Alzheimer’s disease risk gene Wwox protects against amyloid pathology through metabolic reprogramming

Alzheimer’s disease risk gene Wwox protects against amyloid pathology through metabolic reprogramming | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Alzheimer’s disease risk gene Wwox protects against amyloid pathology through metabolic reprogramming View ORCID Profile Hannah J Lucas-Clarke , View ORCID Profile Daniel C Maddison , Leonardo Amadio , View ORCID Profile Edgar Buhl , Katherine O’Hare , View ORCID Profile Clement Regnault , View ORCID Profile Owen M Peters , View ORCID Profile James J L Hodge , View ORCID Profile Gaynor A Smith doi: https://doi.org/10.1101/2025.05.01.651195 Hannah J Lucas-Clarke 1 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University , UK 2 UK Dementia Research Institute at Cardiff University , Wales, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hannah J Lucas-Clarke Daniel C Maddison 1 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University , UK 2 UK Dementia Research Institute at Cardiff University , Wales, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel C Maddison Leonardo Amadio 1 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University , UK 2 UK Dementia Research Institute at Cardiff University , Wales, UK 3 School of Biosciences, Cardiff University , Wales, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edgar Buhl 4 School of Physiology, Pharmacology and Neuroscience, University of Bristol , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Edgar Buhl Katherine O’Hare 1 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University , UK 2 UK Dementia Research Institute at Cardiff University , Wales, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clement Regnault 5 MVLS Shared Research Facilities University of Glasgow , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Clement Regnault Owen M Peters 2 UK Dementia Research Institute at Cardiff University , Wales, UK 3 School of Biosciences, Cardiff University , Wales, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Owen M Peters James J L Hodge 4 School of Physiology, Pharmacology and Neuroscience, University of Bristol , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James J L Hodge Gaynor A Smith 1 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University , UK 2 UK Dementia Research Institute at Cardiff University , Wales, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gaynor A Smith For correspondence: smithga{at}cardiff.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Genome wide association studies have identified multiple loci that mediate the risk of developing late-onset Alzheimer’s Disease (LOAD). The gene WW-domain containing oxidoreductase ( WWOX ) has been identified in recent LOAD risk meta-analyses, yet its function in the brain is poorly understood. Using Drosophila, we discovered that knockdown of the highly conserved Wwox gene impacts longevity and sleep, having roles in both neuronal and glial subtypes. In an amyloid beta 42 (Aβ 42 ) transgenic model of AD, RNAi-mediated knockdown of Wwox significantly decreased both lifespan and locomotion whilst elevating soluble Aβ 42 . Transcriptomic and metabolomic analyses revealed that these effects were accompanied by elevated lactate dehydrogenase ( Ldh ) mRNA and lactate levels, downstream of an increase in the key unfolded protein response protein Atf4. Strikingly, we found that upregulation of Wwox in the Aβ 42 model through CRISPR activation significantly reduced amyloid load, improved longevity and locomotion. Multi-omics analysis revealed Wwox upregulation partially reversed several key Aβ 42 -induced transcriptional pathways in the brain and reduced levels of L-methionine and associated enzymes. These findings support a role for reduced WWOX levels in the genetic risk of developing LOAD via pyruvate metabolism and point towards WWOX activation as a protective therapeutic strategy. Introduction Alzheimer’s Disease (AD) is the most common form of dementia, characterised by pronounced neurodegeneration, causing memory impairment, cognitive decline, and sleep disruption. On a cellular and molecular level, these symptoms follow the accumulation of extracellular amyloid β (Aβ) plaques, hyperphosphorylated tau pathology and reactivity of astrocytes and microglia. AD manifests in two forms, early-(EOAD) and late-onset (LOAD). Whilst EOAD can be caused by monogenic mutations in amyloid precursor protein (APP), presenilin-1 (PSEN1 ) or presenilin-2 (PSEN2 ), LOAD is considered sporadic and a result of the convergence of environmental and genetic risk factors. Single nucleotide polymorphisms (SNPs) at approximately 90 genomic loci have been discovered to contribute to LOAD risk ( 1 ). Determining how genes associated with these SNPs contribute to disease onset and progression is vital to our understanding of LOAD and how it may be prevented or treated. An intergenic SNP located on chromosome 16 between WW-domain containing oxidoreductase ( WWOX ) and MAF bZIP transcription factor ( MAF ) has been associated with an increased the risk of developing LOAD ( 2 ). Genome-wide expression quantitative trait locus (eQTL) mapping between LOAD patients and controls identified WWOX as a significant eQTL in the brain with a negative β value, indicating a decrease in WWOX mRNA in individuals with the associated SNP ( 3 ). A genome-wide significant association has also been made between a SNP within a predicted WWOX enhancer region and a key disease biomarker, Aβ 42 /Aβ 40 ratio ( 4 ). Furthermore, decreases in WWOX have been observed in post-mortem hippocampal tissue of AD patients compared to controls ( 5 ). However, the mechanisms underlying WWOX modulation of AD are not well understood. WWOX is an oxidoreductase and tumour suppressor gene that plays a significant role in cancer cell metabolism, influencing glycolysis, mitochondrial integrity, signalling and oxidative stress ( 6 – 10 ). In addition, WWOX loss of function has been postulated to mediate a Warburg-like effect – a switch to glycolysis despite aerobic conditions, via modulation of HIF1α, a transcription factor with a variety of metabolic targets including glucose transporter 1 ( GLUT1 ) and lactate dehydrogenase ( LDH ) ( 6 , 11 , 12 ). A similar metabolic shift has been observed in Drosophila ( 13 ) and human stem cell-derived neuronal models of AD ( 14 , 15 ), but whether this is influenced by WWOX, in the context of AD, has not yet been explored.We sought to address this question by altering Wwox levels in the Drosophila brain, via neuronal or glial-specific RNA interference (RNAi) and CRISPR activation (CRISPRa). We explored the age associated effects on AD-relevant phenotypes with or without the overexpression of the human Aβ 42 peptide ( 16 ). We show that both neuronal and glial knockdown of Wwox shortened lifespan and disrupted sleep in flies, in the absence of Aß 42 . However, in the presence of Aβ 42, Wwox knockdown exacerbates lifespan and locomotor deficiencies in a neuron-specific manner, whilst also increasing amyloid load. Transcriptomic and metabolomic analyses identified that elevated Ldh , as a consequence of Aβ 42 overexpression, was exacerbated by Wwox knockdown and was accompanied by increased levels of lactate and the integrated stress response (ISR) / unfolded protein response (UPR) regulator Atf4. Strikingly, upregulation of Wwox in neurons significantly reduced amyloid load, rescuing lifespan and locomotor defects but did not alter lactate levels, instead lowering L-methionine. Taken together, these findings provide insight into the mechanisms underlying LOAD risk conferred by genetic variation at the WWOX locus and identify WWOX activation as a strategy for reducing Aβ 42 pathology. We sought to address this question by altering Wwox levels in the Drosophila brain, via neuronal or glial-specific RNA interference (RNAi) and CRISPR activation (CRISPRa). We explored the age associated effects on AD-relevant phenotypes with or without the overexpression of the human Aβ 42 peptide ( 16 ). We show that in the absence of Aß 42 both neuronal and glial knockdown of Wwox shortened lifespan and disrupted sleep. However, in the presence of Aβ 42, Wwox knockdown exacerbates lifespan and locomotor deficiencies in a neuron-specific manner, whilst also increasing amyloid load. Transcriptomic and metabolomic analyses identified that elevated Ldh , as a consequence of Aβ 42 overexpression, was exacerbated by Wwox