Transcriptome and Translatome Regulation of Pathogenesis in Alzheimer’s Disease Model Mice

Abstract

Background:

Defining cellular mechanisms that drive Alzheimer’s disease (AD) pathogenesis and progression will be aided by studies defining how gene expression patterns change during pre-symptomatic AD and ensuing periods of declining cognition. Previous studies have emphasized changes in transcriptome, but not translatome regulation, leaving the ultimate results of gene expression alterations relatively unexplored in the context of AD.

Objective:

To identify genes whose expression might be regulated at the transcriptome and translatome levels in AD, we analyzed gene expression in cerebral cortex of two AD model mouse strains, CVN (APP_SwDI;NOS2^-/-) and Tg2576 (APP_Sw), and their companion wild type (WT) strains at 6 months of age by tandem RNA-Seq and Ribo-Seq (ribosome profiling).

Methods:

Identical starting pools of bulk RNA were used for RNA-Seq and Ribo-Seq. Differential gene expression analysis was performed at the transcriptome, translatome, and translational efficiency levels. Regulated genes were functionally evaluated by gene ontology tools.

Results:

Compared to WT mice, AD model mice had similar levels of transcriptome regulation, but differences in translatome regulation. A microglial signature associated with early stages of Aβ accumulation was upregulated at both levels in CVN mice. Although the two mice strains did not share many regulated genes, they showed common regulated pathways related to AβPP metabolism associated with neurotoxicity and neuroprotection.

Conclusion:

This work represents the first genome-wide study of brain translatome regulation in animal models of AD and provides evidence of a tight and early translatome regulation of gene expression controlling the balance between neuroprotective and neurodegenerative processes in brain.

Keywords

Alzheimer’s disease amyloid microglia RNA-Seq mRNA translation transcriptome regulation

INTRODUCTION

Alzheimer’s disease (AD) is a progressive brain neurodegenerative disorder and the most common cause of dementia. Individuals with AD respond only marginally and briefly to currently available drugs and their long-term care is extremely costly. At the histopathological level, AD has well characterized hallmarks that include extracellular plaques made from amyloid-β (Aβ) peptides, intraneuronal tangles made from the microtubule-associated protein, tau, synapse loss, and neuron death [1].

A comprehensive understanding of the molecular mechanisms underlying AD is paramount for the development of novel therapies that can impede its onset and progression. In particular, impaired mRNA translation has been implicated in other neurological diseases [2 –5], and there are reports linking Aβ and tau to dysregulated translation [6 –12]. For example, local protein synthesis is altered in brain synaptosomes isolated from an AD mouse model that overproduces Aβ [6] and Aβ oligomers induce de novo synthesis of tau itself [8]. Also, tau interacts with ribosomes in vitro [9] and in vivo and decreases global translation [12] and the synthesis of ribosomal proteins [7]. Recently, an antisense transcript-mediated mechanism regulating tau translation has been described and implicated in human brain tau pathologies [13]. Furthermore, Aβ and tau work coordinately to regulate the mTORC1 complex [14, 15], which controls a plethora of cellular functions, including mRNA translation [16].

A diversity of genomics tools have been developed to understand neurodegenerative diseases [17], resulting in powerful studies to explore transcriptome changes associated with AD [18 –21]. However, translatome regulation has barely been examined, and other than one study about microglia [22], we are unaware of any work that directly compared transcriptome and translatome regulation in the context of AD. Here, we describe the use of ribosome profiling, or Ribo-Seq [23 –25], as a genomic screening technique to detect mRNA translation regulation in the brain cortex in transgenic 6-month-old CVN (APP_SwDI;NOS2^-/-) and Tg2576 (APP_Sw) mice, both of which model human AD, and in wild type (WT) mice of the same strain backgrounds. At that age, CVN mice do not yet express any of the AD-like histological or behavioral phenotypes seen later in life (CVN: https://www.alzforum.org/research-models/appswdi-x-nos2-knock-out) but the Tg2576 strain already exhibits measurable cognitive impairment, reduced (long-term potentiation) LTP in the dentate gyrus and limited neuron loss (Tg2576: https://www.alzforum.org/research-models/tg2576).

Beginning with isolated, bulk cortical RNA, we performed RNA-Seq to define mRNA steady-state levels, and in tandem we produced and sequenced ribosome-associated mRNA footprints that revealed the exact positions of active ribosomes on mRNAs in the process of being translated into proteins. Quantitative analysis allowed us to estimate translatome levels at a genome-wide level and to compare translational efficiency [24] between transgenic mice and their WT counterparts. We thereby uncovered translatome regulated genes with a complex signature that implicated genes involved in elevated Aβ production and neurotoxicity as well as in reducing the Aβ load in brain.

MATERIALS AND METHODS

Mice

Three 6-month-old male mice from each of the following genotypes were used: CVN [26], Tg2576 [27], and their respective WT controls (C57/BL6 and B6;SJL, respectively; see Table 1). Animals were maintained, bred, and euthanized in compliance with all policies of the Animal Care and Use Committee of the University of Virginia.

