‘Arc’-hitecture of normal cognitive aging

Craig Myrum; Perla Moreno-Castilla; Peter R Rapp

doi:10.1016/j.arr.2022.101678

. Author manuscript; available in PMC: 2023 Sep 1.

Published in final edited form as: Ageing Res Rev. 2022 Jul 1;80:101678. doi: 10.1016/j.arr.2022.101678

‘Arc’-hitecture of normal cognitive aging

Craig Myrum ¹, Perla Moreno-Castilla ¹, Peter R Rapp ^1,^*

PMCID: PMC9378697 NIHMSID: NIHMS1823599 PMID: 35781092

Abstract

Arc is an effector immediate-early gene that is critical for forming long-term memories. Since its discovery 25 years ago, it has repeatedly surprised us with a number of intriguing properties, including the transport of its mRNA to recently-activated synapses, its master role in bidirectionally regulating synaptic strength, its evolutionary retroviral origins, its ability to mediate intercellular transfer between neurons via extracellular vesicles (EVs), and its exceptional regulation—both temporally and spatially. The current review discusses how Arc has been used as a tool to identify the neural networks involved in cognitive aging and how Arc itself may contribute to cognitive outcome in aging. In addition, we raise several outstanding questions, including whether Arc-containing EVs in peripheral blood might provide a noninvasive biomarker for memory-related synaptic failure in aging, and whether rectifying Arc dysregulation is likely to be an effective strategy for bending the arc of aging toward successful cognitive outcomes.

Keywords: Arc, normal cognitive aging, plasticity, immediate early gene, memory

1. Introduction

Age-related cognitive decline, including the dramatic impairment observed in neurodegenerative disorders like Alzheimer’s disease (AD), is among the most feared and costly consequences of growing old. While some individuals suffer cognitive deficits in late life, a substantial percentage appear resilient. Even excluding AD, the emergence of greater interindividual variability in cognitive abilities in old age is a consistent phenomenon observed in cognitive aging research across all species examined, including mice, rats, non-human primates, and humans (Gray and Barnes, 2015; McQuail et al., 2018; Morrison and Baxter, 2012; Rapp, 2009; Rapp et al., 2020). Animal studies, where undiagnosed AD-associated neuropathology is not a confounding factor, have been particularly well-suited to identifying neurobiological substrates that lead to successful or unsuccessful outcomes in normal cognitive aging.

High throughput studies have aimed to identify overall gene expression patterns that reflect individual differences in age-related cognitive performance and/or chronological age. Here we highlight a single gene from those studies that has attracted particular attention—activity-regulated cytoskeleton-associated protein (Arc)—and the body of work that has sought to unravel its contribution to cognitive outcome in aging. Arc was discovered concurrently by two labs in 1995 and dubbed activity-regulated gene 3.1 (Arg3.1)(Link et al., 1995), and the name more frequently used today, Arc (Lyford et al., 1995). Interest in better understanding Arc’s molecular mechanisms mounted when Arc knockout studies revealed that it is essential for long-but not short-term memory formation (Plath et al., 2006). Although many genes are involved in the plastic properties that together mediate memory, Arc stands out. This is in part attributable to its uniquely close coupling with plasticity processes over neuronal and synaptic activity more broadly (Carpenter-Hyland et al., 2010; Fletcher et al., 2006), its remarkably intricate regulation (Soulé et al., 2012), the rapid transport and specific targeting of its mRNA to recently-activated dendrites (Link et al., 1995; Lyford et al., 1995; Steward et al., 1998), its requirement for both the strengthening and weakening of synapses (Guzowski et al., 2000; Messaoudi et al., 2007; Plath et al., 2006; Waung et al., 2008), and evidence indicating that Arc evolved from a Ty3-Gypsy retrotransposon Gag domain and has retained the topology of a retroviral Gag protein, including a matrix and capsid domain (Campillos et al., 2006). Arc is capable of reversible self-association, forming dimers and higher-order oligomeric species (Myrum et al., 2015a). Remarkably, recombinant Arc was shown to self-assemble into spheroid particles resembling retroviral Gag capsids that are released in extracellular vesicles (EVs) and are capable of transmitting RNA cargo to recipient cells (Ashley et al., 2018; Pastuzyn et al., 2018).

Arc’s “master” role in brain plasticity has been the topic of multiple reviews (Bramham et al., 2010; Korb and Finkbeiner, 2011; Minatohara et al., 2016; Shepherd and Bear, 2011), and others offer focused coverage of various aspects of Arc biology and function (Corrêa, 2018). Evidence that implicates Arc in the mechanisms underlying cognitive decline associated with AD is also reviewed elsewhere (Kerrigan and Randall, 2013). Unravelling Arc’s role in normal cognitive aging has the potential to advance AD research. This is due in part to Arc’s role in Aβ generation and purported role in AD pathogenesis (Wu et al., 2011), but the data leave open the question of whether Arc disruption is a cause, consequence, stage-specific effect of disease, or simply a proxy for the network disruption that may be a more proximal cause of impairment. By illuminating Arc dynamics in aging models uncontaminated by neurodegeneration, the studies discussed here offer insight into the cellular, synaptic, and molecular processes that set the stage for neuropathological outcomes in aging.

Although a multitude of studies indicate that Arc dynamics are disrupted in normal aging, the concordance between results at first glance appears limited. This almost certainly reflects the diversity of species, strains, and models used, the brain region(s) examined, the analytical methods employed (especially whether mRNA or protein is measured), when tissue is collected in relation to behavioral assessment, the cognitive task used, and whether behavioral testing captures the increased interindividual variability prominently observed in normal aging. The present review aims to identify the most consistent themes across studies and contextualize our current understanding of Arc’s role in normal cognitive aging. Given the highly specialized roles of the hippocampus and the neocortex in encoding and retrieval of episodic memories, and the behavioral tasks tailored to probe their functions, here we examine the hippocampus and other cortical regions separately. Since specific hippocampal and neocortical subregions have also been differentially linked to cognitive outcomes in aging (e.g. Haberman et al., 2011), when possible, we further distinguish between subregions. Lastly, we also distinguish the background literature based on whether Arc expression was explicitly experience/activity-induced or measured basal/constitutive levels. We begin with a brief overview of the principal animal models and methods used. We also consider the potential mechanisms underlying disrupted Arc expression, whether Arc is uniquely dysregulated among IEGs in cognitive aging, and whether restoring normal Arc expression might rectify age-related memory deficits.

2. Key animal models and methodology

Insight into Arc dynamics in cognitive aging comes primarily from four strains of rats: outbred Long-Evans (LE) rats, inbred Fischer 344 (F344) rats, F344 × Brown Norway (FBN) F1 hybrid rats, and inbred LOU/C/Jall (LOU) rats. The reliability and high sensitivity of the Morris Water Maze (MWM) (Morris, 1981; Morris et al., 1982) have positioned this behavioral task as a cornerstone for assessing rodent medial temporal lobe function in these models, including studies that demonstrate its value in cognitive aging studies (Gage et al., 1988; Rapp et al., 1987). Other behavioral procedures have also been used, depending on the memory and cognitive domain tested, and the brain region of interest. For example, the object-place paired association task tests cognitive flexibility and relational memory and requires medial prefrontal cortex-medial temporal lobe communication (Hernandez et al., 2018), while the working memory/biconditional association task (WM/BAT) requires interactions between prefrontal, medial temporal, and subcortical structures (Colon-Perez et al., 2019).

LOU rats have been used as a model of “successful” cognitive aging based on their good spatial memory even at 38-42 months of age (Ménard et al., 2014), in addition to intact novel object recognition compared to age-matched Sprague–Dawley (SD) rats (Kollen et al., 2010; Ménard et al., 2014) and intact spatial working memory (using a spontaneous alternation Y-maze task) compared to age-matched Wistar rats (Paban et al., 2013). In contrast, LE rats (Gallagher et al., 1993), FBN rats (McQuail and Nicolle, 2015), and F344 rats (Bizon et al., 2009; McQuail et al., 2018) display substantial interindividual variability such that some perform within the same range as young animals on the MWM task and others display significant deficits. The increased variance in memory capacity among aged rats recapitulates observations in humans, and offers the opportunity to relate variability to underlying neurobiological substrates. Studies using LE rats frequently distinguish well-performing aged animals (i.e., aged unimpaired; AU) that perform on par with young (Y) rats from other, age-matched rats that display learning impairment (aged impaired; AI), while the majority of studies using F344 and FBN rats examine the aged group as a whole. Although many of the studies described here compare Arc in young versus aged rats (i.e. chronological aging), rather than directly to measures of cognitive outcome in aging, those data do not preclude the possibility that Arc is linked to interindividual variability in cognitive aging in those studies. Together these strains and the toolbox of behavioral tasks that have been applied to assess multiple cognitive domains offer a highly complementary approach for modeling the neuropsychology of aging in rats.

The timecourse of Arc induction and intracellular trafficking are well-characterized and important for evaluating studies mapping neuronal activity patterns using Arc. Activity-induced Arc mRNA transcription peaks at around 30 min, while Arc protein peaks later, at 60–120 min (Bottai et al., 2002; Ramírez-Amaya et al., 2005; Soulé et al., 2012). Several studies in F344 and FBN rats have utilized cellular compartment analysis of temporal activity by fluorescence in situ hybridization (catFISH), capitalizing on known Arc temporal and intracellular translocation properties. The method assesses neuronal activation during an initial epoch of behavior, identified by the presence of Arc that has translocated to the cytoplasm ~30 min after activity, and reengagement of those cells during a second activity epoch, signaled by Arc that is in the nucleus ~5 min after activity. The inference is that neurons containing both nuclear and cytoplasmic Arc were active during both epochs of behavior (Guzowski et al., 1999). Studies extrapolating from Arc catFISH data can yield insight into network functional connectivity involved in memory formation and storage, and further, a readout of neural circuits and regional vulnerability and resilience coupled to cognitive aging. In reviewing studies of this sort, it is useful to be aware that, although Arc is a valuable tool for identifying recently activated cellular networks, it remains unclear the extent to which differences in expression levels are the product of differential activation magnitudes, summation of neuronal firing, and region- or cell-type specific expression kinetics.