knockdown and was accompanied by increased levels of lactate and the integrated stress response (ISR) / unfolded protein response (UPR) regulator Atf4. Strikingly, upregulation of Wwox in neurons significantly reduced amyloid load, rescuing lifespan and locomotor defects but did not alter lactate levels, instead lowering L-methionine. Taken together, these findings provide insight into the mechanisms underlying LOAD risk conferred by genetic variation at the WWOX locus and identify WWOX activation as a strategy for reducing Aβ 42 pathology. Materials and Methods Fly husbandry Drosophila stocks (Table.S1) were housed at room temperature and transferred to fresh standard ‘Cornmeal-Molasses-Yeast’ media, set with agar once every 3 weeks. Crosses and experimental lines were housed at 25 °C on a 12:12 hour light-dark schedule. Single cell atlas of the ageing Drosophila brain Wwox expression was assessed using a single cell transcriptomic dataset of the ageing Drosophila brain available on SCOPE ( 17 ). We calculated the percentage of cells within annotated clusters that expressed Wwox . Lifespan Newly eclosed females were allowed to mate for <48 hours, then were separated into vials of n<11 and passed to fresh food 2-3 times a week, whilst scoring deaths. Alive flies that were not transferred successfully into the new vial were censored in the analysis. Kaplan-Meier plots were generated (GraphPad Prism v.10) and analysed using Mantel-Cox and Cox proportional hazard-model tests, correcting for multiple comparisons using 5 % FDR, where necessary. Rapid Iterative Negative Geotaxis (RING) Newly eclosed females were allowed to mate for <48 hours and then divided into vials of n<11. Progeny were flipped onto fresh food 2-3 times a week and assayed at 7-, 14-, 21- and 28-days post eclosion (±1 day). After 5 minutes of acclimatisation, the apparatus was raised to a fixed height (8 cm) and dropped, images were captured once a second for 10 seconds. After 1-minute recovery, this procedure was repeated for a total of five drops. Using FIJI, the mean distance climbed by each fly at the 4 second time point was measured. Data was analysed by repeated measures mixed effect general linear models (genotype x age, matching by vial). If repeated measures were not applicable, ordinary two-way ANOVAs or one-way ANOVAs were performed (GraphPad Prism v.10). For the comparison of two groups, unpaired two tailed t-tests were performed. Significant effects resulting from ANOVA’s were explored further using Šidák multiple comparisons. Assessment of sleep and circadian rhythms Males (0-7 days) were individually housed within Drosophila activity monitors (DAM, Trikinetics Inc) and subjected to a 12-hour light:dark (LD) cycle at 25 °C for 5 days, followed by 5 days of 24-hour darkness (DD). For each run (>2 runs), N=31-80 individual males were assayed per genotype. Activity (beam crosses) was calculated in 1- and 30-minute bins using DAMFileScan v1.11 and an in-house script ( https://github.com/Fly-Cardiff/sleep_analysis ) and subsequently analysed using “Sleep and Circadian Analysis MATLAB Program - SCAMP”( 18 , 19 ) (MATLAB, vR2020b). A bout of sleep was defined, as per convention, as more than five minutes without activity ( 20 , 21 ). Data from the LD phase was derived after exclusion of day 1, to allow for entrainment, and after removal of any deaths. Seventeen metrics of sleep and wakefulness were exported from SCAMP for subsequent analyses: total wake episodes, mean wake episode duration, total activity counts, total time awake, latency to sleep, mean wake duration minus latency, mean activity a minute whilst moving, activity normalised to time awake, the probability of transitioning to an inactive state or active state, the number of sleep episodes, the total, max, mean and median sleep episode duration and sleep stability. All metrics were assessed for inter-correlations (Pearsons) and strongly correlated (r>|0.8|) metrics were excluded from further analysis. Of the remaining metrics, normality of residuals was assessed with Kolmogorov-Smirnov tests and quantile-quantile plots and consequent Box-Cox transformations were performed where data was deemed sufficiently non-Gaussian (RStudio v2022.12, GraphPad Prism v.10). Multiple two-way ANOVAs were then performed on the surviving metrics, corrected by 5% FDR. If a significant interaction was identified, Bonferroni multiple comparison tests were performed for pairwise comparisons of interest (GraphPad Prism v.10) and further corrected for multiple testing across metrics. Assessment of circadian strength was performed for the DD regime, including flies that survive to day 10. Rhythmicity statistic (RS) and period length were derived from SCAMP autocorrelograms. The RS describes the ability of a fly to maintain a cycle, with an RS>1.5 being rhythmic, RS=1-1.5 being weakly rhythmic and flies with an RS< 1 being arrhythmic. Periodicity was calculated for rhythmic and weakly rhythmic flies only. If a fly had a RS<0, it was set to 0 for analysis of rhythmicity. Data was analysed via t-test or non-parametric Mann-Whitney tests dependent on normality (GraphPad Prism v.10). Immunohistochemistry Drosophila heads were dissected and fixed in 4 % PFA in PBS with 0.1 % Triton X-100 (PTX) for 10 minutes. Brains were dissected and fixed for a further 10 minutes. Brains were blocked in 10 % normal goat serum (in PTX) for 1 hour at room temperature, then incubated with primary antibodies – anti-Aβ (1-16, 6E10, mouse, 1:400, Biolegend #803001), anti-Atf4 (rat, 1:200, gifted by Bateman lab ( 22 )) – for 60 and 16 hours respectively at 4 °C in 10 % normal goat serum (in PTX). Brains were washed 5 times in PTX, then incubated with fluorophore-conjugated secondary antibodies AlexaFluor-647 (anti-mouse, 1:500, Invitrogen #A-21235), AlexaFluor-594 (anti-rat, 1:400) for 1-2 hours at room temperature in PTX. Brains were then washed 5 times in PTX, then mounted in VECTASHEILD Antifade (Vector Laboratories, H-1000-10) before imaging on a Cell Observer Spinning Disk Confocal Microscope (ZEISS) with Plan Apo 20x/0.8 dry objective and Axiocam 503 monochromatic camera, using Zen Blue software (ZEISS). Images were processed in FIJI - Aβ aggregates were detected by the Analyze Particles function and Atf4 fluorescence intensity (mean gray value) was averaged across 4 equal sized ROIs (227.5 μm 2 ) per brain. Aβ intensity was analyzed with one-way ANOVA or t-test depending on the number of groups, Atf4 fluorescence was assessed via Mann-Whitney tests (GraphPad Prism v.10). Imaging of sima:GFP fluorescent reporter Brain sima levels were measured using the genetically encoded reporter line UAS-sima:GFP. Unfixed brains were dissected and imaged on a Zeiss Cell Observer spinning disk confocal using 488nm excitation, Plan Apo 20x/0.8 dry objective and Axiocam 503 camera. Fluorescence intensity (mean gray value) was averaged across 4 equal sized (415.1 μm 2 ) ROIs (FIJI) and assessed by unpaired t-test (GraphPad Prism v.10). Electrochemiluminescence quantification of amyloid beta Flies were snap-frozen in liquid nitrogen and heads (n=25 per replicate) separated by vortexing. Heads were then homogenized for 3 mins in 150 μl of soluble extraction buffer (50 mM HEPES pH 7.3, 5 μM EDTA and 1x Protease inhibitor) on ice, vortexed, then incubated for 10 minutes at room temperature. Homogenates were sonicated at 4 °C for 4 minutes (30s on 30s off), then centrifuged at 21,000x g for 5 minutes at 4 °C. The supernatant was separated and used as the soluble fraction. 