clock, Egr2, and Nr4a1 add an additional factor of miRNA regulation to the equation. These transcription factors and their regulated miRNA, coupled with the gene targets of those miRNA, provide a picture of a system undergoing widespread pathological activation of microglia and astrocytes, gliosis, and neuroinflammation, as well as processes attempting to mediate the negative effects of such inflammation.

Table 4: Differentially expressed miRNA and their validated targets also differentially expressed in the RNA-Seq data. Targets combined from DIANA TarBase v.8 and miRWalk 2.0.

MicroRNA	Expression in APP/PS1	Protein coding gene	Expression in APP/PS1	References
miR-146a-5p	↑	Glul	↓	[96]
		Rsad2	↑	[97]

miR-19b-3p	↑	Slc1a2	↓	[98]
		Cav1	↑	[98]
		Clock	↑	[98]
		Pros1	↑	[98]
		Kif21b	↓	[98]

miR-26a-5p	↑	Glul	↓	[96, 99]
		Slc1a2	↓	[98]
		Aif1	↓	[96]
		Galnt15	↑	[98]
		Ccnc	↑	[98]

miR-26b-5p	↑	Glul	↓	[96, 99]
		Aif1	↓	[96]

miR-29b-3p	↑	Glul	↓	[99]
		Pdgfra	↓	[99]
		Spsb4	↓	[99]
		Cav2	↑	[100]
		Dusp2	↑	[101]
		Col3a1	↑	[102]

miR-29c-3p	↑	Cav2	↑	[100]

miR-128-3p	↓	Ip6k3	↑	[99]
		Apbb1ip	↑	[99]
		Plau	↑	[99]
		Stard4	↑	[99]
		Nfe2l2	↑	[99]
		Plek	↑	[99]
		Vsir (4632428N05Rik)	↑	[99]
		Tgfbr2	↑	[99]
		Runx1	↑	[99, 103]

miR-138-5p	↓	Vim	↑	[98]
		Gas2l3	↑	[104]
		Egr2	↓	[105]

miR-204-5p	↓	Pim1	↑	[99]
		Ip6k3	↑	[99]
		Tgfbr2	↑	[99]
		Sox11	↑	[106]
		Runx1	↑	[99]

miR-219a-5p		Pdgfra	↓	[107]

miR-29a-3p	↓	Dusp2	↑	[97]
		Plau	↑	[108]
		Rgs10	↑	[97]
		Cav2	↑	[100]
		Col3a1	↑	[102]

miR-329-3p	↓	Clec7a	↑	[96–99, 104]
		Pak6	↓	[96, 98]
		Pappa	↑	[99, 104]
		Plau	↑	[99]
		Slc11a1	↑	[104]

miR-434-3p	↓	Fblim1	↑	[104]

miR-7a-5p	↓	Pim1	↑	[99]
		Ggta1	↑	[99]
		Osmr	↑	[99]
		Tgfbr2	↑	[99]
		C1qa	↑	[96]
		Runx1	↑	[99]

miR-7b-5p	↓	Irf8	↑	[109]
		Rgs10	↑	[98]
		Fos	↓	[110]
		Casp9	↓	[98]
		Lmo2	↑	[98]
		Glul	↓	[98]
		Slc1a2	↓	[98]

Table 5: Differentially expressed transcription factors and their differentially expressed miRNA targets from the RNA-Seq data, as noted in the TransmiR database.

Transcription factor	Expression in APP/PS1	miRNA target	Expression in APP/PS1
Clock	↑	mmu-miR-101b-3p	↑
		mmu-miR-219a-5p	↓
		mmu-miR-26a-5p	↑
		mmu-miR-26b-5p	↑
		mmu-miR-29b-3p	↑
		mmu-miR-29c-3p	↑
		mmu-miR-3535	↓
		mmu-miR-677-5p	↑
		mmu-miR-7116-5p	↓
		mmu-miR-7a-5p	↓

Lmo2	↑	mmu-miR-101b-3p	↑
		mmu-miR-128-3p	↓
		mmu-miR-26b-5p	↑
		mmu-miR-330-3p	↑
		mmu-miR-7b-5p	↓

Runx1	↑	mmu-miR-101b-3p	↑
		mmu-miR-128-3p	↓
		mmu-miR-19b-3p	↑
		mmu-miR-219a-5p	↓
		mmu-miR-26a-5p	↑
		mmu-miR-26b-5p	↑
		mmu-miR-29a-3p	↓
		mmu-miR-29b-3p	↑
		mmu-miR-330-3p	↑
		mmu-miR-3535	↓
		mmu-miR-5121	↓
		mmu-miR-677-5p	↑
		mmu-miR-7116-5p	↓
		mmu-miR-7a-5p	↓
		mmu-miR-7b-5p	↓

Nfe2l2	↑	mmu-miR-29a-3p	↓
		mmu-miR-29b-3p	↑
		mmu-miR-29c-3p	↑

Egr2	↓	mmu-miR-101b-3p	↑
		mmu-miR-128-3p	↓
		mmu-miR-19b-3p	↑
		mmu-miR-219a-5p	↓
		mmu-miR-26a-5p	↑
		mmu-miR-330-3p	↑
		mmu-miR-3535	↓
		mmu-miR-5121	↓
		mmu-miR-7116-5p	↓
		mmu-miR-7a-5p	↓

c-Fos	↓	mmu-miR-101b-3p	↑
		mmu-miR-128-3p	↓
		mmu-miR-29b-3p	↑
		mmu-miR-29c-3p	↑
		mmu-miR-330-3p	↑
		mmu-miR-677-5p	↑
		mmu-miR-7a-5p	↓