It is important to note that Arc’s critical role in encoding, storing, and recalling information is rooted in its role as an effector molecule that directly affects synaptic properties, unlike many other IEGs that primarily serve as inducible transcription factors that regulate the expression of other genes. Due in large part to this direct effector role, several lines of research demonstrate that disrupted Arc expression has profound consequences on synaptic plasticity and memory formation (Bramham et al., 2010; Corrêa, 2018; Korb and Finkbeiner, 2011; Minatohara et al., 2016; Shepherd and Bear, 2011). This background, together with the work reviewed here, positions Arc as a potential key player in the mechanistic basis of individual differences in the cognitive outcome of aging.

3. Constitutive Arc expression in cognitive aging

Transcriptome analyses have offered substantial insight in nearly all fields of biomedical research, and their use in identifying genes that influence cognitive trajectory throughout the lifespan is no exception. These studies, as well as research that has expressly examined basal Arc expression in aged animals (often to provide cage control data for comparison with experience-dependent regulation), are summarized in Tables 1–2.

Table 1:

Studies examining basal hippocampal Arc expression in aging. Rows are organized first by which hippocampal subregion was examined and second by whether mRNA or protein was measured.

Species	Ages	Behavior	mRNA/protein (method)	Brain region	Finding	Study
Male Wistar and LOU rats	5 and 26 months (both strains)	Basal	mRNA (Microarray)	Whole hippocampus and frontal cortex	Wistar: ↑ Arc in aged rats in both regions, confirmed via RT-qPCR.ns in LOU/C/Jall rats	(Paban et al., 2013)
Male F344 rats	4-6 months and 24-26 months	Basal; another group was tested 21 days after MWM testing.	mRNA (Microarray)	Dorsal hippocampus	↓ Arc with age in both untrained and 21-day post-training.	(Rowe et al., 2007)
Male and female C57BL/6J mice	6 and 24 months	Basal	mRNA (microarray)	Whole hippocampus	↓ in AI compared to Y, confirmed via RT-qPCR.	(Qiu et al., 2016)
Female C57BL/6 mice	3 and 11 months	Basal (Time point not reported with respect to behavioral tasks)	mRNA (RT-qPCR)	Whole hippocampus	↓ in aged versus Y	(Li et al., 2015)
Male and female CD-1 mice	3 and 15 months	Basal	protein (WB)	Whole hippocampus	↑ in aged versus Y; significant correlation between Arc and MWM performance in aged but not Y mice	(Zhang et al., 2020)
Male F344 rats	4, 14, and 24 months	Basal (MWM; sacrificed 24 h after object memory task). Likely includes some activity-induced expression (Ramírez-Amaya et al., 2005)	mRNA (Microarray)	CA1	↓ Arc with age	(Blalock et al., 2003)
Male LE rats	8–9 and 25 months	Basal	mRNA (Microarray)	CA1	ns (Y/AU/AI)	(Haberman et al., 2011)
Male F344 rats	9-12 months and 24-32 months	Basal	mRNA (RT-qPCR and catFISH)	CA1	↓ in aged compared to Y (RT-qPCR) ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Penner et al., 2011)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	Basal	mRNA (catFISH)	CA1	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male FBN rats	4 months and 24 months	Basal	mRNA (catFISH)	CA1 (dorsal and ventral examined separately)	ns % cells expressing Arc in either subfield	(Hernandez et al., 2018)
Male F344 rats	7–9 months old and 24–27 months old	Basal	mRNA (catFISH)	CA1 (proximal and distal examined separately)	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats in either subfield	(Hartzell et al., 2013)
Male LE rats	6 months and 24 months	Basal	mRNA (ISH)	CA1	ns (Y/AU/AI)	(Fletcher et al., 2014)
Male LE rats	6 months and 24 months	Basal	mRNA (RT-qPCR)	CA1	↑ in AI compared to Y and AU	(Myrum et al., 2020)
Male and female C57BL/6J mice	6 and 24 months	Basal	mRNA (ISH)	CA1	↓ in aged versus Y	(Qiu et al., 2016)
Male and female CD-1 mice	3 and 15 months	Basal	mRNA (ISH)	CA1	↑ in aged versus Y; no significant correlations with MWM performance	(Zhang et al., 2020)
Male LE rats	6 months and 24 months	Basal	Protein (WB)	CA1	ns (Y/AU/AI)	(Fletcher et al., 2014)
Male LE rats	6 months and 24 months	Basal	Protein (IHC)	CA1	ns (Y/AU/AI)	(Myrum et al., 2019)
Male LE rats	8–9 and 25 months	Basal	mRNA (Microarray)	CA3	ns (Y/AU/AI)	(Haberman et al., 2011)
F344 rats (Harlan SD)	10-12 months and 24-27 months	Basal	mRNA (ISH)	CA3	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male LE rats	6 months and 24 months	Basal	mRNA (RT-qPCR)	CA3	↑ in AI compared to Y and AU	Myrum et al., 2020
Male LE rats	6 months and 24 months	Basal	mRNA (ISH)	CA3	ns (Y/AU/AI)	(Fletcher et al., 2014)
Male and female C57BL/6J mice	6 and 24 months	Basal	mRNA (ISH)	CA3	↓ in aged versus Y	(Qiu et al., 2016)
Male and female CD-1 mice	3 and 15 months	Basal	mRNA (ISH)	CA3	↑ in aged versus Y; no significant correlations with MWM performance	(Zhang et al., 2020)
Male LE rats	6 months and 24 months	Basal	Protein (WB)	CA3	↑ in AI compared to Y	(Fletcher et al., 2014)
Male LE rats	6 months and 24 months	Basal	Protein (IHC)	CA3	ns (Y/AU/AI)	(Myrum et al., 2019)
Male LE rats	8–9 and 25 months	Basal	mRNA (Microarray)	DG	ns (Y/AU/AI)	(Haberman et al., 2011)
Male F344 rats	9-12 months and 24-32 months	Basal	mRNA (RT-qPCR and ISH)	DG	ns (RT-qPCR; Y/AU/AI) ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Penner et al., 2011)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	Basal	mRNA (ISH)	DG	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male and female C57BL/6J mice	6 and 24 months	Basal	mRNA (ISH)	DG	ns aged versus Y	(Qiu et al., 2016)
Male and female CD-1 mice	3 and 15 months	Basal	mRNA (ISH)	DG	ns aged versus Y; no significant correlations with MWM performance	(Zhang et al., 2020)
Male LE rats	6 months and 24 months	Basal	mRNA (RT-qPCR)	DG	ns (Y/AU/AI)	Myrum et al., 2020
Male SD rats	6 and 22–24 months	Basal (Time point not reported with respect to hole-board test)	Protein (Tandem Mass Tag-Based Quantitative Proteomics)	DG	ns (Y/AU/AI)	(Lubec et al., 2019)

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Abbreviations: AI, aged impaired; AU, aged unimpaired; catFISH, cellular compartment analysis of temporal activity with fluorescence in situ hybridization; DG, dentate gyrus; IHC, immunohistochemistry; ISH, in situ hybridization; ns, not significant; RT-qPCR, real-time quantitative polymerase chain reaction; SD, Sprague–Dawley; WB, western blot; Y, young.

Table 2:

Studies examining basal cortical Arc expression in aging. Rows are organized first by brain region and second by mRNA versus protein.

Species	Ages	Behavior	mRNA/protein (method)	Brain region	Finding	Study
Male F344 rats	7–9 months and 24–27 months	Basal	mRNA (catFISH)	Layer V perirhinal cortex (areas 35 and 36)	ns aged versus Y % of nuclear ISH+ cells ns aged versus Y % of cytoplasmic ISH+ cells ns aged versus Y % of double-positive ISH+ cells	(Burke et al., 2012)
Male FBN rats	4 months and 24 months	Basal	mRNA (catFISH)	Perirhinal cortex (deep and superficial examined separately)	ns % cells expressing Arc in either layer	(Hernandez et al., 2018)
Male LE rats	6 months and 24 months	Basal	mRNA (ISH)	Somatosensory cortex	ns (Y/AU/AI)	(Fletcher et al., 2014)
Male F344 rats	5–6 and 17–22 months	Basal (Set Shifting Operant Task; 7 wk rest; MWM; sacrifice 2 wk after MWM)	mRNA (RNAseq)	mPFC	↑ Arc in mPFC of AI compared to AU with respect to set shift impairment; confirmed via RT-qPCR. ↓ Arc with age in mPFC.	(Ianov et al., 2016)
Male LE rats	6 months and 24 months	Basal	Protein (IHC)	Anterior cingulate Cortex	↑ in AI and AU compared to Y	(Myrum et al., 2019)
Male FBN rats	4 months and 24 months	Basal	mRNA (catFISH)	Prelimbic cortex (deep and superficial examined separately)	↑ aged versus adult % cells expressing Arc in superficial layer	(Hernandez et al., 2018)
Male FBN rats	4 months and 24 months	Basal	mRNA (catFISH)	Prelimbic cortex (deep and superficial examined separately)	↑ (trending) aged versus adult % cells expressing Arc in superficial layer	(Hernandez et al., 2020)
Male LE rats	6 months and 24 months	Basal	Protein (IHC)	Prelimbic cortex	↑ in AI and AU compared to Y	(Myrum et al., 2019)
Male FBN rats	4 months and 24 months	Basal	mRNA (catFISH)	Infralimbic cortex (deep and superficial examined separately)	↑ in aged versus adult % cells expressing Arc in both deep and superficial layers	(Hernandez et al., 2018)
Male LE rats	6 months and 24 months	Basal	Protein (IHC)	Infralimbic cortex	ns (Y/AU/AI)	(Myrum et al., 2019)
Male LE rats	6 months and 24 months	Basal	Protein (IHC)	Retrosplenial cortex	ns (Y/AU/AI)	(Myrum et al., 2019)
Male and female C57BL/6J mice	6 and 24 months	Basal	mRNA (ISH)	Cortex (II/III and V examined separately)	↓ in aged versus Y in both layers	(Qiu et al., 2016)

