Investigating the Role of Exosome-associated Human Endogenous Retroviruses as Biomarkers in Motor Neuron Disease

Investigating the Role of Exosome-associated Human Endogenous Retroviruses as Biomarkers in Motor Neuron Disease | 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 Investigating the Role of Exosome-associated Human Endogenous Retroviruses as Biomarkers in Motor Neuron Disease View ORCID Profile Triparna Roy , View ORCID Profile Misha Ramesh , Nurul Aisha Ahmad Nizam , View ORCID Profile Ammar Al-Chalabi , View ORCID Profile Alfredo Iacoangeli , Ahmad Al Khleifat doi: https://doi.org/10.1101/2025.11.07.686090 Triparna Roy 1 Institute of Psychiatry, Psychology & Neuroscience, King’s College London , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Triparna Roy For correspondence: ahmad.al_khleifat{at}kcl.ac.uk triparna.roy{at}kcl.ac.uk Misha Ramesh 1 Institute of Psychiatry, Psychology & Neuroscience, King’s College London , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Misha Ramesh Nurul Aisha Ahmad Nizam 1 Institute of Psychiatry, Psychology & Neuroscience, King’s College London , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ammar Al-Chalabi 1 Institute of Psychiatry, Psychology & Neuroscience, King’s College London , United Kingdom 2 King’s College Hospital , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ammar Al-Chalabi Alfredo Iacoangeli 1 Institute of Psychiatry, Psychology & Neuroscience, King’s College London , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alfredo Iacoangeli Ahmad Al Khleifat 1 Institute of Psychiatry, Psychology & Neuroscience, King’s College London , United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ahmad.al_khleifat{at}kcl.ac.uk triparna.roy{at}kcl.ac.uk Abstract Full Text Info/History Metrics Preview PDF Abstract Human endogenous retrovirus-K (HERV-K) reactivation is increasingly implicated in amyotrophic lateral sclerosis (ALS), with ongoing clinical trials investigating antiretroviral therapies. However, there is limited understanding of how HERV-K is trafficked in peripheral biofluids, and the role of exosomes nano-sized extracellular vesicles in this process remains largely unexplored. Exosomes offer a stable and cell-specific cargo reservoir that may reflect central pathogenic processes and serve as a minimally invasive biomarker source. In this study, we isolated plasma-derived exosomes from ALS patients (n = 21) and healthy controls (n = 16), and quantified exosomal HERV-K gag , env , and pol transcript levels using SYBR Green qPCR with RNase treatment and normalization to both traditional and exosome-enriched reference genes. HERV-K pol expression was significantly elevated in ALS, with fold-changes ranging from 1.59 to 1.85 ( P = 0.037–0.051). env and gag also showed increased expression, though with greater variability. Normalization to the exosome-specific gene SOD2 provided the most consistent signal. A trend toward higher pol expression in bulbar-onset ALS was observed. These findings suggest that exosomal HERV-K transcripts, particularly pol , could serve as accessible biomarkers for patient stratification and treatment monitoring in HERV-K–targeted ALS trials. This work establishes proof-of-concept for using exosomal cargo to track endogenous retroviral activity in neurodegeneration and supports further investigation of liquid biopsy approaches in ALS precision medicine. 1. Introduction Motor Neuron Disease (MND), also known as amyotrophic lateral sclerosis (ALS), is a devastating and progressive neurodegenerative condition that primarily affects the motor neurons responsible for voluntary muscle control [ 1 , 2 ]. The degeneration of both upper and lower motor neurons results in muscle weakness, atrophy, and eventually respiratory failure, which is the most common cause of death among patients [ 1 , 3 , 4 ]. Despite significant advances in our understanding of the disease’s genetics and pathophysiology, there is no curative treatment available. Current therapeutic strategies focus largely on symptom management and modest slowing of disease progression, highlighting the urgent need for novel diagnostic and prognostic biomarkers, as well as therapeutic targets [ 5 – 8 ]. The genetic underpinnings of MND are increasingly recognized as complex and multifactorial. Familial forms of ALS are linked to mutations in genes such as SOD1, TARDBP, FUS , and C9orf72 , with the latter being the most common cause of both familial and sporadic ALS in some populations [ 9 – 15 ]. The C9orf72 repeat expansion, in particular, has been associated with both ALS and frontotemporal dementia, reflecting a shared neuropathological spectrum [ 16 – 19 ]. Alongside these genetic insights, a growing body of research has focused on dysregulated RNA metabolism, protein aggregation, oxidative stress, and mitochondrial dysfunction as central mechanisms in ALS pathogenesis [ 4 , 11 , 20 ]. In recent years, extracellular vesicles, especially exosomes, have emerged as promising tools for biomarker discovery in neurodegenerative disorders. Exosomes are nano-sized vesicles (30–150 nm) secreted by cells into biological fluids and carry a rich cargo of proteins, RNAs, lipids, and other biomolecules that mirror the physiological or pathological state of their cells of origin [ 21 – 23 ]. Their stability in biofluids and role in cell-to-cell communication position them as ideal candidates for minimally invasive biomarkers in diseases like ALS [ 24 ]. Numerous studies have highlighted altered exosome content in neurodegenerative conditions such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, suggesting a functional role in disease propagation as well as biomarker potential [ 2 , 25 , 26 ]. The application of exosome research to ALS has gained traction with several reports indicating changes in the concentration, size, and molecular cargo of exosomes in ALS patients compared to controls. For instance, exosomal neurofilament light chain and TAR DNA-binding protein 43 (TDP-43) have shown promise as biomarkers of disease progression [ 14 , 23 , 27 , 28 ]. Yet, significant variability in methods for exosome isolation, characterization, and downstream analysis poses challenges to reproducibility and interpretation. Standardized protocols and robust validation across independent cohorts remain a major hurdle in this field [ 23 ]. Another emerging area of interest in ALS research is the role of transposable elements, particularly Human Endogenous Retroviruses (HERVs). HERVs are remnants of ancient viral infections integrated into the human genome, comprising approximately 8% of human DNA [ 29 – 32 ]. Among these, HERV-K is one of the most transcriptionally active families and has been implicated in various neurodegenerative and autoimmune diseases [ 33 ]. In the context of ALS, elevated expression of HERV-K, particularly its pol and env genes, has been documented in both neuronal tissue and peripheral biofluids [ 29 , 34 – 36 ]. One seminal study by Douville et al. demonstrated increased HERV-K expression in the brain tissue of ALS patients, with the env protein showing neurotoxicity in experimental models [ 37 ]. Moreover, HERV-K expression is thought to be modulated by inflammatory and oxidative stress signals, both of which are hallmarks of ALS pathogenesis [ 8 , 31 , 38 ]. Recent studies have further elucidated the role of HERV-K in ALS. For instance, a study by Phan et al. found that HERV-K env levels are significantly elevated in the serum of ALS patients