Metformin decreases RAN proteins, rescues splicing abnormalities and improves behavioral phenotypes in SCA8 BAC mice

Metformin decreases RAN proteins, rescues splicing abnormalities and improves behavioral phenotypes in SCA8 BAC mice | 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 Metformin decreases RAN proteins, rescues splicing abnormalities and improves behavioral phenotypes in SCA8 BAC mice View ORCID Profile Lisa E.L Romano , Setsuki Tsukagoshi , View ORCID Profile Emily E. Davey-Osuch , Ramadan Ajredini , Kamat Manasi , Tala V.R. Ortiz , Eduardo Rijos , Nathan J. Bourgon , S. Elaine Ames , View ORCID Profile Timothy J. Garrett , View ORCID Profile John D. Cleary , View ORCID Profile Eric T. Wang , View ORCID Profile Laura P.W. Ranum doi: https://doi.org/10.1101/2025.08.21.671563 Lisa E.L Romano 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa E.L Romano Setsuki Tsukagoshi 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily E. Davey-Osuch 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emily E. Davey-Osuch Ramadan Ajredini 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kamat Manasi 3 Southeast Center for Integrated Metabolomics, Clinical and Translational Science Institute, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tala V.R. Ortiz 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eduardo Rijos 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathan J. Bourgon 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site S. Elaine Ames 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Timothy J. Garrett 3 Southeast Center for Integrated Metabolomics, Clinical and Translational Science Institute, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Timothy J. Garrett John D. Cleary 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John D. Cleary Eric T. Wang 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL 5 Genetics Institute, University of Florida , Gainesville, FL 6 McKnight Brain Institute, University of Florida , Gainesville FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eric T. Wang Laura P.W. Ranum 1 Center for NeuroGenetics, College of Medicine, University of Florida , Gainesville, FL 2 Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida , Gainesville, FL 5 Genetics Institute, University of Florida , Gainesville, FL 6 McKnight Brain Institute, University of Florida , Gainesville FL 7 Department of Neurology, College of Medicine, University of Florida , Gainesville, FL 8 Norman Fixel Institute for Neurological Disease, University of Florida , Gainesville, FL Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura P.W. Ranum For correspondence: ranum{at}ufl.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Spinocerebellar ataxia type 8 is one member of a larger group of dominantly inherited, debilitating neurological diseases caused by CTG*CAG expansions for which there are no effective disease-targeting treatments. RAN translation, which was discovered in SCA8, has previously been shown to occur across CAG and CUG expansion transcripts, making treatments that work for SCA8 potentially relevant to a much broader group of diseases, including SCA1, 2, 3, 6, 7, 12, Huntington’s Disease (HD) Fuch’s endothelial corneal dystrophy (FECD), and myotonic dystrophy type 1 (DM1). The FDA-approved drug, metformin, has been previously shown to reduce RAN protein levels in cells overexpressing SCA8 CAG repeats. Here we show, using SCA8 BAC transgenic mice, that metformin treatment improves ambulatory performance, including rotarod, DigiGait, and open field measures. At the molecular level, metformin-treated mice show reduced RAN protein levels and improved splicing abnormalities without changing the levels of the expanded RNAs. Metformin-treated mice also show decreased neuroinflammation with reduced levels of astrogliosis and reduced numbers of activated microglia. Taken together, these data provide strong support for testing FDA-approved metformin in clinical trials for SCA8 and potentially the broader group of CAG*CTG repeat expansion disorders. One Sentence Summary Metformin improves behavior, neuropathological and molecular phenotypes in SCA8 BAC transgenic mice. INTRODUCTION Spinocerebellar ataxia type 8 (SCA8), a dominantly inherited form of ataxia with reduced disease penetrance, is caused by a CTG•CAG repeat expansion mutation located in the overlapping ATXN8/ATXN8OS genes 1 . This expansion mutation, traditionally thought to be non-coding, is bidirectionally transcribed, producing both CAG and CUG expansion transcripts 2 , 3 . Similar to DM1, SCA8 CUG expansion transcripts cause RNA gain-of-function effects 3 . Additionally, characterization of the SCA8 expansion mutation led to the discovery of RAN (Repeat-associated non-AUG) translation, a process in which repeat expansion RNAs can produce mutant expansion proteins in all three reading frames without AUG-or AUG-like initiation codons 4 . In SCA8 BAC mice and SCA8 human autopsy tissue, polyGln, polyAla, and polySer RAN protein aggregates have been shown to accumulate in affected tissues, including the cerebellum and brainstem 2 , 4 , 5 . RAN proteins have now been shown to accumulate in a growing number of repeat expansion disorders 6 – 9 , including the most common genetic forms of C9orf72 amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) (C9-ALS/FTD) 10 – 12 and Alzheimer’s disease (AD) 8 , which are caused by intronic GGGGCC 13 , 14 and GGGAGA 8 repeat expansion mutations, respectively. In 2020, Zu et al., 15 showed that RAN translation across multiple repeat motifs is regulated by the double-stranded RNA-dependent protein kinase (PKR) pathway and that PKR inhibition with the dominant-negative mutant PKR-K296R or the FDA-approved drug metformin