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Arc was first identified as an aging-related gene in a microarray screen of aged F344 rats. Basal hippocampal Arc expression declined with age (4, 14, and 24 months), but Arc levels failed to correlate with performance on either of two hippocampus-dependent tasks carried out in that study (MWM and an object recognition task) (Blalock et al., 2003). A late wave of Arc expression occurs 8–24 hr after an initial experience (Ramírez-Amaya et al., 2005). Thus, in Blalock et al. (2003), where CA1 tissue was collected 24 hr after behavior, Arc transcripts therefore include a mixture of recently transcribed mRNA and constitutive pools. A follow-up study by the same lab aimed to more thoroughly identify changes in gene expression associated with age-related cognitive outcome and chronological aging (Rowe et al., 2007). Similar to their previous report, they found that basal Arc expression in dorsal hippocampus was lower in aged animals than young, and here they showed that this occurs in both trained and untrained animals alike. Taking a similar approach, Haberman et al. (2011) also performed microarrays in young and aged rats that were behaviorally characterized on the MWM, but in a notable extension of earlier work, in this case the hippocampal subregions CA1, CA3, and dentate gyrus (DG) were examined separately. Tissue was collected two weeks after behavioral training, aimed at detecting basal, constitutive mRNA profile differences. Despite substantial age- and cognition-related differences in gene expression (and between hippocampal subregions), no significant differences were detected for Arc (Haberman et al., 2011). In a quantitative proteomics study that aimed to identify proteins linked to spatial memory performance, Lubec et al. (2019) found no significant difference in Arc levels in the DG between Y, AU, and AI rats. Related efforts to identify the genes involved in cognitive outcome in aging performed microarrays in LOU rats and age-matched Wistar rats. Those data showed that Arc was significantly elevated in both the hippocampus and frontal cortex of old Wistar rats compared to young, and this increase was confirmed via RT-qPCR. In LOU rats however, Arc was not differentially expressed relative to young in either the hippocampus or frontal cortex (Paban et al., 2013). These results, demonstrating significant effects selectively in a rat strain vulnerable to memory decline in aging, suggest that constitutively elevated basal levels of Arc may be coupled to poor cognitive outcome in aging.

While the studies above identified Arc through genome-wide approaches, others have carried out more targeted analyses. In a report focusing specifically on Arc in aged rats, Penner et al. (2011) found that, compared to young, basal Arc mRNA levels (measured by RT-qPCR) were lower in the CA1 field of aged F344 rats, independent of cognitive status, while the percentage of Arc+ cells was not significantly different between age groups in either CA1 or DG. The authors interpretation is that, in the aged CA1, those cells that express Arc at rest may transcribe less per cell (Penner et al., 2011). Similar null findings for the percentage of basal Arc+ neurons were reported for DG granule cells and CA1 and CA3 pyramidal neurons (Marrone et al., 2012), as well as when dorsal and ventral CA1 were examined separately (Hernandez et al., 2018), or when proximal and distal CA1 were examined separately (Hartzell et al., 2013). Notably, however, whether Arc levels were related to variability in cognitive status in old animals was not examined.

In the LE model of cognitive aging, where aged rats are grouped on the basis of spatial memory capacity, one study reported elevated levels of basal Arc protein in AI rats, above those of Y and AU, in hippocampal CA1 and CA3 (Myrum et al., 2019). Similar findings were reported in another study in LE rats, where basal Arc protein levels in CA1 (measured by western blot) were significantly elevated in AI rats compared to Y rats. In that study, quantification of basal Arc mRNA via in situ hybridization similarly demonstrated numerically elevated levels of Arc mRNA in both CA1 and CA3 of AI rats (Fletcher et al., 2014). Another study in the same model found that basal Arc mRNA expression in AI rats was approximately double the levels seen in young or AU rats in CA1 and CA3 (Myrum et al., 2020). Together these studies reveal that even in the absence of behavioral testing or recent experience aimed at inducing activity-induced gene expression, Arc expression is often coupled to cognitive outcome in aging. This pattern was especially evident at the mRNA level, and most of all when a relatively sensitive tool, RT-qPCR, was used to quantify Arc mRNA (Myrum et al., 2020). In contrast, in a study using inbred C57BL/6J mice that were characterized as Y, AU, or AI based on performance in a Y-maze, hippocampal Arc expression was significantly lower in AI compared to Y, as measured by both microarray and RT-qPCR. In situ hybridization data showed that in the hippocampus, basal levels of Arc mRNA were lower in aged mice compared to young in CA1 and CA3 but not in DG (Qiu et al., 2016). Results from outbred CD-1 mice align more closely with data from LE rats, where Arc mRNA was elevated in CA1 and CA3 of middle-aged mice. However, in that report, higher levels of hippocampal Arc protein in middle-aged mice also correlated with poorer performance on the MWM (Zhang et al., 2020). As noted earlier, discordance between these studies may be attributable to a host of factors, including the degree of genetic heterogeneity of outbred versus inbred strains of animals.

Fewer studies have examined basal Arc expression in cortical regions other than the hippocampus (Table 2). In C57BL/6J mice, basal Arc levels were significantly lower in aged animals in cortical layers II/III and V (cortical area not specified; Qiu et al., 2016). In rats, one study reported that Arc protein is elevated among aged LE rats, independent of cognitive status, in the anterior cingulate cortex (ACg) and prelimbic medial prefrontal cortex (mPFC), but not the infralimbic mPFC or retrosplenial cortex (Myrum et al., 2019). Null findings were also reported in the same animal model for Arc mRNA in the somatosensory cortex (Fletcher et al., 2014). Other studies using FBN rats have reported elevated proportions of Arc+ cells in the superficial layer of the prelimbic as well as in deep and superficial layers of the infralimbic mPFC (Hernandez et al., 2020, 2018), but not in the perirhinal cortex of aged F344 rats (Burke et al., 2012; Hernandez et al., 2018). Taken together, this body of work points to elevated constitutive levels of Arc as a robust feature of aging, largely restricted to the hippocampus, particularly in aged rats with memory impairment.

4. Hippocampal activity-induced Arc expression in cognitive aging

Arc mRNA and protein are rapidly upregulated in response to plasticity-related events in specific neuron populations in brain regions that support learning and memory. Consistent with a critical role in the molecular processes underlying consolidation, failure of Arc upregulation results in the absence of long-term memory formation (Plath et al., 2006). Several lines of evidence indicate that experience-dependent hippocampal Arc expression is blunted in aged animals with memory impairment (Table 3).

Table 3:

Studies examining activity-induced hippocampal Arc expression in aging. Rows are organized first by which hippocampal subregion was examined and second by whether mRNA or protein was measured.

