Connecting HTT intermediate alleles and microRNA dysregulation to enhanced tauopathy in Late-Onset Alzheimer's Disease

Connecting HTT intermediate alleles and microRNA dysregulation to enhanced tauopathy in Late-Onset Alzheimer's Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Connecting HTT intermediate alleles and microRNA dysregulation to enhanced tauopathy in Late-Onset Alzheimer's Disease Juan Castilla-Silgado, Sergio Perez-Oliveira, Paola Pinto-Hernandez, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7621820/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Alzheimer's Research & Therapy → Version 1 posted 9 You are reading this latest preprint version Abstract Background Late-onset Alzheimer´s disease (LOAD) is a heterogenous disorder influenced by genetic factors. In fact, we have previously described intermediate alleles ( IAs ) in the huntingtin ( HTT ) gene as potential modifiers in around 6% of AD population. The caudate nucleus, the most affected region in Huntington's disease, is highly sensitive to these HTT CAG expansions, as they can induce epigenetic changes, including altered microRNA profiles. All this implies a potential source of gene expression deregulation, affecting disease onset and/or progression in LOAD patients with HTT IAs . Methods We investigated the impact of HTT IAs on LOAD progression and neuropathology using a comprehensive approach, genotyping HTT CAG repeats in 323 LOAD patients and 335 healthy controls and further performing histopathological and molecular analyses on caudate nucleus samples in a small subcohort (6 healthy controls, 14 LOAD non- HTT IA carriers, and 13 LOAD HTT IA carriers). Results HTT IAs carriers patients exhibited decreased survival after disease onset, suggesting accelerated progression. Histopathologically, while LOAD patients showed increased soluble HTT levels and altered tau pathology compared to controls, these changes were consistently and markedly exacerbated in HTT IA carriers, characterized by heightened diffuse HTT immunoreactivity, pronounced tau 3R isoform imbalance, and increased 3R tau-enriched ghost tangles. Interestingly, this pathological exacerbation was supported by alterations in key splicing factors, including decreased SRSF6 and increased FUS-SFPQ complex formation. Analysis of microRNA (miRNA) profiling in the caudate nucleus revealed not only a LOAD-associated miRNA dysregulation that was significantly amplified in HTT IA carriers, but also five HTT IA signature miRNAs (miR-100-5p, miR-218-5p, miR-27b-3p, miR-487-3p, and miR-9-3p). In silico analyses, supported by network modeling and direct target validation, demonstrated that altered miRNAs target components of the nuclear spliceosome machinery, such as SRSF family, along with MAPT and HTT genes, suggesting a direct link to the observed tau 3R/4R imbalance. Conclusions Our findings underscore that HTT IAs as critical players in LOAD progression through an intricate network involving miRNA-mediated dysregulation of splicing. Thus, identifying HTT IAs through routine blood genetic screening offers a practical, non-invasive biomarker for patient stratification, taking a step closer to personalized therapeutic strategies in LOAD. proteinopathies alternative splicing prognosis post-transcriptional regulation clinical heterogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Alzheimer’s disease (AD), which accounts for 60–80% of dementia cases, is primarily characterized by the presence of extracellular beta-amyloid plaques and intracellular accumulation of tau protein in the form of neurofibrillary tangles ( 1 ). However, up to 50% of affected individuals present with mixed dementia ( 1 ) associated with the existence of co-pathologies involving the accumulation of proteins, such as alpha-synuclein or TDP-43 ( 2 ), especially in the more advanced stages of the disease ( 1 , 2 ). While the precise cause of this phenomenon is still unknown, genetic factors like the APOE*ε4 allele have been associated with an increased risk of developing co-pathologies ( 1 ). In fact, this polymorphic variant introduces significant variability in AD development between carriers and non-carriers ( 3 , 4 ), causing diverse clinical and phenotypic manifestations that hinder the application of optimal therapeutic and care interventions ( 1 ). Thus, identifying specific genetic risk factors for AD is essential for developing more appropriate, individualized treatment ( 5 , 6 ). Previous results from our laboratory suggest that there is a higher prevalence of intermediate alleles in the huntingtin gene ( HTT ) in AD patients with respect to control subjects (6.03% vs. 2.9%) ( 7 ), extending this fact to other tauopathies, such as frontotemporal dementia (FTD) ( 8 , 9 ), and synucleinopathies ( 10 ). Exon 1 of the HTT gene presents CAG repeats in variable number, being considered normal below 27 repeats. When the number of repeats is above 35, it determines the age of onset of Huntington's disease (HD) ( 11 ), with incomplete penetrance when the range is between 36 and 39 repeats and complete when this number is higher ( 12 ). Between 27 and 35 repeats are referred to as intermediate alleles ( HTT IAs ), which are genetically unstable at the germline level, although they are not considered pathogenic ( 13 ). However, in the case of AD, it has not yet been possible to determine the effect of the presence of HTT IAs . At the neuropathological level, an increase in diffuse HTT accumulation has been found in hippocampal regions associated with memory and in layer III of the frontal cortex in AD patients compared to healthy subjects ( 14 ), although in this case the number of CAG repeats in the HTT gene has not been evaluated. On the other hand, there are studies showing that elderly HD subjects show co-pathology with AD in up to 82% of cases, with prominent dementia in the clinic ( 15 ), and increased aggregation of other proteins, such as phosphorylated tau, alpha-synuclein, and TDP-43, can also be observed in the later stages of HD ( 16 ). It has even been proposed that HD is a 4R tauopathy, as alterations in the splicing of the MAPT gene have been described, modifying tau 3R/4R balance, and the absence of this gene in murine models of HD diminishes the characteristic motor alterations ( 17 ). Therefore, it is reasonable to hypothesize that HTT IAs in AD could also influence MAPT gene splicing, affecting the progression of AD pathology. Studies developed in murine models describe changes in gene expression as a function of CAG repeat length (including intermediate ranges), being the striatum the most sensitive region ( 18 ), and such alteration modifies the expression of microRNAs (miRNAs) in this brain area ( 19 ). miRNAs are small non-coding RNAs (≈ 18–22 nucleotides) that are an integral part of the epigenetic regulatory network, since they can act as modulators that influence gene expression at the post-transcriptional level, including that of other genetic modifiers, such as DNA methyltransferases and histone acetylases ( 20 ). In addition, they themselves are subject to epigenetic regulation, as their expression profile can be altered under pathological conditions, as shown for both HD ( 21 ) and AD ( 22 ) or by lifestyle ( 23 ). Thus, it is possible that both AD and the presence of HTT IAs modify further the profile of miRNAs in carrier individuals, thereby affecting gene expression of key genes for disease progression. Here, we investigated the impact of HTT IAs on the neuropathological features of late-onset AD (LOAD). At the clinical level, we found that the presence of HTT IAs is associated with accelerated disease progression. Building on this observation, we first hypothesized that HTT IAs influence LOAD pathology by altering MAPT gene splicing and, consequently, the tau 3R/4R balance. To investigate this, we focused on the caudate nucleus, demonstrating that its neurons present increased levels of soluble HTT and that HTT IAs further exacerbate this increase. Furthermore, LOAD patients carrying HTT IAs exhibited a pronounced imbalance of tau isoforms, characterized by an excess of 3R tau and a greater abundance of 3R tau-enriched ghost tangles, compared to non-carrier LOAD subjects. These findings were supported by lower levels of the splicing factor SRSF6 and increased formation of FUS–SFPQ nuclear complexes in the caudate nucleus. To elucidate the possible underlying molecular mechanisms driving these changes, we further hypothesized that the presence of HTT IAs modifies LOAD-associated miRNA profile in caudate nucleus. This was confirmed by the first detailed miRNA profiling study in this region. Importantly, the altered miRNAs target key components of the nuclear spliceosome machinery, suggesting a role in the observed tau 3R/4R imbalance. Moreover, the identified miRNA dysregulation represents a promising avenue for novel therapeutic targets in LOAD. Collectively, our findings underscore that HTT IAs serve as critical risk/progression biomarkers in AD, offering a practical, non-invasive genetic biomarker for clinical implementation, thereby facilitating patient classification and personalized therapeutic interventions. Methods Subjects This study included LOAD patients ( N = 323) and healthy controls ( N = 335) from a previously described cohort ( 8 ). Detailed clinical data (diagnosis, gender, onset age, age at death, HTT gene genotyping) are summarized in Table 1 . Based on caudate postmortem sample availability, a sub-cohort was obtained for LOAD non-carrier patients ( N = 14), LOAD HTT IAs carrier patients ( N = 13) and healthy controls ( N = 6) for histological and molecular approaches, matched by demographic and neuropathological variables, which are summarized in Supplementary Table S1 . Additionally, details of the subject samples used in each experiment are listed in Supplementary Table S2 . Table 1 Demographic data of cohorts studied. Demographics Control ( N = 335) LOAD ( N = 323) p-value Gender (female %) a 188 (57.5%) 231 (71.51%) < 0.001 c Onset age b - 75.22 ± 6.02 N. A Death age b - 83.25 ± 6.33 N. A Final follow-up b 70.84 ± 8.47 - N. A Disease duration b - 9.35 ± 4.86 N. A HTT IAs carriers 14 (4.16%) 23 (7.12%) 0.102 d HTT short allele c 17 [16–17] 17 [17–18] 0.045 e HTT long allele c 18 [17–21] 20 [18–22] < 0.0001 e Abbreviations: LOAD, late-onset Alzheimer’s disease; N.A : not available. a Data are shown as n (%). b Data are shown as mean ± SD. c Data are shown as median [IQR]. d Statistical analysis: Fisher's test. e Statistical analysis: Mann Whitney. Material and data supply for patients were provided by the HCB-IDIBAPS Biobank (B.0000575), integrated in the platform ISCIII Biobanks and Biomodels, and by the Principado de Asturias BioBank (PT23/0077), member of the Spanish National Biobanks and Biomodels Network. Both materials and data were processed following standard operating procedures with the appropriate approval of the Ethics and Scientific Committees. The neuropathological LOAD diagnosis was performed according to standard international consensus criteria ( 24 ). All the participants or legal representatives gave written informed consent to participate in the study. All procedures have been approved by the Research Ethics Committee of the Principality of Asturias (CEImPA nº 2022.266). Immunohistochemistry and immunofluorescence For immunohistochemistry, formalin-fixed paraffin-embedded tissue sections (5 µm) were processed using the EnVision FLEX + kit (K8002, Agilent-Dako, Agilent, USA) and the Dako Autostainer. Epitope retrieval was performed at 95 ºC for 20 min in PT-LINK (Dako) with pH 6.0 buffer (K8005) for HTT and tau 3R, or pH 9.0 (58004) for tau 4R. Endogenous peroxidase was blocked and sections were incubated with primary