55 μl of insoluble extraction buffer (soluble extraction buffer with 5 mM Guanidinium HCL) was added to the remaining pellet. The pellet was then homogenized for 2 minutes, vortexed, and incubated for 10 minutes at room temperature. Homogenates were sonicated in a 4 °C water bath for 4 minutes (30s on 30s off), then centrifuged at 21,000x g for 5 minutes at 4°C. The supernatant was separated and used as the insoluble fraction. The MSD V-plex assay was performed according to manufacturer’s instructions. Briefly, the plate was blocked with diluent 35 for 1 hour at room temperature. Standard calibrators were serial diluted in Diluent 35. After blocking, the plate was washed 3 times with 150 μl/well of 0.05 % Tween-PBS. Next, 25 μl of detection antibody (anti-Aβ 17-24, 4G8, mouse, MSD) was added to each well, followed by 25 µl of standard or 0.5 μg/µl brain homogenate from soluble or insoluble fractions. The plate was sealed and incubated whilst shaking for 2 hours. The plate was subsequently washed 6 times with 150 μl/well of 0.05 % Tween-PBS. The final wash was discarded and 150 μl of 2X read buffer was added to each well, before immediately reading the plate on an MSD instrument. Results were analysed via one-way ANOVAs or t-tests depending on number of groups (GraphPad Prism v.10). Bulk RNA sequencing Heads of 14-day old, mated females (n=10) were dissected (N=4 reps per genotype) and RNA extracted using the RNAqueous-Micro kit (AM1931) according to the manufacturer’s instructions. Samples were sent to the Cardiff University School of Biosciences Genomics Research Hub for 6.5M reads paired-end mRNA sequencing on an Illumina NextSeq500 with a fragment length of 75 base-pairs. Quality was screened using FastQC (0.11.8); Poly-G tails, Truseq3 adapters and low-quality bases/regions (phred<3 or sliding window average of<15) were trimmed using Illumina clip (Trimmomatic ( 23 )). Any remaining reads <20 bases in length were also removed. Reads were mapped to the Drosophila melanogaster genome (BDGP6.32) using STAR ( 24 ) and reads counted using featureCounts ( 25 ). Non-coding RNAs, transposons and unassigned features were removed using SARTools ( 26 ). Differential gene expression analysis was then performed using DESeq2 ( 27 ) with log 2 fold-change shrinkage using the ashr algorithm ( 28 ). Dimensionality of variance stabilised data was assessed via principal component analyses (PCA, prcpomp). MA-plots were generated plotting normalised counts against shrunk log 2 fold-change (threshold=±0.58) (ggplot2, RStudio v.4.4.1). Overrepresentation analysis was performed between differentially expressed genes and gene ontology molecular function (GO:MF), gene ontology biological process (GO:BP), KEGG, Wiki Pathways and TRANSFAC databases, via the functional enrichment tool g:Profiler (version e112_eg59_p19_25aa4782) ( 29 ). Metabolomics Heads of 14-day old, mated females were dissected and separated into N=5 replicates of n=10 heads. Metabolites were extracted by homogenising in 1:3:1 chloroform (SLS, C2432), methanol (Fisher, 10284580) and water, followed by an hour agitation at 4 °C. MVLS Shared Research Facilities (University of Glasgow) performed Hydrophilic Interaction Liquid Chromatography (HILC) based metabolomics (UltiMate 3000 RSLC, Thermo Fisher Scientific) using a hydrophilic ZIC-pHILIC column (Merck) on samples and standards (Metabolomics Standards Initiative). Samples, were ionised using a Thermo Orbitrap QExactive (Thermo Fisher Scientific) and mass spectrometry performed in both ionisation modes, detecting metabolites in range of 70-1050 m/z. Data processing, feature annotation (based on exact mass match to compounds in databases) and identification (based on exact mass and retention time match to an authentic standard) was performed using PiMP ( 30 ). PCAs were performed on the processed data in Metaboanalyst 5.0 ( 31 ). Fragmentation analysis of the pooled samples was performed using MVLS Shared Research Facilities in-house software FrAnK. Finally, two-way ANOVAs on the top 200 features (based on variance) were used on log-transformed relative metabolite intensities using 10 % FDR in Metaboanalyst 5.0 ( 31 , 32 ). Results Wwox knockdown in neurons or glia shortens lifespan WWOX is highly conserved between species, with Drosophila Wwox sharing 65% sequence similarity with human WWOX and a score of 16/16 using the DRC Integrative Ortholog Prediction Tool (DIOPT) ( 33 ). Equivalent to its mammalian orthologue ( 34 ) , Drosophila Wwox is expressed throughout the brain. Using a single cell transcriptomic dataset from the ageing Drosophila brain ( 17 ), we identified high levels of Wwox expression in both neuronal and glial cell-types ( Fig.1A ). To dissect the role of Wwox in both cell types, we expressed RNAi constructs under control of elav-GAL4 (pan-neuronal) or repo-GAL4 (pan-glial) drivers and assessed the effect on lifespan and locomotor function. Wwox knockdown in neurons, by two independent UAS-RNAi constructs ( 7 , 8 , 35 ) ( Fig.S1 ), significantly reduced lifespan in comparison to control (RNAi Control, Wwox TRiP , Wwox KK , median lifespan=57, 42 and 40 days respectively, p adj <0.0001, Fig.1B ). Startle-induced locomotor function, assessed by RING, was significantly decreased with age (p<0.0001) but unaffected by neuronal knockdown of Wwox (p=0.4458) or the interaction between the two (p=0.0778, Fig.1C ). Glial knockdown of Wwox caused a reduction in lifespan in comparison to control (median lifespan=70 vs. 74 days, p=0.0003, Fig.1D ), albeit milder than that of neuronal knockdown. Like neuronal knockdown, age significantly worsened startle-induced locomotor activity (p=0.0033), however glial knockdown of Wwox had no effect (p=0.0820), nor was there an interaction (p=0.5431, Fig.1E ). These results suggest that both neuronal and glial Wwox play a role in longevity but is dispensable for startle-induced locomotor function. Download figure Open in new tab Figure 1. Knockdown of neuronal and glial Wwox reduces longevity. A) Analysis of single cell RNAseq data acquired through SCOPE ( 17 ) demonstrates that Wwox is expressed in both glia and neurons in the Drosophila brain. Data is presented as percentage of cells from each cluster expressing detectable levels of Wwox mRNA. B) Lifespan is reduced upon pan-neuronal Wwox knockdown (N=91-105 per genotype). C) Startle induced locomotion is unaffected by neuronal Wwox knockdown (N=3-6, n∼10 flies per N). D) Glial knockdown of Wwox reduces median lifespan (N=98-101). E) Startle induced locomotion is unaffected by glial knockdown of Wwox (N=4-6, n∼10 flies per N). Lifespan was assessed via (multiple) Mantel-Cox tests and locomotion via mixed effect models or two-way ANOVA with factors of age and genotype. Error bars represent standard deviation (SD), data points represent replicates. p adj **<0.01, ***<0.001, ****<0.0001, ns=not significant. Wwox knockdown in neurons or glia decreases wakefulness and increases sleep A common symptom of AD is disruption to circadian rhythms and sleep homeostasis ( 36 – 40 ), leading us to next to assess the contribution of neuronal or glial Wwox expression to sleep and circadian activity regulation. Sleep metrics were derived from activity of individually housed flies using the MATLAB toolbox SCAMP ( 18 , 19 ). The effect of Wwox manipulation was investigated on: Total activity counts, Activity counts normalised to time awake, Max wake episode duration, Mean wake episode duration, Sleep latency, Probability to transition from an active to inactive state (sleep pressure), Number of sleep episodes. Other strongly correlated sleep metrics (r>|0.8|) are shown in Table.S2. Neuronal knockdown of Wwox decreased activity normalised to time awake in the light phase (p adj <0.0001, Fig.2A ) and increased sleep pressure (p adj <0.0001, Fig.2B ), whilst increasing mean sleep episode duration, both in the light and dark phase (both p adj <0.0001, Fig.2C ). Similarly, pan-glial Wwox knockdown reduced activity normalised to time awake (p adj <0.0001, Fig.2D ), increased sleep pressure in the light and dark phases (p adj <0.0001, p adj =0.0129, respectively, Fig.2E ) and increased mean sleep episode duration (p adj =0.0258, Fig.2F ). To understand whether these changes in sleep were due to underlying circadian differences, we evaluated the periodicity of flies in constant darkness without light entrainment cues. After neuronal knockdown of Wwox , all flies remained strongly rhythmic (p=0.8606, Fig.2G , I) with circadian periods comparable to control (p=0.8329, Fig.2H -I). Glial knockdown of Wwox had no effect on rhythmicity (p=0.2955, Fig.2J , L) or period length (p=0.3052, Fig.2K -L). Wwox knockdown driven by the clock specific driver timeless-GAL4 , resulted in a similar decrease in wakefulness ( Fig.S2K-R ) to pan-neuronal and pan-glial knockdown, but flies remained strongly rhythmic ( Fig.S2S ). These results suggest that Wwox knockdown, irrespective of cell type, increases sleep, without disrupting circadian rhythmicity. Download figure Open in new tab Figure 2. Knockdown of Wwox decreases wakefulness and increases sleep. Effect of pan-neuronal Wwox knockdown on A) locomotor activity, B) sleep pressure and C) mean sleep episode duration. D) Effect of pan-glial Wwox knockdown on locomotor activity, E) sleep pressure and F) mean sleep episode duration. Sleep metrics were collected over n>2 independent runs, N=31-80 individual flies per genotype. G) Rhythmicity statistic (RS) and H) period length