Alzheimer's disease is a progressive condition that damages the brain over time. The cause is not clear, but a toxic molecule called Amyloid-β peptide seems to play a part. It builds up in the brains of people with Alzheimer's disease, forming hard clumps called plaques. Yet, though the plaques are a hallmark of the disease, experimental treatments designed to break them down do not seem to help. This raises the question – do Amyloid-β plaques actually cause Alzheimer's disease?

Answering this question is not easy. One way to study the effect of amyloid plaques is to inject clumps of Amyloid-β peptides into model organisms. This triggers Alzheimer's-like brain damage, but it is not clear why. It remains difficult to tell the difference between the damage caused by the injected Amyloid-β peptides and the damage caused by the solid plaques that they form. For this, researchers need a way to trigger plaque formation directly inside animal brains. This would make it possible to test the effects of plaque-targeting treatments, like the drug lithium.

Optogenetics is a technique that uses light to control molecules in living animals. Hsien, Kaur et al. have now used this approach to trigger plaque formation by fusing light-sensitive proteins to Amyloid-β peptides in worms, fruit flies and zebrafish. This meant that the peptides clumped together to form plaques whenever the animals were exposed to blue light. This revealed that, while both the Amyloid-β peptides and the plaques caused damage, the plaques were much more toxic. They damaged cell metabolism and caused tissue loss that resembled late Alzheimer's disease in humans. To find out whether it was possible to test Alzheimer's treatments in these animals, Hsien, Kaur et al. treated them with the drug, lithium. This increased their lifespan, reversing some of the damage caused by the plaques.

Alzheimer's disease affects more than 46.8 million people worldwide and is the sixth leading cause of death in the USA. But, despite over 50 years of research, there is no cure. This new plaque-formation technique allows researchers to study the effects of amyloid plaques in living animals, providing a new way to test Alzheimer's treatments. This could be of particular help in studies of experimental drugs that aim to reduce plaque formation.

Alzheimer’s disease (AD) is a debilitating, age-associated, neurodegenerative disease which affects more than 46.8 million people worldwide and represents the 6th leading cause of death in the United States of America (Ahn et al., 2001; De-Paula et al., 2012; Hawkes, 2016; Kumar et al., 2015; Zhang et al., 2011). Despite extensive efforts over the last 50 years, no disease-modifying therapy has been found and, most recently, several high-profile Phase III clinical trials have failed (Anderson et al., 2017; Lim and Mathuru, 2018; Park et al., 2018). The AD clinical trial landscape has largely been dominated by the amyloid cascade hypothesis, with more than 50% of the drugs targeting amyloid beta (Aβ) in Phase III trials alone (Cummings et al., 2018). The amyloid cascade hypothesis, in its original form, posits the deposition of Aβ, in particular the extracellular Aβ plaque, as the main driver of AD (Hardy and Higgins, 1992). However, the causative role of Aβ plaques has recently been challenged, because of the failure of numerous interventions targeting Aβ plaques in Phase III trials and the observation of Aβ plaques in brains of non-AD symptomatic individuals (Cummings, 2018). Model organisms ranging from nematodes to mice have further shown that AD pathology can be modeled in the absence of obvious Aβ plaques (Duff et al., 1996; Fong et al., 2016; Teo et al., 2019). These observations point to a different mechanism for Aβ neurotoxicity perhaps through Aβ’s intracellular accumulation (LaFerla et al., 2007).

Amyloid plaques are macroscopic extracellular protein aggregates, but intracellular Aβ must function in smaller units of peptides, oligomers or aggregates. Of these, soluble Aβ oligomers appear to be the main toxic species in AD (Ferreira and Klein, 2011). For example, Aβ oligomers have been shown to induce neurotoxic effects including memory loss in transgenic animal models reviewed in Mroczko et al. (2018). These studies, however, mostly rely on in vitro injection of synthetic Aβ oligomers or oligomers extracted from AD brains (Mroczko et al., 2018). There is currently a lack of tools that can directly control Aβ oligomerization in vivo, which would allow direct examination of the effects of Aβ oligomerization.

Here, we describe an optogenetic method to study Amyloid-β (Aβ) protein oligomerization in vivo in different model organisms and apply it to delineating mechanisms of disease progression. Optogenetics hinges on light responsive proteins that can be fused to genes of interest, allowing for the spatial and temporal regulation of proteins in a highly precise manner. Temporal-spatial control is achieved simply by the exposure of the target system to lights of a specific wavelength, without the need to introduce other external agents (Fenno et al., 2011; Möglich and Moffat, 2010). One such optogenetic protein, a modified version of the Arabidopsis thaliana cryptochrome 2 (CRY2) protein, oligomerizes into photobodies quickly and reversibly in the presence of blue light at 488 nm (Más et al., 2000). In cultured cells, expression of the photolyase homology region of CRY2 fused to mCherry led to puncta within 10 s of exposure to blue light and these puncta dispersed within minutes (Bugaj et al., 2013). This basic unit of CRY2-mCherry attached to a variety of proteins have, for instance, been used to study cortical actin dynamics in cell contractility during tissue morphogenesis, the dynamics of Wnt and EGF signaling, plasma membrane composition and transcriptional regulation (Bugaj et al., 2018; Bugaj et al., 2015; Guglielmi et al., 2015; Huang et al., 2017; Idevall-Hagren et al., 2012; Johnson et al., 2017; Kaur et al., 2017; Kennedy et al., 2010).

To address the role of aggregation of intracellular Aβ in the pathophysiological effects of AD, we developed a light-inducible system for use in model organisms. Specifically, we generated an in vivo, light-dependent, oligomerization switch for the formation and dissolution of Aβ oligomers in Drosophila melanogaster, Caenorhabditis elegans and Danio rerio (Figure 1a). This optogenetically-driven model allowed the visualization of microscopic Aβ oligomerization and addressed the question of their relationship to Aβ pathogenesis with spatiotemporal precision.