Table 1

AD model mice

Strain	Gene(s)	Mutations	Modification	WildTtype Background Strain
CVN (APP_SwDI x NOS2^-/-)	APP, NOS2	APP: K670N/M671L, E693Q, D694N	APP: Transgenic NOS2: Knock-Out	C57/BL6
Tg2576	APP	APP: KM670/671NL	APP: Transgenic	B6;SJL

Transcriptomic and ribosome profiling of brain cortex

Cortices (∼250 mg each) were dissected from freshly removed brains in ice cold PBS containing 100μg/ml cycloheximide (CHX; Sigma-Aldrich, catalog # 01810). The tissue was then cut into smaller pieces with a sterile scalpel and Dounce homogenized on ice in lysis buffer (5 mM Tris pH 7.5, 2.5 mM MgCl₂, 1.5 mM KCl, 0.5% Triton X-100, 0.5% sodium deoxycholate, 2 mM 1,4-dithiothreitol and 100μg/ml CHX), using 1 ml of lysis buffer per 100 mg of tissue. A transcriptome sample was then separated, and total RNA was isolated using a mirVana Total RNA Isolation Kit (Invitrogen, catalog # AM1560) according to the vendor’s recommended protocol.

Ribo-Seq was performed as previously described [28, 29]. Briefly, lysate samples isolated as just described were centrifuged twice at 4°C at ∼17,000 g, for 1 and 10 min consecutively, to remove large cellular debris, nuclei, and mitochondria. The post-mitochondrial supernatant (∼1.6 ml; OD₂₆₀ = 5–10 AU) was loaded onto a 12–33.5% (w/v) sucrose cushion prepared in polysome buffer (20 mM HEPES pH 7.5, 5 mM MgCl₂, 100 mM KCl, 100μg/ml CHX) and centrifuged for 2 h at 36,000 rpm (222,228 g_max) in a Beckman SW41Ti rotor at 4°C. The polysome-enriched pellet was resuspended in polysome buffer and digested with 180–200 units of Benzonase nuclease (Millipore, catalog # E1014) for 10 min at room temperature to remove the RNA that was unprotected by ribosomes and thus produce protected ribosome footprints. Digestion was stopped by addition of 3 volumes of mirVana Lysis Buffer, and the RNA was isolated and then concentrated by overnight precipitation with 80% ethanol to maximize small RNA recovery. The concentrated RNA (10–15μg per sample) was then size fractionated by electrophoresis using 15% polyacrylamide-urea gels (ThermoFisher Scientific, catalog # EC68852BOX) run at 200 volts in TBE (89 mM Tris-borate, pH 8.3, 2 mM EDTA) for 65 min to separate ribosome-protected mRNA footprints. Gels were stained using GelRed (Biotium, catalog # 41003) and circular agitation for 10 min in the dark. The ribosome footprint bands were identified using 26-mer and 34-mer RNA oligonucleotides [30] and excised in a dark room under UV light exposure. RNA recovery from gel slices was done overnight at room temperature by gentle mixing on a Nutator [30]. Size, quality, and quantity of both transcriptome and translatome samples were evaluated in an Agilent 2100 Bioanalyzer using Nano and Small RNA kits (Agilent Technologies, catalog #s 5067-1511 and 5067-1548, respectively).

Sequencing

All transcriptome and translatome samples were sequenced at BGI Tech Solutions (Hong Kong). Transcriptome samples were sequenced using an RNA-Seq quantification library protocol with ribosomal RNA (rRNA) removal library preparation, yielding at least 20 million paired-end (2×100 bp) reads. Translatome samples (ribosome footprints) were processed by small RNA library protocol, yielding at least 100 million single-end (50 bp) reads. Raw sequence data are available at the NCBI Sequence Read Archive (SRA; https://trace.ncbi.nlm.nih.gov/Traces/sra/) under BioProject ID PRJNA677972.

Data analysis

Quality control of sequence files was performed using FastQC [31] and then mapped against the Mus musculus genome (mm10/GRCm38 version) using bowtie2 [32] and defaults parameters. Read counts over mRNAs or genes were estimated by featureCounts [33], and differential gene expression analysis of transcriptomes or translatomes was done separately using edgeR [34], comparing each strain with its respective WT parental strain. Normalized counts were exported, and translational efficiency was calculated and contrasted between AD model mouse strains and their WT counterparts using the Xtail R package [35]. For all comparisons (transcriptome, translatome and translational efficiency) differentially expressed genes (DEGs) were defined by a false discovery rate (FDR) adjusted p-value < 0.05 and a fold change of > 1 or > 1.5, as indicated in each case. Functional interpretation and ontology enrichment analysis of DEG lists were performed using Ingenuity Pathway Analysis (IPA; QIAGEN Inc.) [36], online tools like STRING [37] and g:Profiler [38], and in-house software (manuscript in preparation; https://github.com/sradiouy/IdMiner) AmiGO2 database [39] was used to retrieve genes related to AβPP, Aβ, and tau. Plots were generated using R, by general or specific packages, such as pheatmap (https://CRAN.R-project.org/package=pheatmap) and GOplot [40].

RESULTS

High quality datasets of brain transcriptome and translatome differentiate CVN and Tg2576 models

CVN and Tg2576 AD model mice, and the parental WT strains from which they were derived (Table 1 and Fig. 1A) were used to explore transcriptome and translatome gene expression regulation in the brain cortex of six-month-old animals using RNA-Seq and Ribo-Seq, respectively (Fig. 1B). More than 20 million paired-end reads were obtained for transcriptomes and an average of 120 million reads were acquired for translatomes (Supplementary Table 1), derived from total RNA and isolated ribosome footprints (Supplementary Figure 1), respectively. In the transcriptome samples, 88% of the reads aligned over the reference genome, of which 90% mapped to gene regions and 77% to mRNA regions (Supplementary Table 1). On the other hand, in the translatome samples we mapped more than 10 million reads over mRNAs (Supplementary Table 1). In this case, ribosomal RNA depletion was avoided to minimize protocol biases.