of circadian behaviour upon pan-neuronal Wwox knockdown in dark (DD) conditions. I) Representative actograms for pan-neuronal Wwox knockdown. J) RS and K) period length upon pan-glial Wwox knockdown in DD conditions. L) Representative actograms for pan-glial Wwox knockdown. Dotted lines at RS=1 (weakly rhythmic threshold), RS=1.5 (strongly rhythmic threshold) and period length=24 hrs. Circadian rhythmicity was analysed from N=6-60 individual flies per genotype, due to removal of deaths. Whereas circadian period was assessed from N=4-57, due to the further removal of arrhythmic flies. Sleep metrics were analysed via two-way ANOVA followed by Bonferroni multiple comparisons, corrected for multiple comparisons across all sleep metrics by 5% FDR correction. p adj * <0.05, ****<0.0001. Circadian data was analysed via t-test or non-parametric equivalent. Data points represent individual flies. Error bars represent SD. Wwox knockdown modifies Aβ 42 -induced phenotypes and increases Aβ 42 load Next, we explored the impact of Wwox knockdown on amyloid pathology, a hallmark feature of AD. We employed a model of amyloid toxicity whereby neuronal overexpression of aggregate-prone human Aβ 42 harboring the Arctic mutation (E693G) and secretion signal-peptide from the argos gene, drives secretion of the peptide to the extracellular milieu. This causes accumulation of amyloid deposits, leading to brain vacuolation, locomotor deficits and reduction in lifespan in the fly ( 16 ). Neuronal knockdown of Wwox combined with overexpression of human Aβ 42 caused a further reduction in lifespan (RNAi Control, Wwox TRiP , Wwox KK , median lifespan=41, 26 and 24 days, respectively, p <0.0001, Fig.3A ). Cox-proportional hazard modelling of the effects revealed a significant interaction between Aβ 42 overexpression and Wwox knockdown (p<0.0001), indicating that the decrease in lifespan is not simply the additive effect of the two variables but synergistic. As expected, startle-induced locomotor ability was significantly reduced with age (p<0.0001, Fig.3B ), from ∼3.5 cm climbed at day 7 compared to only ∼1.0 cm climbed at day 28 in RNAi Control Aβ 42 flies. There was also a significant effect of genotype (p=0.0002, Fig.3B ) - multiple comparison testing revealed that both RNAi’s led to a reduction in climbing ( Wwox TRiP p =0.0011 and Wwox KK p =0.0004) compared to control with knockdown flies climbing just ∼0.5 cm by day 21. We found no significant interaction between age and genotype (p=0.9323, Fig.3B ). Utilising a dual-binary transgene expression system, we restricted Wwox knockdown to glial cells ( repo-GAL4 ), whilst simultaneously restricting Aβ 42 expression to neurons ( nSyb-QF2 ). Wwox knockdown mildly, albeit significantly, decreased lifespan (median=22 vs. 24 days, p=0.0095, Fig.S3A ) but had no effect on startle-induced locomotion (p=0.3491, Fig.S3B ). These results suggest that neuronal Wwox may be more important than glial Wwox for modulation of Aβ 42 toxicity. Download figure Open in new tab Figure 3. Wwox knockdown exacerbates Aβ 42 related phenotypes. A) Lifespan is reduced upon pan-neuronal Wwox knockdown in the presence of Aβ 42 overexpression (N=45-68). B) Startle-induced locomotion is reduced upon pan-neuronal Wwox knockdown in the presence of Aβ 42 overexpression (N=2-6, n∼10 flies per N). C) Aβ 42 immunocytochemistry (cyan) from day 14 Drosophila brains upon Wwox knockdown. D) Quantification of Aβ 42 staining revealed no changes in number or E) plaque size (N=5-7 brains). F) Schematic of Aβ 42 quantification in brain soluble and insoluble fractions by MSD assay. G) Total and H) soluble Aβ 42 levels quantified by Mesoscale Discovery (MSD) assay increase with Wwox knockdown. I) But identified no changes in insoluble or J) the insoluble/soluble ratio (N=3, n=25 brains). Lifespan was analysed by multiple Mantel-Cox and Cox proportional hazard tests. Startle-induced locomotion was analysed by mixed effect models with factors of age and genotype. MSD assay was analysed by one-way ANOVA. p/p adj *<0.05, **<0.01, ***<0.001, ****<0.0001. Error bars represent SD. Scale bar=100 µm. To understand if neuronal Wwox knockdown directly impacts amyloid accumulation, we probed intact brains from these flies using the anti-Aβ [6E10] antibody. Visible Aβ 42 deposits were quantified ( Fig.3C ), however no significant changes in aggregate number (p=0.1135, Fig.3D ) or median size (p=0.6771, Fig.3E ) were observed. To quantify changes in Aβ 42 deposition with increased sensitivity, we assessed soluble and insoluble fractioned extracts of 14-day old fly heads by an electrochemiluminescence immunoassay (Mesoscale Discovery, MSD) using the anti-Aβ [4G8] antibody ( Fig.3F ). Total Aβ 42 was significantly increased upon Wwox knockdown (p=0.033, Fig.3G ). Interestingly, this effect was led by an increase in soluble Aβ 42 (p=0.0024, Fig.3H ), whereas insoluble fractions were only trending towards an increase (p=0.0602, Fig.3I ). However, the ratio between soluble/insoluble Aβ 42 was not significantly changed (p=0.1131, Fig.3J ). Taken together, these results indicate an increase in Aβ 42 load upon Wwox knockdown, without an associated increase in detectable aggregate size or number. Neuronal Wwox knockdown in the presence of Aβ 42 alters pyruvate metabolism To investigate the mechanisms underlying the effect of neuronal Wwox knockdown on Aβ 42 associated phenotypes, we performed bulk transcriptomics from flies overexpressing Aβ 42 or a LacZ control construct, with or without neuronal Wwox knockdown. The effects of Wwox knockdown and Aβ 42 expression could be separated along PC1 and PC2, explaining 23% and 15% of variance respectively ( Fig.S4 ). Knockdown of Wwox in a control background significantly downregulated 54 genes and upregulated 44 ( Fig.4A , Table.S3). Overrepresentation analysis determined these genes were enriched for oxidoreductase activity and metabolism of ascorbate, aldarate, and porphyrins ( Fig.4B , Table.S3). Overexpressing human Aβ 42 in neurons downregulated 23 genes and upregulated 51 ( Fig.4C , Table.S3); overrepresentation analysis revealed a significant association of these genes with fatty acid and pyruvate metabolism ( Fig.4D , Table.S3) suggesting amyloid expression induces metabolic changes. Knockdown of Wwox in neurons also overexpressing Aβ 42 , significantly downregulated 58 genes and upregulated 54 genes ( Fig.4E , Table.S2). These genes were enriched within the pyruvate metabolism, lipid metabolism and pentose and glucuronate interconversion pathways ( Fig.4F , Table.S3), suggesting that knockdown of Wwox in the presence of Aβ 42 may alter the metabolome further via transcriptional regulation. Download figure Open in new tab Figure 4. Neuronal Wwox knockdown in the presence of Aβ 42 vastly alters the metabolic transcriptome. A) Knockdown of Wwox in a control background differentially expressed 98 genes, 54 down (red) and 44 up (blue). B) These were enriched in within metabolic pathways. C) MA plot summarising the significant up ( 51 ) and down regulated ( 23 ) changes in transcription after Aβ 42 expression. D) These differentially expressed genes were enriched within pyruvate and fatty-acid metabolism pathways. E) Knockdown of Wwox in the presence of Aβ 42 differentially expressed 112 genes, F) enriched within lipid and pyruvate metabolism terms. Dotted lines on MA plots (panels A, C and E) represent log 2 fold-change threshold of ± 0.58. Overrepresentation analysis (panels B, D and F) was performed using g:Profiler, size of datapoints represents the number of genes identified in within the term. Abbreviations represent unique databases which were used for overrepresentation analysis GO:BP Gene ontology biological processes, GO:MF Gene ontology molecular functions, KEGG, TF TRANSFAC, WP Wiki pathways. N=4 replicates of n=10 heads per genotype. Wwox knockdown in the presence of Aβ 42 increases lactate concentrations via Atf4 Since various metabolic pathways were identified by our transcriptomic analysis, we performed Hydrophilic Interaction Liquid Chromatography (HILC)-based metabolomics to assess the effect of overexpression of Aβ 42 and or Wwox knockdown on brain metabolic profiles. Of 2,355 LC-MS peaks, 85 compounds mapped to known standards by m/z and retention time. Neuronal knockdown of Wwox differentially expressed eight standard-mapped metabolites, namely, reduction of L-lysine (2 peaks, both p adj <0.0001), orotate (p adj =0.0046) and 4-trimethylammoniobutanoate (p adj =0.0263) and upregulation