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Generation of transgenic models with light-inducible intracellular Aβ oligomerization in model organisms

We generated transgenic animals expressing the 42-amino-acid human Aβ peptide (Aβ1–42) fused to Cryptochrome 2 and the fluorescent protein mCherry (Aβ-CRY2-mCh, Figure 1a, please note the relative sizes). Drosophila lines were under the control of the GAL4/UAS system (Brand and Perrimon, 1993) and expressed either ubiquitously via AD-Gal4 driver (Figure 1b–c) or specifically in neurons using Elav-Gal4 driver (Figure 1d–e, Figure 1—figure supplement 1). C. elegans lines were made with the heat shock promoter (Figure 1f–h, Figure 1—figure supplement 2). In order to observe oligomerization and clustering of intracellular Aβ, we took a live imaging approach in Drosophila embryos using a lightsheet microscope with a 488 nm laser activating CRY2 and the 561 nm laser imaging mCh over time. We observed clusters of mCh fluorescence forming in embryos exposed to 488 nm light, but fewer in embryos that were not exposed to blue light (Figure 1—figure supplement 3, Videos 1–2). The intensity of the clusters was quantified using Imaris quantification software to show the intensity of oligomer formation (Figure 1c, note color scale for cluster intensity).

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We proceeded to test the effectiveness of light induced clustering of Aβ-CRY2-mCh in C. elegans. As this construct is driven by a heat shock promoter, worms were heat shocked at 35°C for 90 min followed by resting at 20°C before being immobilized and imaged using confocal microscopy (Figure 1f–h). We observed an increase in clusters in worms exposed to blue light over their siblings not exposed to blue light. The results were quantified as fluorescence intensity and represented in (Figure 1h) showing the formation of Aβ clusters.

Induction of chaperones from heat-shock can impact the dynamics of Aβ. In particular, heat-shock treatment in another transgenic C. elegans strain, CL4176, reduced Aβ-mediated paralysis and Aβ oligomerization (Winblad, 1989; Wu et al., 2010). Overexpression of chaperone protein suppressed Aβ-toxicity in C. elegans (Fonte et al., 2008). The use of hsp-16 promoter in the transgenic C. elegans, which requires heat-shock induction, may hence be a limitation of our current approach. In view of this limitation, we performed several other controls to understand the impact of heat-shock treatment on Aβ dynamics, and whether or not heat-shock treatment itself contributes to the metabolic and phenotypic detriments observed in the transgenic animals.

To understand how heat-shock treatment may impact the dynamics of Aβ in our transgenic strains, we compared the dynamics of Aβ between the transgenic Aβ worms with and without heat shock. We found that transgenic animals in both heat-shock (Aβ HS L) and non-heat-shock (Aβ -HS L) condition expressed detectable Aβ-CRY2-mCh protein (Figure 1—figure supplement 2), however animals that had been heat-shocked had significantly higher Aβ levels compared to non-heat-shocked animals, suggesting that heat-shock, as intended, drove higher level of Aβ expression and that this was not compensated for by secondary induction of chaperones.

Even though the CRY2 system predominantly oligomerizes, we also observed the formation of intracellular Aβ aggregates, using a direct stain for Aβ aggregates (Figure 1—figure supplement 4). Importantly, we observed that Aβ aggregation was not reversible as had previously been observed for signaling molecules (Huang et al., 2017; Johnson et al., 2017; Kaur et al., 2017), rather CRY2 appeared to initiate clustering leading to Aβ bundles that did not come apart once blue light was turned off. We further confirmed that turning on blue light alone without the transgene expression did not lead to any Aβ expression or cluster formation (Figure 1—figure supplement 2b, Ab HS L vs Ab –HS L), suggesting that the oligomerization process was CRY2-specific and not an artifact of light. We performed FRAP experiments to establish the stability of the Aβ clusters (Figure 2). When compared to an unrelated CRY2 construct, we observed a very slow recovery for Aβ-CRY2-mCh compared to Arm-CRY2-mCh (Figure 2; Kaur et al., 2017). Together, our results showed that the Aβ-CRY2-mCh transgene was functional in both organisms, with a high specificity to blue light inducing irreversible oligomerization of Aβ.

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Light-inducible Aβ oligomerization causes lifespan and behavioral deficits in transgenic models

To test the functionality of Aβ-CRY2-mCh, we next looked for phenotypes associated with light-induced intracellular Aβ oligomerization. We performed lifespan studies to assess the effects of Aβ-CRY2-mCh by expressing Aβ-CRY2-mCh either ubiquitously (AD-Gal4) or specifically in the nervous system (Elav-Gal4). Newly eclosed adult flies were separated into two groups in each case, one of which was exposed to ambient white light and the other kept in the dark. In both cases, flies exposed to light died much more quickly with half the mean and maximum lifespans (Figure 3a–b) compared to those reared in the dark. We extended this analysis to C. elegans, though starting at Day 6 due to the heat-shock manipulation step. Under light conditions, C. elegans showed markedly decreased lifespans as compared to the transgenic C. elegans kept in dark conditions (Figure 3c). Most significantly, we observed that the lifespan decrease was reversible, as taking animals exposed to light for 24 days and moving them into darkness showed a recovery of their lifespan, or a rescue (Figure 3a–b). To ensure that these reduced lifespans were physiological, we examined the fitness of the animals. Fitness was severely decreased in light-exposed flies and worms as quantified by fertility and locomotive assays (Figure 3—figure supplement 1). Interestingly, light-induced Aβ oligomerization caused further impairment in sensorimotor function of transgenic Aβ nematodes (Figure 3—figure supplement 1c). These results suggesed that light-induced oligomerization of Aβ not only affected lifespan and fitness, but also impaired complex sensorimotor function and behavior.

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Light-inducible Aβ oligomerization leads to physical and metabolic damage in transgenic models

The lethality mediated by light-induced Aβ oligomerization led us to examine the morphological effects of Aβ-CRY2-mCh clustering during embryonic stages in Drosophila. Embryos expressing Aβ in only the nervous system developed at a normal pace and did not show any morphogenetic defects and even hatched in the absence of blue light (Video 1). In contrast, sibling embryos exposed to blue laser light arrested in late neurogenesis stages where the central nervous system stopped developing and the embryos died (Video 2 and Figure 4, Figure 1—figure supplement 3 and Figure 4—figure supplement 1). These embryos showed a variety of abnormalities in the positioning of neurons and glial cells (Compare to complete normal development, Video 3) suggesting that light-induced Aβ oligomerization caused physical, structural damage that affects neuronal development.