Fig. 1

Protocol summary. A) Phenotypes of the CVN (adapted from CVN: https://www.alzforum.org/research-models/appswdi-x-nos2-knock-out) and Tg2576 (adapted from Tg2576: https://www.alzforum.org/research-models/tg2576) mice used in this study. Three 6-month-old male mice were used for each transgenic strain, and for each corresponding WT strain (C57/Bl6 for CVN and B6;SJL for Tg2576). B) Sample preparation. Brain cortex was dissected and homogenized using a glass Dounce homogenizer to yield a transcriptome sample containing total RNA to be used for RNA-Seq, and a post-mitochondrial supernatant, which was ultracentrifuged to isolate polysomes. Ribosome footprints for Ribo-Seq were isolated from the polysomes by an RNA protection assay that digested all RNA not encased inside the ribosomes. Further details are described in the Materials and Methods section.

Expression levels (CPM, counts per million) were estimated for more than 14 thousand different mRNAs in each sample above low/noise signal (Supplementary Figure 2). Inter-replicate correlations indicated high similarity within either transcriptome or translatome samples, but as expected, lower correlations were detected between RNA-Seq (transcriptome) and Ribo-Seq (translatome) data (Supplementary Figure 3). Quality control comparisons between transcriptome and translatome samples revealed the expected difference at the level of triplet periodicity and read distribution among mRNA features (Supplementary Figures 4 and 5). In addition, principal component analysis (PCA) showed a clear separation between genotypes for both RNA-Seq and Ribo-Seq datasets (Supplementary Figure 6).

Gene expression is regulated at the transcriptome and translatome levels in cortices of CVN and Tg2576 mice

We used the edgeR R package [34] to detect differences in cortical gene expression (p-adjusted value < 0.05) with a directionally independent fold change (FC) of > 1 between the transgenic mice and their respective WT controls at both the transcriptome and translatome levels. Here, we will use the term transcriptome regulation to refer to the observed difference obtained from gene expression measurements derived from RNA-Seq. We recognize, though, that this approach reflects several gene expression regulation mechanisms while quantifying only steady-state levels of mRNA. Similarly, we will henceforth refer to translatome regulation to differences derived from Ribo-Seq as they indicate net translatome levels for each mRNA.

For CVN versus their WT parental strain (C57/Bl6), we found 469 DEGs at the transcriptome level and 1,165 DEGs at the translatome level. Of those, 240 and 377 genes had > 1.5-fold differential expression by RNA-Seq and Ribo-Seq, respectively (Table 2 and Fig. 2). For Tg2576 versus their WT counterparts (B6;SJL), we found 343 DEGs at the transcriptome level and 135 at the translatome level with FC > 1. Out of those totals, 140 transcriptome regulated and 94 translatome regulated genes had a FC > 1.5 (Table 2 and Fig. 3). Complete lists of the DEGs detected for CVN and Tg2576 mice are found in Supplementary Tables 2 and 3, respectively.

Table 2

Differentially expressed genes for CVN and Tg2576 mice versus their wild type background strain counterparts. Differentially expressed genes, defined by p-adjusted value < 0.05, are separated by fold change (FC) intervals

CVN versus WT		p-adjusted value < 0.05
		FC > 1.5	FC > 1	FC < – 1.5	FC < – 1
edgeR	RNA-Seq	160	282	80	187
	Ribo-Seq	300	830	77	335
Xtail	Translational Efficiency	88	491	56	306
Tg2576 versus WT		p-adjusted value < 0.05
	FC > 1.5	FC > 1	FC < – 1.5	FC < – 1
edgeR	RNA-Seq	76	194	64	149
	Ribo-Seq	46	66	48	69
Xtail	Translational Efficiency	25	50	25	37

Fig. 2

Differential expression in CVN versus WT (C57/Bl6) cortices determined by edgeR for transcriptomes (A-C), and by Xtail for translational efficiency (D-F). Scatter plots comparing normalized CPM expression between genotypes for transcriptome and translational efficiency values, respectively, are shown in (A) and (D). Volcano plots showing the relationship between fold change and p-adjusted values are shown in (B) and (E). Scatter plots comparing translatome versus transcriptome fold change are shown in (C) and (F).

Fig. 3

Differential expression in Tg2576 versus WT (B6;SJL) cortices determined by edgeR for transcriptomes (A-C), and by Xtail for translational efficiency (D-F). Scatter plots comparing normalized CPM expression between genotypes for transcriptome and translational efficiency values, respectively, are shown in (A) and (D). Volcano plots showing the relationship between fold change and p-adjusted values are shown in (B) and (E). Scatter plots comparing translatome versus transcriptome fold change are shown in (C) and (F).

To disentangle transcriptome from translatome regulation, we estimated translational efficiencies based on the ratios between translatome- and transcriptome-derived expressions levels for each transgenic strain and its WT parental counterpart. For this we used the Xtail R package [35], which revealed that CVN cortices contained 797 translatome regulated genes with FC > 1, of which 144 had a FC > 1.5 (Table 2 and Fig. 2). Similarly, Tg2576 cortices were found to contain 87 translatome regulated genes with FC > 1, of which 50 had a FC > 1.5 (Table 2 and Fig. 3).