of D-glucuronate (p adj =0.0069), 2-hydroxyglutarate (p adj =0.0263, 0.82 log 2 fold-change/1.77-fold), L-lactate (p adj =0.0636, 0.68 log 2 fold-change/1.60-fold) and itaconate (p adj =0.0885, Fig.5A , Table.S4). Aβ 42 expression upregulated two standard-matched metabolites: 2-hydroxyglutarate (p adj =0.0006, 1.33 log 2 fold-change/2.51-fold) and L-lactate (p adj =0.0050, 0.97 log 2 fold-change/1.96-fold, Fig.5B , Table.S4). Both lactate and 2-hydroxyglutarate are generated from pyruvate and α-ketoglutarate, respectively, by Ldh. This is consistent with our transcriptomics analysis, which revealed a 1.19 log 2 fold-change (2.28-fold) upregulation of Ldh after overexpression of Aβ 42 ( Fig.4C , Table.S3). Crucially, the interaction between neuronal Wwox knockdown and Aβ 42 differentially expressed only one standard-mapped metabolite, L-lactate (p adj =0.0174, 1.30 log 2 fold-change/2.46-fold Fig.5C -D, Table.S4), consistent with a 1.33 log 2 (2.51-fold) increase in Ldh transcripts compared to Aβ 42 overexpression alone ( Fig.4E , Table.S3). In addition to this increase in Ldh and lactate, we observed corresponding decreases in the pyruvate metabolism pathway - Pepck2 , CG11052 and Adh - further supporting the role of pyruvate metabolism in the phenotypes observed ( Fig.5D ). An increase in Ldh and lactate suggests a Warburg effect-like shift towards glycolytic energy production. Interestingly however, we observed no differences in ATP levels in the heads of flies as a consequence of Wwox knockdown, Aβ 42 overexpression or both combined (p=0.5720, Fig.S5A ), suggesting that the observed shift towards glycolytic metabolism is sufficient to maintain energy supply and may be a protective response. Download figure Open in new tab Figure 5. Neuronal Wwox knockdown in the presence of Aβ 42 increases L-lactate levels via increased lactate dehydrogenase expression. A) Volcano plots summarising the significant up- and down-regulated metabolites for the main effect of Wwox knockdown, B) Aβ 42 , or C) the statistical interaction between the two (N=5 replicates of n=10 heads). D) Schematic of changes in pyruvate metabolism revealed by the statistical interaction between Wwox knockdown and Aβ 42 overexpression. Differentially expressed transcripts and metabolites are depicted with arrows showing their direction of change, transcripts are represented in boxes. E) Knockdown of lactate dehydrogenase ( Ldh ) rescued startled induced locomotor deficits in Wwox knockdown (+Aβ 42 ) flies at 14, 21 and 28 days (N=4-6 of n∼10 flies). F) Representative images of brains expressing the endogenously tagged Sima:GFP reporter. G) Quantification revealed no differences in sima levels (N=7-9 brains). H) ATF4 ICC in day 14 fly brains, I) demonstrated an upregulation of Atf4 in the brain after Wwox knockdown in the presence of Aβ 42 (N=6 brains). J) Schematic representation of the effect of Wwox knockdown on pyruvate metabolism and Aβ 42 aggregation via Atf4. Statistics=two-way ANOVA (E), t-test (G) and Mann-Whitney Test (I). p adj *<0.05, **<0.01, ***<0.001, ****<0.0001. Error bars represent SD. Scale bars=100 µM. Dotted line in panels A-C represents p adj threshold of 0.01. We next investigated physiological consequences of elevated Ldh and lactate seen in Aβ 42 -transgenic flies with reduced Wwox expression. We utilized RNAi to mediate combined downregulation of both Ldh and Wwox in neurons expressing Aβ 42 and assessed startle-induced locomotion. Multiple comparisons testing revealed that Ldh knockdown rescued climbing deficits seen in Aβ 42 -transgenic flies with downregulated Wwox (at 14-, 21- and 28-days (p adj =0.0081, <0.0001 and <0.0001, respectively, Fig.5E ). These results suggest that Wwox knockdown in the presence of Aβ 42 elevates lactate levels via neuronal Ldh , which is sufficient to exacerbate locomotor defects. Both HIF1α and Atf4 are transcription factors known to promote Ldh expression. We investigated the levels of these proteins upon Wwox knockdown to further understand Ldh regulation in our model. No changes in endogenously tagged sima:GFP (HIF1α homolog) levels were observed in the adult fly brain after neuronal Wwox knockdown in Aβ 42 overexpressing flies (p=0.5185, Fig.5F -G). However, immunocytochemistry revealed a significant upregulation of Atf4 levels in Aβ 42 flies upon Wwox knockdown (p=0.0022, Fig.5H-I ). Since Atf4 is activated downstream of protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) through the ISR and UPR ( 41 ), we also assessed a parallel Ire1-Xbp1 branch of the UPR, by measuring Ire1-mediated Xbp1 splicing with a GFP-based reporter expressed in neurons ( 42 ). However, we observed no differences in neuronal spliced Xbp1 because of Wwox knockdown in Aβ 42 flies (p=0.7574, Fig.S5B-C ). These data suggest that neuronal Wwox knockdown specifically upregulates the PERK-Atf4 branch of the ISR/UPR in Aβ 42 flies and this could be the mechanism via which Ldh and lactate are upregulated, leading to exacerbated Aβ 42 -induced phenotypes ( Fig.5J ). Upregulation of neuronal Wwox rescues Aβ 42 related pathology Having described the detrimental effect of Wwox knockdown on Aβ 42 -induced phenotypes, we wanted to explore whether Wwox upregulation could also affect Aβ 42 toxicity. We employed CRISPRa upregulation of Wwox by expressing UAS-dCas9-VPR and tandem gRNAs targeting the Wwox transcriptional start site ( Wwox gRNA ) or non-targeting (control gRNA ), under pan-neuronal elav-GAL4 control. This was sufficient to achieve ∼2-fold increase in Wwox mRNA levels in the fly head ( Fig.S1 ). In the absence of Aβ 42 , Wwox upregulation had no significant effect on survival (p=0.1420, median=60 vs. 58 days, Fig.S6A ) or startle-induced locomotion (p=0.8770, Fig.S6B ). However, in the presence of neuronal Aβ 42 , upregulation of Wwox significantly improved lifespan (median=47.5 vs. 29 days, p<0.0001, Fig.6A ) and startle-induced locomotion at days 14 and 21 (both p adj <0.0001, Fig.6B ). We observed a similar rescue of lifespan (median=41 vs. 51 days, p=0.0021, Fig.S6C ) and locomotion (p=0.0371, Fig.S6D ) when overexpressing a UAS-cDNA construct of human WWOX ( hWWOX ) in neurons. Download figure Open in new tab Figure 6. Neuronal upregulation of Wwox improves longevity, behaviour, and reduces amyloid load. A) Upregulation of Wwox in neurons expressing Aβ 42 significantly improved lifespan (N=78-91), B) and locomotion (N=3-7 vials). C) Representative images of Aβ 42 staining (cyan) in Drosophila brains. D) Quantification revealed a significant decrease in median plaque size and E) number (N=5-7 brains). Using the MSD assay for amyloid we revealed decreased F) soluble and G) insoluble Aβ 42 (N=3 replicates of n=25 heads). p adj *<0.05, **<0.01, ***<0.001, ****<0.0001. Error bars represent SD. Scale bar=100 µm. Statistics= Mantel-Cox tests (A), two-way ANOVA (B) and t-tests (D-G). Overexpression of Drosophila or hWWOX cDNA in glial cells whilst simultaneously overexpressing Aβ 42 in neurons had no effect on survival (p adj =0.0800 and p adj =0.1045 respectively, Fig.S6E ) or locomotion (p=0.6654, Fig.S6F ) in comparison to controls, further indicating the importance of neuronal, compared to glial Wwox , in the modulation of neuron-derived Aβ 42 toxicity. In the brains of flies with Wwox upregulation in neurons, we found a dramatic reduction in the size (p=0.0003, Fig.6C -D) and number (p=0.0071, Fig.6C , E) of Aβ 42 deposits with confocal microscopy. Similarly, by MSD assay, we found reduced total Aβ 42 (p=0.0313, Fig.S6G ) in both soluble (p=0.0357, Fig.6F ) and insoluble (p=0.0409, Fig.6G ) head lysate fractions of day 14 flies, without altering the ratio of insoluble/soluble Aβ 42 (p=0.2530, Fig.S6H ). This suggests that increasing neuronal Wwox can protect against Aβ 42 associated pathology by lowering total amyloid load. Neuronal upregulation of Wwox reverses the transcriptional profile induced by Aβ 42 To understand how neuronal upregulation of Wwox may provide protection against Aβ 42 and since Wwox knockdown substantially altered the transcriptomic profile of Aβ 42 flies, we again performed bulk transcriptomics on the heads of flies with Wwox upregulation in neurons. Upregulation of Wwox in a control background significantly downregulated 6 genes and upregulated none ( Fig.7A , Table.S5) – these downregulated genes were enriched for fatty acid metabolism terms ( Fig.7B , Table.S5). Overexpression of Aβ 42 significantly downregulated 51 genes and upregulated 28 ( Fig.7C , Table.S5), of