compared to controls, with a strong association to TDP-43 pathology [ 39 ]. Another study demonstrated that peptides derived from HERV-K env elicited a significantly increased antibody response in ALS patients, suggesting a potential role in immune modulation [ 40 ]. Similarly, a study highlighted the presence of elevated HERV-K env transcripts in neuronal extracellular vesicles extracted from plasma, proposing their utility as noninvasive biomarkers for ALS severity [ 33 ]. Additionally, Liu et al. reported that elevated levels of HERV-K Env peptides in sera and cerebrospinal fluid of ALS patients correlated with poor functional performance, suggesting that HERV-K Env contributes to disease progression [ 41 ]. Furthermore, research by Moreno-Martínez et al. confirmed HERV-K overexpression in the ALS brain, highlighting its potential as an accessory diagnostic marker and its interplay with neuroinflammation [ 42 ]. Despite these intriguing findings, the functional significance and mechanisms of HERV-K activation in ALS remain incompletely understood and somewhat controversial. Some researchers argue that HERV-K may play a causal role in neurodegeneration by promoting neuroinflammation and genomic instability, while others suggest that its expression is a secondary consequence of neuronal stress or damage [ 38 , 40 , 43 ]. The reliability of HERV-K as a biomarker is also debated, given the presence of numerous defective and non-functional copies across the genome, and the challenge of distinguishing biologically active variants from background expression [ 36 , 44 ]. The potential interplay between exosomes and HERVs introduces another layer of complexity and opportunity. Exosomes can transport viral elements, including HERV-derived RNA and proteins, and may facilitate their spread between cells [ 45 ]. This raises the possibility that exosomes could act as carriers of HERV-K in ALS, thereby contributing to disease propagation or serving as accessible reservoirs for biomarker discovery. However, evidence for this mechanism in ALS is currently limited, and systematic investigations are lacking. Another study addresses several gaps in our understanding of the exosome-HERV axis in ALS. First, it seeks to characterize exosome size and concentration in plasma samples from ALS patients compared to healthy controls using nanoparticle tracking analysis (NTA) [ 46 ]. Second, it evaluates the quality and yield of RNA and cDNA extracted from these exosomes, with and without RNase treatment to distinguish internal from external nucleic acid sources. Another study assesses the expression of HERV-K (specifically its env , gag , and pol regions) using semi-quantitative PCR and gel electrophoresis and explores the impact of different reference genes on data normalization [ 47 ]. The aim of this study is to evaluate HERV-K transcript levels in plasma-derived exosomes from ALS patients compared to healthy controls and to assess the potential of exosomal HERV-K as a candidate biomarker for further investigation. 2. Materials and Methods 2.1. Participant Recruitment and Sample Processing Plasma samples were obtained from individuals with amyotrophic lateral sclerosis (n = 21) and healthy volunteers (n = 16) through the King’s MND Care and Research Centre. Sample collection was coordinated by the King’s College London MND Biobank. Individuals with ALS were diagnosed according to the revised El Escorial criteria, were aged 18 years or older, and had provided written informed consent. Inclusion required availability of both plasma samples and relevant clinical metadata. None of the ALS samples showed evidence of coexisting neurodegenerative conditions, active systemic disease (including infection or cancer), autoimmune disorders, pregnancy, or incomplete clinical annotation. These conditions were specifically excluded due to reported associations between HERV-K activation which could confound interpretation of HERV-K–related findings. Healthy volunteers were age- and sex-matched, had no history of neurological, autoimmune, or malignant disease (including cancer), and had provided written informed consent. Plasma samples were collected in EDTA tubes, centrifuged at 2000 × g for 10 min at 4°C, and aliquoted into 1.5 mL cryotubes. Samples were stored at –80°C. 2.2. Experimental Workflow This study was designed to enable the isolation, characterization, and molecular profiling of plasma-derived exosomes, followed by targeted quantification of HERV-K transcripts. The process incorporates sequential ultracentrifugation for exosome isolation, nanoparticle tracking for vesicle characterization, RNase treatment to ensure RNA origin specificity, and qPCR with multiple normalization strategies to ensure robust and reproducible gene expression analysis. 2.2.1. Exosome Isolation 300 uL of plasma was centrifuged at 10,000 × g for 15 min at 4°C to remove cellular debris. Supernatants were mixed 1:1 with PBS, then ultracentrifuged twice at 130,000 × g for 90 min at 4°C using a Beckman Optima MAX-XP ultracentrifuge and TLA-55 rotor. Pellets were washed in PBS and ultracentrifuged again under identical conditions. Final exosome pellets were resuspended in 300 µL PBS and stored at –80°C. 2.2.2. Exosome Characterization Exosome size distribution and concentration were assessed using the NanoSight LM10 system (Malvern Panalytical), which utilizes NTA. Each sample was diluted to achieve a concentration of 20– 80 particles per frame and analyzed in triplicate. To ensure consistency and reproducibility across samples, measurements were performed at room temperature (22–23°C) using filtered PBS as the solvent medium. The instrument settings were standardized throughout: screen gain was set to 5, camera level ranged from 11 to 13, and the detection threshold was set at 5. The analysis was conducted at a frame rate of 20–80 frames per second (FPS), assuming water-like viscosity for the medium. 2.2.3. Exosome Lysis and Protein Quantification Lysis was performed using 5× RIPA buffer with protease/phosphatase inhibitors on ice for 20 min. Lysates were centrifuged at 16,000 × g for 5 min, and supernatants collected. Protein concentration was measured using a BCA assay. 2.2.4. RNA Extraction and cDNA Synthesis RNA was extracted using the RNeasy Plus Micro Kit (QIAGEN), which includes gDNA eliminator columns to remove genomic DNA. Additionally, samples underwent DNase digestion for 20 minutes to further eliminate any residual genomic DNA. RNA purity and yield were assessed using NanoDrop spectrophotometry. Reverse transcription was performed using the Quantitect Reverse Transcription Kit with random hexamers and HERV-K pol -specific primers. Primer design targeted HERV-K cDNA specifically, and the absence of genomic DNA contamination was confirmed by melt curve analysis and 2% agarose gel electrophoresis. 2.2.5. RNase Treatment Control To confirm the intravesicular origin of RNA and minimize contamination from external plasma RNA, RNase treatment was applied to a subset of isolated exosome samples. This enzymatic digestion selectively degrades RNA external to vesicles while preserving encapsulated RNA within intact exosomes. RNase treatment followed established protocols commonly used in exosome studies to improve specificity of downstream RNA analyses. 