The global distribution of DEGs values, the relationship between FC and p-adjusted values, and the association between transcriptome and translatome FCs are shown for both AD mouse models in Figs. 2 and 3 and Supplementary Figures 7 and Figs. 2 and 3 and Supplementary 8. As expected, DEGs were distributed along all expression levels (Fig. 2A, 2D, 3A, 3D) and the relationship between FC and p-adjusted values showed classic Volcano plots for both transcriptome and translational efficiency levels (Fig. 2B, 2E, 3B, 3E). When the FC at the translatome level was plotted against the transcriptome, a clear correlation was observed (Fig. 2C, 3C). For example, in CVN mice, 106 out of the 160 DEGs with a FC > 1.5 at the transcriptome level were also DEGs at the same cutoff at the translatome level (Supplementary Figure 9). A similar correlation was evident for the transcriptome downregulated genes; 32 out of 80 were also downregulated at the translatome level (Supplementary Figure 9). Analogous results were also observed in Tg2576 mice (Supplementary Figure 9) and indicated the expected association between transcriptome and translatome samples.

By comparison, translatome regulated genes, although less in number than those regulated at the transcriptome level, showed a different pattern of regulation (Fig. 2F, 3F). In general, translational efficiency regulation involved genes with minimal changes at transcriptome levels (FC ∼1). This pattern was also apparent in the DEGs expression heatmap (Fig. 4). Transcriptome regulated genes expression levels showed classical patterns of regulation in contrast to the translational efficiency regulation. For example, similar expression levels for the transcriptome coupled with an increase or decrease for the translatome implied translational efficiency regulation. This scenario, among others, could be observed in the heatmaps of Fig. 4. Heatmaps of regulated genes exclusively at the translatome level are described in Figures S7D and S8D for CVN and Tg2576 mice, respectively. Collectively, these results demonstrate differential transcriptome and translatome regulation of gene expression in both murine AD models.

Fig. 4

Heatmaps of differentially expressed genes (FC > 1.5 and p-adjusted value < 0.05).

Regulation of distinct biological pathways in cortices of CVN versus Tg2576 mice

We next used IPA [36] to identify biological functions associated with genes that were upregulated or downregulated at FC > 1 in CVN and Tg2576 mice compared to their respective WT counterparts. CVN mice showed a clear functional response at the transcriptome, translatome and translational efficiency levels (Fig. 5, Supplementary Figure 10, and Supplementary Table 4). Downregulated biological functions in CVN mice were dominated by processes indicative of neuronal decline, such as neurodegeneration, demyelination, and decreased axon growth, LTP, and glutamate release. In contrast, indicators of myelination, glial cell abundance, and neurotransmission were identified by IPA as major upregulated processes in CVN mice. IPA also detected an upregulated neuroprotective response for microglia, which may reflect the early accumulation of Aβ. However, IPA also detected activation of neurodegeneration-related processes like reduced axon growth and increased neuron death, especially at the transcriptome level (Supplementary Table 4). Moreover, at the translational efficiency level, IPA reported a marginal (low z-score) decrease in LTP related genes (Fig. 5).

Fig. 5

Functional enrichment analysis of differentially expressed genes in CVN and Tg2576 mice. The top 10 (z-score) decreased and increased functional categories are shown for RNA-Seq, Ribo-Seq, and Translational Efficiency. Analysis was performed using Ingenuity Pathway Analysis and graphical representation was obtained from GOplot R package. Supplementary Table 4 contains the complete set of decreased and increased pathways at each level.

In contrast, only a few overrepresented functional categories in Tg2576 mice were identified with IPA (Fig. 5 and Supplementary Table 4). At the transcriptome level, no categories were found to decrease, and only three categories increased: neuritogenesis, microtubule dynamics, and synaptic transmission. At the translatome level, only the category of quantity of microglia was decreased, but with a low z-score, and no categories increased. No functional categories in translational efficiency were detected.

Using IPA again, we then performed an upstream analysis to infer modulated genes by the observed regulation of their targets. For CVN mice, most of the upstream genes were closely related to AD pathology and implied protective reactions to avoid Aβ accumulation and counter neurodegeneration (Fig. 6). For example, upstream genes that were suppressed included: PSEN1, PSEN2, APOE, B4GALNT1, ST8SIA1, and CTCF at the transcriptome level; SSB at the translatome level; and ADORA2A at the translational efficiency level. Decreased expression levels of PSEN1, PSEN2, and APOE could signal a defense response against Aβ production, as might reduced expression of B4GALNT1 and ST8SIA1, ganglioside synthases that increase AβPP cleavage and affect memory [41, 42]. CTCF encodes a transcription factor that can act as an activator or repressor, and also serves as an insulator protein that defines chromatin domains and can upregulate AβPP expression [43]. CTCF knockout in mouse hippocampus increases cytokine expression and activates microglia [44]. ADORA2A is an adenosine receptor, and its pharmacological inhibition or downregulation restores LTP and reverses memory deficits [45, 46].

Fig. 6

Upstream regulation in CVN mice predicted from differentially expressed gene lists by Ingenuity Pathway Analysis.