Species	Ages	Behavior	mRNA/protein (method)	Brain region	Finding	Study
Male LE rats	6 months and 24 months	MWM; sacrifice 2–3 h after last MWM probe	Protein (WB)	Hippocampus, plus entorhinal, perirhinal and portions of adjacent neocortices	↓ in AI compared to Y and AU Arc correlated with MWM performance	(Ménard and Quirion, 2012)
Male F344 rats	4-6 months and 24-26 months	MWM; sacrifice 1 hr after visible platform testing	mRNA (Microarray)	Dorsal hippocampus	↓ in AI compared to Y and AU (1 h after MWM)	(Rowe et al., 2007)
Male F344 rats (Harlan SD)	9, 15, and 24 months	5 min exploration, 20 min rest, 5 min exploration, sacrifice	mRNA (ISH)	CA1	ns % of ISH+ cells	(Small et al., 2004)
Male F344 rats	9-12 months and 24-32 months	5 min exploration, immediate sacrifice	mRNA (RT-qPCR) mRNA (ISH)	CA1	ns adult versus aged (compared to respective cage controls; RT-qPCR) ↓ aged versus adult (Relative levels; RT-qPCR) ns % of nuclear ISH+ cells	(Penner et al., 2011)
Male F344 rats	9-12 months and 24-32 months	5 min exploration, 20 min rest, 5 min exploration, sacrifice	mRNA (RT-qPCR) mRNA (ISH)	CA1	ns adult versus aged (compared to respective cage controls; RT-qPCR) ↓ aged versus adult (Relative levels; RT-qPCR) ns % of nuclear, cytoplasmic, or double-positive ISH+ cells	(Penner et al., 2011)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	Explore, 8 h rest, explore, sacrifice	mRNA (ISH)	CA1	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	CA1	↓ in AU and AI compared to Y (both single strategy and flexibility)	(Tomás Pereira et al., 2015)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	MECS, 8 h rest, MECS, sacrifice	mRNA (catFISH)	CA1	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male F344 rats	8-12 months and 23-26 months	Either 5 min exploration of novel environment and immediate sacrifice or 5 min exploration of novel environment and 25 min rest in home cage.	mRNA (catFISH)	CA1	↓ replay (double-positive ISH+ cells) in aged rats.	(Gheidi et al., 2020)
Male FBN rats	4 months and 24 months	5 min of object-place paired association (OPPA) 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	CA1 (dorsal and ventral examined separately)	ns Y versus aged for OPPA-induced Arc+ cells ns Y versus aged for alternation-induced Arc+ cells ns Y versus aged for double-positive ISH+ cells (similar results in dorsal and ventral regions)	(Hernandez et al., 2018)
Male F344 rats	7–9 months and 24–27 months	Object exploration 5 min, 20 min rest, 5 min explore, sacrifice. Rats explored either the same environments containing the same objects twice (AA) or two different environments containing identical objects (AB)	mRNA (catFISH)	CA1 (proximal)	ns aged versus Y % of nuclear ISH+ cells (AA and AB) ns aged versus Y % of cytoplasmic ISH+ cells (AA and AB) ↓ aged versus Y % of double-positive ISH+ cells (AA) ns aged versus Y % of double-positive ISH+ cells (AA)	(Hartzell et al., 2013)
Male F344 rats	7–9 months and 24–27 months	Object exploration 5 min, 20 min rest, 5 min explore, sacrifice. Rats explored either the same environments containing the same objects twice (AA) or two different environments that contained identical objects (AB)	mRNA (catFISH)	CA1 (distal)	ns aged versus Y % of nuclear ISH+ cells (AA and AB) ns aged versus Y % of cytoplasmic ISH+ cells (AA and AB) ↓ aged versus Y % of double-positive ISH+ cells (AA) ns aged versus Y % of double-positive ISH+ cells (AA)	(Hartzell et al., 2013)
Male F344 rats	7–9 months and 24–27 months	MECS	mRNA (catFISH)	CA1 (proximal)	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Hartzell et al., 2013)
Male F344 rats	7–9 months and 24–27 months	MECS	mRNA (catFISH)	CA1 (distal)	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Hartzell et al., 2013)
Male LE rats	6 months and 24 months	Water maze, 30 min, sacrifice	mRNA (ISH)	CA1	AI rats fail to induce Arc above baseline levels	(Fletcher et al., 2014)
Male LE rats	6 months and 24 months	Water maze, 2 h, sacrifice	Protein (WB)	CA1	AI rats fail to induce Arc above baseline levels	(Fletcher et al., 2014)
Male and female LOU rats; Male SD	LOU: 6, 12, 24, and 38-42 months; SD: 3 months ad libitumfed, 20 months ad libitumfed, and 20 months calorie-restricted	Novel Object Recognition; Elevated Plus Maze; Open Field; MWM; sacrifice 2–3 h after last MWM probe	Protein (WB)	CA1	ns in LOU rats comparing four age groups ↓ aged SD rats ad libitum versus Y ad libitum and aged calorie-restricted	(Ménard et al., 2014)
Male LE rats	6 months and 24 months	i.p. injection of pilocarpine, 90 min rest, sacrifice	Protein (IHC)	CA1	AI rats fail to induce Arc above baseline levels	(Myrum et al., 2019)
Male F344 rats (Harlan SD)	9, 15, and 24 months	5 min exploration, 20 min rest, 5 min exploration, sacrifice	mRNA (ISH)	CA3	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Small et al., 2004)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	CA3	ns (Y/AU/AI; both single strategy and flexibility)	(Tomás Pereira et al., 2015)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	Explore, 8 h rest, explore, sacrifice	mRNA (catFISH)	CA3	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	MECS, 8 h rest, MECS, sacrifice	mRNA (catFISH)	CA3	ns % of nuclear, cytoplasmic, or double-positive ISH+ cells between Y and aged rats	(Marrone et al., 2012)
Male FBN rats	4 months and 24 months	Object exploration 5 min, 20 min rest, 5 min explore, sacrifice. Note: Young and aged rats displayed a bimodal distribution in discrimination performance. Here we extend the AU/AI distinction to young animals (YU/YI).	mRNA (catFISH)	CA3 (distal and proximal examined separately)	↑ AI versus AU, YU, and YI (% of nuclear, cytoplasmic, or double-positive ISH+ cells across the two epochs) ↑ in AI versus AU, YU, and YI in first epoch (both proximal and distal) ns (YU/YI/AU/AI) in second epoch (both proximal and distal) ns (Y/AU/AI) double-positive ISH+ cells (both proximal and distal)	(Maurer et al., 2017)
Male LE rats	6 months and 24 months	Water maze, 30 min, sacrifice	mRNA (ISH)	CA3	AI rats fail to induce Arc above baseline levels	(Fletcher et al., 2014)
Male LE rats	6 months and 24 months	Water maze, 2 h, sacrifice	Protein (WB)	CA3	↓ in AI and Y compared to AU	(Fletcher et al., 2014)
Male LE rats	6 months and 24 months	i.p. injection of pilocarpine, 90 min rest, sacrifice	Protein (IHC)	CA3	AI rats fail to induce Arc above baseline levels	(Myrum et al., 2019)
Male F344 rats (Harlan SD)	9, 15, and 24 months	5 min exploration, 20 min rest, 5 min exploration, sacrifice	mRNA (ISH)	DG	↓ aged versus Y and middle-aged % of ISH+ cells	(Small et al., 2004)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	Explore, 8 h rest, explore, sacrifice	mRNA (ISH)	DG (infrapyramidal and suprapyramidal blades)	ns aged versus Y % of nuclear ISH+ cells ns aged versus Y % of cytoplasmic ISH+ cells ↓ aged versus Y % of double-positive ISH+ cells	(Marrone et al., 2012)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	DG	ns (Y/AU/AI; both single strategy and flexibility)	(Tomás Pereira et al., 2015)
Male F344 rats (Harlan SD)	10-12 months and 24-27 months	MECS, 8 h rest, MECS, sacrifice	mRNA (catFISH)	DG (infrapyramidal and suprapyramidal blades)	ns aged versus Y % of nuclear ISH+ cells ns aged versus Y % of cytoplasmic ISH+ cells ns aged versus Y % of double-positive ISH+ cells	(Marrone et al., 2012)
Male F344 rats	9-12 months and 24-32 months	5 min exploration, immediate sacrifice	mRNA (RT-qPCR) mRNA (catFISH)	DG	↓ aged versus adult (compared to respective cage controls; RT-qPCR) ↓ aged versus Y (Relative levels; RT-qPCR) ns % of nuclear, cytoplasmic, or double-positive ISH+ cells	(Penner et al., 2011)
Male F344 rats	9-12 months and 24-32 months	5 min exploration, 20 min rest, 5 min exploration, sacrifice	mRNA (RT-qPCR) mRNA (catFISH)	DG	↓ aged versus Y (compared to respective cage controls; RT-qPCR) ↓ aged versus Y (Relative levels; RT-qPCR) ↓ aged versus Y for double-positive ISH+ cells, but ns for nuclear or cytoplasmic alone	(Penner et al., 2011)

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Abbreviations: AI, aged impaired; AU, aged unimpaired; DG, dentate gyrus; IHC, immunohistochemistry; i.p., intraperitoneal injection; ISH, in situ hybridization; MECS, maximum electroconvulsive shock; ns, not significant; RT-qPCR, real-time quantitative polymerase chain reaction; SD, Sprague–Dawley; WB, western blot; Y, young.

In one of the earliest studies to assess activity-induced Arc expression among aged animals, in situ hybridization was carried out in brains from young (9 months), middle-aged (15 months), and old (24 months) rats that engaged in exploratory behavior before sacrifice. The percentage of DG granule cells that was Arc+ significantly decreased with age, but induction in CA1 and CA3 pyramidal cells was preserved (Small et al., 2004). In another study using FISH, adult and aged rats underwent a single 5 min epoch of exploratory behavior, resulting in an approximately equal percentage of Arc+ cells in the two age groups in CA1 and DG (Penner et al., 2011). In separate experiments, rats were exposed to the same environment twice for 5 min each time, separated by 20 min. Here, a similar proportion of CA1 and DG cells were activated by either exploration epoch, but the percentage of re-activated DG granule cells was lower in aged animals compared to adults (Penner et al., 2011). These data complement earlier reports of altered cognitive mapping in the aged hippocampus, where hippocampal place cell recordings indicated that remapping to a novel environment is more rigid and slower to form new spatial representations based on external cues, and that old rats are more prone to remapping upon two visits to the same environment (Barnes et al., 1997; Wilson et al., 2004). To assess relative levels of Arc mRNA in these same animals, Penner et al. (2011) also carried out RT-qPCR. Both the single 5 min exploration epoch and the double exploratory behavior led to a less robust increase in overall Arc mRNA in aged rats compared to adults in both CA1 and DG. Together these experiments demonstrate that, while exploration appears to activate similar proportions of neurons, the amount of mRNA transcribed by each cell is compromised with age. This conclusion is consistent with in situ hybridization evidence, showing that Arc fluorescence intensity is lower in the aged rat hippocampus (Penner et al., 2011).

Behavior-induced Arc transcription is transient in hippocampal and cortical pyramidal neurons, where mRNA remains in the nucleus for ~5 min, translocates to the cytoplasm over the ~30 min after activity, and is then rapidly degraded (Ramírez-Amaya et al., 2005). In DG granule cells by comparison, spatial exploration induces sustained Arc transcription lasting ~8 h (Ramirez-Amaya et al., 2013). Together with Arc’s protracted time course in the DG, the observation noted above that fewer granule cells express Arc in aged rats, and at lower levels, prompted a follow-up investigation assessing the longer term fidelity of Arc expression in DG granule cells (Marrone et al., 2012). In those experiments, animals briefly explored the same environment twice, separated by 8 h. While the percentage of cytoplasm Arc+ cells (reflecting the first exploration epoch) and nuclear Arc+ cells (reflecting the more recent bout of exploration) were similar, the percentage of double-positive cells was significantly lower in aged animals, and their numbers correlated with spatial memory impairment (Marrone et al., 2012). Extending these results to examine potential regional specificity, Hartzell et al. (2013) used catFISH to compare Arc activation in distal CA1 (near subiculum) versus proximal CA1 (near CA2)—regions that receive distinct projections from the lateral and medial entorhinal cortex, respectively (Witter et al., 2017), as well as input from proximal and distal aspects of CA3 (Ishizuka et al., 1990). In these experiments, animals explored two distinct environments, and the interval between them was 20 min, consistent with the known timecouse of Arc dynamics in the pyramidal cell fields. Proximal, but not distal CA1 was responsive to the change in environment in young adults, demonstrated by reduced reactivation of Arc+ cells after exposure to different environments compared with repeat visits to the same setting. Proximal CA1 cells in old rats were less sensitive to this environmental change (Hartzell et al., 2013). Together, these results from studies using Arc to map patterns of neuronal activation under conditions that potently engage hippocampal processing point to differential vulnerability of medial and lateral entorhinal processing streams.