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The severity of the physical damage phenotype in embryos exposed to blue light (Figure 4a–d’’) led us to investigate the differences in mechanism between the two conditions by exploring inflammatory response, mitochondrial health and oxidative damage. Expression of Aβ alone, without light-induced Aβ oligomerization, led to a small increase in inflammatory markers (Figure 4e; Westfall et al., 2018). In embryos exposed to light, two of these markers increased to a much greater level (Figure 4e). We observed a significant reduction in the number of mitochondria in light-treated Aβ animals (Figure 4f). Markers of oxidative damage in light and dark exposed C. elegans showed differential expression of antioxidant defense genes, specifically Catalase, Trx-2 and Sod-3, suggestive of the occurrence of oxidative stress induced by Aβ oligomerization (Figure 5). These results suggested that light-induced Aβ oligomerization phenocopied many of the hallmarks of AD.

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We proceeded to look at light-induced Aβ oligomerization in metabolic impairments in transgenic C. elegans. Heat-shocked mutants exposed to light (Aβ HS L) had lower ATP levels compared to the non-transgenic control (Ctrl -HS D) (Figure 6). However, heat-shocked mutants in the dark condition (Aβ HS D) did not show any significant differences in ATP level compared to the non-transgenic control. This result suggested that presence of Aβ alone is insufficient to induce ATP deficits, and that light-induced oligomerization of Aβ is required for the defect to manifest. Aβ expression alone was also insufficient to affect the nematodes’ maximum respiratory capacity, as heat-shocked mutants in the dark condition (Aβ HS D) did not display differences in maximum and spare respiration capacity compared to control animals (Ctrl -HS D). However, light-induced Aβ oligomerization significantly reduced maximum respiration capacity in C. elegans. Heat-shocked mutants in the light condition (Aβ HS L) displayed significantly lower maximum and spare respiration capacity compared to controls (Ctrl -HS D) and to heat-shocked mutants in the dark condition (Aβ HS D) (Figure 6b–d). Together, these results highlighted the metabolic differences between the two conditions and showed that metabolic deficits were mediated by light-induced Aβ oligomerization.

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To further examine whether the metabolic deficits in transgenic C. elegans were mediated by heat-shock treatment, we included a heat-shocked non-transgenic control in the metabolic and phenotypic assays. We found that heat-shock treatment does not affect ATP levels in the control animals, suggesting that the ATP detriment was a result of Aβ expression, but not heat-shock treatment (Figure 6a). On the other hand, heat-shock treatment appears to reduce the respiratory capacity of the control animals, suggesting that the decline in respiratory capacity was mediated by both heat-shock and Aβ expression (Figure 6c). Comparing the heat-shocked transgenic animals in light and dark conditions, we found that light treatment, which induces oligomerization, worsened the phenotypic and metabolic detriments in the transgenic animals. This suggested that regardless of the effects of heat-shock and chaperone induction, oligomerization still negatively impacted the phenotypes.

Light-inducible Aβ lifespan decrease is rescued by Li⁺

The drug Li⁺ is used for treatment of neurological conditions (Licht, 2012). A high drinking water Li⁺ concentration correlated with lower human mortality (Zarse et al., 2011), and Li⁺ increased the lifespan of C. elegans and Drosophila (Castillo-Quan et al., 2016; McColl et al., 2008; Tam et al., 2014; Teo et al., 2020a; Teo et al., 2020b; Zarse et al., 2011). The molecular mechanisms by which Li⁺ functions include inhibition of inositol monophosphotase (IMPA) and glycogen synthase kinase-3 (GSK-3) (Kerr et al., 2018), reduction of oxidative damage (Kasuya et al., 2009; Kerr et al., 2017; Khan et al., 2015) and longevity via a GSK-3/NRF-2 dependent, hormetic mechanism (Castillo-Quan et al., 2016). Among the mechanisms for neuroprotection, the Wnt signaling pathway plays a role in modulating AD pathology and its progression (De Ferrari et al., 2003; Jin et al., 2017; Parr et al., 2015; Toledo and Inestrosa, 2009). Activation of Wnt signaling through lithium (Li⁺) treatment attenuated Aβ aggregation and increased the lifespan of Aβ models (Toledo and Inestrosa, 2009). By inhibiting the enzyme glycogen synthase kinase-3 (GSK-3), Li⁺ compounds drive the downstream production of intracellular β-catenin activating the Wnt–β-catenin signaling pathway (Klein and Melton, 1996). Li⁺ treatment through Wnt activation also rescued behavioral impairment and neurodegeneration induced by Aβ fibrils (De Ferrari et al., 2003). Taken together, these studies point to a putative AD therapeutic intervention through Li⁺. To test the usefulness of optogenetic Aβ for drug testing, we supplemented the food for Drosophila expressing Aβ-CRY2-mCh with Li⁺. Drosophila exposed to light, but consuming Li⁺ had significantly increased lifespans, suggesting both a rescue effect induced by Li⁺ (Figure 3a–b), and that the optogenetic model can be used for inducible expression drug testing. Rapid drug testing can be also be achieved through cell culture. We extended this assay to HEK293 cells transfected with Aβ-CRY2-mCh where Aβ clusters formed upon stimulation with blue light (Figure 7a). The number of clusters per cell was greatly reduced by the addition of Li⁺ or the specific GSK3 inhibitor CHIR99021, but CHIR99021 additionally reduced the signal intensity of the clusters (Figure 7b–c, quantified 7d). These data demonstrated the potential use of the optogenetics system for drug testing.