On the other hand, upstream genes indicated as activated seem to represent both protective and degenerative responses (Fig. 6). TCF7L2 and CSF1, which we found to be activated at the transcriptome level, probably have a protective function. TCF7L2 may be involved in improving neurogenesis and compensate neuron loss [47] and CSF1 has been linked to microglial activation, prevention of cognitive loss, and reduction of Aβ accumulation [48, 49]. PTF1A and IFNG, identified as upstream activated genes at the translatome level, may also be protective. PTF1A can induce neuronal stem cell generation and improve cognitive dysfunction [50]. IFNG, which encodes interferon gamma (IFN-γ), can activate microglia to suppress Aβ deposition and induce neurogenesis [51 –53]. In contrast, other genes found to be activated, such as TNF, may promote degenerative processes. TNF encodes tumor necrosis factor alpha (TNF-α), a proinflammatory cytokine that exacerbates both Aβ and tau pathologies in vivo. However, the roles of cytokines like IFN-γ and TNF-α are controversial in AD [55].

The upstream activated genes identified from the translational efficiency DEGs, PTF1A (see above) and PRKCG, are both probably protective. Protein kinase C γ, which is encoded by PRKCG, can stimulate AβPP processing by α-secretase to produce soluble fragments of AβPP and reduce Aβ accumulation [56]. In contrast, we did not detect any prominent upstream gene regulation in the Tg2576 mice as might be expected from the uninformative functional enrichment analysis. Collectively, our functional analyses reinforce the conclusion that the CVN and Tg2576 models exhibit differential cortical brain gene regulation at six months of age.

Regulated genes in CVN mice reveal a signature of microglia responding to Aβ

To extend the functional analysis further, we used IdMiner, a tool that we developed in-house (manuscript in preparation; https://github.com/sradiouy/IdMiner) to enhance interpretation of DEGs. This software is a text-mining tool that captures previously reported associations between gene lists and user-defined terms using the PubMed database. We found that among upregulated genes in CVN mice at both the transcriptome and translatome level there was a clear signature of disease-associated microglia [20]. This signature included genes such as TREM2, TYROBP, TLR2, CD68, GPR84, GPNMB, ITGAX, ITGB2, LPL, CLEC7A, CST7, and CCL6 [20 , 57–60]. All these genes encode transmembrane proteins that are highly expressed in a microglial subpopulation that specifically responds to neurodegeneration. Overexpression of these markers orchestrates clearance of Aβ by a subpopulation of disease-associated microglia [20 , 61]. This signature is part of the immune system response in AD, a statistically overrepresented function among upregulated gene networks at both the transcriptome and translatome levels (Supplementary Figures 11 and 12).

Other genes upregulated at both the transcriptome and translatome levels included those expressed primarily either in non-neuronal or neuronal cells, but not in both. For example, GFAP and SERPIN3 are overexpressed in astrocytes in AD [62 –65]. Solute carrier family members and hippocalcin-like proteins were also upregulated, in accordance with their possible neuroprotective functions in AD [66, 67]. Additionally, we found increased expression of MID1, which encodes a member of the MID1 protein complex that binds to and accelerates APP mRNA translation through an mTOR-dependent pathway [68]. The tyrosine kinase gene, FGR, was also upregulated at both transcriptome and translatome levels. Fgr is able to bind tau, and although functional consequences of this interaction are not well understood [69], the Fgr paralog, Fyn, binds and phosphorylates tau [70] causing neurotoxicity [71, 72].

An analysis of the genes upregulated specifically at the translatome level revealed other examples of AD-associated genes. Examples include the histocompatibility 2 class II antigen A gene, H2-AB1, a marker for disease-associated microglia [57, 58], and complement components 4a and 4b, which were upregulated as previously described in response to Aβ plaque increase [65]. The CSF3R gene was also upregulated at the translatome level, as expected, since it is highly expressed in early stages of AD [73]. Deficiency in G-CSF, a ligand for the receptor encoded by CSF3R, has a deep impact in hippocampal structure and function leading to disruption in memory formation and impaired behavioral performance [74]. Likewise, injected G-CSF acts as a neurotrophic factor and induces astrocytic and microglial activation [75, 76], so elevated CSF3R expression may signify a response mechanism to mitigate neurodegeneration.

When studying the list of upregulated genes particularly at translational efficiency level, we detected several notable genes: SERPINA3G, FRMD4A, RRAS2, AV2, ADAM12, PPARG, MID1, LDLRAD3, and CD44. Some of these genes are associated with protective or beneficial functions. For example, FRMD4A, PPARG, and RRAS2 have been shown to reduce high levels of Aβ production [77 –80], while ADAM12, MID1, and LDLRAD3 influence Aβ production or neurotoxicity [68 , 82].

Regarding downregulated genes, we also found examples related to Aβ accumulation. CD59A (complement defense 59A) was downregulated at the transcriptome level, as might be expected in response to Aβ, and could contribute to neuronal vulnerability and loss [83, 84]. Calbindin 2 (calretinin; CALB2) positive interneurons are specifically decreased as early targets of Aβ accumulation in the hippocampus [85] and in the current study its expression was downregulated at the transcriptome and translatome levels. PIN4 was also downregulated at both levels in accordance with the decrease observed for Pin1, a paralog gene, which plays a role in the accumulation of Aβ [86 –88] and tau [89, 90]. Other downregulated genes that may be germane to neurodegeneration include RPLP0 and IGF2 [7, 91], while genes like SLC17A8 and TNC (tenascin-C) may have been downregulated to ameliorate pathology [92].