Junb	↓	mmu-miR-101b-3p	↑
		mmu-miR-146a-5p	↑
		mmu-miR-29b-3p	↑
		mmu-miR-29c-3p	↑
		mmu-miR-330-3p	↑
		mmu-miR-7b-5p	↓

Nr4a1	↓	mmu-miR-329-3p	↓
		mmu-miR-7116-5p	↓

The aberrant transcriptome of APP/PS1 mice

Understanding the molecular complexity underpinning AD is essential for development of disease modifying therapies. APP/PS1 mice exhibit synaptic dysfunction, synaptic and neuronal loss, as well as plaque formation and thus provide a useful tool to study the early stages of AD. The ability of the whole-transcriptome RNA-Seq protocol utilised in this study to analyse complex RNA-RNA interactions offers a unique approach to RNA-Seq data. Using whole-transcriptome RNA-Seq to encapsulate coding and non-coding transcripts, we have provided a unique snapshot of alterations in the hippocampal transcriptome of 15-month-old APP/PS1 mice, identifying key control hubs which may be potential novel therapeutic targets.

Functional enrichment analysis of differentially expressed protein-coding genes was indicative of the widespread activation of microglia and astrocytes. Activation of microglia from their “resting”, homeostatic state to a pro-inflammatory, “reactive” state is a key component of the brain’s response to injury. This activation results in significant changes to the morphology and transcriptome of the cells [111]. It is considered a key driver of AD-pathology [112–114]. Alongside this, we found reduced expression of the astrocyte genes Glul, Slc1a2, and Slc1a3, which are necessary for glutamate metabolism in “normal”. These changes are suggestive of a shift away from healthy astrocyte function towards pathological astrocyte activation. Similar impairment of glutamate metabolism is commonly observed in astrocytes in both mouse models of AD and human AD patients [115–117].

Analysis of enriched transcription factors revealed the transcriptional control hubs related to changes in glial cells. Eight transcription factors – AP-1 (as c-Fos and Junb), Clock, Egr2, Irf8, Lmo2, Nr4a1, Nfe2l2, and Runx1 – seem to underlie most of the gene expression changes observed. Clock, Egr2, Irf8, Lmo2, Nr4a1, Nfe2l2, and Runx1 are all intimately tied to glial function. The results of our transcription factor analysis consistently show downregulation of transcription factors mediating normal/healthy glial function (Egr2, Nr4a1) and upregulation of transcription factors guiding reactivity of glia (Irf8, Lmo2, Nfe2l2, and Runx1). These effects are mirrored in the functional analysis of the miRNA targets of the identified transcription factors, with Egr2 and Nr4a1 regulating expression of several miRNA that suppress microglial activation (miR-128-3p, miR-329-3p, miR-5121, miR-7116-5p, miR-7a-5p), while upregulated transcription factors target miRNA that themselves promote microglial activation and inflammation (e.g. miR-101b-3p, miR-26a-5p, miR-26b-5p, miR-330-3p). These finding highlight potential alternative approaches to therapeutics for AD that could target these genes/proteins, or upstream/downstream pathways. In particular, targeting pathways that are known to be dysregulated in AD – such as TGF-β signalling, Toll-like receptor signalling or more broadly excessive microglial and astrocyte activation and gliosis – but that are currently not being explored in favour of Amyloid-targeting therapies may prove a more fruitful approach to therapeutics given the difficulties in finding effective Amyloid-targeting therapies.

Limitations:

One hurdle to a thorough analysis of such in-depth RNA-Seq data is the status quo of our understanding of the mouse transcriptome. While great strides have been made in identifying, cataloguing, and annotating the various species of non-coding RNA, there remain significant gaps, both in incomplete annotation and knowledge of functional significance. With the RNA-Seq protocol used in this study following a global transcriptome approach, not selecting for specific types of RNA, many differentially expressed RNA were identified that have not yet been annotated (“predicted genes”). Even many genes that have been named have not been functionally explored. While some biotypes of RNA like messenger RNA and miRNA are relatively well-characterised, the workings of lncRNA, piRNA, and pseudogenes are yet to be understood. Furthermore, as of yet, no unified database exists for functional analysis of all biotypes of ncRNA. This makes it difficult to analyse whole-transcriptome data, necessitating lengthy and detailed manual literature searches. However, new databases are in constant development, be they curated or predictive, and such developments will improve the ability to perform functional analysis of large-scale RNA-Seq data.

The APP/PS1 mouse mutations reflect those observed in the rare familial form of AD but lacks several features common to sporadic AD in humans, including tau pathology. Despite this, our results portrayed far more complex and widespread pathological processes than amyloidopathy, with many overlaps with transcriptome changes seen in humans with AD, particularly with regards to microglial complement activation [118–122].

Our research highlights the potential of moving away from solely developing amyloid-targeting therapies, and instead moving towards multifactorial therapies that function more effectively than the sum of their parts. It is clear that runaway inflammation and reactive gliosis are key components to the later stages of AD and should thus be considered as potential targets for symptom alleviation-focused treatments. But such targets may also be useful for developing prevention-focused treatments, since there is some evidence that the use of anti-inflammatory drugs may affect the incidence as well as the severity of AD in humans [123]. Without ignoring the contribution of amyloidosis to AD pathology, combination therapies targeting both reduction of amyloid load and additional up- or down-stream processes may serve as better treatments in the short term.