2.2.6. Quantitative PCR (qPCR) qPCR was performed using PowerUp SYBR Green Master Mix (Thermo Fisher Scientific) and 5 µL cDNA on a QuantStudio 7 (Applied Biosystems). Eight primer sets targeted the HERV-K pol region and one set of each HERV-K gag and env were used to check consistency of gene expression. Housekeeping genes ( GAPDH , ACTB , YWHAZ , SDHA ) and the exosome-specific gene SOD2 exon 3 were used for normalization. Relative gene expression was calculated using the 2 −ΔΔCt method, with control group samples set to a value of 1 for baseline normalization [ 48 ]. To ensure accurate normalization, multiple validated housekeeping genes were employed, allowing robust quantification across samples [ 49 – 52 ]. Although the control group is set as the reference, minor deviations may occur due to biological variability among individual samples. Individual data points were retained to illustrate this variability. ΔCt values were obtained by subtracting the Ct of the housekeeping gene from the Ct of the target gene, and ΔΔCt values were calculated relative to the mean ΔCt of the control group. Agarose gel electrophoresis (2%) was used post-qPCR to visualize amplified products. Melt curve analysis was conducted to confirm specificity. 2.3. Statistical Analysis All statistical analyses were performed using non-parametric methods due to the non-normal distribution of qPCR expression data, as confirmed by Shapiro–Wilk and Kolmogorov–Smirnov tests. Homogeneity of variances across groups was evaluated using Levene’s and Bartlett’s tests. For pairwise comparisons between individuals with ALS patients (n = 21) and healthy controls (n = 16), Mann– Whitney U tests were applied to exosome concentration levels and individual gene expression levels. Kruskal–Wallis tests were used for comparisons across multiple gene targets within the housekeeping-normalized datasets. Site of onset analyses between spinal onset ALS (n = 16) and bulbar onset ALS (n = 5) were conducted using Mann–Whitney U tests, acknowledging unequal sample sizes. HERV-K pol expression comparisons between spinal- and bulbar-onset ALS groups were performed using the same normalization strategy as for the primary case-control analysis. Expression levels were normalized to five housekeeping genes (GAPDH, ACTB, SDHA, YWHAZ, SOD2). Group comparisons were conducted using Mann–Whitney U tests. In addition, multivariate non-parametric approaches, including permutational multivariate analysis of variance (PERMANOVA) and principal component analysis (PCA), were employed to evaluate global gene expression patterns. To control for multiple testing and limit false discovery, Benjamini–Hochberg false discovery rate (FDR) corrections were applied. All statistical analyses were conducted using R software (version 4.3.1), with significance defined as P < 0.05. 3. Results 3.1. Exosome Characterization and Concentration Profiles 3.1.1. Size Distribution and Peak Concentration NTA showed that plasma-derived extracellular vesicles from ALS patients had a broader size distribution, and a rightward skew compared to healthy controls. Both groups displayed peak particle concentrations around 100 nm in diameter. However, the median particle size in ALS samples was 132 nm (IQR: 118–160 nm), compared to 104 nm (IQR: 92–118 nm) in controls, indicating a shift toward larger vesicles in the patient group. Additionally, ALS samples exhibited higher peak concentrations, with an average peak concentration of approximately 1.05 × 10¹⁰ particles/mL, compared to 0.82 × 10¹⁰ particles/mL in controls. The difference in overall EV concentration within the 100–300 nm range was statistically significant ( P = 0.02, Mann–Whitney U test). Concentrations of EVs in the 100–300 nm subpopulation were also significantly higher in ALS patients (median = 4.3 × 10⁹ particles/mL) compared to controls (median = 2.1 × 10⁹ particles/mL; P = 0.018). ( Figure 2 ). Download figure Open in new tab Figure 1. Schematic diagram of exosomes and HERVs as Biomarkers in ALS: Linking Cellular Communication to Neurodegeneration. Created in https://BioRender.com . Download figure Open in new tab Figure 2. NTA of plasma-derived exosomes in ALS patients and healthy controls. Controls showed a sharp peak at ∼100 nm, while ALS samples exhibited a broader distribution from 80–160 nm and extended toward 300 nm. Median EV size was larger in ALS (132 nm, IQR: 118–160 nm) than controls (104 nm, IQR: 92–118 nm), with significantly higher concentrations within the 100–300 nm size range ( P = 0.018) and overall concentration differences ( P = 0.02). 3.2 Protein quantification using BCA assay Protein quantification of the exosome preparations using the BCA assay confirmed the presence of measurable protein content in all samples, indicative of successful vesicle isolation. Mean protein quantification by BCA assay was higher in ALS patients (185.4 µg/mL) compared to healthy controls (162.8 µg/mL), consistent with the increased particle counts observed in NTA measurements. 3.3. RNA and cDNA Quality Assessment 3.3.1. RNase Treatment Effects Samples treated with RNase showed slightly higher RNA yields and improved purity metrics (A260/A280 and A260/A230 ratios). DNA yields increased post-RNase treatment, indicating successful removal of surface-bound external RNA and a clearer representation of encapsulated nucleic acids. 3.3.2. cDNA Synthesis cDNA concentrations were measured and are summarized in Table 4 , separated by ALS patients and control groups. There were no significant differences in cDNA yield between ALS and control samples. RNase-treated samples exhibited improved nucleic acid quality, as indicated by more consistent A260/A280 ratios (>1.8). The DNA concentrations reported in Table 4 represent the total nucleic acid yield measured after reverse transcription to cDNA and before downstream qPCR analysis. This measurement served as a quality control step to assess the efficiency of cDNA synthesis. All RNA samples used for cDNA synthesis were treated with DNase to eliminate genomic DNA contamination prior to reverse transcription, ensuring that subsequent analyses reflected cDNA derived from RNA templates, supporting the utility of RNase treatment in reducing external RNA contamination. View this table: View inline View popup Download powerpoint Table 1. Primer sequences for housekeeping genes and HERV-K gag, env and pol regions for exosomal cDNA expansion. This table lists the forward and reverse primer sequences employed in the quantitative PCR analysis of the exosome-derived cDNA. Housekeeping gene primers (e.g. GAPDH, ACTB, YWHAZ, SDHA ) were used for normalization, while multiple primers targeting conserved regions of the HERV-K pol gene were designed to increase assay sensitivity and specificity for detecting retroviral RNA in MND patient samples. View this table: View inline View popup Download powerpoint Table 2. Demographic and clinical characteristics of participants included in the study. The table summarizes the number of ALS patients and healthy controls, including sex distribution, age range, median age, and clinical onset subtypes among ALS cases. PLS cases are listed separately from classical limb-or bulbar-onset ALS. View this table: View inline View popup Download powerpoint Table 3. Median size and concentration of exosomes in ALS patients vs. Controls. View this table: View inline View popup Download powerpoint Table 4. Summary of nucleic acid yields and purity ratios. View this table: View inline View popup Download powerpoint Table 5: Summary of p-values for each HERV-K region per housekeeping gene. 3.4. HERV-K Expression Analysis Normality of residuals was assessed using both the Shapiro–Wilk test ( P < 0.05 for all genes) and the Kolmogorov–Smirnov test ( P < 0.05), indicating non-normal distribution of expression values. Homogeneity of variances between ALS and control groups was assessed using Levene’s test and Bartlett’s test, both of which indicated unequal variances for at least one normalization method (Levene’s P = 0.041; Bartlett’s P = 0.037). Therefore, non-parametric tests were used for group comparisons. 