antibodies in FLEX antibody diluent (K8006), for 20 min at room temperature and overnight at 4°C. Signal was developed with diaminobenzidine (DAB; DM827 + SM803) and counterstained with hematoxylin reagent (K8008). Negative controls were processed, omitting the primary antibody. All the primary antibodies used are summarized in Supplementary Table S3. Images were acquired using the NanoZoomer-SQ Digital Slide Scanner (C13140-01, Hamamatsu Photonics, Germany) and analyzed with QuPath (version 0.4.2) and Fiji (ImageJ, version 1.6). All acquisition settings, including magnification (40×), were kept constant across experimental groups. For 3R-tau (05-803), 4R-tau (05-804), and HTT clone EM-48 (MAB5374; Merck Millipore, USA) quantification, the number of immunopositive neurons was manually counted in caudate nucleus sections from each patient. Five randomly selected regions of interest (ROI) per case were analyzed, each measuring 1 mm². Results were expressed as the mean number of positive neurons/mm². For HTT clone EPR5526 (ab109115; Abcam, UK), a fixed intensity threshold was applied using Fiji to quantify positive staining in five randomly selected 1 mm² ROIs per section. The signal intensity was then averaged across the five ROIs and expressed as mean signal intensity/mm². For double immunofluorescence, after antigen retrieval, sections were washed with distilled water and subsequently rinsed three times with 0.1M phosphate buffer, followed by a rinse in PBS for 10 min. Tissue sections were then blocked for 45 min at room temperature in a blocking solution consisting of 1% of serum albumin and 1% Triton X-100 in PBS. Primary antibodies (see Supplementary Table S3) were diluted in blocking solution supplemented with normal serum overnight at 4°C. The following day, sections were rinsed with PBS and distilled water. Secondary antibodies (donkey anti-mouse Alexa Fluor 488 and donkey anti-rabbit Alexa Fluor 594, 1:1000; Invitrogen, Thermo Fisher Scientific, USA) were incubated for 2 hours at room temperature. Finally, nuclei were counterstained with DAPI, included in the mounting medium (Fluoroshield™, Sigma-Aldrich), and coverslipped for imaging. Images were acquired on a Leica TSC-SP8X spectral confocal microscope (Leica DMI8 microscope) with excitation lines between 470–670 nm and PLA APO 20X/0.75 IMM CORR CS2 or PLA APO 40X /1.30 CS2 oil-immersion objective (Leica Microsystems, Germany), using Leica Application Suite X software (version 1.8.1, Copyright 1997–2015; Leica Microsystems), and processed with LAS_X_SMALL (version 1.0.0) and Uniovi Fiji Confocal/ImageJ (version 1.6) software. Nuclear colocalization analysis To quantitatively assess the colocalization of FUS (A300-293A; Bethyl Laboratories, USA) and SFPQ (WH0006421M2, Sigma Aldrich, USA) within neuronal nuclei, all sections were imaged under identical confocal microscope settings and laser intensities. Random images were acquired from two distinct regions of the caudate nucleus, capturing six Z-planes (1 µm step size) to cover the full nuclear volume at 63× magnification. Neuronal nuclei were identified based on size (~ 10–12 µm in diameter), and those smaller than 8 µm were excluded from analysis. Colocalization analysis was conducted on 30 randomly selected nuclei per section using the Coloc 2 plug-in in FIJI. Thresholded Manders’ coefficients (tM1 and tM2) [56], along with Pearson’s correlation coefficient (R) ( 25 ), were computed to quantify colocalization and assess the correlation between fluorescence signals. Proximity ligation assay The proximity ligation assay (PLA) for protein complexes detection was carried out as previously described ( 26 ). Briefly, paraffin-embedded brain sections were rehydrated and subjected to antigen retrieval in citrate buffer (pH 6.0) at 95°C for 20 min. Following permeabilization with 0.1% Triton X-100 in PBS for 15 min, sections were blocked for 45 min using the Duolink blocking solution (DUO92009, Sigma-Aldrich). Samples were then incubated overnight at 4°C with primary antibodies diluted in antibody diluent (see Supplementary Table S3). After washing twice in 1× buffer A (DUO82049), sections were incubated for 1 h at 37°C with PLA probes PLUS and MINUS (DUO92002 and DUO92004, respectively; Sigma-Aldrich), each diluted 1:5 in antibody diluent. Ligation was carried out using ligase diluted 1:40 in ligation buffer (DUO92014) at 37°C for 30 min. Signal amplification was performed with polymerase diluted 1:80 in amplification buffer, incubated at 37°C for 100 min. Sections were then washed sequentially in 1× and 0.01× buffer B (DUO82049), mounted with DAPI-containing medium (DUO82040), and stored until imaging. To assess PLA signal intensity, all sections were imaged under identical conditions using a confocal microscope (Leica SP8), with fixed laser power and acquisition settings. For each section, five random Z-stacks were acquired at 1 µm intervals, covering the entire nuclear volume at 63× magnification. Nuclei were selected based on size (10–12 µm diameter); nuclei smaller than 8 µm were excluded. Seventeen nuclei were randomly selected per section for quantification. For each Z-stack, a maximum intensity projection was generated, and mean fluorescence intensity values (range: 0–255 grayscale units) were measured using the stack profile tool in LAS X software (Leica Microsystems), by manually outlining each nucleus. Fluorescence values were normalized to the nuclear area, and the average intensity per nucleus was calculated to yield a representative value per subject. Western blotting Human caudate tissue (30 mg) was homogenized in lysis buffer (20 mM HEPES pH 7.4, 100 mM NaCl, 50 mM NaF, 5 mM EDTA, 1% Triton X-100) with protease inhibitors (cOmplete™, Mini Protease Inhibitor Cocktail; Roche, Switzerland) in a glass homogenizer on ice. Subsequently, the samples were centrifuged for 10 min at 12 000 × g at 4°C and the supernatant was collected for analysis. Protein concentration was determined with the NanoDrop One 2000c spectrophotometer (Thermo Fisher Scientific, USA). Fifty micrograms of total protein were loaded onto 4–12% SDS-polyacrylamide gels (M00653; GeneScript, USA) and transferred to PVDF membranes (0.2 µm, Amersham™ Hybond; Cytiva, USA), which were then blocked in TBS-T (Tris-buffered saline, 1% Tween-20) supplemented with 5% bovine serum albumin (BSA). Membranes were incubated with primary antibodies (see Supplementary Table S3), overnight at 4°C, and, after TBS-T washes, with HRP-conjugated goat anti-mouse IgG (1:20000, ab97040, Abcam, UK) or anti-rabbit IgG (1:20000, ab7090, Abcam) for 1h, at room temperature. Protein detection was performed with Amersham™ ECL™ Prime Western Blotting Detection Reagent (Cytiva). Anti-β-Actin (HRP) conjugated antibody (1:4000, sc-47778, Santa Cruz Biotechnologies, USA) was used as loading control. Western blot images were acquired with a ChemiDoc-It® BioChemi HR Camera (UVP) and quantified using ImageJ (Uniovi Fiji Confocal). mRNA expression analysis by RT-qPCR Frozen postmortem caudate nucleus samples ( N = 33, approximately 5 mg each) were homogenized under cold conditions using Qiazol lysis reagent (Cat. No. 79306, QIAGEN, Germany). Total RNA was extracted using the miRNeasy Micro Kit (Cat. No 74004, QIAGEN) following the manufacturer’ protocol. RNA concentration and purity were determined on a NanoDrop One 2000c spectrophotometer (Thermo Fisher Scientific). RNA integrity was evaluated for each sample using the Agilent 2200 TapeStation system (Agilent Technologies) and the RNA integrity number (RIN) are provided in Supplementary Table S4. Samples were stored at -80°C until further processing. For cDNA synthesis, 500 ng of total RNA were used for cDNA synthesis (StaRT reverse transcription kit, AnyGenes, France). The conditions used were: 10 min at 25°C, 120 min at 37°C, and 5 min at 85°C. Quantification was performed from cDNA using the Perfect Master Mix SYBR Green Kit (AnyGenes) on the 7900HT rapid real-time PCR system (Applied Biosystem, USA). The primers for the MAPT , MAPT 4R , and MAPT 3R genes analyzed were specifically designed by Eurogentec (Belgium). GAPDH and β-ACTIN were used to perform the internal normalization of the results, and the one with the most stable values for our dataset was chosen a posteriori , using the RefFinder tool ( 27 ). Primer information and amplification conditions are listed in Supplementary Tables S5 and S6. The mRNA levels are represented as relative quantification (2 −ΔΔCt ), as described in ( 28 ). small RNA-sequencing Small RNA-Seq was performed by Seqplexing (Sequencing Multiplex S.L., Spain) using 200 ng of caudate nucleus total RNA per sample for miRNA library preparation. Library quality was assessed using the QIAxcel Advanced System (QIAGEN). Sequencing was performed on an Illumina NovaSeq X platform (Illumina, USA), generating paired-end reads (2 x 150bp). The sequencing depth ranged from 8.64 to 144 million reads per sample (median = 25.89 million). Raw sequencing data were obtained in FASTQ format and quality control was performed using FastQC. Adaptor sequences were trimmed and miRTrace software ( 29 ) was used to classify the types of RNA present in each sample, distinguish between miRNAs, rRNA, tRNA and artifacts. miRNA counts were generated using miRDeep2 ( 30 ) and miRNA sequences were mapped to the human genome reference version hg38/GRCh38. Human miRNAs identification was performed using miRBase (v. 22) ( 31 ). All data generated and used in this study are publicly available in ZENODO (DOI: 10.5281/zenodo.15230070 ) and NCBI GEO DataSets (ID: GSE300433; The following secure access code has been created to allow review of record GSE300433 while it remains in private status: mtwfggcozpalrwn). miRPM custom R package A custom R package called miRPM was developed and used, integrating the entire bioinformatics workflow. In this package, the count matrices were normalized using the Reads Per Million (RPM) approach, based on the total number of reads per sample. To further assess the RPM normalization performance, the DANA approach was employed ( 32 ). DANA is an R-based method that evaluates the effectiveness of different normalization strategies in miRNA-Seq data by computing two metrics: concordance correlation coefficient (cc), which measures the preservation of biological signals, and mscr, which quantifies the reduction of handling effects. In our analysis, we incorporated the RPM normalization method into the DANA framework and made several modifications to improve the computation of the concordance correlation coefficient. These changes addressed issues like excessive miRNA filtering and strict cluster size constraints in the original DANA implementation, ensuring a more robust and interpretable evaluation of normalization methods while maintaining the biological integrity of the data. R code is available at https://github.com/sergio30po/miRNA-RPM-DE-Analysis.git . Differential expression of miRNA sequencing data For the differential expression analysis of miRNAs, using the miRPM package, an expression filter with two inclusion criteria, applied consecutively, were considered: 1) more than 50% of valid miRNA data in at least one group; 2) an expression level of > 1000 RPM in all subjects of at least one group. Before conducting the statistical analysis, the necessary assumptions were evaluated to determine the suitability of using non-parametric tests. Data normality was assessed using the Shapiro-Wilk test, and homoscedasticity between groups was evaluated using Levene’s test. Additionally, the coefficient of variation was calculated to evaluate the relative dispersion of the data. Since the assumptions of normality and homogeneity of variances were not met in most cases, non-parametric statistical