Tg2576 mice differentially express genes associated with AβPP metabolism

The number of DEGs in Tg2576 mice was much less than in CVN mice. The Toll like receptor 6 gene (TLR6) was upregulated at both the transcriptome and translatome levels. Tlr6 can dimerize with Tlr4 and interact with CD36 to bind Aβ and activate microglia, and thereby promote neurodegeneration [93]. Calpain 11 was also upregulated at the transcriptome level, as expected since calpains are hyperactivated in AD [94, 95]. CDK5RAP1, an inhibitor of Cdk5, was also upregulated and could have a protective effect since Cdk5 activation causes hyperphosphorylation of AβPP and tau leading to plaques and tangles [96 –98]. Also, Cdk5 has been described as a mediator of Aβ-induced neuronal cell cycle reentry that leads to neurodegeneration [99].

At the translatome level, evidence of other interesting upregulated genes was found. Apolipoprotein D, which has a neuroprotective effect [100] and is induced in hippocampal cells in response to Aβ [101], was increased at the translatome level. Another gene with a neuroprotective function upregulated at the translatome level encodes Metallothionein 3, which is expressed by astrocytes and is thought to facilitate Aβ uptake [102], although this role remains controversial with opposite findings in vitro and in vivo and with different mouse models. For example, a study using the Tg2576 mice model shows that Metallothionein 3 could have opposite effects depending on gender, brain region, and age [103]. Genes upregulated exclusively at the level of translational efficiency were related to Aβ production and disease. Examples include SERF2, a positive regulator of amyloid protein aggregation [104, 105], and HRAS2, an apparent AD biomarker that is stimulated by Aβ and produces a reduction in LTP [78].

Some transcriptome downregulated genes confirmed what was expected from previous reports. For example, KLK6 encodes a peptidase that cleaves AβPP and is downregulated in the cortex of human postmortem samples of AD patients compared to controls [106, 107]. The somatostatin receptor 5 was downregulated at both the transcriptome and translatome level, consistent with the decreased levels reported for these receptors in the cortex of AD patients [108, 109]. The FABP5 gene was found to be downregulated at the translational efficiency level as expected since a Fabp5 deficiency is associated with increased vulnerability to cognitive deficits in mice with AβPP pathology [110, 111]. Thus, Tg2576 mice showed DEGs associated with AβPP metabolism at both the transcriptome and translatome level.

Regulated functions common to both CVN and Tg2576 mice are involved in AβPP metabolism

We next compared the DEGs detected for CVN and Tg2576 mice using a FC cutoff of 1 to explore commonalities shared by both mouse models (Table 2). Common genes were rare, representing < 10% of the genes in our DEG list. When comparing upregulated genes at the transcriptome level between CVN (282 genes) and Tg2576 (195 genes) mice, we found 25 shared genes. Of those, 19 are classified as predicted genes. Exhaustive analysis revealed that those genes mainly encode Zinc finger and α-takusan-like proteins, but they also include a long non-coding RNA (Gm26650). α-takusan proteins represent a large family that regulates synaptic activity and reportedly can mitigate Aβ-induced synaptic loss [112, 113]. Among the common and annotated genes, we found LRRC37A, a leucine-rich repeat containing gene that encodes a plasma membrane protein involved in intracellular vesicle trafficking [114] and OSMR (Oncostatin M Receptor). LRRC37A was previously associated with AD because of its many SNPs detected in genome-wide association studies of APOE4 carriers and its genomic location adjacent to the tau gene, MAPT [115]. On the other hand, it has been reported that Oncostatin M is neuroprotective against Aβ toxicity [116].

When exploring the translatome upregulated genes shared by both transgenic models, several interesting examples were noticed. Due to the small number of such genes in Tg2576 mice (66 genes, Table 2), we only found 6 genes in common. However, among them we found LRRC37A and a predicted gene (GM3173) annotated as an α-takusan-like protein (see above). Other interesting genes found were GFAP and Hemoglobin alpha 1 and 2. Glial fibrillary acidic protein (GFAP) is an intermediate filament protein highly expressed in the reactive astrocytes that surround Aβ plaques [62, 117]. Hemoglobin alpha 1 and 2 (encoded by HBA-A1 and HBA-A2) is expressed by neurons [118] and can bind Aβ and co-localize with plaques [119, 120].

We found 13 downregulated genes common to CVN and Tg2576 mice, 8 of which were downregulated at the transcriptome level and 5 of which were downregulated translationally. Here we highlight TMEM59L, which was downregulated at the transcriptome level and encodes transmembrane protein 59-like, an important paralog of TMEM59. TMEM59 has been identified as a novel modulator of AβPP shedding, controlling AβPP post-translational modification, trafficking, and cleavage [121]. More recently, it has been reported that TMEM59 interacts with TREM2 (see above and Discussion), and that TMEM59 homeostasis is regulated by TREM2 in order to control microglia activity [122]. Common genes downregulated at the translatome level include ribosomal proteins and ribosome biogenesis factors (Rpl5, Rpl32m and Wbscr22), the fatty acid binding protein 5 (FABP5) and adaptor related protein complex 4 (AP4S1). It has been reported that the adaptor protein 4 interacts with AβPP, and that the disruption of this interaction stimulates AβPP cleavage and Aβ production [123].

Finally, despite the low identity overlap of DEGs in CVN versus Tg2576 mice, we also observed biological functions shared by the two AD mouse model strains. For example, several genes affected in each strain are associated with AβPP and Aβ metabolism, either with neuroprotective or neurotoxic functions (Fig. 7).

Fig. 7

Expression regulation of genes related to Aβ, AβPP, or tau metabolism. Several gene ontology categories obtained from AmiGO2, were merged to consider all genes related to Aβ, AβPP, or tau metabolism. Genes were selected by p-adjusted value < 0.05 and the expression levels of those significant genes are shown on the heatmaps.