reduces RAN proteins and improves disease in C9orf72 ALS/FTD mice 15 . Metformin, a widely used FDA-approved drug for type 2 diabetes, has also been shown to have beneficial effects on aging and a number of neurodegenerative disorders 15 – 22 . Additional studies have shown that metformin improves disease in both mice and patients carrying the CAG•CTG mutations, including HD 23 , 24 and DM1 19 , 25 , 26 . While these studies indicate the potential of metformin for improving disease phenotype in CAG•CTG repeat disorders, it remains unclear if metformin decreases RAN protein levels or alters RNA gain-of-function effects. To address the potential therapeutic efficacy and mechanism(s) of metformin in the context of a CAG•CTG repeat expansion disorder, we treated the SCA8 BAC transgenic mice 2 with metformin and examined its effects on behavioral and molecular phenotypes. RESULTS Metformin ameliorates behavioral phenotypes in SCA8 mice Metformin has previously shown beneficial effects in HD mice 23 , 24 , 27 , improved cognitive function in HD patients 28 and improved mobility in DM1 patients 29 . Similar to SCA8, HD and DM1 are caused by a CAG•CTG expansion mutation, which undergoes RAN translation. Based on these studies and evidence that metformin reduces RAN protein levels across CAG and other repeat expansion motifs 15 , we tested the hypothesis that metformin would reduce RAN protein levels and improve disease phenotypes in SCA8 BAC transgenic mice. From 4-7 weeks SCA8 mice and their non-transgenic (NT) littermates were given water with or without 2 mg/ml metformin ( Fig. 1A ). At 8 weeks of age, the dose was increased to 5mg/mL, a concentration previously shown to result in plasma levels of ∼10μM, which is comparable to conventional human doses of 20 mg/kg day used in diabetic patients 30 , 31 . Download figure Open in new tab Fig. 1: Metformin treatment improves behavioral performance of SCA8 mice. A ) Schematic of experimental timeline. B ) Quantification of latency to fall of 32-week-old mice treated with or without metformin. C, D ) Quantification of brake ( C ), step angle ( D ) as examples of two corrected forelimb DigiGait parameters. E, F ) Quantification of % brake stride ( E ) and stride length ( F ) as examples of two corrected hindlimb DigiGait parameters. ( G-I ) Quantification of ambulatory distance ( G ), ambulatory speed ( H ), and resting time ( I ) as three examples of improved open field parameters. Error bars = SEM, n=15 mice/group. Ordinary one-way ANOVA statistical test with Šídák’s multiple comparisons test, *** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05. To assess the effects of metformin on ambulatory function, SCA8 and NT littermates treated with or without metformin underwent rotarod testing at 8 and 32 weeks before and after the onset of overt phenotypes, respectively (n > 14 animals/group). As expected, no differences in performance were found between SCA8 and NT treated and untreated groups at 8 weeks of age ( Fig. S1A ). At 32 weeks of age, metformin treated SCA8 mice showed marked improvement with increased latency to fall (398 ± 38 sec) compared to untreated SCA8 mice (214 ± 33 sec, p=0.0045). Rotarod performance of the metformin treated SCA8 mice was comparable and not significantly different from that of their NT littermates (430 ± 36 sec) ( Fig. 1B ). DigiGait analyses were used to quantify specific features of gait dynamics, posture, and symmetry between forelimb and hindlimb stepping. No differences in performance were found at 16 weeks between asymptomatic SCA8 and NT-treated or untreated groups ( Fig. S1B, C ). At 32 weeks of age, 14 forelimb DigiGait parameters differed between SCA8 and NT animals. Of these, 8 of 14 (57%) DigiGait abnormalities improved in metformin treated SCA8 mice ( Fig. S1D ). Examples of improved parameters of brake (p=0.003) and step angle (p=0.0022) are shown in Fig. 1C and 1D , respectively. Similarly, metformin rescued 9 of 11 hindlimb abnormalities ( Fig. S1E ), including % brake stride (p=0.0058, Fig. 1E ) and stride length (p=0.0018, Fig. 1F ). Open field studies done at 52 weeks identified 9 parameters that differed between untreated SCA8 and NT cohorts. Of these, 6 of 9 parameters were improved in metformin treated vs. untreated SCA8 mice ( Fig. S1F ). Improved phenotypes include ambulatory distance (p=0.0231, Fig. 1G ), ambulatory speed (p=0.0302, Fig. 1H ), and resting time (p=0.0134, Fig. 1I ). Taken together, these data demonstrate that metformin increases ambulatory function and improves various aspects of locomotion in SCA8 mice. Metformin decreases RAN protein levels without changing CAG•CTG repeat length or RNA levels To explore the effects of metformin on RAN proteins, we measured the levels of RAN proteins in the brains of SCA8 mice. IHC analyses using our previously reported C-terminal polySer antibody 5 showed that metformin treatment decreases the number of polySer aggregates in the cerebellum of SCA8 mice (p=0.0013, Fig. 2A, B, C ). Importantly, IHC analysis shows that metformin can also reduce the number of polySer aggregates in the brainstem of SCA8-treated mice (p=0.0001, Fig. 2D, E, F ) and decreases the percentage of polySer area (p=0.0005, Fig. S2A ) in this aggregate-enriched region of the mouse brain. The aggregate characteristics (number, size, and area) were quantified using macro analysis of the deconvoluted images ( Fig. S2B ) in ImageJ. Download figure Open in new tab Fig. 2: Metformin treatment reduced polySer and polyGln aggregates in SCA8 mice brains. A ) Mouse cerebellum schematic showing the area of polySer aggregates accumulation. B ) Quantification of polySer aggregates number in the cerebellum. C ) IHC of cerebellum regions showing reduced polySer aggregates