3.4.1. HERV-K gag Expression Relative expression of HERV-K gag normalized to housekeeping genes was significantly elevated in patient samples ( Figure 3 ). The highest discriminatory power was noted using ACTB and SOD2 . Download figure Open in new tab Figure 3. Bar charts of HERV-K gag expression normalized to GAPDH, SDHA, ACTB, YWHAZ , and SOD2 . Relative expression levels were calculated using the 2 -ΔΔCt method, with the control group set as the reference. Bars represent the mean ± standard deviation of individual biological replicates, which may result in slight deviations from exactly 1 for the control group due to biological variability. Normalization of HERV-K gag expression to various reference genes revealed consistent elevation in ALS patients compared to controls. When normalized to GAPDH, the median fold-change in ALS samples was 1.84 (95% CI: 1.27–2.41; P = 0.026, Mann–Whitney U test). Using ACTB, expression was 1.76-fold higher in ALS (95% CI: 1.15–2.37; P = 0.039). Normalization to YWHAZ showed a 1.69-fold increase (95% CI: 1.10–2.28; P = 0.042), and SOD2 normalization revealed a 2.03-fold difference between groups (95% CI: 1.35–2.70; P = 0.021). Normalization to SDHA yielded a 1.41-fold increase in ALS samples (95% CI: 0.95–1.87), (P = 0.053). A Kruskal–Wallis test showed a significant effect of reference gene choice on expression distribution (H(4) = 10.72, P = 0.031). 3.4.2. HERV-K env Expression The env region was also more expressed in ALS samples as compared to controls ( Figure 4 ). Download figure Open in new tab Figure 4. Bar charts of HERV-K env expression normalized to GAPDH, SDHA, ACTB, YWHAZ , and SOD2 . Relative expression levels were calculated using the 2 -ΔΔCt method, with the control group set as the reference. Bars represent the mean ± standard deviation of individual biological replicates, which may result in slight deviations from exactly 1 for the control group due to biological variability. Normalization of HERV-K env expression to different reference genes revealed variable degrees of group separation. When normalized to GAPDH , the median fold-change in ALS patients was 1.78 (95% CI: 1.22–2.34; P = 0.043, Mann–Whitney U test). Using ACTB , expression was 1.91-fold higher in ALS (95% CI: 1.35–2.49; P = 0.031). SOD2 normalization showed a 1.96-fold increase in ALS samples (95% CI: 1.12–2.61; P = 0.047), while YWHAZ showed a 1.55-fold increase (95% CI: 0.99–2.12; P = 0.050). SDHA normalization yielded a 1.32-fold increase, though not supported by the data ( P = 0.039). To compare expression differences across all housekeeping genes simultaneously, a Kruskal–Wallis test was performed, which revealed a significant effect of normalization method on the distribution of HERV-K env expression (H(4) = 10.24, P = 0.037). 3.4.3. HERV-K pol Expression HERV-K pol displayed the most consistent upregulation across all eight primer sets and housekeeping genes ( Figure 5 ). Download figure Open in new tab Figure 5. Bar charts comparing relative HERV-K pol expression between ALS and control samples, normalized to the five housekeeping genes. Relative expression levels were calculated using the 2 -ΔΔCt method, with the control group set as the reference. Bars represent the mean ± standard deviation of individual biological replicates, which may result in slight deviations from exactly 1 for the control group due to biological variability. Normalization of HERV-K pol expression to multiple housekeeping genes revealed elevated expression in ALS patients compared to controls. When normalized to GAPDH , the median fold-change was 1.72 (95% CI: 1.10–2.34; P = 0.044, Mann–Whitney U test). Using SDHA , expression increased 1.85-fold (95% CI: 1.15–2.56; P = 0.037), while normalization to ACTB showed a 1.78-fold increase (95% CI: 1.12–2.44; P = 0.041). Normalization to SOD2 yielded a 1.66-fold increase (95% CI: 1.01–2.31; P = 0.049), and YWHAZ normalization showed a 1.59-fold increase with P = 0.051. Kruskal–Wallis test indicated a significant effect of normalization method on HERV-K pol expression distribution (H(4) = 9.84, P = 0.043). 3.5. Expression by ALS Onset Type – Bulbar vs. Spinal Onset 3.5.1. HERV-K gag Expression in Bulbar vs. Spinal Onset To assess whether HERV-K gag expression differs by clinical phenotype, patients were stratified into bulbar-onset (n = 5) and spinal-onset (n = 16) groups. Normalization was performed against all five housekeeping genes, consistent with the primary case-control analyses. Comparisons using the Mann–Whitney U test revealed a modest, non-significant elevation of gag expression in bulbar-onset patients ( P = 0.14) ( Figure 6 ). After adjustment for age and sex in a multiple linear regression model, the association between bulbar onset and higher gag expression remained positive but was not significant ( β = 0.22, 95% CI: –0.31 to 0.75, P = 0.18). Visualization of individual data points showed considerable overlap between groups. Download figure Open in new tab Figure 6: Comparison of HERV-K gag expression between spinal-onset and bulbar-onset patients. 3.5.2. HERV-K env Expression in Bulbar vs. Spinal Onset Expression of HERV-K env was also examined by clinical phenotype, normalized to five housekeeping genes. Bulbar-onset patients showed a trend toward higher expression compared to spinal-onset cases. The Mann–Whitney U test yielded a borderline non-significant result ( P = 0.095) ( Figure 7 ). Multiple linear regression adjusting for age and sex confirmed a positive association between bulbar onset and env expression ( β = 0.33, 95% CI: –0.14 to 0.80, P = 0.11), though it did not reach statistical significance. Mean expression levels were consistently higher in bulbar patients across replicates, with greater variability visible in individual data points. Download figure Open in new tab Figure 7: Comparison of HERV-K env expression between spinal-onset and bulbar-onset patients. 3.5.3. HERV-K pol Expression in Bulbar vs. Spinal Onset To assess whether HERV-K pol expression differs by clinical phenotype, patients were stratified into bulbar-onset (n = 5) and spinal-onset (n = 16) groups. Comparisons were normalized to all five housekeeping genes. Statistical significance was assessed using Mann–Whitney U tests, which revealed a trend toward higher pol expression in the bulbar-onset group ( P = 0.082). Mean expression levels were consistently elevated across technical replicates in bulbar-onset cases, and visualization of individual data points revealed greater variability in this group, suggesting underlying biological heterogeneity ( Figure 8 ). After adjustment for age and sex, multiple linear regression showed the association between bulbar-onset and higher pol expression to be positive but did not reach statistical significance ( β = 0.41, 95% CI: –0.07 to 0.89, P = 0.091). Download figure Open in new tab Figure 8. Comparison of HERV-K pol expression between spinal-onset and bulbar-onset patients. To verify the specificity of qPCR amplification products, 2% agarose gel electrophoresis was performed post-amplification on all cDNA samples. Discrete bands corresponding to the expected amplicon sizes were observed for gag , env , and pol , with no non-specific bands or primer-dimer artifacts in negative controls. Notably, band intensity was visually greater in ALS samples, particularly for pol . 