DISCUSSION

We used RNA-Seq and Ribo-Seq to explore transcriptome and translatome gene expression regulation, respectively, in the brain of two transgenic mouse models of AD. Studies of translation regulation using Ribo-Seq in mouse brain tissue have been sparse (see examples in [124 –128]), and, to our knowledge, our work represents the first approach in AD models. Both transgenic mice used in this study are amyloid models since each strain has a transgenic insertion of the APP gene with specific mutations that increase levels of Aβ and amyloid plaques (Table 1). Animals were euthanized at 6 months of age, when CVN mice are asymptomatic both behaviorally and histopathologically (Fig. 1 and CVN: https://www.alzforum.org/research-models/appswdi-x-nos2-knock-out), but Tg2576 mice exhibit synaptic loss in the CA1 region of the hippocampus, reduced long term potentiation in the dentate gyrus, and a variety of cognitive impairments (Fig. 1 and Tg2576: https://www.alzforum.org/research-models/tg2576).

The isolated total RNA and ribosome footprints obtained from the brain cortex (Supplementary Figure 1) yielded more than 20 and 120 million paired-end and single-end reads, respectively (Supplementary Table 1). We specifically used the brain cortex, a region highly degenerated and pathologically compromised in both humans with AD and murine models of the disease. As anticipated, around 80% of the ribosome footprints samples were rRNA fragments derived from the RNAse digestion. We therefore resorted to deep sequencing to obtain workable amounts of non-rRNA reads. By doing so, we obtained more than 10 million ribosome footprints mapping over mRNAs in each sample (Supplementary Table 1), and we defined expression levels for more than 14 thousand genes in each transgenic strain. As expected, almost all genes that we detected were found at both the transcriptome and translatome levels (Supplementary Figure 2). By studying mapping features and read periodicity, we separated transcriptome-derived from translatome-derived reads (Supplementary Figures 4 and 5). For example, translatome-derived reads show the classical three nucleotide mapping pattern periodicity not detected in transcriptome-derived reads, providing evidence of solid transcriptome and translatome datasets.

Differential gene expression analysis was performed over transcriptome and translatome samples separately, comparing transgenic mice versus WT, to explore deregulated genes in each model. However, considering that differences at the translatome level could be explained by regulation at the transcriptome level, we estimated translational efficiency as the ratio between translatome and transcriptome levels to specifically identify translational regulation events [23] (Figs. 2 and 3). Despite using different animals as replicates, which could generate considerable biological variation, samples clustered as expected as evidenced by principal component analysis (Supplementary Figure 6), probably indicating that genotype is the main source of variation. Also, we were able to define lists of genes with a significant statistical difference between genotypes at p-adjusted values < 0.05 (Table 2). Expression levels of DEGs also separates samples as expected and shows the different levels of regulation observed, especially when analyzing translational efficiency regulation (Fig. 4). As a control comparison, we also explored differential gene expression between WT animals, and there is only a limited overlap with previously defined DEGs, and the enriched biological functions observed were either not related to the results previously discussed or absent (Supplementary Figures 13 and 14).

The functional analysis of regulated genes involves different approaches and techniques. We used standard gene ontology and functional enrichment tools, namely IPA and STRING, which revealed a clear signature of decreased neurodegenerative-related process and increased neuroprotective-related functions, particularly for the CVN strain (Figs. 5 and 6). Secondly, we complemented the analysis with an in-house developed tool (https://github.com/sradiouy/IdMiner) to deeply explore DEGs lists in order to find relevant associations between genes and biological functions related to pathology. By using this approach, we detected several genes associated either with the neurodegenerative and neurotoxicity phenotype or with protective and beneficial functions (see below).

In CVN mice, we identified a clear gene signature common in disease-associated microglia (DAM). These genes mainly encode transmembrane proteins that were upregulated at both transcriptome and translatome levels, and mediate immune system processes (Supplementary Figures 11 and 12). Earlier studies of other AD mouse models at the same or older ages suggest that the main functions of this DAM population are to locate and clear Aβ plaques [20 , 58]. It is noteworthy that a DAM population is present in CVN mice at a stage when they do not show any phenotypic signs of disease. Among the genes present in this signature are TREM2 and TYROBP, both of which were upregulated (transcriptome level: FC = 2.12 and 1.66, p-adjusted value < 1.18E-15 and < 6.22E-4, respectively; translatome level: FC = 1.90 and 1.72, p-adjusted value < 4.25E-8 and < 1.39E-4, respectively). The TREM2/TYROBP complex expressed in microglia is necessary to prevent Aβ accumulation and diffusion [20 , 129–131]. TREM2/TYROBP-dependent cell activation seems to be beneficial [132], while TREM2 deficiency impairs cellular metabolism and promotes increased autophagy in microglia in an AD mouse model [133]. Concordantly, in CVN mice we found several members of the TYROBP AD-related pathway [59, 134] regulated at the transcriptome and/or translatome level (Supplementary Figure 15). Regarding TREM2, transgenic overexpression in an AD mouse model of the human version of this gene modified the morphological and functional responses of microglia, which resulted in amelioration of the pathology and memory deficits [135]. Interestingly, it has been reported that TREM2 interacts with TREM59 and modulates microglia activation [122]. Here we found TREM59L as a downregulated gene at the transcriptome level in both transgenic mice models (CVN: FC = –1.89 and p-adjusted value < 3.56E-28; Tg2576: FC = –1.29 and p-adjusted value < 0.01). Regarding functional interpretation of DEGs and considering the upregulated genes at the translatome level, two different sets of genes were observed. One set associated with protective functions, such as reducing Aβ production, and others related to the high levels of Aβ and neurotoxicity. A similar interpretation was made from the lists of downregulated genes.