in SCA8 mice treated with metformin. D ) Mouse brainstem schematic showing area of polySer aggregates accumulation. E ) Quantification of polySer aggregates number in the brainstem. F ) IHC of brainstem regions showing reduced polySer aggregates in SCA8 mice treated with metformin. G ) Mouse brainstem schematic showing area of polyGln aggregates accumulation. H ) Quantification of number of polyGln aggregates in the brainstem. I ) IHC of brainstem regions showing reduced polyGln aggregates in SCA8 mice treated with metformin. ( J ) Bar graph showing relative mRNA levels of ATXN8 and ATXN8OS with and without metformin. FPKM values for each transcript are normalized to FPKM value for ATXN8OS. FPKM=fragments per kilo base of transcript per million mapped reads. ( K , L ) DNA fragment analysis of cerebellum ( K ) and brainstem ( L ). Error bars = SEM, n>10 mice/group. Quantification was performed using a macro analysis in ImageJ software. Statistical one-way ANOVA test **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05. Metformin not only reduces polySer RAN protein, but it also reduces the number of polyGln aggregates (p=0.0076, Fig. 2G ), and the percentage of polyGln area in the brainstem (p=0.0360, Fig. S2C ). Due to the size of polyGln aggregates, the quantification was performed using different ImageJ macros specific to polyGln ( Fig. S2D ). Metformin levels in the brain of SCA8 and NT treated mice were confirmed by mass spectrometry analysis of lysates from cerebellum and brainstem regions of the brain ( Fig. S2E, F ). To rule out the possibility that the decreases in RAN protein levels were not a downstream consequence of decreased ATXN8 and ATXN8OS transcripts, we performed RNA sequencing (RNA-seq) of the cerebellum and brainstem of SCA8 treated and untreated mice. Similar to the previous analysis 5 RNA-seq reads were aligned to a custom human reference genome containing ATXN8 and ATXN8OS plus 10 kb flanking sequences, both upstream and downstream of the repeat. ATXN8OS expression was determined by counting the number of reads that map to exons B, C, and D of the ATXN8OS gene. These exons are unique to ATXN8OS and do not overlap with the ATXN8 sequence. Reads found in the last intron of ATXN8OS , which overlaps the ATXN8 gene, were used to differentiate between the two transcripts and to calculate the relative levels of ATXN8 . These data show that ATXN8 transcripts are expressed ∼6-fold higher than ATXN8OS transcripts in SCA8 mouse brain ( Fig. 2J ) and that metformin treatment does not alter the relative levels of sense and antisense transcripts. Fragment analysis of both cerebellum and brainstem regions showed that the repeat size remains unaltered after metformin treatment in both areas of the brain ( Fig. 2K, L ). These data demonstrate that metformin decreases RAN protein aggregates without affecting RNA levels or repeat length. Metformin decreases neuroinflammation and microglia activation In 2018, we reported oligodendrocyte abnormalities and reactive astrogliosis in polySer-positive regions in SCA8 mouse brains 5 . To measure the extent of reactive astrocytes, following metformin treatment, the intensity of positive GFAP staining was measured in different polyGln and polySer rich areas of the brain, including the brainstem and cerebellum ( Fig. 3A , Fig. 3SA ). Metformin treated SCA8 mice showed markedly reduced GFAP intensity compared to untreated SCA8 mice in the brainstem (p=0.0013, Fig. 3B ) and cerebellum (p=0.0167, Fig. 3C ) and levels similar to their NT littermates. Download figure Open in new tab Fig. 3: Metformin treatment improved neuroinflammation and reduced microglia activation in SCA8 mice. ( A ) Metformin-treated SCA8 mice (60 weeks) show reduced reactive astrogliosis (GFAP) and reduced activated microglia (Iba1) in the regions with prominent polyGln and polySer accumulation. B-D ) Quantification was performed using the IHC profile plugin in ImageJ software for % GFAP positive staining in brainstem ( B ) and cerebellum ( C ), and % positive Iba1 microglia staining ( D ). Error bars = SEM, n>3 mice/group. Statistical one-way ANOVA test, ** p<0.01, * p<0.05. IHC analyses of the levels of Iba1 in the brainstem showed that microglia staining was more abundant in SCA8 compared to NT littermates (p = 0.0479, Fig. 3A, D ) and restored to normal levels in SCA8 mice treated with metformin (p < 0.0286, Fig. 3D ). To determine if metformin treatment influences microglial morphology, including ramification and branching, we analyzed our images using the ImageJ Skeleton analysis plugin 32 . This analysis allows us to measure the number of reactive microglia, the number of branches, branch length, and morphological changes ( Fig. S3B ). Skeleton analysis of Iba1 showed SCA8 mice have an increased number of Iba1 positive microglia compared to NT littermates (p=0.0252, Fig. S3C ) and substantially reduced by metformin treatment (p=0.0105, Fig. S3C) . Similarly, the increases in the total number of branches (p=0.0065, Fig. S3D ) and branch length (p=0.0069, Fig. S3E ) in SCA8 mice compared to NT littermates were dramatically reduced by metformin treatment (number of branches p=0.0049, Fig. S3D ) (branch length p=0.0130, Fig. S3E ). Taken together, these results demonstrate that metformin prevents astrogliosis and microglial abnormalities in RAN positive regions of SCA8 mice brain. Metformin partially rescues SCA8 splicing abnormalities Similar to DM1 33 , RNA foci containing the SCA8 expanded CUG repeats co-localize with MBNL1 in neurons and show several alternative splicing (AS) changes 3 . To further characterize the splicing dysregulation in SCA8, we performed RNA-Seq of the cerebellum and brainstem in SCA8 and NT mice. All libraries met standard