tests were chosen. Thus, Kruskal-Walli’s test was employed to assess overall differences in the expression levels across the three groups. To control the error arising from multiple comparisons, FDR correction was applied. Only those miRNAs with a significant adjusted p-value were subsequently subjected to Dunn’s post-hoc test, which inherently accounts for the multiple pairwise comparisons, to determine which specific group comparisons were significant. Graphical representation of miRNAs levels in heatmap was performed by normalization through Z-score. In silico prediction of target genes and pathway analysis of miRNAs To explore the biological relevance of the differentially expressed miRNAs, enrichment analysis of signaling pathways was performed using DIANA-miRPath v.4, incorporating experimentally validated miRNA-gene interactions from miRTarBase v.8.0 ( 33 ). The analysis was conducted using both Kyoto Encyclopedia of Genes and Genomes (KEGG) and REACTOME repositories, and statistical significance was set at p-value < 0.05 with FDR correction. Among the enriched pathways, particular attention was paid to the spliceosome, given its relevance to tau isoform regulation. For this pathway, validated gene targets associated with the miRNAs of interest were retrieved from miRTarBase v.8.0. The subset of genes involved in the spliceosome was then subjected to Gene Ontology (GO) analysis using PantherDB v.19.0 tool, to characterize enriched biological processes, molecular functions, and cellular components in which they are predominantly represented. GO terms meeting the significance threshold ( p-value < 0.05) were retained. In silico graphs were created using the SRPlot module ( 34 ). Finally, gene interaction networks were generated to visualize validated miRNA–target connections. Interaction data were imported into Cytoscape v3.10.3 for visualization and network analysis. General statistical analysis Statistical analyses were performed using IBM SPSS Statistic 27.0 software (IBM, USA). Categorical variables were described as frequencies and percentages. For between-group comparisons of the frequencies of HTT IAs , APOE alleles or gender frequency, Chi-square and Fisher tests were performed. To determine the relationship between the presence of HTT IAs and the probability of survival in the LOAD groups, the Kaplan-Meier estimation method and the log-rank test were used. Survival rate was calculated as the time elapsed from pathology diagnosis to age at death (event = 1 per subject). Additionally, multivariate Cox-PH regression models were run to identify predictors of survival risk, such as age or the presence of HTT IAs . Kolmogorov-Smirnov and Shapiro-Wilks normality tests (for N < 50) were performed and the corresponding parametric or nonparametric tests were then applied. Mean and standard deviation or median and interquartile range, as appropriate, were used to describe quantitative variables. To compare age at onset, age at death, disease duration, immunohistochemistry and immunofluorescence results between controls vs . LOAD group or between LOAD subgroups, Student’s t-test or Mann-Whitney U-test was performed, as appropriate. Also, the parametric analysis of variance (ANOVA) test, followed by Tukey's comparison, or the nonparametric Kruskal-Wallis’ test, followed by Dunn's test, was applied when comparisons were made considering the three groups. To determine whether miRNAs levels correlate with the recording of different clinical variables, a Spearman correlation test was applied. The threshold for statistical significance was set at p < 0.05. The exact levels of the p-values are indicated in each figure or additional table, if applicable. Graphs were created using GraphPad Prism 10.2.0 (GraphPad, USA). Results The presence of HTT IAs modifies LOAD progression We have previously analyzed the possible influence of the HTT IAs on the development of tauopathies, including AD, and our results suggested that LOAD patients had a higher frequency of HTT IAs than patients with early-onset AD ( 8 ). However, we have not established whether this could be affecting disease progression. Thus, to further explore the effect of HTT IAs on LOAD, we considered for this study only LOAD patients from our previous cohort and a group of healthy subjects ( 8 ). The age of LOAD onset was 75.22 ± 6.02 years and disease duration until death was 9.35 ± 4.86 years, with a higher percentage of women in the LOAD group versus the control (Table 1 ). The distribution frequency of the different APOE gene isoforms was consistent with previous reports (Table S7) ( 8 ). The presence of subjects carrying HTT IAs was higher in the case of LOAD patients versus healthy controls, with a frequency similar to that observed in previous studies (7.12% vs. 4.16%; p-value = 0.102; Fisher's test, Table 1 ) ( 7 , 8 ). Moreover, the number of CAG repeats was significantly higher in the case of long HTT allele of LOAD subjects with respect to healthy controls (20 [18–22] vs. 18 [17–21], respectively; p-value < 0.0001; Mann Whitney; Table 1 and Figure S1 B). Importantly, within the range of IAs in the LOAD group, the most frequent repeat was 27 CAGs, with subjects presenting even 35 CAGs (Figure S2 ). To study the effect of the presence of HTT IAs specifically in LOAD donors and clinical variables, we divided this group into non-carrier ( N = 300) and carrier ( N = 23) patients (Table 2 ). The number of CAG repeats in the long HTT allele was higher in carriers versus non-carriers (28 [27–30] vs. 19 [18–22]; p-value = 0.001, Mann Whitney; Table 2 ). No significant differences were found in the percentage of females, onset age and death age between the two groups. Table 2 Demographics data of LOAD cohort studied. Demographics LOAD non-HTT IAs ( N = 300) LOAD HTT IAs ( N = 23) p-value Gender (female %) a 216 (66.87%) 15 (65.21%) 0.488 d Onset age b 75.24 ± 5.98 75.75 ± 6.64 0.855 e Death age b 84.33 ± 6.31 83.43 ± 6.79 0.492 e Disease duration b 9.50 ± 4.96 7.40 ± 2.89 0.053 e HTT short allele c 17 [17–17] 17 [13–18] 0.065 f HTT long allele c 19 [18–22] 28 [27–30] < 0.0001 f Abbreviations: LOAD, late-onset Alzheimer’s disease a Data are shown as n (%). b Data are shown as mean ± SD. c Data are shown as median [IQR]. d Statistical analysis: Fisher's test. e Statistical analysis: Student’s t-test. f Statistical analysis: Mann-Whitney. However, the disease duration, after diagnosis, was shorter in carrier subjects with respect to non-carrier (7.40 ± 2.89 years vs. 9.50 ± 4.96 years, respectively; p-value = 0.053, Student’s t-test; Table 2 ). In fact, the survival rate after diagnosis showed a clear reduction of this rate in LOAD donors with HTT IA s ( p-value = 0.0017; Kaplan-Meier estimator; Fig. 1 ). The disease duration was estimated based on the available clinical records of age at disease onset and age at death, allowing us to calculate survival time from onset to death. To verify this result, logistic regression models were used. The model proposed revealed a significant fit (Cox-Snell R 2 = 0.01) and a significant association between disease duration and the presence of HTT IAs ( p-value = 0.012). These results suggest that the HTT IAs modifies LOAD progression, decreasing patient survival after disease onset. Increased diffuse HTT protein levels in caudate neurons of LOAD patients are further exacerbated by HTT IAs It has been previously described that HTT levels are increased in the neurons of hippocampus and frontal cortex in LOAD ( 14 ). To determine whether HTT IAs affect HTT total protein burden and its distribution in LOAD patients, a histopathological analysis was performed on caudate nucleus (Fig. 2 ). First, we explored an antibody that recognizes both wild-type and mutant HTT (Fig. 2 A, EPR5526 clone). The results show significantly higher HTT intensity signal in the cytoplasm of caudate neurons in both LOAD HTT IAs non-carriers and carriers compared to healthy controls ( p-value = 0.0002 and p-value < 0.0001 respectively, ANOVA followed by Tuckey´s test; Fig. 2 B), as reported by Axenhus et al. ( 14 ). Notably, LOAD group carrying HTT IAs had an even higher intensity signal in HTT-positive neurons, compared to non-carriers ( p-value < 0.0001, ANOVA followed by Tuckey´s test; Fig. 2 B), which has not been described before. Given this, we next sought to ascertain the presence of intranuclear inclusions, a typical hallmark of HD pathology (Fig. 2 B, EM-48 clone). However, HTT inclusions were only detected in HD samples used as positive controls. Interestingly, we observed cytoplasmic HTT EM-48 labeling in a subset of neurons, exhibiting a cytoplasmic pattern consistent with our previous analysis with HTT EPR5226. Quantitatively, both LOAD HTT IAs non-carrier and carrier groups showed a significant increase in these HTT-positive neurons compared to control subjects ( p-value < 0.0001, ANOVA followed by Tuckey´s test; Fig. 2 C). Furthermore, LOAD HTT IA s carriers displayed a higher number of HTT-positive neurons than non-carriers ( p-value < 0.0001, ANOVA followed by Tuckey’s test; Fig. 2 C) and HD subjects revealed a significantly greater increase compared to all three other groups ( p-value < 0.0001, ANOVA followed by Tuckey´s test; Fig. 2 C). These results were consistent with previously published data ( 14 ), extending the regions in which there is an increase of HTT-positive neurons in the brains of LOAD patients. Moreover, this finding was further exacerbated in HTT IAs carriers, although no sign of HTT aggregation or inclusion formation was observed. All in all, the increased HTT levels observed in LOAD patients represent another component in the co-occurrence of proteinopathies ( 16 ) at the brain level. Furthermore, the presence of HTT IAs appears to amplify this phenomenon, potentially promoting a more disturbed neuronal environment that detrimentally modifies disease progression in these subjects. HTT IAs increase tau 3R and 4R isoforms in the caudate nucleus of LOAD patients An imbalance in tau 3R and 4R isoforms has been previously reported in the striatum and cerebral cortex of HD patients ( 17 ). Thus, we explored whether a disruption of the 3R/4R balance of tau isoforms could also be present in LOAD patients with HTT IAs . We first analyzed mRNA expression levels of total MAPT and its MAPT 3R and MAPT 4R transcripts in the caudate nucleus of healthy controls and LOAD patients (Figs. 3 A-B). No significant differences in total MAPT and MAPT 3R mRNA levels were observed among the three groups. However, MATP 4R mRNA levels were significantly higher in the non-carrier LOAD group compared to controls ( p-value = 0.02, Kruskal-Wallis followed by Dunn´s test; Fig. 3 B). Next, total tau protein and its 3R and 4R isoforms were analyzed by Western blot (Fig. 3 C and Figure S3). Due to comprised integrity, control samples could not be reliable included in this protein analysis, thus comparisons were restricted to the LOAD groups. Total tau levels were significantly higher in LOAD carrier patients ( p-value = 0.0006, Student´s t-test; Fig. 3 D), with a trend toward higher levels of the 3R isoform in the HTT IAs carrier group ( p-value = 0.06, Student´s t-test; Fig. 3 E). No changes in tau 4R levels were detected between the LOAD groups (Fig. 3 F). To further evaluate the distribution and abundance of tau 3R and 4R isoforms, we performed immunohistochemical analysis of tau 3R- and 4R-positive neurons across different regions of the caudate nucleus in healthy subjects and in both LOAD groups (Fig. 3 G). Tau 4R quantification revealed a higher number of tau 4R-positive neurons in LOAD patients compared to controls, although this increase was only statistically significant in the HTT IAs carriers ( p-value = 0.0017, Kruskal-Wallis