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Finally, we also extended these findings to a vertebrate system. We generated a zebrafish permanent line with UAS:Aβ-CRY2-mCh in the transgenic TgBAC(gng8:GAL4)^c416 background (Hong et al., 2013) that limited expression to a few cells. We observed a diffuse mCh fluorescence in a small subset of neurons in the olfactory epithelium, the interpeduncular nucleus and in a few neurons sparsely distributed in the forebrain at 5 dpf. Upon blue light exposure (488 nM) the detectable intensity of the fluorescence increased rapidly (Figure 8—figure supplement 1). Several neurons among those expressing appeared to bleb and began to die within 40 to 50 min of initial exposure to the blue light (Video 4). Such damage was not observable in matched controls expressing Aβ-mCh driven by a CMV promotor lacking the CRY2 protein (Video 5). Our approach leads to the expression of Aβ-CRY2-mCh in only a small subset of neurons making analysis of AD hallmarks difficult, so we used transient expression of Aβ-CRY2-mCh driven by a ubiquitin promotor in 48 hr old embryos to quantify the effects of light-induced Aβ oligomerization on mitochondria health and metabolic deficits. Similar to the impairments observed in C. elegans, Aβ-CRY2-mCh expressing embryos showed lower maximum and spare respiration capacity compared to un-injected control (Figure 8). In addition, light exposure of Aβ-CRY2-mCh expressing embryos caused a significant reduction in ATP levels (Figure 8a).

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Aggregation of Aβ has been studied extensively, but our results represent the first study to demonstrate that induced oligomerization of intracellular Aβ can be used to separate different pathologies (induced by Aβ oligomerization vs expression alone). A second approach to making inducible aggregates using a chemically controlled fluorescent protein has been published, but not yet widely tested (Miyazaki et al., 2016). Other previous approaches lacked an inducible oligomerization tool and could only demonstrate Aβ oligomers’ toxicity through exogenous injection of Aβ oligomers (Mroczko et al., 2018). We use a tool to investigate the pathological effects of intracellular Aβ oligomerization in nematodes and flies, human kidney cells, and in the vertebrate model organism zebrafish (Figure 8—figure supplement 1, Video 4). Despite the reversibility of CRY2 clustering in previous findings, we find that CRY2 initiated clustering of Aβ appeared to be irreversible likely due to the structure of the aggregates (Antzutkin et al., 2012). Consistent with existing literature showing that Aβ oligomers induce oxidative stress and inflammation (Butterfield et al., 2013; Forloni and Balducci, 2018), we show that light-induced oligomerization of Aβ, but not Aβ expression alone, is necessary for the metabolic defects, loss of mitochondria and inflammation in C. elegans and Drosophila. Light-induced oligomerization also leads to physical damage of the nervous system in D. rerio, resembling the loss of brain tissue in AD (Figure 8—figure supplement 1). The embryonic assay for physical damage phenocopies brain lesions only to some degree as the extent of the damage is enhanced by the forces applied to the developing neural tissue. The irreversibility of Aβ aggregates once initiated as seen here, also hints at intrinsic properties that may be unique or unusual to this peptide and warrants further investigation. It is also likely to prove a good model for testing anti-aggregation compounds.

We anticipate that our approach will serve as an attractive tool for carrying out drug screens and mechanistic studies for the treatment of AD. This optogenetic strategy will also undoubtedly complement other techniques due to the high level of spatiotemporal specificity. For example, it could be optimally positioned to gain insights into the unresolved mechanism of Aβ - whether oligomerization and subsequent accumulation or lack of effective clearance underpins AD pathologies. The separation of two phases of AD progression also suggests that single drug treatments will not suffice, and perhaps combinatorial approaches should be tried.

All data generated or analysed during this study are included in the manuscript and supporting files.

1. 1. Ahn NG
   2. Nahreini TS
   3. Tolwinski NS
   4. Resing KA

   (2001) Pharmacologic inhibitors of MKK1 and MKK2

   Methods in Enzymology 332:417–431.

   https://doi.org/10.1016/s0076-6879(01)32219-x

   • PubMed
   • Google Scholar
2. 1. Anderson RM
   2. Hadjichrysanthou C
   3. Evans S
   4. Wong MM

   (2017) Why do so many clinical trials of therapies for alzheimer's disease fail?

   The Lancet 390:2327–2329.

   https://doi.org/10.1016/S0140-6736(17)32399-1

   • Google Scholar
3. 1. Antzutkin ON
   2. Iuga D
   3. Filippov AV
   4. Kelly RT
   5. Becker-Baldus J
   6. Brown SP
   7. Dupree R

   (2012) Hydrogen bonding in Alzheimer's amyloid-β fibrils probed by 15N{17O} REAPDOR solid-state NMR spectroscopy

   Angewandte Chemie 51:10289–10292.

   https://doi.org/10.1002/anie.201203595

   • PubMed
   • Google Scholar
4. 1. Bischof J
   2. Maeda RK
   3. Hediger M
   4. Karch F
   5. Basler K

   (2007) An optimized transgenesis system for Drosophila using germ-line-specific phiC31 integrases

   PNAS 104:3312–3317.

   https://doi.org/10.1073/pnas.0611511104

   • PubMed
   • Google Scholar
5. 1. Brand AH
   2. Perrimon N

   (1993)

   Targeted gene expression as a means of altering cell fates and generating dominant phenotypes

   Development 118:401–415.

   • Google Scholar
6. 1. Bugaj LJ
   2. Choksi AT
   3. Mesuda CK
   4. Kane RS
   5. Schaffer DV

   (2013) Optogenetic protein clustering and signaling activation in mammalian cells

   Nature Methods 10:249–252.

   https://doi.org/10.1038/nmeth.2360

   • PubMed
   • Google Scholar
7. 1. Bugaj LJ
   2. Spelke DP
   3. Mesuda CK
   4. Varedi M
   5. Kane RS
   6. Schaffer DV

   (2015) Regulation of endogenous transmembrane receptors through optogenetic Cry2 clustering

   Nature Communications 6:6898.

   https://doi.org/10.1038/ncomms7898

   • PubMed
   • Google Scholar
8. 1. Bugaj LJ
   2. Sabnis AJ
   3. Mitchell A
   4. Garbarino JE
   5. Toettcher JE
   6. Bivona TG
   7. Lim WA

   (2018) Cancer mutations and targeted drugs can disrupt dynamic signal encoding by the Ras-Erk pathway

   Science 361:eaao3048.