Leveraging the catFISH approach in a slightly different way, Ghedi et al. (2020) examined Arc transcription to assess memory-related replay of recent spatial exploration. More specifically, while some animals were killed immediately after a bout of exploratory activity, others were euthanized after a 25 min delay. This design tested the hypothesis that plasticity is disrupted during the “offline” processing of memory in aged rats. Indeed, in young F344 rats, CA1 cells that transcribed Arc during the delay were more likely than chance to be the same cells that were active during exploration. In aged rats, by comparison, reactivation of cells during the memory delay was no more frequent than chance (Gheidi et al., 2020). A valuable extension of this work would be to examine whether these age-related changes in wakeing replay are similarly disrupted during the neuronal ensemble replay observed in sleep, and importantly, whether aberrant sleep replay is coupled with memory impairment.

Studies using LE rats are well-suited for distinguishing neurobiological substrates that differ based on age-related cognitive outcome from those linked to chronological age. In immunoblots from whole hippocampus (plus adjacent cortical regions), behavior-induced Arc expression was lower in AI rats compared to Y and AU. The same study also reported that MWM performance correlated with Arc expression, where lower levels were associated with longer escape latency (Ménard and Quirion, 2012). In Fletcher et al. (2014), rats classified as AI or AU on the MWM underwent a second water maze test and tissue was collected either 5 min or 2 h after the last trial to examine mRNA levels (via ISH) and protein levels (via western blot). While behavioral training induced Arc expression in CA1 and CA3 in young and AU rats, AI rats failed to induce Arc mRNA transcription above baseline levels in these regions. A deficit in activity-induced Arc protein was also observed in CA1 (Fletcher et al., 2014), as well as in both CA1 and CA3 in Myrum et al. (2019), where neuronal activity in the same aging model was induced with a subconvulsive, low dose of the muscarinic acetylcholine receptor agonist, pilocarpine. Previous analyses of IEG induction with pilocarpine revealed neuronal activation differences coupled with both age (Bucci et al., 1998) and age-related cognitive status (Haberman et al., 2017). In a recent extension, higher levels of pilocarpine-induced Arc expression correlated with poorer memory performance in AU and AI groups, but not with chronological aging (AU + AI), or intact memory (Y + AU) (Myrum et al., 2019). The failure of AI rats to induce Arc expression above baseline levels in CA1, CA3, and DG was also observed in another recent study, where Arc protein levels following spatial exploration were actually significantly lower than basal levels in CA3 (Myrum et al., 2020). These findings are consistent with earlier microarray data from behaviorally characterized F344 rats. In that study, Arc expression in the dorsal hippocampus 1 h after MWM testing was significantly lower in AI rats compared to Y and AU animals (Rowe et al., 2007).

As described above, LOU rats have been used as a model of successful outcomes in cognitive aging, based on their normal object recognition and MWM performance relative to younger animals (Kollen et al., 2010; Ménard et al., 2014; Paban et al., 2013). In Ménard et al., (2014), MWM-induced Arc protein levels in CA1 of LOU rats were comparable in rats across the lifespean, at 6, 12, 24, and even 38–42 months of age. In striking contrast, 20-month-old Sprague–Dawley rats in that study displayed impaired MWM performance and significantly lower Arc protein levels relative 3-month-old controls of the same strain.

Together, research exploring experience-dependent regulation offers the most consistent finding concerning Arc in cognitive aging; blunted activity-induced Arc expression in the hippocampus is associated with poor cognitive outcome. Although the subfields of the hippocampus are thought to support specialized roles in memory, the relationship between Arc expression and cognitive aging has been observed throughout the DG, CA3, and CA1 fields. Results across rat models are generally complementary, demonstrating that while memory impaired LE and F344 rats display deficits in Arc expression, memory intact aged LOU and unimpaired LE rats retain normal activity-induced Arc expression. As noted earlier, some seemingly contradictory reports may in fact be attributable to differences in experimental design, including behavioral assessments, detection methods, and mRNA versus protein measures.

5. Neocortical activity-induced Arc expression in cognitive aging

The hippocampal formation is richly interconnected with widely distributed neocortical sites, forming functional networks important for encoding and consolidating new episodic memories. The role of neocortical regions has therefore been of great interest in cognitive aging research. Multiple lines of evidence, including electrophysiology and neuroimaging experiments, suggest that hippocampal-neocortical connectivity plays a prominent role in episodic memory impairment and other cognitive deficits associated with aging (Thomas and Gutchess, 2020). The medial prefrontal cortex (mPFC) is frequently implicated, often in the context of working memory (Bizon et al., 2012; Morrison and Baxter, 2012), but increasingly with reference to the role of mPFC interactions with the perirhinal (PER) cortex and other parahippocampal cortices in cognitive aging (Burke et al., 2018). The work surveyed here provides insight into these interactions and the related possibility that age-related changes in Arc regulation are non-selectively distributed across brain regions.

As in research examining Arc expression in the hippocampus, catFISH has been adopted to study aging effects on Arc dynamics in neocortical circuits (Table 4). Burke et al. (2012), for example, documented the status of object recognition memory in F344 rats, and probed with catFISH to assess ensemble activation/reactivation of neurons in layer V of the PER across two bouts of exploratory activity. Despite similar object recognition abilities in young and aged animals, the percentage of double-labeled Arc+ cells was significantly lower in aged rats compared to young, regardless of whether the environment of the second epoch was novel or familiar. These data point to PER as a brain region vulnerable to aging (Burke et al., 2012). Taking a similar approach, another catFISH study in F344 rats focused on lateral entorhinal cortex (LEC) projections to the CA3 field. The results demonstrated that, following two epochs of random foraging in novel environments, aged rats with object discrimination deficits had a higher percentage of Arc+ LEC cells than aged rats with intact discrimination (Maurer et al., 2017). In another related study, rats were tested successively, separated by 20 min, on procedures emphasizing cognitive flexibility and associative learning capacities that require the hippocampus, mPFC, and PER, i.e., an object-place paired association task and an alternation procedure. Among the cortical regions examined, deep layers of the PER and the prelimbic subregion of the mPFC had more Arc+ cells in aged rats than young rats, while the superficial layers of these regions had significantly fewer positive cells relative to baseline. Follow-up tracer studies demonstrated that these layer-specific differences primarily involved PER to mPFC projection neurons (Hernandez et al., 2018). The same experimental strategy, except using cognitive multitasking and alternation assesments, was likewise employed to examine functional connectivity of prelimbic cortical projections to PER (Hernandez et al., 2020). Lower Arc expression in the deep layers of the prelimbic cortex was associated with lower multitasking ability in aged, but not young rats. Unlike the PER, however, age-related differences in mPFC Arc expression were equally likely between projecting and non-projecting neurons. These data demonstrate highly region- and lamina-specific age-related vulnerability of long-range projections in relation to cognitive aging.

Table 4:

Studies examining activity-induced cortical and striatal Arc expression in aging. Rows are organized first by brain region and second by mRNA versus protein.