followed by Dunn´s test; Fig. 3 I). Interestingly, both LOAD non-carrier and carrier groups presented a higher number of tau 3R-positive neurons compared to controls (Fig. 3 H). Furthermore, LOAD subjects carrying HTT IAs exhibited a significantly more pronounced increase in tau 3R-positive neurons compared to LOAD non-carriers ( p-value < 0.0001, ANOVA followed by Tuckey´s test; Fig. 3 H). Collectively, our immunohistochemical findings demonstrate a complex modulation of tau 3R and 4R isoform levels and distribution within the caudate nucleus of LOAD patients. While both isoforms show elevated neuronal counts in LOAD patient groups, the significantly more pronounced increase of the 3R isoform in LOAD patients carrying HTT IAs strongly suggest that the presence of these alleles distinctively influences the tau 3R/4R balance. Final stage neurofibrillary tangles predominate in LOAD patients with HTT IAs In AD, both 3R and 4R tau isoforms are present in neurofibrillary tangles (NFTs), key elements in AD pathophysiology. This contrasts with other tauopathies like Pick’s disease, which predominantly features 3R tau, or Progressive Supranuclear Palsy (PSP), which mainly involves tau 4R ( 35 – 38 ). NFTs begin as fibrillar bundles in neurons, evolve into mature tangles, and are externalized after neuronal death ( 39 , 40 ). This progression appears to be unidirectional and correlates with the sequential predominance of tau isoforms. Ghost tangles, representing the final stage of NFTs degeneration (Fig. 4 A), are primarily composed of the 3R isoform, suggesting a temporal shift from 4R to 3R tau during the course of AD pathology ( 40 ). Accordingly, we hypothesized that the increased number of 3R-positive neurons observed in LOAD patients carrying HTT IAs may reflect a more advanced stage of NFT maturation in these individuals. We first analyzed the proportion of each stage of NFT maturation intra-group. Within non-carrier subjects, we observed a significantly higher number of pretangle-positive cells and mature tangles compared to ghost tangles ( p-value = 0.024 and p-value = 0.004, respectively; Kruskal-Wallis followed by Dunn’s test; Fig. 4 B). Conversely, within HTT IAs carrier patients, there was a significantly less pretangles regarding mature tangles ( p-value = 0.0005; Kruskal-Wallis followed by Dunn’s test), with no significant differences observed in ghost tangles compared to other stages (Fig. 4 B). When comparing NFTs structures between LOAD groups, the primary differences emerged at the early and late stages of maturation. Specifically, LOAD patients with HTT IAs displayed significantly fewer pretangles ( p-value = 0.002; Mann Whitney U-test) and a greater number of ghost tangles than the non-carrier group ( p-value = 0.003; Student’s t-test; Fig. 4 B). These findings suggest that the presence of HTT IAs is associated with faster neuropathological progression, affecting tau aggregation and indicating a more accelerated neurodegenerative process in LOAD patients carrying HTT IAs. SRSF6 splicing factor is decreased in caudate nucleus of LOAD patients with HTT IAs Given the observed increase in tau 3R isoform levels in LOAD HTT IAs patients, we explored the possibility of disturbances in the spliceosome pathway, as it has been well documented in AD and other neurodegenerative pathologies ( 41 , 42 ). Within the factors involved in MAPT splicing, the serine/arginine splicing factor family (SRSF) stands out ( 43 ). In fact, increased levels of SRSF6 protein, which is involved in the inclusion of exon 10 of the MAPT gene, and potentially related to increased tau 4R isoform, have been previously described in HD patients ( 17 ). In our study, we found no changes in SRSF6 mRNA expression between groups (Fig. 5 A). However, SRSF6 immunoblot analysis in LOAD patients showed that this protein is decreased in HTT IAs carriers compared to non-carriers ( p-value = 0.017; Student’s t-test; Figs. 5 B-C; Figure S4A, B). These results suggest that downregulation of SRSF6 protein could be one of the factors contributing to the observed increase in tau 3R in LOAD HTT IAs patients. We also conducted an exploratory analysis of mRNA expression levels for other members of the SRSF family known to be involved in MAPT splicing ( 43 ). The results showed that SRSF1 and SRSF9 , both implicated in the inclusion of MAPT exon 10, had lower expression levels in LOAD patients with HTT IAs compared to controls ( p-value = 0.01 and p-value = 0.02, respectively; Kruskal-Wallis followed by Dunn’s test; Figure S4B). Lower mRNA expression levels were also observed in two other family members involved in MAPT exon 10 exclusion, such as SRSF3 (although not significantly) and SRSF4 ( p-value = 0.003; Kruskal-Wallis followed by Dunn’s test) in carrier patients. However, due to the low RIN levels of the RNA samples and the absence of the corresponding protein levels analyses for the specific splicing factors, these mRNA findings should be interpreted with caution. In summary, our findings demonstrate a significant decrease in SRSF6 protein levels in LOAD patients with HTT IAs , which potentially contributes to the observed tau 3R isoform imbalance. While exploratory mRNA analyses suggest broader alterations in other SRSF family members, these observations require further validation, particularly at the protein level, to definitively ascertain whether the presence of HTT IAs induces a widespread alteration within the SRSF family. Increased formation of nuclear FUS-SFPQ complexes in caudate neurons of HTT IAs carrier LOAD patients Previous studies have demonstrated that the formation of a nuclear complex between fused in sarcoma (FUS) and the proline/glutamine splicing factor (SFPQ) plays a critical role in the regulation of MAPT pre-mRNA splicing, facilitating exon 10 (E10) exclusion through the assembly of an intranuclear dimer ( 44 ). This interaction is disrupted in several neurodegenerative diseases, including tauopathies, where it contributes to splicing defects and the aberrant expression of tau isoforms ( 25 ). To elucidate the role of the FUS-SFPQ complex, and its possible modulation by the presence of HTT IAs , we assessed the levels of FUS and SFPQ in the caudate nucleus. While no differences were detected at the mRNA expression level (Figs. 6 A–B), protein levels of both FUS and SFPQ were significantly elevated in LOAD patients carrying HTT IAs compared to non-carriers ( p-value = 0.03 and p-value = 0.005, respectively; Student’s t-test; Figs. 6 D–E; Figure S5). This upregulation of FUS and SFPQ protein levels in HTT IAs carriers suggested enhanced assembly and/or stability of the FUS–SFPQ complex, which, given its crucial role in MAPT splicing and E10 inclusion/exclusion ( 25 ), could profoundly influence LOAD pathogenesis. Therefore, we explored whether this elevation involved a change in FUS-SFPQ complex formation. To assess complex formation, we investigated nuclear localization and colocalization of FUS and SFPQ (Fig. 6 F). Quantitative analysis using Pearson’s correlation coefficient (R) revealed increased nuclear colocalization in LOAD HTT IA carriers, with no differences observed between controls and non-carrier patients ( p-value < 0.0001 carriers vs . other groups; Kruskal-Wallis followed by Dunn’s test; Fig. 6 G). These findings were further supported by Mander’s threshold colocalization coefficients (tM1 and tM2; Fig. 6 H). For more validation at protein–protein interaction level, we employed proximity ligation assays (PLA; Figs. 6 I–J). Quantification of the PLA signal demonstrated significantly higher interaction levels in LOAD HTT IA carriers than in the other groups ( p-value < 0.0001 carriers vs . other groups; Kruskal-Wallis followed by Dunn’s test; Fig. 6 J), consistent with enhanced FUS–SFPQ complex formation. Collectively, these results provide compelling evidence that the presence of HTT IAs in LOAD patients promotes aberrant assembly of splicing regulatory complexes in the caudate nucleus. This mechanism may underlie the observed shift in tau isoform expression, reinforcing the emerging role of RNA-binding proteins in modulating tau pathology through alternative splicing dysregulation. HTT CAG repeat size modulates caudate nucleus miRNA profiles in LOAD patients Our previous findings have revealed that LOAD patients with HTT IAs presented a shift in tau isoform balance towards tau 3R and an increased burden of ghost tangles in the caudate nucleus, consistent with the lower survival rate observed in these patients. These histopathological changes could be due to alterations in splicing factors dynamics, as evidenced by reduced SRSF6 levels and increased FUS/SFPQ complex formation, providing a potential mechanistic link to the observed increase of tau 3R. Beyond alterations in splicing factor activity, post-transcriptional gene regulation by small non-coding RNAs, such as miRNAs, plays a critical role in the intricate molecular landscape of neurodegenerative diseases, including MAPT alternative splicing ( 45 ). Interestingly, impaired miRNA expression as a function of the number of CAG repeats in the HTT gene was observed in different brain regions in HD mouse models, with the striatum the most vulnerable area ( 19 ). Therefore, we hypothesized that the caudate nucleus of LOAD patients carrying HTT IAs might exhibit an altered miRNA profile. We performed small RNA-Seq on postmortem caudate nucleus samples from a subset of individuals within the study cohort, carefully selected based on demographic factors such as sex and age at death to minimize potential confounding effects, and Braak stage in the case of LOAD patients to account for disease progression (see Table S2 for details). Initial analysis identified a total of 1953 miRNAs with at least 1 RPM in at least one subject. To focus on consistently expressed miRNAs, we applied a first inclusion criterion requiring expression in at least 50% of the subjects within at least one of the groups. This reduced the database to 1187 miRNAs. Subsequently, to prioritize highly abundant miRNAs likely to have a greater biological impact, we applied a second inclusion criterion, preserving those miRNAs that exhibited an expression level > 1000 RPM in all subjects within at least one of the groups, retaining 39 miRNAs (Figure S6A). To further assess the robustness of our normalization strategy and the biological integrity of the data, we employed the DANA approach ( 32 ), incorporating RPM normalization method into its framework. This analysis demonstrated that our RPM normalization effectively preserved biological signals and mitigated handling effects, providing robust and interpretable evaluation of the miRNA-Seq data (Figure S6B). Based on this filtered set, differential expression analysis was performed using our custom R package, miRPM, which integrates the entire bioinformatics workflow for miRNA-Seq data analysis. From the retained 39 miRNAs, we performed a principal component analysis (PCA; Figure S6C), from which PC1 and PC2 components explained 58.9% of the variance observed in the subjects, exhibiting a consistent distribution pattern. In the PCA plot shown in Figure S6C, the control group appeared separated from the LOAD groups along PC1 and PC2, with an observable gradient within the LOAD group, in which HTT IAs carriers tended to position further along these components compared to non-carriers. However, despite these discernible trends, the overall separation was not sufficient to establish a clear distinction between the three groups solely based on PCA. The analysis among the three groups revealed that 26 of the 39 miRNAs that passed the screening process were significantly altered (Fig. 7 A and Table 3 ). Of these 26 miRNAs, all of them exhibited