   https://doi.org/10.1126/science.aao3048

   • PubMed
   • Google Scholar
9. 1. Butterfield DA
   2. Swomley AM
   3. Sultana R

   (2013) Amyloid β-peptide (1-42)-induced oxidative stress in alzheimer disease: importance in disease pathogenesis and progression

   Antioxidants & Redox Signaling 19:823–835.

   https://doi.org/10.1089/ars.2012.5027

   • PubMed
   • Google Scholar
10. 1. Castillo-Quan JI
    2. Li L
    3. Kinghorn KJ
    4. Ivanov DK
    5. Tain LS
    6. Slack C
    7. Kerr F
    8. Nespital T
    9. Thornton J
    10. Hardy J
    11. Bjedov I
    12. Partridge L

    (2016) Lithium promotes longevity through GSK3/NRF2-Dependent hormesis

    Cell Reports 15:638–650.

    https://doi.org/10.1016/j.celrep.2016.03.041

    • PubMed
    • Google Scholar
11. 1. Colosimo PF
    2. Tolwinski NS

    (2006) Wnt, hedgehog and junctional Armadillo/beta-catenin establish planar polarity in the Drosophila embryo

    PLOS ONE 1:e9.

    https://doi.org/10.1371/journal.pone.0000009

    • PubMed
    • Google Scholar
12. 1. Cummings J

    (2018) Lessons learned from alzheimer disease: clinical trials with negative outcomes

    Clinical and Translational Science 11:147–152.

    https://doi.org/10.1111/cts.12491

    • PubMed
    • Google Scholar
13. 1. Cummings J
    2. Lee G
    3. Ritter A
    4. Zhong K

    (2018) Alzheimer's disease drug development pipeline: 2018

    Alzheimer's & Dementia 4:195–214.

    https://doi.org/10.1016/j.trci.2018.03.009

    • PubMed
    • Google Scholar
14. 1. De Ferrari GV
    2. Chacón MA
    3. Barría MI
    4. Garrido JL
    5. Godoy JA
    6. Olivares G
    7. Reyes AE
    8. Alvarez A
    9. Bronfman M
    10. Inestrosa NC

    (2003) Activation of wnt signaling rescues neurodegeneration and behavioral impairments induced by beta-amyloid fibrils

    Molecular Psychiatry 8:195–208.

    https://doi.org/10.1038/sj.mp.4001208

    • PubMed
    • Google Scholar
15. 1. De-Paula VJ
    2. Radanovic M
    3. Diniz BS
    4. Forlenza OV

    (2012) Alzheimer's disease

    Sub-Cellular Biochemistry 65:329–352.

    https://doi.org/10.1007/978-94-007-5416-4_14

    • PubMed
    • Google Scholar
16. 1. Duff K
    2. Eckman C
    3. Zehr C
    4. Yu X
    5. Prada CM
    6. Perez-tur J
    7. Hutton M
    8. Buee L
    9. Harigaya Y
    10. Yager D
    11. Morgan D
    12. Gordon MN
    13. Holcomb L
    14. Refolo L
    15. Zenk B
    16. Hardy J
    17. Younkin S

    (1996) Increased amyloid-beta42(43) in brains of mice expressing mutant presenilin 1

    Nature 383:710–713.

    https://doi.org/10.1038/383710a0

    • PubMed
    • Google Scholar
17. 1. Fenno L
    2. Yizhar O
    3. Deisseroth K

    (2011) The development and application of optogenetics

    Annual Review of Neuroscience 34:389–412.

    https://doi.org/10.1146/annurev-neuro-061010-113817

    • PubMed
    • Google Scholar
18. 1. Ferreira ST
    2. Klein WL

    (2011) The aβ oligomer hypothesis for synapse failure and memory loss in Alzheimer's disease

    Neurobiology of Learning and Memory 96:529–543.

    https://doi.org/10.1016/j.nlm.2011.08.003

    • PubMed
    • Google Scholar
19. 1. Fong S
    2. Teo E
    3. Ng LF
    4. Chen CB
    5. Lakshmanan LN
    6. Tsoi SY
    7. Moore PK
    8. Inoue T
    9. Halliwell B
    10. Gruber J

    (2016) Energy crisis precedes global metabolic failure in a novel Caenorhabditis elegans alzheimer disease model

    Scientific Reports 6:33781.

    https://doi.org/10.1038/srep33781

    • PubMed
    • Google Scholar
20. 1. Fong S
    2. Ng LF
    3. Ng LT
    4. Moore PK
    5. Halliwell B
    6. Gruber J

    (2017) Identification of a previously undetected metabolic defect in the complex II Caenorhabditis elegans mev-1 mutant strain using respiratory control analysis

    Biogerontology 18:189–200.

    https://doi.org/10.1007/s10522-016-9672-6

    • PubMed
    • Google Scholar
21. 1. Fonte V
    2. Kipp DR
    3. Yerg J
    4. Merin D
    5. Forrestal M
    6. Wagner E
    7. Roberts CM
    8. Link CD

    (2008) Suppression of in vivo beta-amyloid peptide toxicity by overexpression of the HSP-16.2 small chaperone protein

    Journal of Biological Chemistry 283:784–791.

    https://doi.org/10.1074/jbc.M703339200

    • PubMed
    • Google Scholar
22. 1. Forloni G
    2. Balducci C

    (2018) Alzheimer's Disease, Oligomers, and Inflammation

    Journal of Alzheimer's Disease 62:1261–1276.

    https://doi.org/10.3233/JAD-170819

    • PubMed
    • Google Scholar
23. 1. Groth AC
    2. Fish M
    3. Nusse R
    4. Calos MP

    (2004) Construction of transgenic Drosophila by using the site-specific integrase from phage phiC31

    Genetics 166:1775–1782.

    https://doi.org/10.1534/genetics.166.4.1775

    • PubMed
    • Google Scholar
24. 1. Guglielmi G
    2. Barry JD
    3. Huber W
    4. De Renzis S

    (2015) An optogenetic method to modulate cell contractility during tissue morphogenesis

    Developmental Cell 35:646–660.