which Ldh was the second most significantly upregulated gene with a 2.85 log 2 fold-change (7.21-fold) compared to control. Overrepresentation analysis revealed dysregulation of translation repression, membrane transport and oxidative phosphorylation ( Fig.7D , Table.S4). Strikingly, upregulation of neuronal Wwox in the presence of Aβ 42 , downregulated 305 genes and upregulated 59 ( Fig.7E , Table.S4). Overrepresentation analysis revealed metabolic and immune enrichments including a dysregulation of cysteine and methionine metabolism lead largely by these downregulated genes ( Fig.7F , Table.S5). To further understand these transcriptional changes, we identified 40 commonly differentially expressed genes between the analysis of Aβ vs LacZ and Wwox gRNA Aβ vs. control gRNA Aβ (Table.S6). Interestingly the log 2 fold-changes of these genes were significantly negatively correlated (r=-0.8341, p<0.0001, Fig.7G ). Notably, every gene differentially expressed in Aβ 42 vs LacZ comparison was differentially expressed in the opposing direction by the Wwox gRNA Aβ vs. control gRNA Aβ 42 comparison ( Fig.7G , Table.S6). Overrepresentation analysis revealed that those genes upregulated by Aβ 42 and downregulated by Wwox overexpression were enriched within immune response terms, whereas genes with the opposing change were enriched for synaptic terms ( Fig.7G ). Taken together, these results suggest that upregulation of Wwox reverses the transcriptional reprogramming of metabolic, immune and synaptic pathways caused by Aβ 42 upregulation. Download figure Open in new tab Figure 7. Neuronal overexpression of Wwox reverses the metabolic, immune and synaptic transcriptomic reprogramming caused by Aβ 42 expression. A) MA plot for the effect of Wwox overexpression in a control background reveals dysregulation of 6 genes, B) g:Profiler analysis reveals enrichment within fatty acid metabolism. C) Overexpression of Aβ 42 downregulated 51 genes and upregulated 28, D) these genes were enriched within translation repression and metabolic terms. E) Overexpression of Wwox in the presence of Aβ 42 differentially expressed 364 genes. F) Overrepresentation analysis revealed many metabolic, immune and synaptic terms. Size of datapoints (panels B, D, F) represents the number of genes identified within the term. G) 40 genes were commonly dysregulated between Aβ 42 expression alone and Aβ 42 expression with Wwox overexpression. The log 2 fold-change of each of these genes were significantly negatively correlated suggesting a reversal of the transcriptional profile. Those genes that were upregulated by Aβ 42 and subsequently downregulated with Wwox overexpression were enriched within immune pathways (left) whereas genes that change in the opposing direction were enriched with synaptic transmission. Abbreviations represent databases used for enrichment analysis: gene ontology biological processes (GO:BP), gene ontology molecular functions (GO:MF), KEGG, TRANSFAC (TF) and Wiki Pathways (WP). Dotted lines on MA plots (panels A, C and E) represent log 2 fold-change threshold of +/-0.58. N=4 replicates of n=10 pooled heads. Neuronal upregulation of Wwox in the presence of Aβ 42 reduces L-methionine levels To understand how this transcriptional reprogramming may impact the neuronal metabolism of these flies, we performed unbiased metabolomic profiling on flies expressing Aβ 42 , overexpressing Wwox , or overexpressing Wwox in the presence of Aβ 42 . Of 2414 peaks, 86 compounds were exact matches to known standards by m/z and retention time. Expression of Aβ 42 differentially altered four standard-matched metabolites, including a reduction in sn-glycero-3-phosphocholine (p adj =0.0006) and an increase in betaine (p adj =0.0667), 2-hydroxyglutarate (p adj =0.0001) and citrate (p adj =0.0667, Fig.8A , Table.S7). This increase in 2-hydroxyglutarate is consistent with our previous finding of a 2.85 log 2 fold-change (7.21-fold) increase in Ldh transcripts in Aβ 42 expressing flies compared to control – the second most significant differentially expressed gene in this comparison (Table.S5). Neuronal Wwox overexpression downregulated five standard-matched metabolites – L-Leucine (p adj =0.0545), sn-glycero-3-phosphocholine (p adj =0.0667), L-methionine (p adj =0.0047 and 0.0091) and a suspected isomer of methyl-L-histidine (N-pi) (p adj =0.0545, Fig.8B , Table.S7). However, the interaction between Wwox overexpression and Aβ 42 revealed just two significantly downregulated standard-matched metabolites, both representing L-methionine (p adj =0.0073 and p adj =0.0133, Fig.8C , Table.S7). Consistently, in our transcriptomics analysis we identified downregulation of seven genes within the cysteine and methionine metabolism KEGG pathway ( Fig.8D ). Surprisingly, the reduction of amyloid load and improvement in lifespan and locomotion of these flies therefore occurred without restoring lactate levels but instead caused a reduction in transcripts encoding numerous enzymes within the cysteine and methionine metabolism pathway, and a corresponding decrease in the metabolite L-methionine ( Fig.8D -E). Download figure Open in new tab Figure 8. Neuronal overexpression of Wwox in the presence of Aβ 42 reduces L-methionine levels. Volcano plots A) illustrating the differentially expressed metabolites due to Aβ 42 expression, B) Wwox overexpression and the C) statistical interaction between the two. D) Schematic of downregulated transcripts (boxes) and metabolites (L-methionine, arrow) within the cysteine and methionine metabolism KEGG pathway revealed in our analysis of Wwox overexpression in the presence of Aβ 42 . E) Schematic to show key metabolic pathways during Wwox inhibition vs activation. N=5 replicates of n=10 pooled heads. Discussion In this study we investigated the multifaceted, cell-type specific effects of Wwox manipulation in the Drosophila brain, in combination with the effects of ageing and human Aβ 42 overexpression. Like mammals ( 34 ), Drosophila Wwox is expressed throughout the brain in both neuronal and glial cell types. Using RNAi, we reduced Wwox levels in these cells to recapitulate the dysregulation observed in LOAD ( 3 , 5 ). Whilst startle-induced locomotion was unaffected, Wwox knockdown in either cell type reduced lifespan, suggesting its important role in both neuronal and glial health. Additionally, Wwox knockdown in either cell type affected sleep-wake behaviour – decreasing activity and increasing sleep – indicating its involvement in regulating sleep homeostasis, a phenomenon also observed in early-stage LOAD ( 39 , 40 ). Furthermore, a study was published reporting a genome wide association between SNPs at the WWOX locus and self-aware sleep duration, whilst also demonstrated sleep defects in Wwox knockout Drosophila , supporting our findings ( 43 ). Our findings further show a strong interaction between sleep patterns in Wwox knockdown in controls animals and in our AD model, suggesting this gene could affect both age-related sleep decline and abnormalities seen in LOAD. Wwox knockdown in the Aβ 42 model of AD exacerbated neurotoxicity, leading to increased Aβ 42 load, impaired locomotor function and reduced lifespan. However, Wwox knockdown in glia had no effect on the toxicity of neuron-derived Aβ 42 , suggesting that neuronal Wwox plays a cell-autonomous protective role against Aβ 42 -mediated toxicity. Metabolic exploration of Aβ 42 expression revealed an increase of 2-hydroxyglutarate and L-lactate – the products of Ldh conversion of α-ketoglutarate and pyruvate, respectively. Consistently we found Ldh transcripts significantly increased with Aβ 42 expression. This enzymatic reduction could explain previous reports of increased lactate levels in Aβ 42 -expressing Drosophila ( 13 ) and AD patients ( 44 , 45 ). The level of Wwox in the brain provides a link between this metabolic mechanism and genetic risk of disease. Keeping Ldh at normal levels in the brain could be key to maintaining normal metabolic homeostasis and Ldh overexpression has been found to shorten lifespan, disrupt circadian rhythms and induces neurodegeneration ( 46 , 47 ). Interestingly, Ldh knockdown has also been shown to cause detrimental effects in fly Aβ 42 models, suggesting that Ldh expression and lactate levels need to be tightly controlled ( 13 , 48 ). Here we identified that Wwox knockdown combined with Aβ 42 expression further elevated L-lactate levels, likely mediated by the further increase in Ldh expression observed in our transcriptomics