quality metrics in FastQC (Version 0.12.0) 34 and sequenced to a depth between 38 and 53 million input reads, providing sufficient coverage for the analysis of AS changes. The percentage spliced in (PSI) values were estimated using rMATS (replicate multivariate analysis of transcript splicing) 35 . Significant mis-splicing events were defined as events with > 10% change in mean PSI in either direction and an FDR < 0.05. Our data identified 554 differentially spliced exons in SCA8 vs. NT littermate controls. Of these, there are 266 skipped exons (SE), 104 alternative 5ʹ splice sites (A5SS), 107 alternative 3ʹ splice sites (A3SS), 58 retained introns (RI), and 19 mutually exclusive exons (MXE) events ( Fig. S4A ). Heatmap ( Fig. 4A ) and scatterplot ( Fig. S4B ) show global profiling of significant dysregulated AS events in SCA8 compared to NT controls. Metascape gene ontology (GO) enrichment analyses 36 were used to identify disease-relevant pathways. The most enriched GO terms fall into three main categories: 1) regulation of mRNA regulation and splicing; 2) DNA damage and repair; 3) synaptic signaling ( Fig. 4B ). Transcripts mis-spliced in SCA8 include those expressed from a number of genes previously implicated in other neurological diseases, including Tardbp 37 – 39 , Adarb1 40 , Rad52 41 , Nfib 42 , Unc13b 43 , 44 , Baiap2 45 , and Clcc1 46 , 47 ( Fig. S4C-E ). Download figure Open in new tab Fig. 4: Metformin partially rescued dysregulation of alternative splicing in the SCA8 mouse model affected brain regions. A ) Heatmap showing 554 significantly regulated splicing events in SCA8 when compared to NT, using FDR |0.1|. B ) Enrichment of gene ontology (GO) terms identified using metascape analysis of the 554 skipped exon events dysregulated in SCA8 mice. Broad functional categories of terms are indicated by bar color. C ) Venn diagram showing the overlapping mis-splicing events between SCA8 water vs. NT water when compared to SCA8 water vs. SCA8 metformin treated mice. D ) Heatmap showing the overlapping 114 significant mis-splicing events. E-G ) Percent spliced in (PSI) for transcripts belonging to specific GO terms, including DNA damage and repair (green)( E ), synaptic signal (orange)( F ) and the mRNA processing category (yellow)( G ). Metformin has previously been shown to affect splicing regulation in cancer 48 , 49 and DM1 25 . To understand the effects of metformin on AS in SCA8, we compared the 554 significant mis-splicing events between SCA8 vs. NT animals with dysregulated AS events in SCA8 vs. SCA8 metformin treated mice. The Venn diagram in Fig. 4C shows that 114 of the AS events in SCA8 vs. NT animals are also changed and potentially corrected in SCA8 metformin treated mice vs. SCA8 water treated animals. The heatmaps in Fig. 4D , S4F show that metformin treated SCA8 mice show a partial rescue of these mis-splicing events. Examples of dysregulated splicing events that are corrected by metformin include transcripts from genes involved in DNA damage and repair, such as Pms2 50 and Ppp2r5c 51 , 52 ( Fig. 4E ), synaptic signaling including Cacna1b 53 , Kcnq2 54 , and Tbccd1 ( Fig. 4F ) and RNA processing Celf4 55 , 56 ( Fig. 4G ). Taken together, these data show that in addition to RAN protein accumulation, SCA8 mice also exhibit widespread AS dysregulation and that metformin substantially improves both of these molecular phenotypes. DISCUSSION Here we show that metformin improves multiple facets of disease in SCA8 BAC transgenic mice, resulting in: 1) increased ambulatory function by DigiGait, rotarod, and open field analyses; 2) reduced RAN protein aggregate burden; 3) decreased neuroinflammation as evidenced by reduced astrogliosis and microglial activation; and 4) partial rescue of alternative splicing abnormalities. These data identify metformin as a promising drug that mitigates key behavioral and molecular phenotypes driven by the repeat expansion mutation. These data position this FDA-approved drug for rapid transition into clinical trials to test the safety and efficacy of metformin in SCA8, and potentially the broader group of CAG•CTG expansion disorders. Metformin, a commonly prescribed drug for type 2 diabetes, has been reported to have benefits on aging 18 , 57 and neurodegenerative disorders 18 , 20 . Metformin treatment also was shown to decrease RAN protein levels and improve behavior in a BAC mouse model of C9orf72 ALS/FTD, a disease caused by a hexanucleotide GGGGCC repeat expansion 15 . This strong pre-clinical evidence has led to an ongoing open-label clinical trial ( NCT04220021 ) into the safety and therapeutic potential of metformin for C9orf72 ALS patients. In the context of CAG•CTG repeat expansion disorders, metformin has been shown to improve cognitive phenotypes in HD patients 28 and three different HD mouse models 23 , 24 , 27 . Similarly, metformin improved motor phenotypes in DM1 patients 29 . In a separate study using patient-derived DM1 myoblasts, 4 of 20 alternative splicing events were rescued by metformin treatment 25 . While these HD and DM1 studies examined behavior and select RNA splicing changes, respectively, both of these mutations have also been shown to express RAN proteins 4 , 58 , raising the question of whether the beneficial effects of metformin in these diseases result from unexamined RAN protein and/or RNA effects. Our data showing that metformin reduces SCA8 RAN protein levels and RNA splicing abnormalities strongly suggests that this FDA-approved drug will also improve these repeat-driven pathologies across a broad range of CAG•CTG diseases, including multiple SCAs (SCA1, 2, 3, 6, 7, 12), DM1. HD, and FECD. Although sense and antisense transcripts and RAN proteins have been reported for more than a dozen repeat