    https://doi.org/10.1016/j.devcel.2015.10.020

    • PubMed
    • Google Scholar
25. 1. Han SK
    2. Lee D
    3. Lee H
    4. Kim D
    5. Son HG
    6. Yang JS
    7. Lee SV
    8. Kim S

    (2016) OASIS 2: online application for survival analysis 2 with features for the analysis of maximal lifespan and healthspan in aging research

    Oncotarget 7:56147–56152.

    https://doi.org/10.18632/oncotarget.11269

    • PubMed
    • Google Scholar
26. 1. Hardy JA
    2. Higgins GA

    (1992) Alzheimer's disease: the amyloid cascade hypothesis

    Science 256:184–185.

    https://doi.org/10.1126/science.1566067

    • PubMed
    • Google Scholar
27. 1. Hawkes N

    (2016) Sixty seconds on . . . solanezumab

    BMJ 355:i6389.

    https://doi.org/10.1136/bmj.i6389

    • Google Scholar
28. 1. Hong E
    2. Santhakumar K
    3. Akitake CA
    4. Ahn SJ
    5. Thisse C
    6. Thisse B
    7. Wyart C
    8. Mangin JM
    9. Halpern ME

    (2013) Cholinergic left-right asymmetry in the habenulo-interpeduncular pathway

    PNAS 110:21171–21176.

    https://doi.org/10.1073/pnas.1319566110

    • PubMed
    • Google Scholar
29. 1. Huang A
    2. Amourda C
    3. Zhang S
    4. Tolwinski NS
    5. Saunders TE

    (2017) Decoding temporal interpretation of the morphogen bicoid in the early Drosophila embryo

    eLife 6:e26258.

    https://doi.org/10.7554/eLife.26258

    • PubMed
    • Google Scholar
30. 1. Idevall-Hagren O
    2. Dickson EJ
    3. Hille B
    4. Toomre DK
    5. De Camilli P

    (2012) Optogenetic control of phosphoinositide metabolism

    PNAS 109:E2316–E2323.

    https://doi.org/10.1073/pnas.1211305109

    • Google Scholar
31. 1. Iijima K
    2. Chiang HC
    3. Hearn SA
    4. Hakker I
    5. Gatt A
    6. Shenton C
    7. Granger L
    8. Leung A
    9. Iijima-Ando K
    10. Zhong Y

    (2008) Abeta42 mutants with different aggregation profiles induce distinct pathologies in Drosophila

    PLOS ONE 3:e1703.

    https://doi.org/10.1371/journal.pone.0001703

    • PubMed
    • Google Scholar
32. 1. Jin N
    2. Zhu H
    3. Liang X
    4. Huang W
    5. Xie Q
    6. Xiao P
    7. Ni J
    8. Liu Q

    (2017) Sodium selenate activated wnt/β-catenin signaling and repressed amyloid-β formation in a triple transgenic mouse model of Alzheimer's disease

    Experimental Neurology 297:36–49.

    https://doi.org/10.1016/j.expneurol.2017.07.006

    • PubMed
    • Google Scholar
33. 1. Johnson HE
    2. Goyal Y
    3. Pannucci NL
    4. Schüpbach T
    5. Shvartsman SY
    6. Toettcher JE

    (2017) The spatiotemporal limits of developmental erk signaling

    Developmental Cell 40:185–192.

    https://doi.org/10.1016/j.devcel.2016.12.002

    • PubMed
    • Google Scholar
34. 1. Kasuya J
    2. Kaas G
    3. Kitamoto T

    (2009) Effects of lithium chloride on the gene expression profiles in Drosophila heads

    Neuroscience Research 64:413–420.

    https://doi.org/10.1016/j.neures.2009.04.015

    • PubMed
    • Google Scholar
35. 1. Kaur P
    2. Saunders TE
    3. Tolwinski NS

    (2017) Coupling optogenetics and light-sheet microscopy, a method to study wnt signaling during embryogenesis

    Scientific Reports 7:16636.

    https://doi.org/10.1038/s41598-017-16879-0

    • PubMed
    • Google Scholar
36. 1. Kennedy MJ
    2. Hughes RM
    3. Peteya LA
    4. Schwartz JW
    5. Ehlers MD
    6. Tucker CL

    (2010) Rapid blue-light-mediated induction of protein interactions in living cells

    Nature Methods 7:973–975.

    https://doi.org/10.1038/nmeth.1524

    • PubMed
    • Google Scholar
37. 1. Kerr F
    2. Sofola-Adesakin O
    3. Ivanov DK
    4. Gatliff J
    5. Gomez Perez-Nievas B
    6. Bertrand HC
    7. Martinez P
    8. Callard R
    9. Snoeren I
    10. Cochemé HM
    11. Adcott J
    12. Khericha M
    13. Castillo-Quan JI
    14. Wells G
    15. Noble W
    16. Thornton J
    17. Partridge L

    (2017) Direct Keap1-Nrf2 disruption as a potential therapeutic target for alzheimer's disease

    PLOS Genetics 13:e1006593.

    https://doi.org/10.1371/journal.pgen.1006593

    • PubMed
    • Google Scholar
38. 1. Kerr F
    2. Bjedov I
    3. Sofola-Adesakin O

    (2018) Molecular mechanisms of lithium action: switching the light on multiple targets for dementia using animal models

    Frontiers in Molecular Neuroscience 11:297.

    https://doi.org/10.3389/fnmol.2018.00297

    • PubMed
    • Google Scholar
39. 1. Khan A
    2. Jamwal S
    3. Bijjem KR
    4. Prakash A
    5. Kumar P

    (2015) Neuroprotective effect of hemeoxygenase-1/glycogen synthase kinase-3β modulators in 3-nitropropionic acid-induced neurotoxicity in rats

    Neuroscience 287:66–77.

    https://doi.org/10.1016/j.neuroscience.2014.12.018

    • PubMed
    • Google Scholar
40. 1. Kibat C
    2. Krishnan S
    3. Ramaswamy M
    4. Baker BJ
    5. Jesuthasan S

    (2016) Imaging voltage in zebrafish as a route to characterizing a vertebrate functional connectome: promises and pitfalls of genetically encoded indicators