In this study we show that a whole-transcriptome RNA sequencing approach can identify gene expression changes of both coding and non-coding genes in a murine model of late-stage AD. This approach reduces additional biases from separation and purification of individual RNA subtypes. To our knowledge, this is the first such exploration of the transcriptome in 15-month-old APP/PS1 mice, and these data show widespread dysfunction of immune processes and dysregulation of microglia- and astrocyte-specific pathways. This research also identifies networks of potential RNA-RNA interactions and predict altered transcription factors driving and underlying these changes. These findings may open doors to therapeutic interventions that target either these genes/proteins, or upstream/downstream pathways.

Consent to participate

Not applicable

Consent for publication

Not applicable

Author Contributions

Nikita Potemkin, Jo-Ann Stanton, and Joanna Williams contributed to the study conception and design. RNA extraction, sequencing library preparation, and data analysis were performed by Nikita Potemkin. Diane Guévremont contributed to sample preparation. Sophie Cawood performed the RT-qPCR. Bruce Mockett maintained the transgenic mouse colony. Jackson Treece performed the RNA sequencing. Nikita Potemkin and Joanna Williams prepared the manuscript. All authors read and approved the final manuscript.

Funding

This study was funded by Health Research Council of New Zealand (Grant no. 16-597) and The University of Otago.

Competing Interests

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

Data Availability

Ethics Approvals

All work was approved through the Ngāi Tahu Consultation process. All animal use was compliant with the New Zealand Animal Welfare Act 1991 and performed under guidelines and approval of the University of Otago Animal Ethics Committee (approval number DET09/15). The reporting in this manuscript follows the recommendations in the Animal Research: Reporting on In Vivo Experiments (ARRIVE) guidelines [28].