expansion diseases 6 , 59 , 60 , most therapeutic strategies (e.g., antisense oligonucleotides or ASOs) for these disorders have been focused on targeting the sense transcripts. Unfortunately, this strategy has repeatedly failed for multiple expansion diseases, including C9orf72 amyotrophic lateral sclerosis (C9-ALS) 61 , HD 62 , SCA2 63 , and SCA3 64 . While it is not yet clear why each of these trials failed, the answer may be that sense and antisense RNAs are known to regulate each other. For example, previous studies in SCA7 65 and HD 66 have shown that knock-down of sense expansion transcripts leads to the upregulation of the antisense expansion transcript. Therefore, decreases in the levels of sense transcripts with ASOs may disrupt this sense/antisense transcript balance leading to unintended increases in antisense transcript levels and toxicity. An advantage of metformin treatment, as shown in this study, is that it improved both RAN and RNA splicing phenotypes without changing the levels of sense or antisense expansion RNAs. Our data also shows that metformin reduces astrogliosis and microglia activation at sites of RAN protein pathology. Metformin has been previously shown to decrease neuroinflammation and its associated effects across multiple different diseases including Alzheimer’s disease (AD), Parkinson’s Disease (PD), multiple sclerosis (MS) and HD 18 . While to date, PD and MS have not been associated with repeat expansion mutations, RAN protein aggregates are known to accumulate in HD 58 and a recent study shows RAN protein aggregates also accumulate in substantial fraction of AD autopsy brains 8 . Our SCA8 data raise the possibility that by decreasing RAN protein aggregate burden, metformin reduces neuroinflammation in SCA8 and other repeat expansion disorders. Metformin has been suggested to reduce neuroinflammation by acting on the AMP-activated protein kinase (AMPK) pathway 16 , 67 , 68 in other diseases. While it is clear that metformin reduces RAN protein levels in SCA8 and C9orf72 mice 15 , additional research is needed to understand the mechanisms by which metformin reduces neuroinflammation. CAG•CTG trinucleotide repeat expansion mutations underlie SCA8, DM1, HD and multiple SCAs. In DM1, in addition to RAN translation, RNA gain-of-function of CUG expansion transcripts has been shown to play an important role in disease through the sequestration of the muscleblind-like (MBNL) family of proteins 33 and global disruption of alternative splicing. Alternative splicing in SCA8 3 and other SCAs 69 is not as widely characterized as in DM1. Here, we demonstrate substantial dysregulation of alternative splicing in SCA8 BAC transgenic mice. with alternations in transcripts important for synaptic signaling, RNA processing/splicing, and DNA damage and repair. Metformin treatment rescued ∼20% of these dysregulated alternative splicing events including transcripts linked to mRNA metabolism and synaptic signaling. While additional work will be needed to understand the role of and the relative contributions of RNA processing abnormalities versus RAN protein effects, it is important to note that metformin treatment dramatically improves disease relevant behavioral and neuroinflammatory phenotypes in SCA8 mice. In summary, our work shows that metformin treatment improves behavior, reduces RAN protein aggregates, and neuroinflammation in SCA8 BAC transgenic mice without altering sense or antisense RNA levels. A distinct advantage of metformin over strategies that target and degrade sense expansion RNAs is that reducing RAN protein levels using metformin is predicted to lower both sense and antisense pathogenic proteins. Metformin’s strong RAN protein lowering and anti-inflammatory properties, together with its long track record as a safe and affordable drug, make metformin an excellent candidate for clinical trials for SCA8 and potentially other CAG•CTG expansion disorders. MATERIALS AND METHODS Study design In this study, we sought to determine whether metformin can prevent and/or reverse the behavioral and pathological phenotype of SCA8 transgenic mice. Because RAN proteins start to accumulate early in the disease, we designed our study by treating adult female and male SCA8 BAC transgenic mice beginning at the age of 4 weeks, well before the development of behavioral and pathological phenotypes. During the treatment period of 60 weeks, animals were assessed for behavioral defects at 8, 16,32, and 52 weeks. Final take down was done at 60weeks of age. Mouse model Mice used in this study were housed and treated in accordance with the NIH Guide for the Care and Use of Laboratory Animals. The Animal Care and Use Committee approved all animal studies at the University of Florida. Previously described SCA8 BAC transgenic lines on the FVB background (BAC EXP2, 2878) were used 2 . Metformin administration Male SCA8-BAC mice were bred with female FVB/N mice obtained from Jackson Laboratory to generate cohorts of mice for metformin treatment. Hemizygous mice with the SCA8 BAC transgene were genotyped by PCR as previously described 2 . Female and male SCA8 positive mice were separated into different cohorts, along with their NT littermates, and were treated with or without metformin in the drinking water. SCA8 and NT control mice in the treatment groups received 2 mg/mL up to 7 weeks of age, after that, they received 5 mg/mL metformin in the drinking water up to 60 weeks of age. Blinding procedure Researchers were blinded and did not know which groups of animals were receiving metformin treatment or knew the genotype of the mice. This blinding was done during the following experiments: 1) in vivo efficacy study and all IHC downstream analysis. 