Species	Ages	Behavior	mRNA/protein (method)	Brain region	Finding	Study
Male F344 rats	7–9 months and 24–27 months	MECS	mRNA (catFISH)	Layer V perirhinal cortex (areas 35 and 36)	ns aged versus Y % of nuclear ISH+ cells ns aged versus Y % of cytoplasmic ISH+ cells ns aged versus Y % of double-positive ISH+ cells	(Burke et al., 2012)
Male F344 rats	7–9 months and 24–27 months	Object exploration 5 min, 20 min rest, 5 min explore, sacrifice. Rats explored either the same environments containing the same objects twice (AA) or two different environments that contained identical objects (AB)	mRNA (catFISH)	Layer V perirhinal cortex (areas 35 and 36)	ns aged versus Y % of nuclear ISH+ cells (AA and AB) ns aged versus Y % of cytoplasmic ISH+ cells (AA and AB) ↓ in aged versus Y % of double-positive ISH+ cells (AA and AB)	(Burke et al., 2012)
Male FBN rats	4 months and 24 months	5 min of object-place paired association (OPPA) 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Perirhinal cortex (deep layer)	↑ in aged versus Y for OPPA-induced Arc+ cells ↑ in aged versus Y for alternation-induced Arc+ cells ↑ in aged versus Y for double-positive ISH+ cells	(Hernandez et al., 2018)
Male FBN rats	4 months and 24 months	5 min of object-place paired association (OPPA), 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Perirhinal cortex (superficial layer)	↓ in aged versus Y for OPPA-induced Arc+ cells ↓ in aged versus Y for alternation-induced Arc+ cells ns in aged versus Y for double-positive ISH+ cells	(Hernandez et al., 2018)
Male FBN rats	4 months and 24 months	Object exploration 5 min, 20 min rest, 5 min explore, sacrifice. Note: Young and aged rats displayed a bimodal distribution in discrimination performance. Here we extend the AU/AI distinction to young animals (YU/YI).	mRNA (catFISH)	Layer II dorsal region of the lateral entorhinal cortex	ns (AU/AI/YU/YI (% of nuclear, cytoplasmic, or double-positive ISH+ cells across the two epochs) ↑ in AI versus AU, YU, and YI in first epoch among; also observed for CA3-projecting neurons ns (YU/YI/AU/AI) in second epoch ns (Y/AU/AI) double-positive ISH+ cells (both proximal and distal)	(Maurer et al., 2017)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	Orbitofrontal cortex	↓ in AU and AI compared to Y (both single strategy and flexibility) AU insensitive to demands on strategy switching	(Tomás Pereira et al., 2015)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	Cingulate cortex	↓ in AU and AI compared to Y (both single strategy and flexibility)	(Tomás Pereira et al., 2015)
Male FBN rats	4 months and 24 months	Working memory/biconditional association task (WM/BAT) or spatial alternation task, 20 min rest, then whichever task had not yet been performed. Order was counterbalanced.	mRNA (catFISH)	Anterior cingulate cortex (deep and superficial examined separately)	ns aged versus Y % positive during WM/BAT (neither deep nor superficial layers) ns aged versus Y % positive during alternation (neither deep nor superficial layers)	(Colon-Perez et al., 2019)
Male LE rats	6 months and 24 months	i.p. injection of pilocarpine, 90 min rest, sacrifice	Protein (IHC)	Anterior cingulate cortex	↑ in AI compared to Y and AU	(Myrum et al., 2019)
Male FBN rats	4 months and 24 months	5 min of object-place paired association (OPPA) 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Prelimbic cortex (deep layer)	↑ in aged versus Y for OPPA-induced Arc+ cells ↑ in aged versus Y for alternation-induced Arc+ cells ↑ in aged versus Y for double-positive ISH+ cells	(Hernandez et al., 2018)
Male and female FBN rats	4-7 months and 23-28 months	5 min of working memory biconditional association task (WM/BAT), 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Prelimbic cortex (deep layer)	↓ in aged versus Y for WM/BAT-induced Arc+ cells ↓ in aged versus Y for alternation task-induced Arc+ cells Number of Arc+ cells correlated with WM/BAT task performance in aged but not young rats	(Hernandez et al., 2020)
Male and female FBN rats	4-7 months and 23-28 months	5 min of working memory biconditional association task (WM/BAT), 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Prelimbic cortex (superficial layer)	↓ in aged versus Y for WM/BAT-induced Arc+ cells ↓ in aged versus Y for alternation task-induced Arc+ cells	(Hernandez et al., 2020)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	Prelimbic cortex	↓ in AU and AI compared to Y (both single strategy and flexibility) AU insensitive to demands on strategy switching	(Tomás Pereira et al., 2015)
Male FBN rats	4 months and 24 months	5 min of object-place paired association (OPPA) 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Prelimbic cortex (superficial layer)	↓ in aged versus Y for OPPA-induced Arc+ cells ↓ in aged versus Y for alternation-induced Arc+ cells ↓ in aged versus Y for double-positive ISH+ cells	(Hernandez et al., 2018)
Male LE rats	6 months and 24 months	i.p. injection of pilocarpine, 90 min rest, sacrifice	Protein (IHC)	Prelimbic cortex	↑ in AI compared to Y	(Myrum et al., 2019)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	Infralimbic cortex	↓ in AU and AI compared to Y (both single strategy and flexibility) AU insensitive to demands on strategy switching	(Tomás Pereira et al., 2015)
Male FBN rats	4 months and 24 months	5 min of object-place paired association (OPPA) 20 min rest, 5 min of alternation task, sacrifice	mRNA (catFISH)	Infralimbic cortex	ns Y versus aged for OPPA-induced Arc+ cells ns Y versus aged for alternation-induced Arc+ cells ns Y versus aged for double-positive ISH+ cells	(Hernandez et al., 2018)
Male LE rats	6 months and 24 months	i.p. injection of pilocarpine, 90 min rest, sacrifice	Protein (IHC)	Infralimbic cortex	ns (Y/AU/AI)	(Myrum et al., 2019)
Male LE rats	6 months and 24 months	i.p. injection of pilocarpine, 90 min rest, sacrifice	Protein (IHC)	Retrosplenial cortex	ns (Y/AU/AI)	(Myrum et al., 2019)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	Dorsal striatum	↓ in AU and AI compared to Y (both single strategy and flexibility)	(Tomás Pereira et al., 2015)
Male LE rats	6 months and 24-28 months	MWM; days later, swimming T-maze task where one group learned a single strategy (place or response) and another group that required cognitive flexibility (strategy required was switched); sacrifice 30 min	mRNA (ISH)	Ventral striatum	ns (Y/AU/AI; both single strategy and flexibility)	(Tomás Pereira et al., 2015)
Male FBN rats	4 months and 24 months	Working memory/biconditional association task (WM/BAT) or spatial alternation task, 20 min rest, then whichever task had not yet been performed. Order was counterbalanced.	mRNA (catFISH)	Dorsal striatum (medial and lateral examined separately)	↑ aged versus Y % positive during WM/BAT (both medial and lateral regions) ns aged versus Y % positive during alternation (neither deep nor superficial layers)	(Colon-Perez et al., 2019)
Male C57BL/6 Mice	2–4 months and 18–20 months	Sleep deprivation by gentle handling.	mRNA (RNAseq)	mPFC	ns in aged versus Y	(X. Guo et al., 2019)

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A related examination of connectivity between brain regions, and how it changes with age, combined resting state fMRI and cognitive training on a spatial working memory (WM)/biconditional association task (BAT) with catFISH analyses in FBN rats. MRI scans collected over the course of training revealed increased functional connectivity between the anterior cingulate cortex (ACC) and dorsal striatum (DS) in cognitively impaired aged rats, but not young animals. This network alteration was associated with an age-related elevation in Arc expression in DS, but not the ACC (Colon-Perez et al., 2019). Together, studies using Arc as a proxy provide compelling evidence of substantial region- and layer-specific alterations in brain connectivity at the level of individual neurons in the aged brain. Whether these changes contribute to the altered network functional connectivity that has been widely reported in the human neuroimaging literature on aging (Cabeza, 2002; Davis et al., 2008; Reuter-Lorenz and Cappell, 2008) remains an important area for investigation.

Fewer studies have examined activity-induced neocortical Arc dynamics in LE rats (Table 4). In Tomás Pereira et al. (2015), Y, AU, and AI rats learned competing hippocampus-dependent place (i.e., go East or West) and corticostriatal response strategies (i.e., turn Left or Right), alternated across days in a water T-maze. While young rats learned place strategies faster than response, AI rats displayed the opposite pattern, acquiring response strategies faster than places. Interestingly, AU rats learned both strategies equally quickly, without disruption when switching between the two. Just before sacrifice, for the first time during training, rats were required to switch between place and response strategies within the same test session, a manipulation intended to increase demands on cognitive flexibility. ISH analyses for Arc showed that aged rats had lower behavioral Arc induction relative to young across multiple brain regions (see Table 4). More illuminating, while strategy switching was associated with robust Arc induction in multiple regions of the mPFC in both Y and AI rats, no such activation was observed in AU rats (Tomás Pereira et al., 2015). This pattern is reminiscent of results observed in F344 rats in response to a set shifting operant task (Ianov et al., 2016), where Arc was identified in an RNA-seq study optimized to identify genes associated with impaired medial mPFC function. The data showed that Arc levels were significantly lower in aged animals overall. However, evaluating results in relation to set shifting performance, Arc expression was significantly higher specifically in AI rats compared to AU, while AI levels more closely matched those of young rats (Ianov et al., 2016). In another study in LE rats, pilocarpine-induced Arc expression was greater in AI compared to Y and AU rats in the anterior cingulate cortex and the prelimbic cortex, while no differences were observed for the infralimbic prefrontal or retrosplenial cortex. Drug-induced Arc levels correlated with water maze performance across all subjects, although the specific nature of the correlation differed across brain regions (Myrum et al., 2019).

These studies highlight that Arc expression in many cortical areas is exquisitely sensitive to aging, but that the specific nature of these effects is dependent upon the particular region examined and the cognitive demands of testing used to induce activity. As proposed by Nikolaienki et al. (2017), multiple pools of Arc protein may exist, including rapidly induced and more stable pools regulated by different molecular mechanisms, and with distinct functional roles. It should also be noted that although basal levels of Arc may reflect spontaneous neural activity, some indications are that even basal Arc may reflect prior behavioral history (Marrone et al., 2008). As we learn more about the various cellular and molecular roles by which Arc supports memory, and how they are instantiated across different neural systems and cognitive demands, it will be important to examine the fidelity of each in the aged brain. Our current understanding of what might underlie these age-related changes in Arc expression is discussed next.

6. Molecular causes and consequences of dysregulated Arc expression in cognitive aging

As described above, an abundance of data links altered Arc expression to various aspects of cognitive aging. Our knowledge regarding the molecular causes and consequences directly associating Arc and cognitive aging, however, is still limited. For example, it is unclear if, or to what degree, ARC genetic variation plays a role in interindividual difference in cognitive abilities. In a study examining associations between common ARC variants in healthy middle-aged adults and a panel of cognitive measures, only nominal trends were identified for a subset of outcomes (Myrum et al., 2015b), suggesting that common genetic ARC variants fail to account for variability in human cognitive abilities. Nevertheless, a genetic variant in the ARC 3′ untranslated region was reported to confer a decreased risk of AD in a Swedish sample (Landgren et al., 2012), while another variant in the same region was associated with increased genetic risk of AD in a cohort of Han Chinese (Bi et al., 2018). Interestingly, this variant was also associated with increased hippocampal Arc mRNA expression, reminiscent of findings in cognitively impaired aged rats (Myrum et al., 2020).

In contrast to the weak associations between ARC variants and cognition, mutations and variation in genes found in Arc protein interaction networks have been shown to contribute to cognitive performance and increased risk of cognitive disorders. Using a gene set built from a proteomics experiment (Fernández et al., 2017), members of the Arc protein complex were found to be enriched in de novo copy-number variants (Kirov et al., 2012; Marshall et al., 2017), rare disruptive mutations (Purcell et al., 2014), non-synonymous de novo single-nucleotide variants, and small insertions or deletions (indels) (Fromer et al., 2014; Pocklington et al., 2015) in schizophrenia cases, which are characterized by prominent deficits in episodic memory (Guo et al., 2019). Taking a similar approach, but using a curated ARC complex based on biochemically validated Arc interacting proteins, genetic variation within this complex was associated with general intellectual function in children, as well as in adults with AD (Myrum et al., 2017). Taken together, these reports show that genetic variation in the Arc protein interaction networks, rather than the Arc gene itself, seem to contribute to cognitive performance and cognitive disorders in humans.