    Journal of Neurogenetics 30:80–88.

    https://doi.org/10.1080/01677063.2016.1180384

    • PubMed
    • Google Scholar
41. 1. Klein PS
    2. Melton DA

    (1996) A molecular mechanism for the effect of lithium on development

    PNAS 93:8455–8459.

    https://doi.org/10.1073/pnas.93.16.8455

    • PubMed
    • Google Scholar
42. 1. Kumar A
    2. Singh A
    3. Ekavali

    (2015) A review on Alzheimer's disease pathophysiology and its management: an update

    Pharmacological Reports 67:195–203.

    https://doi.org/10.1016/j.pharep.2014.09.004

    • PubMed
    • Google Scholar
43. 1. LaFerla FM
    2. Green KN
    3. Oddo S

    (2007) Intracellular amyloid-beta in Alzheimer's disease

    Nature Reviews Neuroscience 8:499–509.

    https://doi.org/10.1038/nrn2168

    • PubMed
    • Google Scholar
44. 1. Licht RW

    (2012) Lithium: still a major option in the management of bipolar disorder

    CNS neuroscience & Therapeutics 18:219–226.

    https://doi.org/10.1111/j.1755-5949.2011.00260.x

    • PubMed
    • Google Scholar
45. 1. Lim C
    2. Mathuru A

    (2018) Modeling Alzheimer’s and Other Age Related Human Diseases in Embryonic Systems

    Journal of Developmental Biology 6:1.

    https://doi.org/10.3390/jdb6010001

    • Google Scholar
46. 1. Markstein M
    2. Pitsouli C
    3. Villalta C
    4. Celniker SE
    5. Perrimon N

    (2008) Exploiting position effects and the gypsy retrovirus insulator to engineer precisely expressed transgenes

    Nature Genetics 40:476–483.

    https://doi.org/10.1038/ng.101

    • PubMed
    • Google Scholar
47. 1. Más P
    2. Devlin PF
    3. Panda S
    4. Kay SA

    (2000) Functional interaction of phytochrome B and cryptochrome 2

    Nature 408:207–211.

    https://doi.org/10.1038/35041583

    • PubMed
    • Google Scholar
48. 1. McColl G
    2. Killilea DW
    3. Hubbard AE
    4. Vantipalli MC
    5. Melov S
    6. Lithgow GJ

    (2008) Pharmacogenetic analysis of lithium-induced delayed aging in Caenorhabditis elegans

    Journal of Biological Chemistry 283:350–357.

    https://doi.org/10.1074/jbc.M705028200

    • PubMed
    • Google Scholar
49. 1. Miyazaki Y
    2. Mizumoto K
    3. Dey G
    4. Kudo T
    5. Perrino J
    6. Chen LC
    7. Meyer T
    8. Wandless TJ

    (2016) A method to rapidly create protein aggregates in living cells

    Nature Communications 7:11689.

    https://doi.org/10.1038/ncomms11689

    • PubMed
    • Google Scholar
50. 1. Möglich A
    2. Moffat K

    (2010) Engineered photoreceptors as novel optogenetic tools

    Photochemical & Photobiological Sciences 9:1286–1300.

    https://doi.org/10.1039/c0pp00167h

    • PubMed
    • Google Scholar
51. 1. Mroczko B
    2. Groblewska M
    3. Litman-Zawadzka A
    4. Kornhuber J
    5. Lewczuk P

    (2018) Amyloid β oligomers (AβOs) in Alzheimer's disease

    Journal of Neural Transmission 125:177–191.

    https://doi.org/10.1007/s00702-017-1820-x

    • PubMed
    • Google Scholar
52. 1. Müller HA
    2. Wieschaus E

    (1996) Armadillo, bazooka, and stardust are critical for early stages in formation of the zonula adherens and maintenance of the polarized blastoderm epithelium in Drosophila

    The Journal of Cell Biology 134:149–163.

    https://doi.org/10.1083/jcb.134.1.149

    • PubMed
    • Google Scholar
53. 1. Okazaki A
    2. Sudo Y
    3. Takagi S

    (2012) Optical silencing of C. elegans cells with arch proton pump

    PLOS ONE 7:e35370.

    https://doi.org/10.1371/journal.pone.0035370

    • PubMed
    • Google Scholar
54. 1. Park J
    2. Wetzel I
    3. Marriott I
    4. Dréau D
    5. D'Avanzo C
    6. Kim DY
    7. Tanzi RE
    8. Cho H

    (2018) A 3D human triculture system modeling neurodegeneration and neuroinflammation in Alzheimer's disease

    Nature Neuroscience 21:941–951.

    https://doi.org/10.1038/s41593-018-0175-4

    • PubMed
    • Google Scholar
55. 1. Parr C
    2. Mirzaei N
    3. Christian M
    4. Sastre M

    (2015) Activation of the wnt/β-catenin pathway represses the transcription of the β-amyloid precursor protein cleaving enzyme (BACE1) via binding of T-cell factor-4 to BACE1 promoter

    The FASEB Journal 29:623–635.

    https://doi.org/10.1096/fj.14-253211

    • PubMed
    • Google Scholar
56. 1. Rival T
    2. Page RM
    3. Chandraratna DS
    4. Sendall TJ
    5. Ryder E
    6. Liu B
    7. Lewis H
    8. Rosahl T
    9. Hider R
    10. Camargo LM
    11. Shearman MS
    12. Crowther DC
    13. Lomas DA

    (2009) Fenton chemistry and oxidative stress mediate the toxicity of the beta-amyloid peptide in a Drosophila model of alzheimer's disease

    European Journal of Neuroscience 29:1335–1347.

    https://doi.org/10.1111/j.1460-9568.2009.06701.x

    • PubMed
    • Google Scholar
57. 1. Stackley KD
    2. Beeson CC
    3. Rahn JJ
    4. Chan SS

    (2011) Bioenergetic profiling of zebrafish embryonic development

    PLOS ONE 6:e25652.

    https://doi.org/10.1371/journal.pone.0025652

    • PubMed
    • Google Scholar
58. Book

    1. Stiernagle T

    (2006) Maintenance of C. Elegans WormBook

    Oxford University Press.