2) blinded researchers performed IHC and histological staining; quantification was performed by a researcher who did not perform the staining and using images taken by researchers who did not perform the experiments. Rotarod analysis Rotarod training was performed at 8 and 32 weeks of age using an accelerating rotarod (Ugo Basille, Comerio, Italy) as described 5 . Three trials were run per day for four days: averages of the three trials on day four are presented. One way-ANOVA test followed by post-hoc multiple comparison analysis (Holm-Sidak) was performed to assess differences in rotarod performance between groups (non-transgenic, SCA8, and SCA8 metformin-treated mice). Gait analysis Digigait analysis was performed at 16 and 32 weeks of age in all experimental group. Digital video images of the underside of the mouse were collected with a high-speed video camera from below the transparent belt of a motorized treadmill (DigiGaitTM Imaging system, Mouse Specific). Each mouse was allowed to explore the treadmill compartment with the motor speed set to 14 cm/s for 1 min then the motor speed was increased to 24 cm/s for video recording. Only video recordings in which the mouse walked straight ahead with a constant relative position with respect to the camera were used for analysis. Data from each paw was analyzed with DigiGait automated gait analysis software (Mouse Specifics). All analyses were performed in a blinded fashion. Open field analysis Open field analysis was performed at 52 weeks by testing mouse behavior during a 30-minute session in a completely dark open chamber (17”x17”) (Med Associates). Approximately two hours before the start of analysis, mice were placed in the behavior room to allow for acclimation to the room. Mice were then placed in the center of the darkened activity-monitoring chamber. The trace path and center time were recorded and analyzed with Activity Monitor (MED Associates, Inc.) software. All analyses were performed in a blinded fashion Tissue processing for histopathological and downstream analysis For histological analysis, animals were anesthetized using 100 mg of ketamine and 10 mg of xylazine per kg of body weight and perfused through the ascending aorta with 15 ml of isotonic saline. Half of the brain was collected for histopathological analysis and fix in 10% buffered formalin. The other half of the brain was harvested, dissected and snap-frozen for total subsequent RNA isolation or protein isolation. Histology and Immunohistochemistry For the detection of polySer RAN protein in fixed brain tissue, animals were perfused transcardially with 1× PBS. Brain was dissected and fixed in 10% formalin for 24 h and later removed into 70% ethanol. After histological processing and paraffin embedding, five-micrometer sagittal sections were cut using a microtome. Sections were deparaffinized in xylenes (2x15 minutes) and rehydrated through an alcohol gradient (100%, 100%, 95%, 80%; 10 minutes each step). Sections were then treated with the following antigen retrieval steps: first, 1 μg/ml proteinase K treatment in 1 mM CaCl 2 , 50 mM Tris buffer (pH = 7.6) for 30 min at 37°C; second, steam in 10 mM EDTA (pH = 6.5) for 30 min using a steamer; and third, 95% formic acid treatment for 5 min. Endogenous peroxidase was blocked in 3% H 2 O 2 methanol for 12 min. To block non-specific binding, a non-serum block (Biocare Medical, Pacheco, CA) was applied for 15 min. Primary antisera were diluted in 1:10 non-serum block/water at the concentrations indicated below and incubated at 4 ° C overnight. Rabbit Linking Reagent (streptavidin) was applied for 30 minutes at RT. Secondary antibodies were Biotin-Avidin/Streptavidin labeled using ABC reagent (Vector laboratories, Inc.). Each step was followed by 3x5 minutes washing in PBS. The detection of antibody was performed by exposure to the Vector Red Substrate Kit (Vector Laboratories, Inc.) for polySer and DAB for 1C2. Slides were washed in running water (10 mins) and counterstained using Hematoxylin QS (Vector Laboratories) at RT (2 mins). After washing in water for 5 minutes, slides were dehydrated (ethanol 80%, 95%, 100%, xylene) and mounted using Cytoseal 60 (Electron Microscopy Sciences). Images were captured with an Olympus BX51 light microscope. For the detection of polyGln the antigen retrieval steps are different, and they include 95% formic acid treatment for 5 min and steaming in 10 mM Citrate buffer (pH = 6.8) for 30 min using a steamer. For the detection of astrocytes and microglia, the protocol is similar to the one described above, except the antigen retrieval steps are different. For the detection of GFAP, slides are incubated in 10mM citrate buffer (pH=6.8) for 30 minutes. For the detection of Iba1, slides are steamed in 10 mM citrate buffer (pH=6.8) for 30 minutes, and then they are incubated for five minutes in 95% formic acid for 5 min. Primary antibodies/sera were used at the following conditions in mice tissue: polySerCT RAN proteins 5 (NEP, Project #1306, Rabbit #F3672, 1:10000), polyGln 1C2 (Millipore, mouse, 1:15000), astrocytes GFAP (Abcam, Ab7260-GFAP, 1:5000) and microglia Iba1 (Abcam, Ab 1:5000). Tissue for RNA isolation, RNA-seq library preparation Mouse tissue from cerebellum and brainstem was lysed and homogenized with 800 μL TRIzol reagent (Thermo Fisher Scientific, 15596018) in 2-mL tubes pre-filled with 1.5-mm high-impact zirconium beads (Benchmark Scientific, D1032-15). Tubes were agitated at 10,000 × g for 1 min using a Bead Ruptor 12 (OMNI International, 19-050A). Then, they were placed on ice for 1 min, for a total of four repetitions for complete tissue lysis. RNA was then extracted following the Direct-zol RNA Miniprep kit protocol, with DNase I treatment (Zymo Research, R2070). RNA concentration was measured