Although Tg2576 mice experience cognitive deficits at 6 months, amyloid plaques typically do not appear in this strain until 11–13 months (Fig. 1). Nevertheless, we found a group of genes that were regulated at the transcriptome and/or translatome levels in the 6-month-old Tg2576 mice and have been reported to be altered in response to the presence of Aβ. In light of the dearth of plaques, we suspect that expression of these genes was affected by accumulation of Aβ oligomers in cortical brain tissue. Also, as observed for CVN mice, we found genes associated with neuroprotective functions and others with a neurodegenerative phenotype related to AβPP metabolism (Fig. 7).

We also explored DEGs that were common to both AD strains at the transcriptome or translatome levels. Despite the facts that CVN and Tg2576 mice are derived from different WT parental strains (Table 1), exhibit different patterns of phenotypic alterations (Fig. 1A), and distinct regulation levels (Table 2), it is noteworthy that they share genes that are regulated at the transcriptome and translatome levels. These DEGs, such as α-takusan proteins, LRRC37A, OSMR, GFAP, hemoglobin alpha 1 and 2, TMEM59L, and FABP5, are seen at early stages of disease and modulate functions that could be associated with AD pathology. Despite the lack of plaques in 6-month-old CVN mice, most of the DEGs common to both strains are implicated in AβPP metabolism in a neuroprotective fashion that responds to Aβ accumulation. However, the low number of common regulated genes was unexpected. Likely reasons for this include the previously mentioned possibilities plus the fact that the NOS2 gene is deleted in CVN, but not Tg2576 mice (Table 1).

It is important to note that we analyzed gene expression of a complex brain tissue that contains a heterogeneous population of cells. In addition to specific regulation events occurring in particular cell types that could be underestimated here, the high abundance of neurons adds an extra layer of complexity, because of the large dimensions and high polarity these cells can achieve. Considering the relevant contribution of local regulation events in neurons [136 –139] and in particular previous evidence in the context of AD [11], we cannot exclude the possibility that local protein synthesis events may regulate neuron response to local stimulus in the brain cortices. To specifically disentangle these regulatory events, one would need to conduct new experiments using tools designed to examine functional genomics in specific neuronal compartments [17].

Overall, this study documents the contribution of the different layers of gene expression regulation involved with AD pathology, especially at the level of translation. The two amyloid-producing animal models used here show relevant gene expression regulation at the transcriptome and translatome levels. Although the number of DEGs was markedly different, both models reveal several regulated genes associated with AβPP and Aβ metabolism. However, we noticed that each transgenic mouse was able to modulate different groups of genes that represent specific biological functions. For example, although the 6-month-old CVN mice that we used were asymptomatic for AD-like traits, their gene expression profiles were skewed towards neuroprotection. Degeneration processes were decreased, protective functions were increased, and a specific microglia subpopulation was induced as a presumptive response to Aβ accumulation prior to plaque formation. On the other hand, 6-month-old Tg2576 mice displayed signs of simultaneous neuroprotection and neurodegeneration. Perhaps this reflects a more rapidly progressing AD-like phenotype manifest as cognitive impairment at that age for Tg2576 mice (Tg2576: https://www.alzforum.org/research-models/tg2576), as compared to ∼13 months for CVN mice (CVN: https://www.alzforum.org/research-models/appswdi-x-nos2-knock-out). Nevertheless, the gene expression regulation described here at the transcriptome and translatome levels is derived from transgenic mouse models that overexpress mutated versions of human APP throughout life. Conclusions based on these mouse studies must be considered cautiously when they are extrapolated to bona fide sporadic AD in humans.

Recent advances in genomic views of translation have been essential to widen our current concepts about the dynamic properties of protein translation as a process. More specifically, such advances increase the relevance that regulation of translation has on particular sets of mRNAs, and by extension on proteostasis. Besides pointing to novel transcriptome regulation events in AD disease models, here we provide evidence that translation is important in regulating key AD related genes at early asymptomatic stages. Several of the dysregulated pathways observed here will need further study to elucidate regulation mechanisms, but their identification implies that both translation and transcription are dysregulated during both pre-symptomatic and symptomatic phases of AD.

Footnotes

ACKNOWLEDGMENTS

We thank members of the Sotelo-Silveira lab for the results discussion and intellectual input. We also thank members of the Bloom lab, especially Drs. Andrés Norambuena and Antonia Silva, for their support with wet lab experiments, and Dr. Dora Bigler-Wang and Nutan Shivange for handling mice and brain dissections. We also acknowledge the following sources of financial support: PhD fellowships from Agencia Nacional de Investigación e Innovación (ANII) to G.E. (POS NAC 2016 1 129959), Programa de Desarrollo de las Ciencias Básicas (PEDECIBA) to G.E. and J.R.S-S.,PROLAB travel grant (PABMB/ASBMB/IUBMB) to G.E.; the Owen’s Family Foundation, the Cure Alzheimer’s Fund, and the Rick Sharp Alzheimer’s Foundation to G.S.B., J.S.L. and E.R.S., and NIH grant RF1 AG051085 to G.S.B.

Authors’ disclosures available online ().

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

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