higher expression levels in LOAD HTT IA carriers compared to control group, while 21 showed significant differences between controls and LOAD non-carriers (Table 3 ). Thus, specifically, miR-100-5p, miR-218-5p, miR-27b-3p, miR-487b-3p, and miR-9-3p were differentially expressed regarding controls in LOAD HTT IAs patients. On the other hand, comparison between the two LOAD groups showed that 14 miRNAs were more overexpressed in HTT IA carriers than in non-carriers. Collectively, these findings indicate that the miRNA profile is significantly affected by the AD pathology itself, and that HTT IAs further amplifies these alterations. Table 3 Differentially expressed miRNAs in healthy controls vs. LOAD groups. miRNAs Control LOAD non - HTT IAs [Adjusted p-value vs. Control] LOAD HTT IAs [Adjusted p-value vs. Control | LOAD non - HTT IAs ] miR-99b-5p 706.99 ± 284.88 1213.29 ± 232.88 [0.0170] 1682.73 ± 332.49 [< 0.0001 | 0.0024] miR-9-5p 16386.6 ± 7417.38 32643.36 ± 5013.56 [0.0096] 39014.65 ± 5207.62 [< 0.0001 | 0.0076] miR-30d-5p 2819.36 ± 1657.27 5793.21 ± 1134.76 [0.0084] 7545.85 ± 1484.60 [< 0.0001 | 0.0102] miR-128-3p 4182.43 ± 2453.05 7897.71 ± 1813.42 [0.0228] 11928.67 ± 3091.31 [< 0.0001 | 0.0026] miR-23b-3p 626.34 ± 269.65 1428.61 ± 284.35 [0.0068] 1691.89 ± 213.97 [< 0.0001 | 0.0174] miR-191-5p 746.22 ± 424.02 1596.30 ± 297.25 [0.0083] 1932.93 ± 390.83 [< 0.0001 | 0.0193] miR-151a-5p 918.83 ± 460.58 1704.72 ± 375.72 [0.0127] 2079.86 ± 455.23 [< 0.0001 | 0.0168] miR-125a-5p 837.72 ± 400.65 2889.29 ± 859.72 [0.0021] 4390.30 ± 3066.51 [< 0.0001 | n. s. ] miR-30a-5p 3941.43 ± 2223.77 7719.60 ± 1488.43 [0.0089] 9224.76 ± 1615.98 [< 0.001 | 0.0282] miR-487b-3p 889.57 ± 571.90 1477.67 ± 402.52 [ n.s ] 2171.85 ± 648.72 [0.0002 | 0.0044] miR-125b-5p 4169.52 ± 2147.22 11502.07 ± 3279.17 [< 0.0029] 13890.95 ± 4082.34 [0.0002 | n. s. ] miR-221-3p 946.90 ± 466.80 1827.74 ± 641.08 [0.0052] 2250.74 ± 693.33 [< 0.0004 | n. s. ] miR-139-5p 669.33 ± 319.52 1345.48 ± 420.42 [0.0043] 1698.56 ± 453.94 [0.0006 | n. s. ] miR-103a-3p 3531.15 ± 2064.04 7974.13 ± 2293.12 [0.0034] 9017.74 ± 2049.48 [0.0008 | n. s. ] miR-99a-5p 3093.67 ± 1188.20 5094.84 ± 1597.55 [0.0225] 6288.39 ± 1970.75 [0.0011| n. s. ] miR-29a-3p 5212.68 ± 2435.92 9829.22 ± 2232.98 [0.0043] 10515.86 ± 3491.47 [0.0016 | n. s. ] miR-218-5p 1495.30 ± 904.06 2005.07 ± 656.07 [ n. s. ] 2685.15 ± 433.87 [0.0017 | 0.0024] miR-126-3p 1127.99 ± 647.37 1832.21 ± 638.10 [0.0421] 2354.87 ± 704.51 [0.0019 | 0.0398] miR-100-5p 1946.82 ± 799.29 2979.13 ± 803.40 [ n.s ] 3582.45 ± 951.83 [0.0033 | 0.0289] miR-124-3p 3016.37 ± 1393.79 6776.95 ± 3470.01 [0.0053] 6340.28 ± 2815.46 [0.0033 | n. s. ] miR-24-3p 1305.32 ± 611.40 2751.71 ± 557.81 [0.0012] 2583.71 ± 471.96 [0.0035 | n. s. ] let-7a-5p 5551.12 ± 2778.08 9428.42 ± 1717.47 [0.0084] 10713.78 ± 3368.41 [0.0043 | n. s. ] miR-181a-5p 3125.08 ± 1920.87 5420.40 ± 1409.52 [0.0428] 7066.36 ± 2796.03 [0.0049] miR-30c-5p 1855.95 ± 964.69 3017.19 ± 536.48 [0.0148] 3246.25 ± 798.72 [0.001 | n. s. ] miR-9-3p 8417.09 ± 6358.31 9191.86 ± 1803.44 [ n.s ] 12182.08 ± 1913.93 [0.0084 | 0.0042] miR-27b-3p 2717.82 ± 1559.54 3526.19 ± 808.69 [ n.s ] 4626.31 ± 1250.03 [0.0095 | 0.0265] Data are shown as mean ± SD. Statistical analysis: Kruskal-Wallis (miRNAs selected based on FDR-adjusted p-value < 0.05). Pairwise comparisons were performed using Dunn’s post-hoc test, with p-values adjusted for multiple comparisons. Ordered from highest to lowest significance according to LOAD HTT IAs. To investigate whether altered miRNA expression is associated with clinical features in this genetic context of LOAD, we examined the relationship between the 14 differentially expressed miRNAs between the LOAD groups (Table 3 ) and key clinical variables. No significant correlations were observed with age at onset, age at death, disease duration, or Braak stage (Table S8). In contrast, CAG repeat size showed a positive, and significant, correlation with the expression of six miRNAs (Fig. 7 B and Table S8): miR-128-3p (R = 0.48, p-value = 0.011), miR-99b-5p (R = 0.59, p-value = 0.001), miR-9-5p (R = 0.45, p-value = 0.017), miR-9-3p (R = 0.58, p-value = 0.001), miR-218-5p (R = 0.52, p-value = 0.005), and miR-27b-3p (R = 0.46, p-value = 0.015). Of these, the last three are among the five miRNAs that we have previously found to be specific to the presence of HTT IAs . These results suggest that the altered miRNA profile in the caudate nucleus is significantly influenced by HTT CAG repeat length, potentially contributing to the accelerated disease progression observed in LOAD individuals carrying HTT IAs . In silico analysis reveals spliceosome pathway as a key target of dysregulated miRNAs in LOAD patients with HTT IAs Since the LOAD-associated miRNA profile in caudate is even more impaired by the presence of HTT IAs , we next explored whether this miRNA pattern could be affecting genes specifically involved in the splicing of the MAPT gene. As a first approach to our in silico study, we performed an initial enrichment analysis using validated gene targets. This analysis revealed that the miRNAs differentially expressed between LOAD donors and controls, as well as those distinguishing the LOAD subgroups, were significantly enriched in 151 and 147 biological pathways, respectively. Notably, 13 of these KEGG pathways were directly associated with neurodegenerative processes (Figure S6D-E, Table S9 and S10). To further explore the molecular mechanisms potentially regulated by these miRNAs, we performed an additional enrichment analysis using the REACTOME encyclopedia. This analysis identified over 470 potentially modulated processes, among which ten stood out with highly significant relevance and were all related to RNA biology (Fig. 7 C, Table S11), including mRNA splicing and metabolism of non-coding RNAs (such as miRNAs themselves). These findings reinforce our hypothesis of a functional connection between the identified miRNA profile and the spliceosome pathway in this subgroup of patients. To explore this pathway more thoroughly, we retrieved the genes included in the spliceosome-related pathway from REACTOME and subjected them to a Gene Ontology (GO) enrichment analysis using PantherDB v.19.0. A total of 246 genes were extracted and categorized according to cellular component, molecular function, and biological process. In terms of cellular component, the nucleus was the most enriched compartment (47%), followed by the cytosol (38%) (Fig. 7 D, Table S12). Regarding molecular function, the majority of genes were associated with nucleic acid-related activities, with DNA binding (31%) and RNA binding (26.14%) being the most prominent (Fig. 7 E, Table S13). When analyzing enrichment in biological processes, a large proportion of genes were involved in RNA processing (25.4%), RNA splicing (19.8%), and, more specifically, mRNA splicing (16.07%) (Fig. 7 F, Table S14). These data indicate that the spliceosome machinery is among the most enriched pathways targeted by the miRNAs identified in our analysis. Finally, experimentally validated miRNA-mRNA interactions were retrieved from miRTarBase, focusing on the 246 genes linked to the spliceosome pathway. Subsequent network analysis using Cytoscape revealed that ten out of the 14 miRNAs differentially expressed between LOAD subgroups shared common target genes with ten members of the SRSF family (Fig. 7 G). Taken together, these findings provide compelling evidence for a significant functional interplay between the miRNA expression profile identified in the caudate nucleus and the regulation of the spliceosome pathway. This suggests that dysregulation of specific miRNAs may critically influence alternative splicing mechanisms, potentially contributing to the molecular pathology underlying LOAD. These findings underscore the importance of spliceosome as a key regulatory center targeted by miRNAs in this neurodegenerative context, warranting further experimental validation. Inter-relationship between dysregulated miRNAs , HTT CAG repeat size, and key neuropathological hallmarks To better integrate our findings within a neuropathological framework, we next investigated the associations between miRNA expression profiles, CAG repeat length, and the severity of tau pathology, including both pretangles and ghost tangles, as well as the soluble HTT signal in caudate neurons (Fig. 8 A, Table S15). We observed a consistent and statistically significant positive correlation between the intensity of soluble HTT immunoreactivity and the expression levels of eleven miRNAs, with miR-218-5p and miR-27b-3p, included in the group of five miRNAs specific for LOAD HTT IAs patients, showing the strongest associations. This suggests a transcriptional footprint associated with the presence of an exacerbated HTT protein profile in the caudate nucleus. Only miR-30d-5p, miR-100-5p, and miR-126-3p failed to show significant associations with increased HTT soluble levels. Conversely, the burden of pretangles exhibited a predominantly negative correlation with miRNA levels, reaching statistical significance for 6 out of the 14 miRNAs analyzed (Fig. 8 A, Table S15). In contrast, ghost tangles, indicative of more advanced tau pathology, displayed a positive correlation with miR-218-5p and miR-30a-5p (Fig. 8 A, Table S15). Interestingly, the number of CAG repeats correlated positively with both HTT soluble signal and the abundance of ghost tangles, while showing an inverse correlation with pretangle burden. This finding reinforces the notion that CAG repeat length correlates with HTT protein burden and is associated with a shift toward more mature tau aggregates. Building upon our in silico target analysis, we examined experimentally validated miRNA-mRNA interactions involving the MAPT and HTT genes (Fig. 8 B). Network modeling identified three miRNAs (miR-23b-5p, miR-99-5p, and miR-128-3p) that are validated regulators of both MAPT and HTT . Additionally, other miRNAs showed gene-specific interactions: miR-191-5p targets MAPT exclusively, while miR-27b-3p is selectively linked to HTT . These direct validated interactions provide a molecular basis for the correlations observed, strengthening the evidence that the post-transcriptional effects of HTT extend beyond its canonical role in HD, reshaping the miRNA scenario in a way that promotes tau pathology in the context of LOAD. Such miRNA-mediated cross-talk between HTT and MAPT may contribute to the acceleration of tau-driven neurodegeneration. Collectively, these data underscore a dual role for HTT IAs : both as histopathological hallmarks, by increased HTT and ghost tangles, and as modulators of miRNA-mediated gene regulation, synergistically accelerating tau-mediated neurodegeneration. However, further experimental studies are necessary to elucidate the precise molecular mechanisms and the biological significance of these miRNAs in the context of this accelerated neurodegeneration. Discussion Late-onset Alzheimer´s disease (LOAD) patients represent a heterogeneous population. In fact, there is a complex influence of genetic, environmental, and other factors on disease development and progression. Our previous studies showed a higher frequency of intermediate alleles in the HTT gene among AD patients ( 7 , 8 ). Therefore, in this work, we aimed to investigate whether this genetic characteristic influences LOAD progression and/or its neuropathological hallmarks, with a special focus on MAPT gene splicing. A key finding from our study is that LOAD patients carrying HTT IAs exhibited a lower survival rate after disease onset compared to non-carrier patients, a clinical outcome not previously described. We have analyzed the caudate nucleus, a region particularly sensitive to alterations in HTT CAG repeat