using a Nanodrop One spectrophotometer (Thermo Fisher Scientific, ND-ONE-W). RNA integrity was assessed by measuring the RNA integrity number using capillary electrophoresis (Agilent, M5310AA) paired with the RNA kit (Agilent, DNF-471-0500). Samples with an RQN > 8 were further processed. RNA-seq libraries were constructed using the NEBNext Ultra II Directional RNA library prep kit for Illumina, using ribosomal RNA depletion followed by strand-specific RNA-seq preparation. Samples were amplified with PCR for 9–11 cycles and sequenced using the Illumina NextSeq 2000. A total of 300 ng RNA was used as input for library preparation. rRNA was depleted from samples (NEB, E7405), and libraries were prepared for paired-end Illumina sequencing (NEB, E7760L) with the following adjustments to the manufacturer’s protocol: 1:40 adaptor dilution, universal and indexing primers at 1 μM final concentration, and 10 cycles for PCR enrichment of adaptor-ligated DNA. Library quality was assessed via capillary gel electrophoresis (Agilent, M5310AA) paired with the High Sensitivity Next Generation Sequencing Fragment Kit (Agilent, DNF-474-0500). Library concentrations were determined using a Library Quant Kit for Illumina (NEB, E7630L) following the manufacturer’s protocol. Sequencing was performed on the Illumina NextSeq 2000 platform (Illumina, 20038897) using the onboard denature and dilute protocol, paired with P3 reagents for 200 cycles (Illumina, 20040560). Sequencing output was demultiplexed and converted to FASTQ format via Basespace BCL Convert (version 2.3.0). Read mapping, splicing isoform quantitation, and Gene ontology enrichment analysis FASTQ file quality and total number of reads were assessed using FASTQC (version 0.11.9) and datasets with an average read depth >35 million paired-end reads were accepted for this study. FASTQ files were aligned to the GRCm39/mm39 reference genome, which was modified to include the human ATXN8OS coding sequence, using STAR. To compare expression levels of ATXN8 and ATXN8OS , a human reference genome containing ATXN8OS and 10 kb upstream and downstream sequence was indexed using HiSat2 as described before 5 and raw RNA-Seq reads were aligned to this custom reference genome. Fragment per kilobase million (FPKM) of ATXN8 and ATXN8OS was calculated by normalizing the read count mapped to gene-specific regions of ATXN8 and ATXN8OS to the length. The difference was calculated by dividing both FPKM values by ATXN8OS FPKM value. This analysis was done on four biological replicates. Using BAM files as input, PSI values were quantitated using rMATS-turbo (version 4.2.0) 35 to identify dysregulated exons. AS events were considered to be dysregulated according to the FDR (≤0.05) and absolute value of the inclusion level difference (|Δ PSI| ≥ 0.1) when comparing SCA8 mice to NT mice. rMATS raw files are provided in Supplementary Table 1 and Supplementary Table 2. Heatmaps were generated using the ComplexHeatmap (2.10.0) R package. Gene Ontology analysis was performed using Metascape (version v3.5.20230101) 36 with default express analysis. Mass Spectrometry (UHPLC-HRMS/MS) Cerebellum and brainstem protein lysates from mice were quantified and diluted to make a 250 µg/mL solution in RIPA. 50 µL of each sample was transferred to a labelled Eppendorf tube. Metformin calibration curve : Calibration curve was prepared by spiking metformin standard in albumin at levels - 0.25 ng/mL, 1 ng/mL, 10 ng/mL, 25 ng/mL, 50 ng/mL, 100 ng/mL, 200 ng/mL, 400 ng/mL and 800 ng/mL. QCs were prepared at 15 ng/mL, 150 ng/mL and 600 ng/mL levels. Metformin-d6 (1 µL of 500 ng/mL concentration) was added as internal standard to each calibration level, QCs and samples. Extraction was performed by adding 200 µL of acetonitrile to precipitate proteins. The samples were then centrifuged, and the supernatant was transferred to LC vials for analysis by UHPLC-HRMS/MS. Instrumentation : Samples were analyzed on a Thermo Q-Exactive Orbitrap mass spectrometer with Dionex UHPLC by positive heated electrospray ionization. Mass resolution of 35,000 for full scan and 17,500 for MS/MS. Full scan range – m/z 70-1000 and MS/MS – m/z 130.1087 (Metformin) and 136.1463 (Metformin-d6) at NCE 55. Mobile phase A – 95:5 - 10 mM ammonium formate in water with 0.1% formic acid: acetonitrile; Mobile phase B – 95:5 – acetonitrile: water with 0.1% formic acid; Column – Agilent HILIC Plus RRHD 1.8 µm; 2.1 X 150 mm; Flow rate – 0.4 mL/min; Injection volume – 2 µL. DNA fragment analysis PCR for SCA8 mice using 3730xl Analyzer To examine the ATXN8 repeat length, genomic samples from mice cerebellum and brainstem were analyzed by a PCR reaction consisting of 1X Phire reaction buffer, 0.5 mM dNTPs, forward primer (0.5 μM), reverse primer (0.5 μM), DNA 20-50 ng/reaction, 1U Phire Hot Start II DNA polymerase (Thermo Fisher). PCR cycling conditions were 98 °C for 3min, followed by 30cycles of 98 °C for 20 s, 62 °C for 20 s, 72 °C for 1 min 15 s and a final extension at 72 °C for 3 min. Primers sequences are SCA8-1F-FAM: 56-FAM/TTTGAGAAAGGCTTGTGAGGA, SCA8-1R: TCTGTTGGCTGAAGCCCTAT. FAM-labeled PCR products were mixed with GeneScan 1200LIZ dye Size Standard (Applied Biosystems, 437995), analyzed on an ABI3730xl DNA analyzer (Applied Biosystems) and the data was analyzed using GeneMarkers software (version 1.75, SoftGenetics). Statistical analysis Sample sizes needed for behavioral, histological and biochemical experiments, were based on previously published papers using this model (1, 5) (e.g. n>14 animals / group for RAN proteins staining comparisons; n>14 animals/group for DigiGait, rotarod and open field analyses; n>3 animals/group for neuroinflammation comparison; n=4 animal/group for RNAseq, splicing and transcript