data. Taken together, these data provide evidence that some genetic risk loci, like WWOX , can influence the metabolome to control pathology, thereby influencing disease progression. The transcription factors Atf4 and HIF1α are both known to increase transcription of Ldh ( 11 , 13 ). While WWOX has been shown to inhibit HIF1α ( 6 ), our data suggest that Atf4, rather than the HIF1α orthologue sima, is primarily responsible for the observed Ldh upregulation in this fly model. Indeed, Atf4-dependent, sima-independent upregulation of Ldh has been previously reported in Aβ 42 -expressing Drosophila ( 13 ), further supporting these results. The stimulation of the PERK arm of the ISR/UPR pathway results in phosphorylation of eif2α, suppressing global translation whilst specifically promoting Atf4 translation. WWOX has previously been shown to interact in proximity with PERK, suggesting it could be directly affecting PERK activation ( 49 ). This provides a potential mechanism via which Wwox knockdown promotes Atf4 translation in the presence of Aβ 42 . Hippocampal neurons exposed to Aβ 42 oligomers have been shown to increase transport of Atf4 mRNA to axons, where it is locally translated before retrograde transport to the soma, leading to prolonged CHOP expression and CHOP-dependant cell-death ( 50 ). Whilst no Drosophila CHOP orthologue has been identified, prolonged PERK/Atf4 signalling has been shown to cause cell death in Drosophila overexpressing Presenilin , which encodes the catalytic subunit of Aβ-liberating γ-secretase ( 51 ). Interestingly, WWOX has also been shown to interact with GCN1 ( 47 ), an activator of Eukaryotic Translation Initiation Factor 2 Alpha Kinase 4 (GCN2) – a kinase known to phosphorylate eif2α ( 52 ). GCN2 depletion has previously been shown to be detrimental in the 5xFAD mouse model of Aβ toxicity, via enhanced PERK activation and Atf4 elevation ( 53 ). WWOX regulation of GCN2 could thus also explain the elevated Atf4 and aggravation of Aβ toxicity in our Wwox knockdown model. Our findings suggest that the combined effect of Wwox knockdown and Aβ 42 leads to chronic ISR/UPR activation, characterized by elevated Atf4, Ldh and lactate, above that of Aβ 42 overexpression alone. Notably, we found that reducing Ldh levels in neurons rescued Wwox knockdown-induced locomotor deficits in the presence of Aβ 42, suggesting that elevated neuron-derived lactate levels, downstream of Atf4 signalling, could be driving the detrimental effects of Wwox knockdown in Aβ 42 overexpressing flies. Lactate has been shown to be detrimental in amyloid models previously – for instance, lactic acid accumulation may increase APP-Grp78 binding, subsequent amyloidogenic cleavage and UPR disinhibition ( 54 ). Additionally, aerobic glycolysis has been suggested to directly modulate Aβ 42 accumulation ( 55 , 56 ), implying a direct link between lactate and amyloid. However, further research is needed to understand the complicated dynamics between amyloid and lactate in this model of AD and how well this mirrors the human disease. In addition to identifying deleterious effects of Wwox knockdown, recapitulating human risk, we found that upregulation of Wwox remarkably reduced amyloid load, rescued lifespan and locomotion of Aβ 42 overexpressing flies. Surprisingly however, omics analyses show this rescue occurs without a reduction in Ldh or lactate, suggesting a different mechanism of protection. We observed a dramatic reversal of transcriptional changes after Wwox overexpression in the presence of amyloid, identifying a downregulation of metabolic and inflammatory related genes, including cysteine and methionine metabolism. Consistent with this, we observed a reduction in L-methionine as a result of this interaction. L-methionine is an essential amino acid; however, it has been suggested that diets high in methionine can result in worsened memory ( 57 , 58 ), neurodegeneration and increased Aβ deposition in mouse models ( 58 ). Interestingly chronic L-methionine administration has also been shown to increase inflammatory cytokine release from microglial cells ( 59 ). These results suggest that the reversal of amyloid phenotypes by Wwox overexpression may arise through reduction of cysteine and methionine metabolism and consequently reduction of L-methionine, inflammation and amyloid deposition. In conclusion, our study provides new insights into the multifaceted relationship between Aβ 42 expression and Wwox levels in neurons. We demonstrate that Wwox plays a critical role in neuronal health and the modulation of Aβ 42 toxicity. Additionally, we identified metabolic alterations associated with Aβ 42 expression and Wwox knockdown. Excitingly, increasing Wwox expression in neurons caused a dramatic rescue of amyloid load. Future studies should explore the potential therapeutic implications of targeting these pathways for treatment of AD. Conflicts The authors report no conflicts of interest. Declarations The authors have no declarations. Data availability Data analysis code is available at https://github.com/Fly-Cardiff . Raw reads and feature counts for the transcriptomics experiment can be accessed on Array Express, for both the knockdown (E-MTAB-14948) and overexpression (E-MTAB-14949) experiments. The metabolomics data have been deposited to MetaboLights ( 60 ) repository with the study identifier MTBLS12344. Contributions GS, DM and HLC conceptualized this project and wrote the manuscript. HLC and DM performed experiments, and analysis. LA, EB and KO aided in the experiments. CR performed metabolomic experiments and analyses. OP, JH and GS were members of HLC supervisory team. Supplementary Methods Quantitative polymerase chain reaction (qPCR) 12-24 hrs post eclosion, flies were snap-frozen in liquid nitrogen and heads were separated by vortexing (n=15 heads per N=3 replicate). RNA was isolated from fly heads homogenised in TRIzol reagent (Invitrogen, 15596026), according to manufacturer’s instructions. Genomic DNA contamination was removed by TURBO DNase kit (Invitrogen, AM1907) and cDNA was synthesised using the QuantiTect Reverse Transcription kit (Thermo Scientific, K0221) according to manufacturer’s instructions. cDNA was amplified using Maxima SYBR Green/ROX master mix (Invitrogen) and the following primers at 300 nM final concentration; Wwox forward : ATGATAGCCCTACCCGACACA, Wwox reverse: TGGTTCACATAGCAAACGGTG , rp49 forward: AGCATACAGGCCCAAGATCG, rp49 reverse: TGTTGTCGATACCCTTGGGC. The QuantStudio 7 thermocycler was used with the following conditions: Initial denaturation (95°C, 600 s), denaturation (95°C, 15 s), annealing (60 °C, 30 s), extension (72 °C, 30 s) (40 cycles), melt curve (60-95°C). Melt curve analysis was performed to ensure single-product amplification. Raw amplification data was processed using the quantitative RT PCR package in R Studio and statistical significance was calculated using a permutations approach ( 62 ). XBP1 imaging We utilised the fluorescent genetically encoded reporter line UAS-Xbp1:EGFP:HG driven pan-neuronally to measure alternatively spliced Xbp1 . Fresh, unfixed, brains (N=6-10) were dissected and mounted on bridge slides for immediate imaging on a Zeiss Cell Observer spinning disk confocal with an Axiocam 503 camera, using an excitation wavelength of 488 nm and 20x objective. Four regions of interest were drawn over the brain and mean gray values were calculated, avoiding trachea (FIJI, version 2.3.0). Data was analysed via t-test (GraphPad Prism v. 10) – exploring the effect of Wwox knockdown in the presence of amyloid. Celltiter-Glo 2.0 ATP concentrations were measured via the Celltiter-Glo 2.0 luciferase assay (Promega, G9242) from 14-day old flies. n=3 brains were dissected per replicate (N=4) per genotype and homogenised in 100 μl 0.1% PTX. Debris was removed via centrifugation and the supernatant was subsequently equilibrated to room temperature (30 mins) before addition of an equal volume of Celltiter-glo and a further 2 mins incubation (with rotation). After a further 10 mins luminescence was measured on a FLUostar Omega plate reader with 520 nm filter and 1.24 s exposure per well. Luminescence was averaged across four replicate wells per genotype and background luminescence subtracted. Results were then analysed via two-way ANOVA (GraphPad Prism v. 10). Supplementary Figure Legends Download figure Open in new tab Figure S1. qPCR of Wwox mRNA levels from fly heads upon elavGAL4 driven expression of Wwox targeting TRiP RNAi for knockdown, or gRNA for