number ( 18 ). Histopathological and molecular analysis of this brain region revealed that while LOAD non-carriers subjects already display alterations (e.g., increased soluble HTT levels or altered miRNA profiles) compared to healthy controls, these changes were consistently and further exacerbated in HTT IAs carrier patients. This observed severity in neuropathology included a heightened diffuse HTT immunoreactivity, in a non-aggregated state, distinguishing it from the profound pathology seen with fully mutated HTT in HD, and a more advanced tau pathology. Both factors likely contribute to the compromised survival in HTT IAs carrier patients by favoring a gain of toxic function of the non-aggregated HTT. The tau 3R predominance observed here, characterized by a tau 3R isoform imbalance and a significant increase in 3R tau-enriched ghost tangles, aligns with its association with axonal cytoskeleton destabilization and reduced neuronal survival ( 46 ). This also positions HTT IA -associated LOAD in later stages of disease, where tau 3R becomes more prevalent ( 47 , 48 ) and may promote the progression of NFTs toward their final, neurotoxic stage ( 40 , 49 , 50 ). Our study elucidates molecular mechanisms underlying this acceleration in neuropathology. It represents the first detailed miRNA profiling in the caudate nucleus of LOAD patients, showing that this profile is already altered in LOAD, and profoundly amplified in HTT IA carriers. We employed a novel RPM-based analysis method (miRPM) validated by the DANA approach ( 32 ). This stringent analysis revealed that while 26 miRNAs were generally altered in LOAD versus controls, HTT IAs specifically exacerbated these changes, with 14 miRNAs being significantly more overexpressed in HTT IA carriers compared to non-carriers. Five miRNAs (miR-100-5p, miR-218-5p, miR-27b-3p, miR-487-3p, and miR-9-3p) were uniquely altered in LOAD HTT IAs patients regarding healthy controls, highlighting their specificity, and emerging as a HTT IA signature miRNAs. Notably, miR-100-5p has also been linked to disease progression in HD ( 21 ), suggesting a shared molecular pathway across HTT-related conditions. Furthermore, a subset of miRNAs showed a positive correlation with CAG repeat length, indicating a CAG-dependent modulation. Importantly, miR-218-5p was the only miRNA analyzed that exhibited positive correlations with CAG repeat length, HTT protein levels, and ghost tangles, and negative with pretangles. This suggests miR-218-5p may play an essential role in the alterations leading to the accelerated disease progression observed in these patients. Our in silico studies and network analysis provide mechanistic insights, suggesting that these altered miRNAs directly target key components of the splicing machinery and both MAPT and HTT genes. This strongly supports our observations at the histopathological levels regarding tau isoform imbalance and splicing factors dysregulation, such as SRSF6 and FUS-SFPQ, in HTT IAs carriers. This miRNA-mediated effect suggests that the presence of HTT IAs significantly modifies the regulatory environment of gene expression to aggravate splicing machinery alterations and accelerate tau-mediated neurodegeneration. This scenario of HTT-tau co-occurrence, where HTT IAs favor increased HTT and tau 3R levels, driving accelerating NFTs maturation, is in line with studies suggesting HTT pathology as a trigger for multiple proteinopathies, including TDP-43, alpha-synuclein, beta-amyloid, and tau alterations in advanced HD ( 16 ). While tau isoform shifts differ between HD (4R) ( 17 ) and LOAD (3R), the positive correlation of tau alterations with CAG repeat length in both conditions is striking. This suggests that the final HTT protein state (mutated versus intermediate) may dictate the precise tau imbalance. Our proposed mechanism integrating these findings is schematically represented in Fig. 9 . Conclusions These findings collectively underscore the profound impact of HTT IAs on LOAD pathogenesis, offering another clue to understand the disease’s clinical heterogeneity. This comprehensive neuropathological and molecular characterization reveals HTT IAs as critical risk biomarkers. Crucially, as HTT genotyping is a widely used and easily accessible technique, our results can contribute to improved clinical practice by enabling a more precise stratification of LOAD patients for clinical trials and facilitating the development of more focused, personalized, therapeutic interventions. The identified miRNA dysregulation, particularly the HTT IA signature miRNAs, represents a promising avenue for novel therapeutic targets, as their dysregulation offers a precise avenue for future intervention. Limitations Working with postmortem human brain tissue, while invaluable for understanding neuropathology, presents unique limitations that can influence study design and sample inclusion. We acknowledge the variability in sample size ( N ) across our different experimental assays (e.g., RT-qPCR, Western blot, immunohistochemistry), which is primarily due to the finite nature of our well-characterized donor cohort and the differing quality requirements of each analytical method. For RNA-based analyses, such as RT-qPCR or mRNA expression and miRNA-Seq, we faced challenges with RNA integrity. As indicated by the low RIN values in Table Supplementary 4, RNA is highly susceptible to postmortem degradation. While this significantly affects mRNA expression levels, miRNA sequencing data is less compromised, as several studies indicate that miRNAs, being small nucleotide chains with high resistance to degradation, allow for reliable data acquisition despite low RIN levels ( 51 – 53 ). Similarly, the integrity of proteins in postmortem brain tissue poses significant challenges for Western blot analysis. We encountered difficulties due to the degradation of protein samples, especially for control subjects, which limited their inclusion. However, it is important to note that the primary objective of our Western blot analyses was to elucidate differences between LOAD HTT IAs carriers and LOAD non-carrier patients, a critical comparison that remained fully addressed by the available samples. For immunohistochemistry and immunofluorescence, optimal tissue morphology and antigen preservation are critical for accurate high-resolution analysis and cell counting. Despite differences in N across the different techniques, consistent trends and complementary information observed at various molecular levels (e.g., miRNA, mRNA, protein, and IHC profiles) derived from these human samples significantly enhance the overall robustness and interpretability of our conclusions. Abbreviations AD: Alzheimer’s disease APOE : Apolipoprotein E E10: exon 10 of MAPT gene FDR: false discovery rate FTD: frontotemporal dementia FUS: fused in sarcoma protein GO: gene ontology HD: Huntington’s disease HTT IAS : intermediate alleles in HTT gene HTT : huntingtin protein KEGG: Kyoto Encyclopedia of Genes and Genomes LOAD: Late- onset Alzheimer’s disease MAPT: microtubule associated tau gene miRNAs: microRNAs NDs: neurodegenerative diseases NFTs: neurofibrillary tangles PCA: principal component analysis PLA: proximity ligation assay PMI: postmortem interval PSP: progressive supranuclear palsy RBP: RNA binding protein RPM: reads per million RIN: RNA integrity number ROI: region of interest RPM: read per million SFPQ: Proline/Glutamine rich splicing factor SRSF: Serine/Arginine rich splicing factor Declarations Ethical approval All procedures were performed after obtained the approval of Ethical Committees of Neurological Tissue Bank (NTB) of the Hospital Clinic-FRCB-IDIBAPS (Barcelona, Spain), the Principado de Asturias BioBank and Research Ethics Committee of the Principality of Asturias (CEImPA nº 2022.266). Consent for publication All authors have approved the content of this manuscript and provided consent for publication. Availability of data and materials All data generated and used in this study are publicly available in ZENODO (DOI: 10.5281/zenodo.15230070) and NCBI GEO DataSets (ID: GSE300433; The following secure access code has been created to allow review of record GSE300433 while it remains in private status: mtwfggcozpalrwn). R code used to analyze miRNA database is available at https://github.com/sergio30po/miRNA-RPM-DE-Analysis.git. Competing interests The authors declare no conflicts of interest. Author's contribution J.C-S performed all histological and molecular experiments, conducted data analysis, created the figures and wrote the manuscript. S.P-G performed genotyping the initial samples to establish the studied cohort, developed the miRPM R package, and carried out bionformatic analysis. P.P-H, M.F-S and E.I-G provided guidance on the analysis and interpretation of RNAseq and miRNAs experiments. MD.C-T developed the technical tasks for the preparation of the samples provided by the Biobank of the Principality of Asturias. V.A and M.M-G conceptualized, designed and obtained funding for the study, interpreted the epidemiological data, executed the genetic objective of the study, and supervised the final versions of the manuscript. C.T-Z designed and supervised the molecular and histopathological analysis experiments, interpreted the data, and supervised the different versions of the manuscript. Acknowledgments We extend our gratitude to the research staff for their valuable support, including: J. Bermejo-Pampliega (Physiology Area of University of Oviedo; AYUD/2021/5134); M. Alonso-Guervos (Microscopy and Image Processing Unit, Scientific and Technical Services, University of Oviedo); the Molecular Histopathology Service in Animal Cancer Models at Instituto Universitario de Oncología del Principado de Asturias (IUOPA), and the Molecular Genetics Laboratory of Hospital Universitario Central de Asturias (HUCA). We also acknowledge the collaboration of the Principado de Asturias BioBank (PT23/0077), financed by Servicio de Salud del Principado de Asturias and ISCIII/FEDER, and HCB-IDIBAPS Biobank for sample and data procurement. Finally, we are indebted to Asociación Parkinson Asturias-Obra Social Cajastur and to all the patients and their families for their invaluable contributions. Funding This study was funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union (FEDER/FSE) through PI21/00467 (V.A., M. M-M). J.C-S was supported by ISCIII grant AC20/00017, co-founded by EuroNanoMed III (20-0084, M. 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Supplementary Files CastillaSilgadoetalSupplementarytables.pdf CastillaSilgadoetalSupplementaryfigures.pdf Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Alzheimer's Research & Therapy → Version 1 posted Editorial decision: Revision requested 30 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 28 Sep, 2025 Editor assigned by journal 17 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7621820","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526362344,"identity":"2bf88423-5426-48df-96e8-d99a842f8e6d","order_by":0,"name":"Juan Castilla-Silgado","email":"","orcid":"","institution":"University of Oviedo","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Castilla-Silgado","suffix":""},{"id":526362346,"identity":"2f95bd37-feba-44c7-b413-da0cd28de554","order_by":1,"name":"Sergio Perez-Oliveira","email":"","orcid":"","institution":"Health Research Institute of the Principality 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1","display":"","copyAsset":false,"role":"figure","size":181684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe presence of HTT IAs modifies LOAD progression.