analyses). For quantification of polySer and polyGln aggregates, we use ImageJ scripts/macros explicitly designed for each protein. For GFAP and Iba1 we used IHC profiler 70 to quantify the total positive signal and skeleton plugin was use to quantify specifically Iba1 32 . Differences between groups were determined by a multiple comparisons ANOVA test in GraphPad software. Alternative Splicing statistical tests comparing WT and SCA8 and treated SCA8 were performed concurrently with rMATS-turbo with default settings. Data availability All data are available in the main text or the supplementary materials. All RNA-seq data will be available in GEO. Download figure Open in new tab Supp. Fig. 1: Metformin treatment improves behavioral performance of SCA8 mice. ( A ) Quantification of latency to fall of 8-week-old mice (pre-disease) treated with or without metformin. B, C ) Quantification of brake ( B ), step angle ( C ) performance of 16-week-old mice (pre-disease) as examples of two parameters that are impaired in SCA8 mice after onset of the disease. ( D ) Fractions of total parameters of Digigait forelimb behavioral test that are significantly different in SCA8-NT. The graph represents p-value comparisons of 14 DigiGait parameters among SCA8-NT and SCA8-SCA8met cohorts. ( E ) Fractions of total parameters of Digigait hindlimb behavioral test that are significantly different in SCA8-NT. The graph represents p-value comparisons of 11 DigiGait parameters among SCA8-NT or SCA8-SCA8met cohorts. ( F ) Fractions of total parameters of open field behavioral test that are significantly different in SCA8-NT. The graph represents p-value comparisons of 11 DigiGait parameters among SCA8-NT or SCA8-SCA8met cohorts. In all the graphs, yellow boxes define regions of significance compared to SCA8-NT and SCA8-SCA8met. Red datapoints indicate parameters with p ≤ 0.05 that are rescued and so within the yellow box. Black datapoints define parameters that are significantly different in SCA8-NT but are not rescued by metformin (n ≥ 14/group). Download figure Open in new tab Supp. Fig. 2: Metformin treatment reduced RAN protein aggregate total area in SCA8 mice brain. A ) Quantification of polySer shows reduced polySer aggregate total area in the brainstem of SCA8 mice treated with metformin. B ) Representative picture of automated quantification for polySer comparing SCA8 with NT mice. C ) Quantification of polyGln shows reduced polyGln aggregate total area in the brainstem of SCA8 mice treated with metformin. D ) Representative picture of automated quantification for polyGln comparing SCA8 with NT mice. E ) Metformin levels measured by mass spectrometry in the brainstem. F ) Metformin levels measured by mass spectrometry in the cerebellum. Error bars = SEM, n>3 mice/group. Statistical one-way ANOVA test **** p<0.0001, *** p<0.001, * p<0.05 Download figure Open in new tab Supp. Fig. 3: Metformin treatment reduced microglia activation in SCA8 mice. A ) Representative IHC panel showing reactive astrogliosis (GFAP) and activated microglia (Iba1) in the regions with prominent polyGln and polySer accumulation. ( B ) Representation of skeleton 3D analysis using ImageJ plugin. ( C-E ) Quantification outcomes of skeleton 3D analysis showing total number of skeletons ( C ), Total number of Iba1 branches ( D ), and average length of Iba1 branches ( E ). Error bars = SEM, n>3 mice/group. Statistical one-way ANOVA test, ** p<0.01, * p<0.05. Download figure Open in new tab Supp. Fig. 4: Dysregulation of alternative splicing in the SCA8 mouse model. A ) Significantly mis-spliced skipped exons (SE) retained introns (RI), mutually exclusive exons (MXE), alternative 5’ splice sites (A5SS), and alternative 3’ splice sites (A3SS) events in SCA8 versus NT mice, number of each event shown on bar, |ΔPSI| > 0.1, FDR 0.1, FDR < 0.05) and are highlighted in purple. Mis-splicing events in light pink represent the example reported in the following figure. C-E ) Percent spliced in (PSI) for select mis-splicing events in NT and SCA8 mice. F ) Comprehensive heatmap showing PSI values for all 554 significantly dysregulated events in SCA8 compared to NT and their trend in SCA8 metformin-treated mice (samples in which there are fewer than 5 reads for a given event are represented as a grey area in the heatmap). References and Notes 1. ↵ Koob , M. D. et al. An untranslated CTG expansion causes a novel form of spinocerebellar ataxia (SCA8) . Nat Genet 21 , 379 – 384 , doi: 10.1038/7710 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 2. ↵ Moseley , M. L. et al. Bidirectional expression of CUG and CAG expansion transcripts and intranuclear polyglutamine inclusions in spinocerebellar ataxia type 8 . Nat Genet 38 , 758 – 769 , doi: 10.1038/ng1827 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 3. ↵ Daughters , R. S. et al. 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Metformin decreases RAN proteins, rescues splicing abnormalities and improves behavioral phenotypes in SCA8 BAC mice Lisa E.L Romano , Setsuki Tsukagoshi , Emily E. Davey-Osuch , Ramadan Ajredini , Kamat Manasi , Tala V.R. Ortiz , Eduardo Rijos , Nathan J. Bourgon , S. Elaine Ames , Timothy J. Garrett , John D. Cleary , Eric T. Wang , Laura P.W. Ranum bioRxiv 2025.08.21.671563; doi: https://doi.org/10.1101/2025.08.21.671563 Share This Article: Copy Citation Tools Metformin decreases RAN proteins, rescues splicing abnormalities and improves behavioral phenotypes in SCA8 BAC mice Lisa E.L Romano , Setsuki Tsukagoshi , Emily E. Davey-Osuch , Ramadan Ajredini , Kamat Manasi , Tala V.R. Ortiz , Eduardo Rijos , Nathan J. Bourgon , S. Elaine Ames , Timothy J. Garrett , John D. Cleary , Eric T. Wang , Laura P.W. 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