4. Discussion The aim of this study was to assess whether HERV-K expressions within plasma-derived exosomes differ between ALS patients and healthy controls, and whether such differences could support HERV-K as a potential biomarker or therapeutic target in ALS. The data generated through NTA, qPCR amplification, and normalization against both traditional and exosome-specific housekeeping genes offer new insights into exosomal biology in ALS and reinforce the relevance of retroviral reactivation in disease pathogenesis. Initial NTA analysis indicated that while both control and ALS samples exhibited peak exosome concentrations near 100 nm, typical of canonical exosomes, the distribution curve in ALS samples was broader and flatter. In controls, the size distribution was tightly clustered, with a sharp peak at ∼100 nm and a steep drop-off thereafter. In contrast, ALS-derived exosomes showed elevated concentrations extending into the 100–300 nm range, suggesting a greater abundance of larger extracellular vesicles in ALS. These findings align with prior work reporting broader exosome size profiles in neurodegenerative conditions, which may reflect altered exosome biogenesis or impaired vesicle clearance [ 28 , 53 ]. Larger EVs in ALS may originate from diseased astrocytes or microglia undergoing stress-induced vesiculation [ 27 ]. The enrichment of 100–300 nm vesicles could be indicative of pathologically altered multivesicular body (MVB) dynamics or heightened vesicle fusion with the plasma membrane. Interestingly, while total exosome concentration was not significantly different, the qualitative shift in vesicle size distribution may point toward a disease-specific EV phenotype, adding granularity to previous claims that EV number alone may not be a sufficient biomarker [ 28 , 54 ]. RNase treatment served as a critical step in validating the specificity of exosomal RNA. Treated samples showed a slight increase in total RNA yield and a corresponding increase in cDNA concentration post-extraction, consistent with removal of external, plasma-circulating RNA and the preservation of intravesicular nucleic acids. RNase treatment of exosome preparations provided validation that the RNA detected was predominantly intravesicular, reducing confounding effects from extracellular plasma RNA contamination. However, because RNase treatment was applied only to a subset of samples, variability in RNA purity and yield may have influenced some results. This limitation highlights the need for consistent and standardized approaches in future studies assessing exosomal RNA. This observation corroborates findings from other studies where RNase treatment improved signal-to-noise ratio for vesicle-contained RNA while reducing contamination from free-floating RNA fragments [ 20 ]. Interestingly, cDNA concentrations were largely comparable between ALS and controls, with improved purity metrics (A260/A280) noted in RNase-treated samples. This suggests that exosome encapsulation protects retroviral transcripts from degradation, and that RNase pre-treatment may enhance biomarker fidelity for downstream qPCR. However, this step also introduced some inter-sample variability, possibly reflecting inconsistencies in exosome membrane integrity or residual RNase activity, which should be addressed through protocol standardization in future studies. qPCR analysis targeting gag, env, and pol regions of HERV-K revealed significant upregulation in ALS-derived exosomes compared to healthy controls. This aligns with historical reports implicating retroviral reactivation in ALS, particularly for HERV-K elements, which are among the few retroviral families with intact open reading frames capable of producing functional viral-like particles [ 55 , 56 ]. Housekeeping gene selection represents a critical variable in qPCR-based biomarker studies, as variability in reference gene stability can directly impact relative expression values and, consequently, the interpretation of disease-associated changes. This is particularly relevant for exosomal transcriptomics, where RNA content differs markedly from cellular RNA, and traditional tissue-agnostic housekeeping genes (e.g., GAPDH, ACTB ) may not reflect compartment-specific stability. In this study, normalization to SOD2 , an exosome-enriched mitochondrial gene, yielded higher discriminatory power for HERV-K env , while results for gag and pol were broadly consistent across reference genes. By employing five different housekeeping genes and reporting all outcomes, we aimed to reduce the risk of bias arising from single-reference normalization and to improve reproducibility. These findings highlight the importance of validating reference genes for the specific biological matrix and disease context under investigation. Relative expression of HERV-K gag normalized to multiple housekeeping genes showed a consistent elevation in ALS patients. Notably, statistical significance was reached for GAPDH ( P = 0.043), ACTB ( P = 0.031), and SOD2 ( P = 0.047), while YWHAZ approached significance ( P = 0.050), and SDHA remained non-significant ( P = 0.099). This suggests that while HERV-K gag is upregulated in ALS, normalization to exosome-specific markers (e.g., SOD2 ) enhances discriminatory sensitivity. This pattern suggests robust activation of the gag region across several endogenous loci. The gag gene encodes the core viral capsid proteins and has previously been implicated in the formation of toxic viral-like particles in neuronal models of ALS [ 43 , 56 ]. Similar upregulation was observed in env expression, which encodes the envelope glycoprotein responsible for host-cell fusion. Significant expression differences were found across GAPDH ( P = 0.026), ACTB ( P = 0.039), YWHAZ ( P = 0.042), and SOD2 ( P = 0.021), with SDHA again showing borderline results ( P = 0.053). The env protein of HERV-K has been shown to elicit neuroinflammatory responses and cytotoxicity in in vitro models of motor neurons, further supporting its pathogenic potential [ 57 ]. The pol region of HERV-K demonstrated the most reproducible upregulation across analyses, as it was targeted by eight independent primer sets and consistently detected across samples. Statistically significant differences in pol expression between ALS patients and controls were observed when normalized to GAPDH ( P = 0.044), SDHA ( P = 0.037), ACTB ( P = 0.041), and SOD2 ( P = 0.049), with borderline significance using YWHAZ ( P = 0.051). This consistency across diverse normalization strategies suggests that pol may offer increased sensitivity for detecting HERV-K dysregulation while mitigating the impact of inter-sample variability. Importantly, the pol region encodes reverse transcriptase and integrase enzymes, which are functionally relevant to retrotransposon activity and potential genomic reintegration events. Prior studies have proposed that pol transcripts may represent intermediate products of disease-associated HERV-K reactivation [ 58 , 59 ]. The robust signal obtained across primer sets and reference genes underscores pol as a promising biomarker candidate for exosome-based liquid biopsy assays in ALS. Normalization to the exosome-specific housekeeping gene SOD2 consistently revealed higher relative HERV-K expression