\u003c/strong\u003eKaplan-Meier survival curves comparing LOAD patients with HTT IA (N = 20) to non-HTT IAs carriers (N = 191), showing reduced survival after clinical diagnosis in HTT IA carrier group. Statistical significance was determined by Log-rank test and p-value is indicated in the graph.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/a84ec7219659db17e6475815.png"},{"id":93251789,"identity":"07846dee-b650-44d0-96c6-9668d3d98702","added_by":"auto","created_at":"2025-10-10 15:52:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2152639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated diffuse HTT in caudate of LOAD patients is further increased by HTT intermediate alleles.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Representative immunohistochemistry (IHC) images of the caudate nucleus showing HTT distribution. The upper panel uses anti-HTT EPR5526 clone (detects normal and mutated HTT), while the lower panel uses anti-HTT EM-48 clone (specific for mutant HTT). Arrows indicate cytoplasmic HTT pattern, consistent across groups. An arrowhead in the HD patient, detected with anti-HTT EM-48, highlights an intranuclear HTT inclusion. Scale bars: 20 µm. \u003cstrong\u003e(B)\u003c/strong\u003eQuantification of relative intensity of cytoplasmic HTT immunoreactivity per mm2 using anti-HTT EPR5526. \u003cstrong\u003e(C)\u003c/strong\u003eQuantification of HTT-positive cells per mm2 using anti-HTT EM-48. Data are shown as mean ± SD, with each point representing one subject. Statistical significance was calculated by one-way ANOVA followed by Tukey’s post hoc test; p-values are indicated in the graphs. Control subjects (\u003cem\u003eN\u003c/em\u003e = 5), LOAD patients (\u003cem\u003enon-HTT IAs\u003c/em\u003e carriers, \u003cem\u003eN \u003c/em\u003e= 10; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 9), and HD patients (\u003cem\u003eN\u003c/em\u003e= 2).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/39c34da09c92edfd8532d2a4.png"},{"id":93248888,"identity":"9345d7af-7799-443f-868b-a09eeed5f4bd","added_by":"auto","created_at":"2025-10-10 15:36:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2384633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHTT IAs increase tau 3R and 4R isoforms in LOAD patients’ caudate nucleus. (A-B)\u003c/strong\u003e Relative mRNA levels of total MAPT, MAPT 3R, and MAPT 4R isoforms in the caudate nucleus, analyzed by RT-qPCR. GAPDH was used as normalizer, and relative levels were calculated using the 2-ΔΔCt method, with the control group as reference. Data are represented as median ± SD, and each point represents one subject. Statistical significance was assessed using Kruskal-Wallis followed by Dunn's multiple comparison test. Controls (\u003cem\u003eN\u003c/em\u003e = 5) and LOAD patients (\u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 13; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 13). \u003cstrong\u003e(C-F)\u003c/strong\u003e Immunoblots for total tau, tau 3R, and tau 4R proteins in LOAD patients are shown in panel C, with molecular weights (kDa) is indicated. Quantification of total tau \u003cstrong\u003e(D)\u003c/strong\u003e, tau 3R \u003cstrong\u003e(E)\u003c/strong\u003e, and tau 4R \u003cstrong\u003e(F)\u003c/strong\u003e protein levels are expressed as arbitrary optical density units. Data are represented as mean ± SD. Statistical significance was assessed Student´s t-test. LOAD \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 7; \u003cem\u003eLOAD HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 7). \u003cstrong\u003e(G-I)\u003c/strong\u003eImmunohistochemistry for tau 3R and tau 4R. Representative images are shown in panel G, where arrows indicate regions magnified in insets. Scale bar: 50 µm. Quantification of tau 3R+ \u003cstrong\u003e(H)\u003c/strong\u003e and tau 4R+ \u003cstrong\u003e(I)\u003c/strong\u003e neurons per mm2 in the caudate nucleus. Data are shown as mean ± SD, with each point representing one subject. Statistical significance was calculated by one-way ANOVA followed by Tukey’s post hoc test, and p-values indicated in graphs. Controls (N = 5) and LOAD patients (\u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 9; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 9).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/7993f7b3d93e7f63dd709b43.png"},{"id":93248879,"identity":"e0d81340-7047-4b63-8161-079a4d6734c3","added_by":"auto","created_at":"2025-10-10 15:36:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":700903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdvanced neurofibrillary tangles (NFT) maturation in LOAD patients are linked to HTT IAs. (A)\u003c/strong\u003e Representative immunohistochemistry images NFTs immunoreactive for tau 4R and tau 3R in the caudate nucleus of LOAD patients’ subgroups. \u003cstrong\u003e(B)\u003c/strong\u003e Quantification of NFTs per mm2 in caudate neurons differentiated by tau 4R and tau 3R immunoreactivity. Data are shown as total structures per mm2 for each indicated tangle condition. Statistical significance was determined by Student’s t-test, for intragroup comparisons (a) and by one-way ANOVA followed by Tukey’s post hoc test for intergroup comparisons (b); p-values are indicated in graphs. LOAD \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 9; HTT IAs, \u003cem\u003eN\u003c/em\u003e = 9.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/1dacd34511d3af125b341bb6.png"},{"id":93248885,"identity":"1a6f9e5b-32dd-4c70-90dc-f62256855d37","added_by":"auto","created_at":"2025-10-10 15:36:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":133113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSRSF6 splicing factor decreased caudate nucleus levels in LOAD HTT IA carriers. (A)\u003c/strong\u003e Relative SRSF6 mRNA expression levels in the caudate nucleus, analyzed by RT-qPCR. GAPDH mRNA was used as normalizer, and relative expression was calculated using the comparative Ct method (2-ΔΔCt), with the control group as a reference. Results are shown as mean ± SD, with each point representing one subject. Statistical significance was calculated by one-way ANOVA followed by Tukey’s post hoc test. Control subjects (N = 5), and LOAD patients (\u003cem\u003enon-HTT IAs\u003c/em\u003e carriers, \u003cem\u003eN\u003c/em\u003e = 14; \u003cem\u003eHTT IAs\u003c/em\u003e, N = 13). \u003cstrong\u003e(B)\u003c/strong\u003e SRSF6 immunoblot for protein determination in LOAD patients. Molecular weights (kDa) are indicated. \u003cstrong\u003e(C)\u003c/strong\u003e Quantification of relative SRSF6 protein levels, expressed as arbitrary optical density units. The non-HTT IA carriers’ group was used control. Results are shown as mean ± SD, with each point representing one subject. Statistical significance was determined by Student’s t-test. LOAD \u003cem\u003enon-HTT IAs\u003c/em\u003ecarriers, \u003cem\u003eN\u003c/em\u003e = 7; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 7. P-values are indicated in the graphs.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/a6752cb13d59b1ea1e72e319.png"},{"id":93248882,"identity":"bed9b971-5d4c-4129-a5c2-e9d48ed76749","added_by":"auto","created_at":"2025-10-10 15:36:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1645461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFUS-SFPQ complexes are increased in the caudate nucleus of LOAD HTT IA patients. (A-B).\u003c/strong\u003e Relative mRNA levels for FUS \u003cstrong\u003e(A)\u003c/strong\u003e and SFPQ \u003cstrong\u003e(B)\u003c/strong\u003e levels in the caudate nucleus, determined by RT-qPCR. GAPDH expression was used as normalizer, and relative expression was calculated using the 2-ΔΔCt method, with the control group as a reference. Results are represented as median ± SD and each point representing one subject. Statistical significance was determined using Kruskal-Wallis followed by Dunn’s test. Controls (\u003cem\u003eN\u003c/em\u003e = 5) and LOAD patients ( \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 14; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 13). \u003cstrong\u003e(C)\u003c/strong\u003e FUS and SFPQ immunoblots in LOAD patients. Molecular weights (kDa) are indicated. \u003cstrong\u003e(D-E)\u003c/strong\u003eQuantification of relative FUS \u003cstrong\u003e(E) \u003c/strong\u003eand SFPQ \u003cstrong\u003e(F)\u003c/strong\u003e protein levels, expressed as arbitrary optical density units. The \u003cem\u003enon-HTT IA \u003c/em\u003ecarriers’ group served as control. Results are shown as mean ± SD, with each point representing one subject. Statistical analysis was performed by Student’s t-test. LOAD \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 7; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 7). \u003cstrong\u003e(F)\u003c/strong\u003eDouble-fluorescence images showing SFPQ-FUS colocalization in caudate neuron nuclei. Scale bar, 20 µm. (G-H) Colocalization analysis using Pearson's R \u003cstrong\u003e(G)\u003c/strong\u003e and Threshold Mander’s coefficients (tM1: SFPQ/FUS; tM2: FUS/SFPQ; \u003cstrong\u003eH\u003c/strong\u003e). Thirty nuclei per subject were analyzed using the Coloc2 plugin (FIJI). Data are represented as median and interquartile ranges. Statistical significance was calculated using Kruskal-Wallis followed by Dunn’s post hoc test. Controls\u003cem\u003e \u003c/em\u003e(\u003cem\u003eN\u003c/em\u003e= 3) and LOAD patients (LOAD \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 4; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 4). \u003cstrong\u003e(I)\u003c/strong\u003e Representative proximity ligation assay (PLA) images of caudate neuron nuclei. Scale bar, 10 µm. \u003cstrong\u003e(J)\u003c/strong\u003e Quantification of PLA colocalization signal intensity, obtain by summing maximum Z-plane projections. Data are represented as median and interquartile ranges \u003cstrong\u003e(G-J)\u003c/strong\u003e. Statistical analysis was determined by Kruskal-Walli’s test followed by Dunn’s post hoc test. Controls: \u003cem\u003eN\u003c/em\u003e= 2; LOAD: \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 2; \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 2. P-values are indicated in the graphs.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/17d350e9df3a81e9949c3d04.png"},{"id":93250730,"identity":"abd4f976-93e5-4603-ba7c-e39129def126","added_by":"auto","created_at":"2025-10-10 15:44:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":874320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiRNA profile in the caudate nucleus links HTT CAG size to spliceosome pathway regulation. (A)\u003c/strong\u003e Heat maps showing miRNAs expression after RNA-seq analysis in caudate nucleus samples comparing control subjects vs. LOAD subgroups (Controls: N = 6; LOAD: \u003cem\u003enon-HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 14\u003cem\u003e; HTT IAs\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e = 13). The color scale illustrates miRNA expression levels from red (the highest) to blue (the lowest), with significance indicated by p-value (Dunn’s test vs. control comparison \u0026lt;0.05) and Z-score. \u003cstrong\u003e(B)\u003c/strong\u003e Spearman correlation analysis between CAG repeat size in HTT gene and miRNA expression. Dot size is proportional to the correlation coefficient, and p-value is represented by the color scale (* p-value \u0026lt; 0.05, ** p-value \u0026lt; 0.01). \u003cstrong\u003e(C)\u003c/strong\u003e Enrichment diagram based on REACTOME database analysis, highlighting RNA-related processes targeted by differentially expressed miRNAs between LOAD subgroups. Circle size corresponds to the number of target genes associated with the miRNAs in each process. \u003cstrong\u003e(D-F)\u003c/strong\u003e Gene ontology (GO) analysis of validated target genes within the spliceosome pathway (identified using Panther DB platform), showing their distribution by Cellular Component \u003cstrong\u003e(D)\u003c/strong\u003e, Molecular function \u003cstrong\u003e(E)\u003c/strong\u003e and Biological process \u003cstrong\u003e(F)\u003c/strong\u003e. Note: a gene may be assigned to more than one category, so the total number of genes assigned may exceed the total recognized. (G) Diagram of validated interactions, generated in Cytoscape v.3, between miRNAs and SRSF gene family members identified from the miRWalk database.