Prince M, Wimo A, Guerchet M, et al (2015) World Alzheimer Report 2015: The Global Impact of Dementia - An analysis of prevalence, incidence, cost and trends. Alzheimers Dis Int 84. https://doi.org/10.1111/j.0963-7214.2004.00293.x
Hyman B, Van Hoesen G, Damasio A, Barnes C (1984) Alzheimer’s Disease : Cell-Specific Pathology Isolates the Hippocampal Formation. Science 225:1168–1170
Glenner GG, Wong CW (1984) Alzheimer’s disease: Initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res Commun 120:885–890. http://dx.doi.org/10.1016/S0006-291X(84)80190-4
Grundke-Iqbal I, Iqbal K, Quinlan M, et al (1986) Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J Biol Chem 261:6084–6089
Idda ML, Munk R, Abdelmohsen K, Gorospe M (2018) Noncoding RNAs in Alzheimer’s disease. WIREs RNA 9:e1463. https://doi.org/10.1002/wrna.1463
Zhang Y, Zhao Y, Ao X, et al (2021) The Role of Non-coding RNAs in Alzheimer’s Disease: From Regulated Mechanism to Therapeutic Targets and Diagnostic Biomarkers. Front Aging Neurosci 13:
Patel N, Hoang D, Miller N, et al (2008) MicroRNAs can regulate human APP levels. Mol Neurodegener 3:1–6. https://doi.org/10.1186/1750-1326-3-10
Vilardo E, Barbato C, Ciotti MT, et al (2010) MicroRNA-101 regulates amyloid precursor protein expression in hippocampal neurons. J Biol Chem 285:18344–18351. https://doi.org/10.1074/jbc.M110.112664
Long JM, Lahiri DK (2011) MicroRNA-101 downregulates Alzheimer’s amyloid-β precursor protein levels in human cell cultures and is differentially expressed. Biochem Biophys Res Commun 404:889–895. https://doi.org/10.1016/j.bbrc.2010.12.053
Long JM, Ray B, Lahiri DK (2012) MicroRNA-153 physiologically inhibits expression of amyloid-β precursor protein in cultured human fetal brain cells and is dysregulated in a subset of Alzheimer disease patients. J Biol Chem 287:31298–31310. https://doi.org/10.1074/jbc.M112.366336
Banzhaf‐Strathmann J, Benito E, May S, et al (2014) Micro RNA ‐125b induces tau hyperphosphorylation and cognitive deficits in Alzheimer’s disease. EMBO J 33:1667–1680. https://doi.org/10.15252/embj.201387576
Wang X, Tan L, Lu Y, et al (2015) MicroRNA-138 promotes tau phosphorylation by targeting retinoic acid receptor alpha. FEBS Lett 589:726–729. https://doi.org/10.1016/j.febslet.2015.02.001
Wang G, Huang Y, Wang LL, et al (2016) MicroRNA-146a suppresses ROCK1 allowing hyperphosphorylation of tau in Alzheimer’s disease. Sci Rep 6:. https://doi.org/10.1038/srep26697
Taganov KD, Boldin MP, Chang KJ, Baltimore D (2006) NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci U S A 103:12481–12486. https://doi.org/10.1073/pnas.0605298103
Shin VY, Jin H, Ng EKO, et al (2011) NF-κB targets miR-16 and miR-21 in gastric cancer: Involvement of prostaglandin E receptors. Carcinogenesis 32:240–245. https://doi.org/10.1093/carcin/bgq240
Nazari-Jahantigh M, Wei Y, Noels H, et al (2012) MicroRNA-155 promotes atherosclerosis by repressing Bcl6 in macrophages. J Clin Invest 122:4190–4202. https://doi.org/10.1172/JCI61716
Guévremont D, Tsui H, Knight R, et al (2022) Plasma microRNA vary in association with the progression of Alzheimer’s disease. Alzheimers Dement Diagn Assess Dis Monit 14:e12251. https://doi.org/10.1002/dad2.12251
Faghihi MA, Modarresi F, Khalil AM, et al (2008) Expression of a noncoding RNA is elevated in Alzheimer’s disease and drives rapid feed-forward regulation of β-secretase. Nat Med 14:723–730. https://doi.org/10.1038/nm1784
Massone S, Ciarlo E, Vella S, et al (2012) NDM29, a RNA polymerase III-dependent non coding RNA, promotes amyloidogenic processing of APP and amyloid β secretion. Biochim Biophys Acta - Mol Cell Res 1823:1170–1177. https://doi.org/10.1016/j.bbamcr.2012.05.001
Cao B, Wang T, Qu Q, et al (2018) Long Noncoding RNA SNHG1 Promotes Neuroinflammation in Parkinson’s Disease via Regulating miR-7/NLRP3 Pathway. Neuroscience 388:118–127. https://doi.org/10.1016/j.neuroscience.2018.07.019
Zhao MY, Wang GQ, Wang NN, et al (2019) The long-non-coding RNA NEAT1 is a novel target for Alzheimer’s disease progression via miR-124/BACE1 axis. Neurol Res 41:489–497. https://doi.org/10.1080/01616412.2018.1548747
Wang Z, Zhao Y, Xu N, et al (2019) NEAT1 regulates neuroglial cell mediating Aβ clearance via the epigenetic regulation of endocytosis-related genes expression. Cell Mol Life Sci 76:3005–3018. https://doi.org/10.1007/s00018-019-03074-9
Zeng A, Yin J, Li Y, et al (2018) MiR-129-5p targets Wnt5a to block PKC/ERK/NF-κB and JNK pathways in glioblastoma. Cell Death Dis 9:1–16. https://doi.org/10.1038/s41419-018-0343-1
Rogelj B, Hartmann CEA, Yeo CH, et al (2003) Contextual fear conditioning regulates the expression of brain-specific small nucleolar RNAs in hippocampus. Eur J Neurosci 18:3089–3096. https://doi.org/10.1111/j.1460-9568.2003.03026.x
Aranda-Abreu GE, Hernández Aguilar ME, Durán FR, et al (2016) Tau alternative splicing in alzheimer’s disease. In: Alternative Splicing and Disease. Nova Science Publishers, Inc., pp 39–50
Roy J, Sarkar A, Parida S, et al (2017) Small RNA sequencing revealed dysregulated piRNAs in Alzheimer’s disease and their probable role in pathogenesis. Mol Biosyst 13:565–576. https://doi.org/10.1039/c6mb00699j
Jankowsky JL, Fadale DJ, Anderson J, et al (2004) Mutant presenilins specifically elevate the levels of the 42 residue β-amyloid peptide in vivo: Evidence for augmentation of a 42-specific γ secretase. Hum Mol Genet 13:159–170. https://doi.org/10.1093/hmg/ddh019
Percie du Sert N, Hurst V, Ahluwalia A, et al (2020) The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLOS Biol 18:e3000410. https://doi.org/10.1371/JOURNAL.PBIO.3000410
Ryan MM, Guévremont D, Mockett BG, et al (2018) Circulating Plasma microRNAs are Altered with Amyloidosis in a Mouse Model of Alzheimer’s Disease. J Alzheimers Dis JAD 66:835–852. https://doi.org/10.3233/JAD-180385
Potemkin N, Cawood SMF, Treece J, et al (2022) A method for simultaneous detection of small and long RNA biotypes by ribodepleted RNA-Seq. Sci Rep 12:. https://doi.org/10.1038/s41598-021-04209-4