compared to traditional reference genes. Since SOD2 (superoxide dismutase 2), a mitochondrial antioxidant enzyme, is known to be enriched in exosomes derived from both neural and glial cell types [ 60 , 61 ], it more accurately reflects exosomal RNA content than tissue-agnostic housekeeping genes like GAPDH or ACTB . These latter genes may underestimate disease-specific expression changes due to differences between total cellular and exosomal RNA profiles. Thus, using compartment-specific reference controls such as SOD2 improves the sensitivity and specificity of exosome transcriptomic analyses. However, it is important to note that the stability and suitability of SOD2 as a reference gene requires further validation in larger, independent cohorts before it can be adopted as the sole normalization control in exosome transcriptomics. Stratified analysis by clinical onset subtype revealed a trend toward higher HERV-K pol expression in bulbar-onset ALS (n = 5) compared to spinal-onset (n = 16), though this difference did not reach statistical significance ( P = 0.082). Bulbar-onset ALS is associated with faster disease progression, earlier cognitive involvement, and poorer survival outcomes [ 1 , 18 ]. The elevated expression of retroviral elements in this group may reflect a more aggressive or systemic disease biology. However, given the limited sample size, this trend must be interpreted cautiously. Future studies incorporating larger bulbar-onset cohorts and longitudinal data could help elucidate whether HERV-K activity contributes to phenotypic heterogeneity in ALS. The use of ultracentrifugation for exosome isolation, coupled with RNase-based purification and multi-gene normalization strategies, provides a rigorous framework for assessing vesicle-contained transcripts. Unlike previous studies using crude EV preparations or total plasma RNA, this approach offers improved specificity and analytical precision. However, limitations remain. The sample size, though larger than earlier pilot work, still restricts statistical power, particularly for subgroup analyses. RNase treatment introduced some variability in cDNA quality, underscoring the need for protocol harmonization. Additionally, the study focused solely on qPCR-based transcript quantification; future work should validate protein expression using immunoassays or explore transcript heterogeneity via RNA-seq. The findings presented here offer preliminary insights into the feasibility and challenges of this dual biomarker approach. Initial results suggest that exosome concentration is higher in ALS patients, with a broader size distribution and a shift towards larger vesicles. RNase treatment appears to improve RNA purity, although it introduces variability in downstream DNA yields [ 55 , 62 ]. HERV-K expression, particularly of the pol region elevated in ALS patients, and normalization to SOD2 offers more consistent results than traditional housekeeping genes. However, differences in expression across clinical subtypes (bulbar vs. spinal) were not statistically significant, reinforcing the need for further investigation with adequately powered studies. Although ALS patients and controls were age- and sex-matched with comparable median ages, we considered age as a potential confounding factor given its known influence on disease onset, progression, and retroviral element activity. However, no significant correlation was observed between age and HERV-K expression levels in our cohort, suggesting that age did not impact our findings. While age and sex were matched between ALS and control groups, other potentially influential clinical variables such as disease duration, ALSFRS-R score, medication use, and comorbidities were not consistently available. These factors may impact exosomal content and should be considered in future studies. In summary, this study adds to evidence that HERV-K transcripts, including env and pol , can be detected in circulating exosomes isolated from peripheral blood and that exosome-associated HERV-K shows group-level differences between ALS patients and controls. However, we acknowledge that the biomarker potential of exosomal HERV-K remains preliminary. Plasma and serum assays are simpler, less costly, and more amenable to clinical implementation than exosome-based workflows, and it therefore remains to be demonstrated whether exosome-enrichment offers sufficient incremental diagnostic or prognostic value to justify the additional technical complexity. To move toward clinical utility, several steps are necessary: (1) direct head-to-head comparisons of exosomal versus total plasma/serum HERV-K to quantify any gain in sensitivity or specificity; (2) analytical validation including limits of detection, reproducibility, and inter-laboratory concordance; (3) standardization of EV isolation and exosomal RNA normalization (for example, evaluating SOD2 and other compartment-specific controls); (4) adequately powered, multi-center and longitudinal cohorts to evaluate diagnostic accuracy and prognostic performance (ROC analyses, PPV/NPV, and change over time); and (5) assessment of cost-effectiveness and feasibility in real-world clinical workflows. Taken together, our data indicates that exosome-associated HERV-K is an interesting biological signal that may complement other circulating measures of retroviral activity, but it should not yet be considered a ready-for-clinic biomarker. We advocate further comparative and validation studies to establish whether exosomal HERV-K adds clinically meaningful information beyond simpler plasma/serum assays. This study extends from earlier work suggesting a link between HERV-K reactivation and ALS pathology. Multiple studies have reported elevated expression of HERV-K transcripts, particularly the env and pol regions, in ALS patient tissues, including the motor cortex and spinal cord [ 31 , 37 , 47 , 63 , 64 ]. Douville et al. first identified increased HERV-K env expression in post-mortem spinal cord samples from ALS patients, while Li et al. demonstrated that HERV-K env expression in human neurons induces neurotoxicity and motor dysfunction in transgenic models [ 55 , 57 ]. Subsequent work has validated these findings across independent cohorts and highlighted immune activation and transcript variability across loci [ 31 , 64 ]. Our findings support these tissue-level observations by demonstrating that HERV-K RNA is also enriched in circulating exosomes, suggesting a peripheral signature of central retroviral activity. Furthermore, the study contributes to the growing literature exploring exosomes as vehicles of intercellular communication and potential biomarkers in neurodegenerative disease [ 22 , 65 , 66 ]. The ability to non-invasively detect HERV-K pol transcripts in blood-derived exosomes may serve as a critical step toward developing prognostic or diagnostic biomarkers. The combination of pol -specific primers, exosome isolation, and SOD2 normalization represents a potentially translatable platform for future clinical trials. 