    https://doi.org/10.1895/wormbook.1.101.1

    • Google Scholar
59. 1. Tam ZY
    2. Gruber J
    3. Ng LF
    4. Halliwell B
    5. Gunawan R

    (2014) Effects of lithium on Age-related decline in mitochondrial turnover and function in Caenorhabditis elegans

    The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 69:810–820.

    https://doi.org/10.1093/gerona/glt210

    • Google Scholar
60. 1. Teo E
    2. Ravi S
    3. Barardo D
    4. Kim HS
    5. Fong S
    6. Cazenave-Gassiot A
    7. Tan TY
    8. Ching J
    9. Kovalik JP
    10. Wenk MR
    11. Gunawan R
    12. Moore PK
    13. Halliwell B
    14. Tolwinski N
    15. Gruber J

    (2019) Metabolic stress is a primary pathogenic event in transgenic Caenorhabditis elegans expressing pan-neuronal human amyloid beta

    eLife 8:e50069.

    https://doi.org/10.7554/eLife.50069

    • PubMed
    • Google Scholar
61. 1. Teo E
    2. Fong S
    3. Tolwinski N
    4. Gruber J

    (2020a) Drug synergy as a strategy for compression of morbidity in a Caenorhabditis elegans model of alzheimer's disease

    GeroScience 1:1.

    https://doi.org/10.1007/s11357-020-00169-1

    • Google Scholar
62. 1. Teo E
    2. Lim SYJ
    3. Fong S
    4. Larbi A
    5. Wright GD
    6. Tolwinski N
    7. Gruber J

    (2020b) A high throughput drug screening paradigm using transgenic Caenorhabditis elegans model of Alzheimer’s disease

    Translational Medicine of Aging 4:11–21.

    https://doi.org/10.1016/j.tma.2019.12.002

    • Google Scholar
63. 1. Toledo EM
    2. Inestrosa NC

    (2009) Activation of wnt signaling by lithium and rosiglitazone reduced spatial memory impairment and neurodegeneration in brains of an APPswe/PSEN1DeltaE9 mouse model of Alzheimer's disease

    Molecular Psychiatry 15:272–285.

    https://doi.org/10.1038/mp.2009.72

    • PubMed
    • Google Scholar
64. 1. Tolwinski NS
    2. Wieschaus E

    (2001)

    Armadillo nuclear import is regulated by cytoplasmic anchor axin and nuclear anchor dTCF/Pan

    Development 128:2107–2117.

    • PubMed
    • Google Scholar
65. 1. Tsujimoto T
    2. Nakase Y
    3. Sugano T

    (1970)

    Firefly luminescence for ATP assay

    Wakayama Medical Reports 14:29–32.

    • PubMed
    • Google Scholar
66. 1. Westfall S
    2. Lomis N
    3. Prakash S

    (2018) Longevity extension in Drosophila through gut-brain communication

    Scientific Reports 8:8362.

    https://doi.org/10.1038/s41598-018-25382-z

    • PubMed
    • Google Scholar
67. 1. Winblad B

    (1989)

    Bengt Winblad, geriatric researcher: the need for care increases with more and more people over the age of 80

    Lakartidningen 86:599–601.

    • PubMed
    • Google Scholar
68. 1. Wu Y
    2. Cao Z
    3. Klein WL
    4. Luo Y

    (2010) Heat shock treatment reduces beta amyloid toxicity in vivo by diminishing oligomers

    Neurobiology of Aging 31:1055–1058.

    https://doi.org/10.1016/j.neurobiolaging.2008.07.013

    • PubMed
    • Google Scholar
69. 1. Zarse K
    2. Terao T
    3. Tian J
    4. Iwata N
    5. Ishii N
    6. Ristow M

    (2011) Low-dose lithium uptake promotes longevity in humans and metazoans

    European Journal of Nutrition 50:387–389.

    https://doi.org/10.1007/s00394-011-0171-x

    • PubMed
    • Google Scholar
70. 1. Zhang YW
    2. Thompson R
    3. Zhang H
    4. Xu H

    (2011) APP processing in Alzheimer's disease

    Molecular Brain 4:3.

    https://doi.org/10.1186/1756-6606-4-3

    • PubMed
    • Google Scholar

Author details

1. Chu Hsien Lim

   Science Division, Yale- NUS College, Singapore, Singapore

   Contribution

   Conceptualization, Investigation, Methodology

   Contributed equally with

   Prameet Kaur

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6691-8277

2. Prameet Kaur

   Science Division, Yale- NUS College, Singapore, Singapore

   Contribution

   Data curation, Investigation, Methodology

   Contributed equally with

   Chu Hsien Lim

   Competing interests

   No competing interests declared

3. Emelyne Teo

   Science Division, Yale- NUS College, Singapore, Singapore

   Contribution

   Data curation, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5050-4109

4. Vanessa Yuk Man Lam

   Science Division, Yale- NUS College, Singapore, Singapore

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

5. Fangchen Zhu

   Science Division, Yale- NUS College, Singapore, Singapore

   Contribution

   Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

6. Caroline Kibat

   1. Science Division, Yale- NUS College, Singapore, Singapore
   2. Institute of Molecular and Cell Biology (IMCB), Singapore, Singapore
   3. Department of Physiology, YLL School of Medicine, Singapore, Singapore

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

7. Jan Gruber

   1. Science Division, Yale- NUS College, Singapore, Singapore
   2. Department of Biochemistry, National University of Singapore, Singapore, Singapore

   Contribution

   Resources, Supervision, Funding acquisition, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3329-3789

8. Ajay S Mathuru

   1. Science Division, Yale- NUS College, Singapore, Singapore
   2. Institute of Molecular and Cell Biology (IMCB), Singapore, Singapore
   3. Department of Physiology, YLL School of Medicine, Singapore, Singapore

   Contribution

   Resources, Data curation, Supervision, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4591-5274

9. Nicholas S Tolwinski

   Science Division, Yale- NUS College, Singapore, Singapore

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology

   For correspondence

   nicholas.tolwinski@yale-nus.edu.sg

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8507-2737

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