Franceschini A, Lin J, Von Mering C, Jensen LJ (2016) SVD-phy: Improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Bioinformatics 32:1085–1087. https://doi.org/10.1093/bioinformatics/btv696
Franceschini A, Szklarczyk D, Frankild S, et al (2013) STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:. https://doi.org/10.1093/nar/gks1094
Jensen LJ, Kuhn M, Stark M, et al (2009) STRING 8 - A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37:. https://doi.org/10.1093/nar/gkn760
Snel B, Lehmann G, Bork P, Huynen MA (2000) String: A web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res 28:3442–3444. https://doi.org/10.1093/nar/28.18.3442
Szklarczyk D, Gable AL, Lyon D, et al (2019) STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613. https://doi.org/10.1093/nar/gky1131
Szklarczyk D, Morris JH, Cook H, et al (2017) The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368. https://doi.org/10.1093/nar/gkw937
Szklarczyk D, Franceschini A, Wyder S, et al (2015) STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452. https://doi.org/10.1093/nar/gku1003
Szklarczyk D, Franceschini A, Kuhn M, et al (2011) The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39:. https://doi.org/10.1093/nar/gkq973
von Mering C, Jensen LJ, Kuhn M, et al (2007) STRING 7 - Recent developments in the integration and prediction of protein interactions. Nucleic Acids Res 35:. https://doi.org/10.1093/nar/gkl825
von Mering C, Jensen LJ, Snel B, et al (2005) STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 33:. https://doi.org/10.1093/nar/gki005
von Mering C, Huynen M, Jaeggi D, et al (2003) STRING: A database of predicted functional associations between proteins. Nucleic Acids Res 31:258–261. https://doi.org/10.1093/nar/gkg034
Enright AJ, Van Dongen S, Ouzounis CA (2002) An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 30:1575–1584. https://doi.org/10.1093/nar/30.7.1575
Brohée S, van Helden J (2006) Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7:488. https://doi.org/10.1186/1471-2105-7-488
Chen EY, Tan CM, Kou Y, et al (2013) Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:. https://doi.org/10.1186/1471-2105-14-128
Kuleshov MV, Jones MR, Rouillard AD, et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90–W97. https://doi.org/10.1093/nar/gkw377
Wang Z, Civelek M, Miller CL, et al (2018) BART: a transcription factor prediction tool with query gene sets or epigenomic profiles. Bioinformatics 34:2867–2869. https://doi.org/10.1093/bioinformatics/bty194
Ma W, Wang Z, Zhang Y, et al (2021) BARTweb: a web server for transcriptional regulator association analysis. NAR Genomics Bioinforma 3:. https://doi.org/10.1093/nargab/lqab022
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, et al (2018) DIANA-TarBase v8: A decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res 46:D239–D245. https://doi.org/10.1093/nar/gkx1141
Wang J, Lu M, Qiu C, Cui Q (2009) TransmiR: A transcription factor microRNA regulation database. Nucleic Acids Res 38:. https://doi.org/10.1093/nar/gkp803
Tong Z, Cui Q, Wang J, Zhou Y (2019) TransmiR v2.0: An updated transcription factor-microRNA regulation database. Nucleic Acids Res 47:D253–D258. https://doi.org/10.1093/nar/gky1023
Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210. https://doi.org/10.1093/nar/30.1.207
Herrera-Molina R, Von Bernhardi R (2005) Transforming growth factor-β1 produced by hippocampal cells modulates microglial reactivity in culture. Neurobiol Dis 19:229–236. https://doi.org/10.1016/j.nbd.2005.01.003
Swardfager W, Lanctot K, Rothenburg L, et al (2010) A meta-analysis of cytokines in Alzheimer’s disease. Biol Psychiatry 68:930–941. https://doi.org/10.1016/j.biopsych.2010.06.012
Tesseur I, Zou K, Esposito L, et al (2006) Deficiency in neuronal TGF-β signaling promotes neurodegeneration and Alzheimer’s pathology. J Clin Invest 116:3060–3069. https://doi.org/10.1172/JCI27341
Walker DG, Link J, Lue L-F, et al (2006) Gene expression changes by amyloid β peptide-stimulated human postmortem brain microglia identify activation of multiple inflammatory processes. J Leukoc Biol 79:596–610. https://doi.org/10.1189/jlb.0705377
Gurzov EN, Ortis F, Bakiri L, et al (2008) JunB inhibits ER stress and apoptosis in pancreatic beta cells. PLoS ONE 3:. https://doi.org/10.1371/journal.pone.0003030
Ness JM, Harvey CR, Washington JD, et al (2008) Differential activation of c-Fos and caspase-3 in hippocampal neuron subpopulations following neonatal hypoxia-ischemia. J Neurosci Res 86:1115–1124. https://doi.org/10.1002/jnr.21573
Masuda T, Tsuda M, Yoshinaga R, et al (2012) IRF8 Is a Critical Transcription Factor for Transforming Microglia into a Reactive Phenotype. Cell Rep 1:334–340. https://doi.org/10.1016/j.celrep.2012.02.014
Pajares M, Jiménez-Moreno N, García-Yagüe ÁJ, et al (2016) Transcription factor NFE2L2/NRF2 is a regulator of macroautophagy genes. Autophagy 12:1902–1916. https://doi.org/10.1080/15548627.2016.1208889
Bellissimo DC, Chen CH, Zhu Q, et al (2020) Runx1 negatively regulates inflammatory cytokine production by neutrophils in response to Toll-like receptor signaling. Blood Adv 4:1145–1158. https://doi.org/10.1182/bloodadvances.2019000785
Palazzo AF, Lee ES (2015) Non-coding RNA: what is functional and what is junk?
Pan J, Ma N, Yu B, et al (2020) Transcriptomic profiling of microglia and astrocytes throughout aging. J Neuroinflammation 17:97. https://doi.org/10.1186/s12974-020-01774-9
Jeong A, Cheng S, Zhong R, et al (2021) Protein farnesylation is upregulated in Alzheimer’s human brains and neuron-specific suppression of farnesyltransferase mitigates pathogenic processes in Alzheimer’s model mice. Acta Neuropathol Commun 9:. https://doi.org/10.1186/S40478-021-01231-5
Li L, Sun Q, Li Y, et al (2015) Overexpression of SIRT1 Induced by Resveratrol and Inhibitor of miR-204 Suppresses Activation and Proliferation of Microglia. J Mol Neurosci 56:858–867. https://doi.org/10.1007/s12031-015-0526-5
He Q, Wang Q, Yuan C, Wang Y (2017) Downregulation of miR-7116-5p in microglia by MPP+ sensitizes TNF-α production to induce dopaminergic neuron damage. Glia 65:1251–1263. https://doi.org/10.1002/glia.23153