CRISPRa upregulation. Wwox mRNA levels were normalised to rp49 housekeeping gene and each group was also normalised to its corresponding control genotype. mean ± SD; pairwise fixed reallocation randomization test, *** p<0.001, N=3 (15 heads per n). Download figure Open in new tab Figure S2. Wwox manipulation in neurons, glia or the clock decreases activity and increases sleep. Neuronal knockdown of Wwox decreases A) total activity counts in the light (p adj <0.0001) and dark (p adj <0.0001), B) maximum wake episode duration (p adj <0.0001) and C) mean wake episode duration (light p adj <0.0001, dark p adj =0.0128). D) The number of sleep episodes decreases (p adj =0.0031) E) as does the time taken to fall asleep (p adj <0.0001). Similar effects of activity are seen after glial knockdown of Wwox . F) Total activity decreases (light p adj <0.0001 and dark p adj =0.0444), G) max wake episode is shortened (p adj <0.0001), H) and mean wake episode is shorter during the light phase (p adj <0.0001). I) The number of sleep episodes is increased in the light phase (p adj =0.0107) and J) the latency to fall asleep is shorter (p adj <0.0001). Clock knockdown of Wwox decreases K) activity counts normalized to time awake (p adj <0.0001), L) increases probability to sleep (p adj =0.0019), and M) increases mean sleep episode duration (p adj =0.0151). Like neuronal and glial knockdown – N) clock knockdown decreases total activity (p adj <0.0001), O) and max wake episode duration (p adj =0.0403). However, no effects were seen on P) mean wake episode duration (p adj =0.5272), Q) number of sleep episodes (p adj =0.1940) or R) latency to sleep (p adj =0.4339). S) Circadian rhythmicity remained strong after clock knockdown of Wwox (p=0.0018) and T) circadian period was significantly lengthened (p=0.0006). two-way ANOVA followed by Bonferroni multiple comparisons were performed in A-R, P values were further corrected by 5% FDR to account for the number of metrics being assessed. Unpaired t-tests were performed in S and Mann-Whitney test in T. Dotted lines in panels S represent RS=1 (weakly rhythmic threshold), RS=1.5 (strongly rhythmic threshold) and panel T period length=24 hours. p/p adj *<0.05, **<0.01, ***<0.001, ****<0.0001. Data points represent individual flies: A-E N=31-80, F-J N=53-56, K-R N=53-61, S-T N=47-60 per genotype. Error bars represent SD. Download figure Open in new tab Figure S3. Knockdown of Wwox in glia whilst neurons expressed Aβ 42 mildly effected lifespan. A) Wwox knockdown in glia, whilst expressing amyloid in neurons, decreased lifespan (p=0.0095, Mantel-Cox tests N=100-115 individual flies), B) but had no effect on locomotion (p= 0.3491, unpaired t-test, N=5-6 vials of n∼10). Error bars represent SD. ** p<0.01. Download figure Open in new tab Figure S4 Principal component analyses show clear separation of genotypes. Genotypes from neuronal knockdown of Wwox transcriptomics experiment separate along A) PC1 (23%) vs. PC2 (15%) B) PC1 vs. PC3 (11%) and C) PC2 vs. PC3. PC1 appears to explain the effect of amyloid whereas PC2 the effect of Wwox knockdown. N=4 replicates from n=10 heads. Download figure Open in new tab Figure S5. ATP levels and Xbp1-splicing is unaltered by Wwox knockdown in the presence of Aβ 42 . A) Celltitre-glo background subtracted luminescence from brain, revealed no differences in ATP concentrations after Wwox knockdown (p=0.5720). Mean ± SD, N=4 (n=3 brains per sample, averaged across 4 technical replicate wells). Statistics: two-way ANOVA. B) There are no differences in the levels of Xbp1:GFP:HG (reporter which only fluoresces when Xbp1 is alternatively spliced) after Wwox knockdown in the presence of amyloid (p=0.2848). Error bars represent SD, statistics = t-test, N=6-10 brains. C) Representative images, scale bar=100 μm. Download figure Open in new tab Figure S6. Overexpression of Wwox in control neurons or overexpression of Wwox in glia whilst expressing neuronal Aβ 42 has no effect on behavior. A) There is no difference between the survival curves of flies expressing control gRNA or Wwox gRNA for CRISPRa overexpression in neurons ( elav-GAL4 ) in a control background (p=0.1420, N=76-86). B) Startle-induced locomotion of flies expressing neuronal control gRNA or Wwox gRNA revealed no effect of genotype (p=0.8770, N=3-6). C) Expressing hWWOX into neurons also expressing Aβ 42 could significantly rescue lifespan (p=0.0021, N=85-106) and D) improves climbing at 7-days (p=0.0371, N=5 vials). E) However, overexpression of Wwox ( Drosophila or human) in glial cells, whilst expressing Aβ 42 in neurons, had no effect on lifespan (p adj =0.8000, p adj =0.1045, N=84-154) or F) startle induced locomotion (p=0.6654, N=4-7) compared to an empty vector control. G) The MSD assay for amyloid revealed decreased total amyloid after Wwox overexpression, H) but no difference in the ratio of insoluble to soluble species (N=3 replicates of n=25 heads). Statistics: Mantel-Cox tests (A, C, E), two-way ANOVA (B), t-test (D, G, H), one-way ANOVA (E). Error bars represent SD. p/p adj * <0.05, **<0.01, ***<0.001, ns=non-significant. Download figure Open in new tab Figure S7. Principal component analyses for overexpression transcriptomics. Principal component analysis reveals that 29% of the variation in the dataset can be explained by PC1, 16% by PC2 and 9% by PC3. A) Comparison of PC1 vs PC2 reveals separation of control and Wwox overexpression along PC1. There is less clear separation between the genotypes when comparing B) PC1 and PC2, or C) PC2 and PC3. N=4 replicates of n=10 heads. Supplementary Tables Uploaded in a separate file with this manuscript. Acknowledgements We would like to thank the School of Biosciences Genomics Research Hub and Prof. Valentina Escott-Price at Cardiff University for their expertise regarding the transcriptomics experiments in this manuscript. We would also like to give thanks to Phil Whitfield at MVLS Shared Research Facilities (University of Glasgow) for their contribution concerning analysis of the metabolomic experiments. Schematic figures were generated with BioRender.com. This work was funded by the UKRI MRC Momentum Award (MC_PC_16030/1 to GS), the Leverhulme Trust project grant (RPG-2020-369 to GS), and the UKRI MRC Momentum Award (MC_PC_16030/2 to OP). HLC was funded by a Wellcome Trust Integrative Neuroscience PhD studentship [108891/B/15/Z]. EB and JH are supported by Alzheimer’s Research UK grant (ARUK-IRG2019B-003) and BBSRC (BB/W000865/1) awarded to JH. This work is further supported by the UK Dementia Research Institute through UK DRI Ltd, principally funded by the Medical Research Council. Funder Information Declared Medical Research Council, https://ror.org/03x94j517 , MC_PC_16030/1 , MC_PC_16030/2 Wellcome Trust , 108891/B/15/Z Leverhulme Trust , RPG-2020-369 Alzheimer’s Research UK , ARUK-IRG2019B-003 Biotechnology and Biological Sciences Research Council , BB/W000865/1 Footnotes https://github.com/Fly-Cardiff https://www.ebi.ac.uk/biostudies/arrayexpress https://www.ebi.ac.uk/metabolights/ References 1. ↵ Bellenguez C , Küçükali F , Jansen IE , Kleineidam L , Moreno-Grau S , Amin N , et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias . Nat Genet . 2022 ; 54 ( 4 ): 412 – 36 . 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Share Alzheimer’s disease risk gene Wwox protects against amyloid pathology through metabolic reprogramming Hannah J Lucas-Clarke , Daniel C Maddison , Leonardo Amadio , Edgar Buhl , Katherine O’Hare , Clement Regnault , Owen M Peters , James J L Hodge , Gaynor A Smith bioRxiv 2025.05.01.651195; doi: https://doi.org/10.1101/2025.05.01.651195 Share This Article: Copy Citation Tools Alzheimer’s disease risk gene Wwox protects against amyloid pathology through metabolic reprogramming Hannah J Lucas-Clarke , Daniel C Maddison , Leonardo Amadio , Edgar Buhl , Katherine O’Hare , Clement Regnault , Owen M Peters , James J L Hodge , Gaynor A Smith bioRxiv 2025.05.01.651195; doi: https://doi.org/10.1101/2025.05.01.651195 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7624) Biochemistry (17651) Bioengineering (13871) Bioinformatics (41882) Biophysics (21424) Cancer Biology (18566) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13365) Ecology (19867) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22476) Immunology (17714) Microbiology (40331) Molecular Biology (17148) Neuroscience (88483) Paleontology (666) Pathology (2828) Pharmacology and Toxicology (4817) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)