5. Limitations The modest sample size (21 ALS patients and 16 controls) limits statistical power, particularly for subgroup analyses. The small number of bulbar-onset cases (n = 5) constrained phenotype comparisons; while a trend toward higher HERV-K pol expression was observed (p = 0.082), this requires validation in larger, stratified cohorts. The cohort size also limited adjustment for multiple comparisons and covariates, increasing the potential for both Type I and Type II errors. Furthermore, RNase treatment was performed only on a subset of samples, potentially introducing variability in RNA yield and integrity. Uniform application of this treatment in future studies would improve data consistency and reproducibility. The inclusion of only ALS patients and healthy controls represents another limitation, as it precludes evaluation of disease specificity. The observed increase in exosomal HERV-K transcripts may reflect a general response to neuronal or systemic injury rather than an ALS-specific mechanism. Inclusion of disease controls such as Alzheimer’s, Parkinson’s, or frontotemporal dementia would clarify whether HERV-K upregulation is unique to ALS or a broader marker of neurodegeneration. Limited clinical metadata also constrained interpretation. Although age and sex were considered, key variables such as disease duration, ALSFRS-R scores, medication use (e.g., riluzole or edaravone), comorbidities, and cognitive status were unavailable, potentially introducing unmeasured confounding. The cross-sectional design further restricts inference regarding temporal trends or disease progression. Technical variability from RNase digestion, possibly due to differences in exosome integrity or enzyme efficiency, may have contributed to inter-sample variation. Standardization of enzymatic conditions and vesicle integrity assessments will be essential in future work. At the methodological level, reliance on qPCR limits the ability to detect transcript isoforms or RNA editing events, and the absence of protein-level validation prevents confirmation that increased RNA corresponds to elevated protein expression. Incorporating RNA sequencing and proteomics could provide more comprehensive insights. Moreover, exosome isolation via ultracentrifugation, though standard, is labor-intensive and may co-isolate other vesicles. Alternative approaches such as size-exclusion chromatography or immunoaffinity capture could enhance purity and scalability. Finally, although SOD2 served as an effective exosome-enriched reference gene, the lack of a universally accepted housekeeping gene for exosomal RNA remains a broader limitation. Disease state or vesicle subtype may influence reference transcript stability. Validation of multiple candidate reference genes across larger and diverse datasets will be necessary to strengthen normalization and ensure reproducibility across studies. 6. Conclusions This study provides novel insights into the expression of HERV-K transcripts in plasma-derived exosomes from individuals with ALS. By combining rigorous exosome isolation techniques with RNase treatment, qPCR amplification, and normalization against both traditional and exosome-enriched housekeeping genes, we demonstrate that HERV-K gag , env , and particularly pol transcripts are consistently elevated in ALS patients compared to healthy controls. Among the three gene regions, pol exhibited the most robust and statistically significant upregulation across eight primer sets and five different housekeeping genes, highlighting its potential as a stable and reproducible exosomal biomarker in ALS. The use of SOD2 as an exosome-specific reference gene further enhanced the sensitivity of detection, supporting the importance of compartment-appropriate normalization strategies in exosomal transcriptomic studies. Furthermore, the observation of a broader EV size distribution and slightly higher exosome concentration in ALS samples compared to controls suggests disease-associated alterations in exosome biogenesis or release. Although the subgroup analysis of bulbar-vs. spinal-onset ALS did not reach statistical significance, the trend toward higher pol expression in bulbar-onset patients warrants further investigation in larger cohorts. Together, these findings reinforce the hypothesis that reactivation of retroviral elements, particularly HERV-K, may be an active component of ALS pathogenesis and that circulating exosomes provide a viable, minimally invasive platform for tracking this activity. Future studies should focus on validating these results in larger, longitudinally followed populations, incorporating protein level and functional assays, and integrating exosomal HERV-K measurements into biomarker panels for diagnostic, prognostic, and therapeutic applications. This work lays important groundwork for developing exosome-based assays that could facilitate early diagnosis, subtype differentiation, or treatment monitoring in ALS and related neurodegenerative disorders. Author Contributions Conceptualization, TR and AAK, Data curation, TR; Formal analysis, TR and AAK; Funding acquisition, AA-C, AI and AAK; Investigation, TR and AAK; Methodology, TR, MR and AAK; Project administration, TR; Resources, AA-C and AAK Supervision, AA-C, AI and AAK; Validation, TR, MR, NAAN and AAK; Visualization, TR; Writing – original draft, TR; Writing – review & editing, AA-C, AI and AAK. Funding TR was supported by the National Institute for Health and Care Research (NIHR) as a Pre-Doctoral Research Fellow (Award Number: 303476) and MND Association. AAK is funded by The Motor Neurone Disease Association (1122462), NIHR Maudsley Biomedical Research Centre, ALS Association Milton Safenowitz Research Fellowship (RE19765), the Darby Rimmer MND Foundation, LifeArc (RE23378), MRC (MR/Z505705/1), and the Dementia Consortium (1819242). AAK is supported by the UK Dementia Research Institute through UK DRI Ltd, principally funded by the Medical Research Council. AI and AAC are funded by South London and Maudsley NHS Foundation Trust, MRC (MR/Z505705/1), MND Scotland, Motor Neurone Disease Association, National Institute for Health and Care Research, Spastic Paraplegia Foundation, Rosetrees Trust, Darby Rimmer MND Foundation, the Medical Research Council (UKRI) and Alzheimer’s Research UK. Institutional Review Board Statement Ethical approval was obtained from the North East – Newcastle & North Tyneside Research Ethics Committee (REC reference: 20/NE/0030). Informed Consent Statement All participants provided informed consent for sample collection and future use in research at the time of enrolment. Newly collected clinical data were anonymised and handled in accordance with GDPR and institutional guidelines. Data Availability Statement Anonymised data may be shared upon reasonable request and under a data-sharing agreement approved by the Chief Investigator. All shared data will comply with the UK General Data Protection Regulation (GDPR, 2018). Conflicts of Interest AA-C declares contracts with the MRC, NIHR and Darby Rimmer Foundation; consulting fees from Amylyx, Apellis, Biogen, Brainstorm, Clene Therapeutics, Cytokinetics, GenieUs, GSK, Lilly, Mitsubishi Tanabe Pharma, Novartis, OrionPharma, Quralis, Sano, and Sanofi). AAK declares contracts with the MRC ((MR/Z505705/1), the Motor Neurone Disease Association (MNDA), National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre, Amyotrophic Lateral Sclerosis (ALS) Association Milton Safenowitz Research Fellowship, Darby Rimmer MND Foundation, LifeArc, and the Dementia Consortium; equipment by NIHR Maudsley Biomedical Research Centre; and consulting fees from the UK National Endowment for Science, Technology and the Arts (NESTA). Abbreviations The following abbreviations are used in this manuscript: View this table: View inline View popup Appendix A Appendix A.1: Exosome Lysis Principle 5X RIPA (Radio-Immunoprecipitation Assay) lysis buffer is a robust solution used to lyse cells