Zhou Y, Lu M, Du R-H, et al (2016) MicroRNA-7 targets Nod-like receptor protein 3 inflammasome to modulate neuroinflammation in the pathogenesis of Parkinson’s disease. Mol Neurodegener 11:28. https://doi.org/10.1186/s13024-016-0094-3
Saba R, Gushue S, Huzarewich RLCH, et al (2012) MicroRNA 146a (miR-146a) Is Over-Expressed during Prion Disease and Modulates the Innate Immune Response and the Microglial Activation State. PLoS ONE 7:e30832. https://doi.org/10.1371/journal.pone.0030832
Thounaojam MC, Kaushik DK, Kundu K, Basu A (2014) MicroRNA-29b modulates Japanese encephalitis virus-induced microglia activation by targeting tumor necrosis factor alpha-induced protein 3. J Neurochem 129:143–154. https://doi.org/10.1111/jnc.12609
Costa V, Esposito R, Aprile M, Ciccodicola A (2012) Non-coding rnaand pseudogenes in neurodegenerative diseases: “the (un)usual suspects.” Front Genet 3:. https://doi.org/10.3389/fgene.2012.00231
Sekar S, McDonald J, Cuyugan L, et al (2015) Alzheimer’s disease is associated with altered expression of genes involved in immune response and mitochondrial processes in astrocytes. Neurobiol Aging 36:583–591. https://doi.org/10.1016/j.neurobiolaging.2014.09.027
Barbash S, Simchovitz A, Buchman AS, et al (2017) Neuronal-expressed microRNA-targeted pseudogenes compete with coding genes in the human brain. Transl Psychiatry 7:e1199. https://doi.org/10.1038/tp.2017.163
Garcia-Esparcia P, Sideris-Lampretsas G, Hernandez-Ortega K, et al (2017) Altered mechanisms of protein synthesis in frontal cortex in Alzheimer disease and a mouse model. Am J Neurodegener Dis 6:15–25
Hee Lee S, Kim I, Chul Chung B (2007) Increased urinary level of oxidized nucleosides in patients with mild-to-moderate Alzheimer’s disease. Clin Biochem 40:936–938. https://doi.org/10.1016/j.clinbiochem.2006.11.021
Oliveira SR, Dionísio PA, Gaspar MM, et al (2021) miR-335 Targets LRRK2 and Mitigates Inflammation in Parkinson’s Disease. Front Cell Dev Biol 9:661461. https://doi.org/10.3389/fcell.2021.661461
Che Y, He J, Li X, et al (2022) Overexpression of microRNA-381-3p ameliorates hypoxia/ischemia-induced neuronal damage and microglial inflammation via regulating the C-C chemokine receptor type 2 /nuclear transcription factor-kappa B axis. Bioengineered 13:6839–6855. https://doi.org/10.1080/21655979.2022.2038448
Feng Y, Li T, Xing C, et al (2021) Effective inhibition of miR-330/SHIP1/NF-κB signaling pathway via miR-330 sponge repolarizes microglia differentiation. Cell Biol Int 45:785–794. https://doi.org/10.1002/cbin.11523
Ashraf U, Zhu B, Ye J, et al (2016) MicroRNA-19b-3p Modulates Japanese Encephalitis Virus-Mediated Inflammation via Targeting RNF11. J Virol 90:4780–4795. https://doi.org/10.1128/JVI.02586-15
Zeng X, Shen J, Li D, et al (2023) CEBPα/miR-101b-3p promotes meningoencephalitis in mice infected with Angiostrongylus cantonensis by promoting microglial pyroptosis. Cell Commun Signal 21:31. https://doi.org/10.1186/s12964-023-01038-y
Kang Y-C, Zhang L, Su Y, et al (2018) MicroRNA-26b Regulates the Microglial Inflammatory Response in Hypoxia/Ischemia and Affects the Development of Vascular Cognitive Impairment. Front Cell Neurosci 12:154. https://doi.org/10.3389/fncel.2018.00154
Wang R, Li Q, He Y, et al (2020) miR-29c-3p inhibits microglial NLRP3 inflammasome activation by targeting NFAT5 in Parkinson’s disease. Genes Cells Devoted Mol Cell Mech 25:364–374. https://doi.org/10.1111/gtc.12764
Kumar A, Bhatia HS, de Oliveira ACP, Fiebich BL (2015) microRNA-26a modulates inflammatory response induced by toll-like receptor 4 stimulation in microglia. J Neurochem 135:1189–1202. https://doi.org/10.1111/jnc.13364
Bai Y-Y, Niu J-Z (2020) miR-222 regulates brain injury and inflammation following intracerebral hemorrhage by targeting ITGB8. Mol Med Rep 21:1145–1153. https://doi.org/10.3892/mmr.2019.10903
Feng X, Hu J, Zhan F, et al (2021) MicroRNA-138-5p Regulates Hippocampal Neuroinflammation and Cognitive Impairment by NLRP3/Caspase-1 Signaling Pathway in Rats. J Inflamm Res 14:1125–1143. https://doi.org/10.2147/JIR.S304461
Zhao F, Zhao H, Fan J, et al (2021) MiR-29a Knockout Aggravates Neurological Damage by Pre-polarizing M1 Microglia in Experimental Rat Models of Acute Stroke. Front Genet 12:642079. https://doi.org/10.3389/fgene.2021.642079
Yang Z, Xu J, Zhu R, Liu L (2017) Down-Regulation of miRNA-128 Contributes to Neuropathic Pain Following Spinal Cord Injury via Activation of P38. Med Sci Monit Int Med J Exp Clin Res 23:405–411. https://doi.org/10.12659/MSM.898788
Zhao C, Deng Y, He Y, et al (2021) Decreased Level of Exosomal miR-5121 Released from Microglia Suppresses Neurite Outgrowth and Synapse Recovery of Neurons Following Traumatic Brain Injury. Neurotherapeutics 18:1273–1294. https://doi.org/10.1007/s13311-020-00999-z
He J, Xuan X, Jiang M, et al (2021) Long non-coding RNA SNHG1 relieves microglia activation by downregulating miR-329-3p expression in an in vitro model of cerebral infarction. Exp Ther Med 22:1148. https://doi.org/10.3892/etm.2021.10581
Zhang C, Zhang X, Gong Y, et al (2020) Role of the lncRNA-mRNA network in atherosclerosis using ox-low-density lipoprotein-induced macrophage-derived foam cells. Mol Omics 16:543–553. https://doi.org/10.1039/d0mo00077a
Lee K-H, Hwang H-J, Cho J-Y (2020) Long Non-Coding RNA Associated with Cholesterol Homeostasis and Its Involvement in Metabolic Diseases. Int J Mol Sci 21:8337. https://doi.org/10.3390/ijms21218337
Crestini A, Napolitano M, Piscopo P, et al (2006) Changes in cholesterol metabolism are associated with PS1 and PS2 gene regulation in SK-N-BE. J Mol Neurosci 30:311–322. https://doi.org/10.1385/JMN:30:3:311
Chang TY, Yamauchi Y, Hasan MT, Chang C (2017) Cellular cholesterol homeostasis and Alzheimer’s disease. J Lipid Res 58:2239–2254. https://doi.org/10.1194/jlr.R075630
White RR, Milholland B, MacRae SL, et al (2015) Comprehensive transcriptional landscape of aging mouse liver. BMC Genomics 16:899. https://doi.org/10.1186/s12864-015-2061-8
Hales CM, Seyfried NT, Dammer EB, et al (2014) U1 small nuclear ribonucleoproteins (snRNPs) aggregate in Alzheimer’s disease due to autosomal dominant genetic mutations and trisomy 21. Mol Neurodegener 9:. https://doi.org/10.1186/1750-1326-9-15