Other evidence points to potential epigenetic mechanisms (Figure 1A) of age-related changes in Arc transcription (Figure 1B). For example, Penner et al. (2011) found a number of age-related differences in F344 rats compared to young rats: 1) under basal conditions, in CA1, there was greater methylation at the ARC minimal promoter and intragenic region; 2) under basal conditions there was less methylation in the DG at the ARC intragenic region; 3) following spatial exploration, intragenic methylation in the DG went down while methylation increased in young rats; 4) following spatial exploration, in CA1, intragenic methylation was lower (Penner et al., 2011). These data demonstrate that ARC methylation is affected by age, and that the changes differ across subregions of the hippocampus and specific gene regulatory regions. Another study, focusing on the hippocampal CA3 field in Y/AU/AI LE rats, identified two CpG sites in the ARC minimal promoter where methylation was significantly elevated under basal conditions in AI rats compared to Y and AU rats. While there were no overall age-related differences in methylation in the synaptic activity-responsive element (SARE; located ~ 7 kb upstream of the rat ARC transcription initiation site) or the intragenic region, methylation of the ARC minimal promoter was significantly elevated in AI rats above Y and AU levels under basal conditions. Overall methylation in Y and AU rats was not affected in response to spatial exploration in CA3, but exploration-induced methylation was decreased in AI (Myrum et al., 2020). Upstream of these changes in methylation, DNA methyltransferase-3a (DNMT3a) is reduced in the hippocampus and cortex of 18-month-old C57Bl/6 mice relative to young animals and is associated with reduced Arc and BDNF expression, but not with altered c-Fos or Egr-1/Zif268 (Oliveira et al., 2012). Findings from Myrum et al. (2020) both confirm an age-related change in ARC methylation in the aged brain and they underscore that these changes are not universally observed across all ARC regulatory regions. Nor are changes in methylation observed across all brain regions. Whole genome bisulfite sequencing of the mPFC in behaviorally tested F344 rats, aimed at identifying DNA methylation patterns associated with aging and age-related cognitive impairment, failed to detect sites of differentially methylated ARC (Ianov et al., 2017). Together these data suggest that, compared with the mPFC, the hippocampus displays more robust age-related changes in ARC methylation.

Histone modifications, which alter chromatin structure and thus gene expression, are also associated with altered Arc expression in aging. For example, binding of histone deacetylase 2 (HDAC2) and decreased H3K9 acetylation at the ARC promoter were shown to parallel age-related decreases in hippocampal Arc expression across 2-, 7-, and 18-month old mice (Singh and Thakur, 2018). In the LE model, Myrum et al. (2020) found that elevated basal Arc mRNA in AI rats was coupled with enrichment of H3K9Me2 at the ARC promoter, which is linked to transcriptional repression (Black et al., 2012), and enrichment of H3K9AcS10p, which is responsive to synaptic activity (Oey et al., 2015). The same study also reported preliminary evidence for age-related Arc dysregulation at a higher level of epigenetic regulatory organization, i.e., nucleosome positioning (Myrum et al., 2020). Overall, it remains unclear whether altered epigenetic regulation is a primary determinant of dysregulated Arc expression in cognitive aging. Data suggesting that other epigenetic mechanisms are also altered in aging, including hydroxymethylation (Irier et al., 2014) and non-coding RNAs (Barter and Foster, 2018), underscore to the need for examining a broader range of influences on Arc expression.

Upon translation, Arc RNA is subject to rapid nonsense-mediated decay (Giorgi et al., 2007). While mRNA decay mechanisms were largely intact in aged rats, as assessed by measuring eukaryotic initiation factor 4A3 levels (Figure 1B), AI-specific differences were observed for a key rate-limiting step and core component of cap-dependent translation, eukaryotic initiation factor 4E (eIF4E). More specifically, basal levels of the eIF4E were elevated in AI rats and insensitive to recent behavioral experience, suggesting that basal cap-dependent Arc translation may be elevated in AI compared to young and cognitively intact aged rats (Figure 1C). Elevated levels of basal Arc protein might also reflect another effect observed in AI, i.e., lower levels of the proteosome-targeting protein Ube3a, which targets Arc for degradation by ubiquitination (Fletcher et al., 2014). Together these findings indicate that multiple mechanisms involved in Arc synthesis and degradation are altered in cognitively impaired aged rats. Unraveling the directionality and initial cause of the multi-level dysregulation of Arc in the cognitively impaired aged brain is an important challenge, as we discuss next.

7. Outstanding puzzles about Arc in aging

7.1. Is the expression of Arc more tightly coupled to age-related changes in synaptic plasticity in comparison to other IEGs?

It is worth asking whether Arc is somehow unique among IEGs, or if its emergence as a key gene in neurocognitive aging simply reflects experimental design considerations or other extraneous factors. Many studies of cognitive aging in the FBN model, for example, have exploited Arc at least in part due to its convenient temporal dynamics for identifying neural network changes in catFISH analyses. Other IEGs can similarly be used for catFISH analyses, albeit with different temporal dynamics. At least in adult SD rats, Arc+ and Homer1a+ in hippocampal and cortical neuronal networks are predominantly overlapping (Vazdarjanova et al., 2002). Other examples of co-expression include multiple IEGs in spiny projection neurons in the dorsal striatum of mice following acute exposure to cocaine (Gonzales et al., 2020), Arc and Egr-1/Zif268 in the DG of LE rats following fear conditioning (Lonergan et al., 2010), and Arc, Nr4a1, and c-Fos in the neocortex following sleep deprivation in mice (Thompson, 2010). Following chemical LTP induction in cultured hippocampal neurons, ~83% of Arc+ neurons were also c-Fos+ (Jiang and VanDongen, 2021). Under some conditions, however, distinct IEG expression patterns have also been reported, for example between Arc and c-Fos in the marmoset primary visual cortex (Nakagami et al., 2013) and Nptx2 cell populations in the neocortex following sleep deprivation in mice were markedly different from other IEGs (Thompson, 2010). It will therefore be important to directly test how IEG expression profiles compare with one another, within animal models of cognitive aging.

In LE rats, where elevated basal Arc levels are coupled to poor cognitive outcomes in aging (Fletcher et al., 2014; Myrum et al., 2020), basal Nptx2, Homer1a, c-Fos, and Egr1 mRNA levels are comparable across Y/AU/AI groups. While those data point to an expression pattern potentially unique to Arc, activity-induced expression of IEGs with similar kinetics—Arc, Egr1, and c-Fos—all demonstrate a failure to induce expression above basal levels (Myrum et al., 2020). In another study that reported lower hippocampal activity-induced Arc protein levels in AI rats, expression profiles for Egr-1/Zif268 and Homer1a were markedly different. While Homer1a was enriched in hippocampal postsynaptic densities in AU rats over Y and AI, there were no significant differences between Y, AU, and AI animals for Egr-1/Zif268 (Ménard and Quirion, 2012). However, age-related deficits have been reported in experience-dependent Egr-1/Zif268 transcription in the DG of F344 rats (Penner et al., 2016). In CA1, they observed lower Egr-1/Zif268 expression in aged rats than young, but normal mRNA levels induced by spatial exploration. Similar to Arc, they also observed age-related, site-specific changes in methylation in the promoter region of the Egr-1 gene.

While IEG expression is often used simply as a tool to map the distribution of recent neuronal activity, the dysregulation of genes that encode synaptic or secretory proteins, such as Arc, BDNF, Nptx2, and Homer1a, would be expected to have serious cell biological consequences, reflecting their direct effector role in neurons. This is not unique to these IEGs, and of course others that encode for inducible transcription factors (e.g., c-Fos, Egr-1, and c-Jun) are also positioned to regulate the expression of downstream late-response genes involved in neuron function. Nonetheless, some data suggest that Arc expression is more closely linked to memory-related plasticity mechanisms than to neuronal activity, per se, potentially providing a specific proxy for disrupted plasticity in aging (Fletcher et al., 2006; Guzowski et al., 2001).

Other activity-regulated genes may well emerge as key players in the mechanistic substrates of neurocognitive aging as we begin to better understand their roles in neural network organization. For example, current evidence indicates that neuronal PAS domain protein 4 (Npas4) potently influences excitatory/inhibitory balance (Sun and Lin, 2016), including DG/CA3 circuitry that exhibits hyperactivity in relation to poor cognitive outcome in aging (Gallagher et al., 2019). Considered alongside evidence that Npas4 expression induced by contextual fear conditioning is largely restricted to CA3 (Ramamoorthi et al., 2011), the basis for supposing Npas4 might play a role in cognitive aging appears compelling. Consistent with this view, Qiu et al. (2016) found that hippocampal Npas4 expression is lower in aged C57BL/6J mice with spatial memory impairment (Qiu et al., 2016). Another IEG, neuronal pentraxin 2 (Nptx2), may also be relevant in this context given its role in adaptive strengthening of inhibitory interneuron circuits (Pelkey et al., 2015), together with data suggesting that Nptx2 levels in CSF provide a reliable biomarker in AD (Swanson and Willette, 2016; Xiao et al., 2017). Indeed, recent advances may herald a new horizon in which non-invasive ‘liquid biopsies’ for Arc or other IEGs offer a sensitive means of detecting failing plasticity in the aged brain, as well as the response to treatment. Many of the studies reviewed here examined Arc at time points when expression would be expected to peak, based on normative findings in young adults. The assumption is thus that differences in expression as a function of age or cognitive statu are related to the maximal capacity of induction and/or degradation of Arc. Examining neuronal activity captured at a single time point, however, fails to address possible age-related slowing or acceleration of transcriptional and/or translational machinery. Live in vivo imaging could test this possibility, using recently established techniques for tracking endogenous Arc (Das et al., 2018), from transcription and mRNA degradation to translation and protein turnover. Arc mRNA and protein localization are also highly regulated, and accordingly, a related challenge will be to determine whether Arc intracellular trafficking dynamics remain intact in the aged brain (Figure 1B).

7.2. What are the molecular mechanisms and the cascade of cellular events responsible for disrupted Arc regulation?

The accumulated evidence reviewed here links impaired cognition in aging to altered regulation of Arc’s highly dynamic system, from the level of transcriptional regulation, to translation and Arc degradation (Figure 1). Understanding the directionality and specific contributions of these alterations to the plasticity of relevant brain circuits in cognitive aging is a key challenge. More work is clearly warranted, including an examination of whether Arc posttraslational modifications, intra- or inter- Arc protein-protein interactions, or Arc biophysical or structural properties change with age. For example, the oligomeric conformation of Arc may impede its normal interaction and degradation (Figure 1D) (Nikolaienko et al., 2018), resulting in altered downstream cellular signaling and consequent disruption of synaptic plasticity and cognition. It will be similarily important to examine age-related changes at the level of Arc’s expansive protein network interactions (Myrum et al., 2017; Nikolaienko et al., 2018) or DNA- and RNA-binding proteins affecting Arc dynamics in the aged brain (Figure 1C).