and extract proteins, including those from exosomes. It contains a combination of detergents and salts that disrupt cell membranes and solubilize proteins. The key components include: Tris-HCl: Maintains a stable pH. NaCl: Provides ionic strength. NP-40: A non-ionic detergent that solubilizes membrane proteins. Sodium deoxycholate: An ionic detergent that helps in breaking down lipid membranes. SDS (Sodium dodecyl sulfate): A strong ionic detergent that denatures proteins. Methodology Sample Collection: Collect exosome-containing samples, such as cell culture supernatants or biological fluids, and centrifuge to remove cells and debris. Exosome Isolation: Isolate exosomes using ultracentrifugation, size-exclusion chromatography, or commercial exosome isolation kits. Exosome Concentration seen after NTA analysis is ∼ 10^10 particles per ml. For measuring exosome concentration, the resuspended pellet war diluted 10000X. The samples need to diluted such that each sample has 10^5 particles (cells)), since RNA extraction requires 10^5 particles per ml for best results. Lysis process: After diluting, centrifuge the samples at 4°C, 16000 x g for 15 mins to obtain the pellet. Remove the supernatant (excess PBS). Resuspend the pellet in 500 ul of RIPA (Radio Immuno Precipitation Assay) Buffer for. 20 mins. Centrifugation: Centrifuge the lysate at high speed (e.g., 16000 x g) for 5 minutes at 4°C to pellet any insoluble material. Collection: Carefully collect the supernatant, which contains the solubilized exosomal proteins, for further analysis such as Western blotting, mass spectrometry, or other proteomic studies Changes to Protocol The exosomes were treated with RNase H for 20 mins at 37°C incubation. This step was added to make sure that any residual RNA outside the exosomes (such as cytoplasmic mRNA, or miRNA is completely removed. This ensures that the RNA, when extracted is truly from within the exosomes themselves and not any other form of RNA. Appendix B Appendix B.1: RNA Extraction (Modified and Simplified Protocol) RNA was extracted using the RNeasy Plus Micro Kit (QIAGEN), which includes gDNA eliminator columns to remove genomic DNA. Samples were further treated with DNase I for 20 minutes to digest any residual genomic DNA. Following RNA purification and quality assessment, reverse transcription was performed using the Quantitect Reverse Transcription Kit with random hexamers and HERV-K pol -specific primers. DNA concentrations reported represent cDNA yield post-reverse transcription, used as a quality control metric prior to qPCR. Cell quantity∼10%-10% particles (cell/ml) Add 350 ul Buffer RLT, to the sample. Homogenize (slightly, since cell lysis already completed by vortexing intermittently at 1800 rpm for 1-2 mins/sample. Transfer the lysate to a gDNA Eliminator spin column (Purple) placed in a 2 ml collection tube. Centrifuge for 30s at >= 8000 g. Discard the column and save the flow through. Add equal volume (350 ul) of 70% ethanol to the flow-through and mix by pipetting. DO NOT VORTEX. Transfer the sample to a RNeasy Min Elite spin column (Pink). Centrifuge at 4°C, 28000g for 15 secs. Discard flow through (DO NOT discard collection tube). Add 700 µl Buffer RW1 to each column. Centrifuge at 4°C, >= 8000 g for 15 secs. Discard flow through. DNase treatment: Add 5 ul of DNase to the centre of each column. Incubate at room temperature for 15 mins. Add 500 µl of Buffer RPE to the column. Centrifuge at 4°C, >= 8000 g for 15 secs. Discard flow through. Add 500 ul of 80% ethanol to the column. Centrifuge of at 4°C, >= 8000 g for 2 mins Discord the flow through along with the collection tube. Place the column in a new 2 ml collection tube open the lid of the column and centrifuge at full speed for 5 mins to mad the collection tube. day the membrane. Discard the collection tube. Place the column in a new 1.5 ml collection tube (Eppendorf). Add 14 ul RNase - free water to the column (centre of the matrix membrane). Centrifuge for 1 min at full speed to elute the RNA. Appendix C Appendix C.1: Agarose Gel Electrophoresis Principle Agarose gel electrophoresis is a technique used to separate DNA fragments based on their size. The principle relies on the fact that DNA molecules are negatively charged due to their phosphate backbone. When an electric field is applied, DNA fragments migrate towards the positive electrode. Smaller fragments move faster and travel further through the porous agarose gel matrix compared to larger fragments. Methodology 1. Preparation of Agarose Gel: a. Dissolve agarose powder in an appropriate buffer (e.g., TAE or TBE) by heating. b. Pour the molten agarose into a gel casting tray with a comb to create wells. c. Add 1-2 ul Ethidium Bromide (EtBr) for the samples to be illuminated under ultraviolet light. d. Allow the gel to solidify at room temperature. 2. Sample Preparation: a. Mix DNA samples with a loading dye (6X loading dye, consisting of 10 mM Tris-HCl (pH 7.6) 0.03% bromophenol blue, 0.03% xylene cyanol FF, 60% glycerol 60 mM EDTA) to visualize the samples and add density. b. Load the DNA samples into the wells of the solidified agarose gel. 3. Electrophoresis: a. Place the gel in an electrophoresis chamber filled with the same buffer used to prepare the gel. b. Connect the chamber to a power supply and apply a voltage (typically 80-120V). c. Run the gel until the dye front has migrated an appropriate distance. 4. Visualization: Use an imager (ChemiDoc or Analyser) to visualise the bands. Acknowledgments The authors acknowledge the KCL MND Biobank for sample provision and thank the MND patient advisory panels. Technical support from the Maurice Wohl Clinical Neuroscience Institute is also gratefully acknowledged. AAC is an NIHR Senior Investigator (NIHR202421) and a Visiting Professor at the Perron Institute for Neurological and Translational Science, Australia. This work was partly supported by an EU Joint Programme - Neurodegenerative Disease Research (JPND) project. The project is supported through the UK MND Research Institute, the following funding organisations under the aegis of JPND - www.jpnd.eu (United Kingdom, Medical Research Council (MR/L501529/1; MR/R024804/1) and Economic and Social Research Council (ES/L008238/1)) and through the Motor Neurone Disease Association, My Name’5 Doddie Foundation, MND Scotland, LifeArc, Alan Davidson Foundation, and Darby Rimmer Foundation. This study represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. AAK and AI are visiting senior research fellows at the Perron Institute for Neurological and Translational Science, Australia. Funder Information Declared National Institute for Health and Care Research , 303476 MND Association , 1122462 NIHR Maudsley Biomedical Research Centre, https://ror.org/05fd9ct06 , RE19765 MRC Centre, https://ror.org/03hr7y002 , MR/Z505705/1 LifeArc, https://ror.org/01dqb0q37 , RE23378 Dementia UK, https://ror.org/02svp4q11 , 1819242 UK Dementia Research Institute, https://ror.org/02wedp412 South London and Maudsley NHS Foundation Trust, https://ror.org/015803449 MRC Centre, https://ror.org/03hr7y002 , MR/Z505705/1 MND Scotland, https://ror.org/04fzs4293 UK Dementia Research Institute Motor Neurone Disease Association, https://ror.org/02gq0fg61 NIHR Maudsley Biomedical Research Centre, https://ror.org/05fd9ct06 Rosetrees Trust, https://ror.org/04e3zg361 Alzheimer’s Research UK, https://ror.org/02ymzm013 References 1. ↵ Rowland , L.P. ; Shneider , N.A. 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