Love JE, Hayden EJ, Rohn TT (2015) Alternative Splicing in Alzheimer’s Disease. J Park Dis Alzheimers Dis 2:. https://doi.org/10.13188/2376-922X.1000010
Jutzi D, Akinyi MV, Mechtersheimer J, et al (2018) The emerging role of minor intron splicing in neurological disorders. Cell Stress 2:40–54. https://doi.org/10.15698/cst2018.03.126
Schug J, McKenna LB, Walton G, et al (2013) Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver. BMC Genomics 14:. https://doi.org/10.1186/1471-2164-14-264
Loeb GB, Khan AA, Canner D, et al (2012) Transcriptome-wide miR-155 Binding Map Reveals Widespread Noncanonical MicroRNA Targeting. Mol Cell 48:760–770. https://doi.org/10.1016/j.molcel.2012.10.002
Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486. https://doi.org/10.1038/nature08170
Zhang X, Zuo X, Yang B, et al (2014) MicroRNA directly enhances mitochondrial translation during muscle differentiation. Cell 158:607–619. https://doi.org/10.1016/j.cell.2014.05.047
He A, Zhu L, Gupta N, et al (2007) Overexpression of micro ribonucleic acid 29, highly up-regulated in diabetic rats, leads to insulin resistance in 3T3-L1 adipocytes. Mol Endocrinol 21:2785–2794. https://doi.org/10.1210/me.2007-0167
Li Z, Hassan MQ, Jafferji M, et al (2009) Biological functions of miR-29b contribute to positive regulation of osteoblast differentiation. J Biol Chem 284:15676–15684. https://doi.org/10.1074/jbc.M809787200
Van Rooij E, Sutherland LB, Thatcher JE, et al (2008) Dysregulation of microRNAs after myocardial infarction reveals a role of miR-29 in cardiac fibrosis. Proc Natl Acad Sci U S A 105:13027–13032. https://doi.org/10.1073/pnas.0805038105
Motohashi N, Alexander MS, Casar JC, Kunkel LM (2012) Identification of a novel microRNA that regulates the proliferation and differentiation in muscle side population cells. Stem Cells Dev 21:3031–3043. https://doi.org/10.1089/scd.2011.0721
Leung AKL, Young AG, Bhutkar A, et al (2011) Genome-wide identification of Ago2 binding sites from mouse embryonic stem cells with and without mature microRNAs. Nat Struct Mol Biol 18:237–245. https://doi.org/10.1038/nsmb.1991
Adilakshmi T, Sudol I, Tapinos N (2012) Combinatorial action of mirnas regulates transcriptional and post-transcriptional gene silencing following in vivo pns injury. PLoS ONE 7:. https://doi.org/10.1371/journal.pone.0039674
Shaham O, Gueta K, Mor E, et al (2013) Pax6 Regulates Gene Expression in the Vertebrate Lens through miR-204. PLoS Genet 9:. https://doi.org/10.1371/journal.pgen.1003357
Dugas JC, Cuellar TL, Scholze A, et al (2010) Dicer1 and miR-219 Are Required for Normal Oligodendrocyte Differentiation and Myelination. Neuron 65:597–611. https://doi.org/10.1016/j.neuron.2010.01.027
Marzi MJ, Puggioni EMR, Dall’Olio V, et al (2012) Differentiation-associated microRNAs antagonize the Rb-E2F pathway to restrict proliferation. J Cell Biol 199:77–95. https://doi.org/10.1083/jcb.201206033
Dou C, Zhang C, Kang F, et al (2014) MiR-7b directly targets DC-STAMP causing suppression of NFATc1 and c-Fos signaling during osteoclast fusion and differentiation. Biochim Biophys Acta - Gene Regul Mech 1839:1084–1096. https://doi.org/10.1016/j.bbagrm.2014.08.002
Lee HJ, Palkovits M, Young WS (2006) miR-7b, a microRNA up-regulated in the hypothalamus after chronic hyperosmolar stimulation, inhibits Fos translation. Proc Natl Acad Sci U S A 103:15669–15674. https://doi.org/10.1073/pnas.0605781103
Eggen BJL, Raj D, Hanisch UK, Boddeke HWGM (2013) Microglial phenotype and adaptation. J Neuroimmune Pharmacol 8:807–823. https://doi.org/10.1007/s11481-013-9490-4
Overmyer M, Helisalmi S, Soininen H, et al (1999) Reactive microglia in aging and dementia: an immunohistochemical study of postmortem human brain tissue. Acta Neuropathol (Berl) 97:383–392. https://doi.org/10.1007/s004010051002
Hardy J, Salih D (2021) TREM2-mediated activation of microglia breaks link between amyloid and tau. Lancet Neurol 20:416–417. https://doi.org/10.1016/S1474-4422(21)00133-2
Guan Y-H, Zhang L-J, Wang S-Y, et al (2022) The role of microglia in Alzheimer’s disease and progress of treatment. Ibrain 8:37–47. https://doi.org/10.1002/ibra.12023
Meda L, Baron P, Scarlato G (2001) Glial activation in Alzheimer’s disease: The role of Aβ and its associated proteins. Neurobiol Aging 22:885–893. https://doi.org/10.1016/S0197-4580(01)00307-4
Cassano T, Serviddio G, Gaetani S, et al (2012) Glutamatergic alterations and mitochondrial impairment in a murine model of Alzheimer disease. Neurobiol Aging 33:1121.e1-1121.e12. https://doi.org/10.1016/j.neurobiolaging.2011.09.021
Hefendehl JK, LeDue J, Ko RWY, et al (2016) Mapping synaptic glutamate transporter dysfunction in vivo to regions surrounding Aβ plaques by iGluSnFR two-photon imaging. Nat Commun 7:. https://doi.org/10.1038/ncomms13441
Yasojima K, Schwab C, McGeer EG, McGeer PL (1999) Up-regulated production and activation of the complement system in Alzheimer’s disease brain. Am J Pathol 154:927–936. https://doi.org/10.1016/S0002-9440(10)65340-0
Tacnet-Delorme P, Chevallier S, Arlaud GJ (2001) β-Amyloid Fibrils Activate the C1 Complex of Complement Under Physiological Conditions: Evidence for a Binding Site for Aβ on the C1q Globular Regions. J Immunol 167:6374–6381. https://doi.org/10.4049/jimmunol.167.11.6374
Sim RB, Kishore U, Villiers CL, et al (2007) C1q binding and complement activation by prions and amyloids. Immunobiology 212:355–362. https://doi.org/10.1016/j.imbio.2007.04.001
Wilcox KC, Lacor PN, Pitt J, Klein WL (2011) Aβ oligomer-induced synapse degeneration in Alzheimer’s disease. Cell Mol Neurobiol 31:939–948. https://doi.org/10.1007/s10571-011-9691-4
Stephan AH, Barres BA, Stevens B (2012) The complement system: An unexpected role in synaptic pruning during development and disease. Annu Rev Neurosci 35:369–389. https://doi.org/10.1146/annurev-neuro-061010-113810
Rivers-Auty J, Mather AE, Peters R, et al (2020) Anti-inflammatories in Alzheimer’s disease—potential therapy or spurious correlate? Brain Commun 2:. https://doi.org/10.1093/braincomms/fcaa109