7.3. Is the altered Arc expression a cause or a consequence of impaired cognition in aging?

A particularly thorny issue in this area of investigation is that the direction of potential causality between disrupted Arc expression and poor cognitive outcome is difficult to resolve. Is memory impaired in aging because Arc is dysregulated, or do Arc dynamics appear disrupted in the aged brain because aged animals fail to learn or learn using different strategies? Network dysfunction in aging may be the more proximal cause of age-related memory impairment, whereas dysregulated Arc expression has emerged as a convenient proxy to quantify and characterize those disrupted neuronal networks. Intervention studies, discussed in a subsequent section, will be illuminating, providing a powerful means for examining the effects of correcting Arc expression on cognitive outcome in aging. Research incorporating a longitudinal component will also be be informative, with the potential to document whether disrupted Arc dynamics emerge earlier in the lifecourse and predict the emergence of behaviorally detectable cognitive decline. Preliminary evidence that memory-related neuronal Arc content can be measured in blood-derived EVs may open new avenues for testing this proposal (Moreno-Castilla et al., 2019; Figure 1D).

8. Restoration of Arc-mediated plasticity: a potential therapeutic target?

More than one study has demonstrated that despite age-related impairments in hippocampal Arc expression in response to exploratory behavior, given sufficient stimulation (e.g. maximum electroconvulsive stimulation), aged neurons are able to express Arc (Burke et al., 2012; Marrone et al., 2012). These findings indicate that endogenous Arc induction is still possible in aged-impaired brains and lends hope that pharmacological restoration of Arc function is viable and might be effective in treating age-related cognitive decline. At least one study has reported encouraging early discovery results, successfully identifying compounds that directly modulate Arc protein function. In a screen of ~2,700 FDA-approved chemical agents, two, thioridazine and trifluoperazine, were confirmed to bind directly to the Arc N-lobe. Given that this region binds TARPγ2/stargazin, which is involved in AMPA receptor trafficking, the study offers hope that pharmacological manipulation of Arc function could directly affect synaptic function (Zhang et al., 2015). Although so far unexplored in the context of memory, precedent includes evidence that acute overexpression of a lentivirus containing Arc can extend the critical period in adult mice and reinstate juvenile-like plasticity in the visual cortex (Jenks et al., 2017).

It seems reasonable to suppose that enhancing overall activity might restore deficits in Arc expression. But broad, unrestrained induction of Arc is also likely to produce deleterious effects. For example, in Arc knock-in mice, where ubiquination sites are mutated and Arc accumulates, animals display reduced Arc turnover and deficits in reversal learning (Wall et al., 2018). Similarly, high levels of Arc are seen in a number of diseases where cognitive deficits are prominent, including Angelman syndrome (Cao et al., 2013; Greer et al., 2010; Kühnle et al., 2013; Mabb et al., 2014; Pastuzyn and Shepherd, 2017), fragile X syndrome (Park et al., 2008), Gordon Holmes syndrome (Husain et al., 2017), and AD (Kerrigan and Randall, 2013). Together these findings demonstrate that Arc expression and localization adhere to the Goldilocks principle, and that any effort to restore Arc function would require precise coordination of Arc spatial and temporal dynamics. A seemingly challenging goal, intriguing evidence suggests that moderate dietary or lifestyle changes may be sufficient to correct aberrant Arc expression (Ménard et al., 2014). In one study, long-term caloric restriction in SD rats prevented age-related body weight increases, partially blunted the cognitive deficits observed in old ad libitum rats, and increased MWM-induced Arc expression to levels comparable to young SD rats. The effects of other lifestyle changes in advanced age, like circadian phase shift (Benloucif et al., 1997), improved sleep quantity and quality (Mander et al., 2017; Scullin and Bliwise, 2015), or exercise (Clark et al., 2011; Voss et al., 2013) merit similar examination.

Preclinical studies generally indicate that non-specific pharmacological enhancement of Arc function can improve memory. For example, in a rat model of depression, chronic treatment with the selective serotonin reuptake inhibitor (SSRI) escitalopram restored emotional memory performance and Arc transcription (Eriksson et al., 2012). In rats chronically treated with lipopolysaccharide (LPS) to induce inflammation, the NMDA receptor antagonist memantine, widely prescribed for symptomatic treatment in AD, similarly restores Arc expression and spatial memory (Rosi et al., 2006). Fewer studies have examined avenues for restoring Arc expression in the aged brain. In 18-month-old mice, treatment with histone deacetylase-2 (HDAC2) antisense, or with the general HDAC inhibitor (HDACi) sodium butyrate, improved performance on a novel object recognition test and increased Arc protein levels (Singh and Thakur, 2018). Arc expression is also modulated by a large number of other non-specific treatments that include certain drugs of abuse and psychotropic agents known to act on the glutamatergic, serotonergic, and dopaminergic systems (see review by Yakout et al., 2021). The degree to which these drugs restore normal Arc levels, and thus enhance plasticity and memory in the aged brain, remains to be fully examined. The development of more precise molecular tools and pharmacological strategies for manipulating Arc function will be instrumental in advancing this area of work, and in more directly tackling the direction of association between disrupted Arc expression and cognitive outcome in aging.

9. Conclusion

A major goal in the field of aging is to identify the cellular and molecular mechanisms underpinning individual differences in cognitive outcome. Given the worldwide growth in the elderly population, and associated increase in the number of individuals facing cognitive decline, this undertaking has never been more urgent. While the neurobiological substrates driving cognitive outcome in late life are undoubtedly complex, the studies reviewed here indicate that Arc—in its so-called “master regulator” role in brain plasticity—is well-positioned to play a substantive role. Altered Arc dynamics, from the level of transcriptional regulation, to Arc protein function and degradation, are linked to impaired cognition in aging. Defining the influence of these alterations on age-related deficits in synaptic plasticity is an important but largely unmet challenge. As we advance our understanding of the basic biology of Arc, we will therefore not only gain insight into the molecular mechanisms of memory, but also illuminate how these processes diverge during the lifespan to influence cognitive outcome. Key to that endeavor will be: (i) To incorporate new technologies like in vivo imaging techniques with improved spatio-temporal resolution and strategies for tracking gene expression in real time; (ii) To reduce ambiguity in interpretation by using animal models and behavioral evaluations finely tuned to attribute changes in Arc dynamics to cognitive outcome in aging versus chronological aging; and (iii) Longitudinal studies will be particularly illuminating, with the potential to identify midlife predictors of late life cognitive trajectory. The encouraging implication based on the evidence reviewed here is that Arc might ultimately provide an early biomarker of failing brain plasticity, holding the key to intervention aimed at bending the arc of cognitive aging.

Highlights.

Arc is a critical effector protein in memory consolidation.
As a marker, Arc has identified networks associated with normal cognitive aging.
Dysregulated Arc expression is associated with poor cognitive outcome in aging.
Age-related Arc changes are region- and task-specific.
Arc could serve as a therapeutic target in cognitive aging.

Funding:

This work was supported entirely by the Intramural Research Program of the National Institutes of Health, National Institute on Aging.

Abbreviations

ARC: human gene
ARC: rodent gene
Arc: mRNA
Arc: protein or unspecified/generic usage

Footnotes

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Relevant conflicts of interest/financial disclosures: The authors declare that they have no conflicts of interest.

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PERMALINK

‘Arc’-hitecture of normal cognitive aging

Craig Myrum

Perla Moreno-Castilla

Peter R Rapp

Abstract

1. Introduction

2. Key animal models and methodology

3. Constitutive Arc expression in cognitive aging

Table 1:

Table 2:

4. Hippocampal activity-induced Arc expression in cognitive aging

Table 3:

5. Neocortical activity-induced Arc expression in cognitive aging

Table 4:

6. Molecular causes and consequences of dysregulated Arc expression in cognitive aging

Figure 1. Overview of the ‘Arc’-hitecture of normal cognitive aging.

7. Outstanding puzzles about Arc in aging

7.1. Is the expression of Arc more tightly coupled to age-related changes in synaptic plasticity in comparison to other IEGs?

7.2. What are the molecular mechanisms and the cascade of cellular events responsible for disrupted Arc regulation?

7.3. Is the altered Arc expression a cause or a consequence of impaired cognition in aging?

8. Restoration of Arc-mediated plasticity: a potential therapeutic target?

9. Conclusion

Highlights.

Funding:

Abbreviations

Footnotes

References

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

‘Arc’-hitecture of normal cognitive aging

Craig Myrum

Perla Moreno-Castilla

Peter R Rapp

Abstract

1. Introduction

2. Key animal models and methodology

3. Constitutive Arc expression in cognitive aging

Table 1:

Table 2:

4. Hippocampal activity-induced Arc expression in cognitive aging

Table 3:

5. Neocortical activity-induced Arc expression in cognitive aging

Table 4:

6. Molecular causes and consequences of dysregulated Arc expression in cognitive aging

Figure 1. Overview of the ‘Arc’-hitecture of normal cognitive aging.

7. Outstanding puzzles about Arc in aging

7.1. Is the expression of Arc more tightly coupled to age-related changes in synaptic plasticity in comparison to other IEGs?

7.2. What are the molecular mechanisms and the cascade of cellular events responsible for disrupted Arc regulation?

7.3. Is the altered Arc expression a cause or a consequence of impaired cognition in aging?

8. Restoration of Arc-mediated plasticity: a potential therapeutic target?

9. Conclusion

Highlights.

Funding:

Abbreviations

Footnotes

References

ACTIONS

PERMALINK

RESOURCES

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