OnlineResource1.csv
Online Resource 1: Full list of genes differentially expressed between APP/PS1 and wild-type littermate control mice, using unadjusted p-value < 0.05.
OnlineResource2.pdf
Online Resource 2: Bar and dot plot showing log2 fold-change of the genes Trem2, Tyrobp, and Cst7in APP/PS1 mice compared to wild-type controls as measured by qPCR. Bars indicate SD, and individual datapoints are also shown. Cst7 reported a 200 ± 97-fold change (log FC 7.7, p = 0.0016), while Trem2 and Tyrobpshowed fold-changes of 3.5 ± 0.63 and 3.44 ± 0.32 respectively (p = 0.0059 and p < 0.0001).
OnlineResource3.csv
Online Resource 3: Transcription factors predicted to target differentially expressed genes in APP/PS1 mice, predicted by BART. TFs themselves differentially expressed are highlighted in red.

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Whole transcriptome RNA-Seq reveals drivers of pathological dysfunction in a transgenic model of Alzheimer’s disease

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Abstract

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Introduction

Methods

Data availability

Results

RNA-Seq reveals genes differentially expressed between APP/PS1 and wild-type control mice at 15 months.

Functional enrichment analysis highlights key gene clusters and pathways changed in the APP/PS1 mouse model of AD.

Analysis of enriched Transcription Factors

Biotype analysis of RNA-Seq data

Functional enrichment analysis of non-coding RNA

A synthesis of RNA expression – investigating RNA-RNA interactions

Discussion

Limitations:

Conclusions

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Consent for publication

Author Contributions

References

Supplementary Files

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Version 1