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/ceadfc048dedaadc621951a4.png"},{"id":93250731,"identity":"0253f08c-886e-49e7-99f8-89e9802d0ec3","added_by":"auto","created_at":"2025-10-10 15:44:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":223249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAmplifying/accelerating role of HTT IAs as critical disease modifiers. (A)\u003c/strong\u003e Spearman correlation matrix illustrates the relationships between miRNA levels, HTT CAG repeat size and histopathological markers related to tau and HTT pathology. The color of each grid determines the degree of correlation. Statistical significance for each correlation, statistical significance is denoted by p-value: * p-value \u0026lt; 0.05, ** p-value \u0026lt; 0.01, and *** p-value \u0026lt; 0.001. Exact correlation coefficients and p-values are provided in Table S16. \u003cstrong\u003e(B)\u003c/strong\u003e Interaction diagram, generated using Cytoscape v.3.10.3, depicting regulatory relationships between selected miRNAs and the MAPT and HTT transcripts, based on experimentally validated targets from miRTarBase v.8.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/5b48cafc9116c2ecda1bf131.png"},{"id":93248890,"identity":"fe5db2f4-cf71-4cea-9c5d-e5361adb5421","added_by":"auto","created_at":"2025-10-10 15:36:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":512559,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed mechanism: HTT intermediate alleles (HTT IAs) accelerate LOAD pathogenesis through miRNA-mediated splicing dysregulation.\u003c/strong\u003eApproximately 6% of LOAD patients carry HTT IAs. At the caudate nucleus level, the presence of these alleles is associated with increased levels of diffuse HTT in the neuronal cytoplasm and profoundly reshapes the LOAD-associated miRNA profile. These miRNA alterations directly impact genes related to the splicing machinery. This molecular dysregulation translates into alterations in key proteins and complexes involved in MAPT gene splicing, specifically a decrease in SRSF6 and increased formation of FUS-SFPQ complexes, leading to increased tau 3R levels. Ultimately, this results in an increase in NFTs at the most advanced stage (ghost tangles), leading to aggravation of the pathophysiological state in HTT IA carrier patients. The five LOAD HTT IA signature miRNAs (miR-9-3p, miR-27b-3p, miR-218-5p, miR-487b-3p, miR-100-5p) are strongly implicated in this cascade, correlating with CAG repeat length, HTT protein levels, and tau pathology. Among these, miR-218-5p stands out for its potential involvement in the increase of HTT levels in neurons, its role in MAPT splicing favoring tau 3R increase, and its association with mature NFT formation, suggesting a central regulatory role in this accelerated neurodegeneration.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/5d6f88f8ef832fa34f31b059.png"},{"id":107928192,"identity":"fb74e179-de27-4804-a25f-6800675845cc","added_by":"auto","created_at":"2026-04-27 16:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8808492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/86c0a547-d04c-4815-8c2f-3de6da394a3b.pdf"},{"id":93248877,"identity":"2c204378-6f0c-4345-adc3-c02fea73efef","added_by":"auto","created_at":"2025-10-10 15:36:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":446567,"visible":true,"origin":"","legend":"","description":"","filename":"CastillaSilgadoetalSupplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/539f3c6c09175e185bfec26c.pdf"},{"id":93248875,"identity":"82d35be8-42c3-47f7-a82f-46b23e17a2fe","added_by":"auto","created_at":"2025-10-10 15:36:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":922026,"visible":true,"origin":"","legend":"","description":"","filename":"CastillaSilgadoetalSupplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7621820/v1/61cd007e794ce1af09a232bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Connecting HTT intermediate alleles and microRNA dysregulation to enhanced tauopathy in Late-Onset Alzheimer's Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD), which accounts for 60\u0026ndash;80% of dementia cases, is primarily characterized by the presence of extracellular beta-amyloid plaques and intracellular accumulation of tau protein in the form of neurofibrillary tangles (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, up to 50% of affected individuals present with mixed dementia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) associated with the existence of co-pathologies involving the accumulation of proteins, such as alpha-synuclein or TDP-43 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), especially in the more advanced stages of the disease (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). While the precise cause of this phenomenon is still unknown, genetic factors like the \u003cem\u003eAPOE*ε4\u003c/em\u003e allele have been associated with an increased risk of developing co-pathologies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In fact, this polymorphic variant introduces significant variability in AD development between carriers and non-carriers (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), causing diverse clinical and phenotypic manifestations that hinder the application of optimal therapeutic and care interventions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Thus, identifying specific genetic risk factors for AD is essential for developing more appropriate, individualized treatment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious results from our laboratory suggest that there is a higher prevalence of intermediate alleles in the huntingtin gene (\u003cem\u003eHTT\u003c/em\u003e) in AD patients with respect to control subjects (6.03% vs. 2.9%) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), extending this fact to other tauopathies, such as frontotemporal dementia (FTD) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and synucleinopathies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Exon 1 of the \u003cem\u003eHTT\u003c/em\u003e gene presents CAG repeats in variable number, being considered normal below 27 repeats. When the number of repeats is above 35, it determines the age of onset of Huntington's disease (HD) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), with incomplete penetrance when the range is between 36 and 39 repeats and complete when this number is higher (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Between 27 and 35 repeats are referred to as intermediate alleles (\u003cem\u003eHTT IAs\u003c/em\u003e), which are genetically unstable at the germline level, although they are not considered pathogenic (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, in the case of AD, it has not yet been possible to determine the effect of the presence of \u003cem\u003eHTT IAs\u003c/em\u003e. At the neuropathological level, an increase in diffuse HTT accumulation has been found in hippocampal regions associated with memory and in layer III of the frontal cortex in AD patients compared to healthy subjects (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), although in this case the number of CAG repeats in the \u003cem\u003eHTT\u003c/em\u003e gene has not been evaluated. On the other hand, there are studies showing that elderly HD subjects show co-pathology with AD in up to 82% of cases, with prominent dementia in the clinic (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and increased aggregation of other proteins, such as phosphorylated tau, alpha-synuclein, and TDP-43, can also be observed in the later stages of HD (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It has even been proposed that HD is a 4R tauopathy, as alterations in the splicing of the \u003cem\u003eMAPT\u003c/em\u003e gene have been described, modifying tau 3R/4R balance, and the absence of this gene in murine models of HD diminishes the characteristic motor alterations (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Therefore, it is reasonable to hypothesize that \u003cem\u003eHTT IAs\u003c/em\u003e in AD could also influence \u003cem\u003eMAPT\u003c/em\u003e gene splicing, affecting the progression of AD pathology.\u003c/p\u003e\u003cp\u003eStudies developed in murine models describe changes in gene expression as a function of CAG repeat length (including intermediate ranges), being the striatum the most sensitive region (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and such alteration modifies the expression of microRNAs (miRNAs) in this brain area (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). miRNAs are small non-coding RNAs (\u0026asymp;\u0026thinsp;18\u0026ndash;22 nucleotides) that are an integral part of the epigenetic regulatory network, since they can act as modulators that influence gene expression at the post-transcriptional level, including that of other genetic modifiers, such as DNA methyltransferases and histone acetylases (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In addition, they themselves are subject to epigenetic regulation, as their expression profile can be altered under pathological conditions, as shown for both HD (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and AD (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) or by lifestyle (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Thus, it is possible that both AD and the presence of \u003cem\u003eHTT IAs\u003c/em\u003e modify further the profile of miRNAs in carrier individuals, thereby affecting gene expression of key genes for disease progression.\u003c/p\u003e\u003cp\u003eHere, we investigated the impact of \u003cem\u003eHTT IAs\u003c/em\u003e on the neuropathological features of late-onset AD (LOAD). At the clinical level, we found that the presence of \u003cem\u003eHTT IAs\u003c/em\u003e is associated with accelerated disease progression. Building on this observation, we first hypothesized that \u003cem\u003eHTT IAs\u003c/em\u003e influence LOAD pathology by altering \u003cem\u003eMAPT\u003c/em\u003e gene splicing and, consequently, the tau 3R/4R balance. To investigate this, we focused on the caudate nucleus, demonstrating that its neurons present increased levels of soluble HTT and that \u003cem\u003eHTT IAs\u003c/em\u003e further exacerbate this increase. Furthermore, LOAD patients carrying \u003cem\u003eHTT IAs\u003c/em\u003e exhibited a pronounced imbalance of tau isoforms, characterized by an excess of 3R tau and a greater abundance of 3R tau-enriched ghost tangles, compared to non-carrier LOAD subjects. These findings were supported by lower levels of the splicing factor SRSF6 and increased formation of FUS\u0026ndash;SFPQ nuclear complexes in the caudate nucleus. To elucidate the possible underlying molecular mechanisms driving these changes, we further hypothesized that the presence of \u003cem\u003eHTT IAs\u003c/em\u003e modifies LOAD-associated miRNA profile in caudate nucleus. This was confirmed by the first detailed miRNA profiling study in this region. Importantly, the altered miRNAs target key components of the nuclear spliceosome machinery, suggesting a role in the observed tau 3R/4R imbalance. Moreover, the identified miRNA dysregulation represents a promising avenue for novel therapeutic targets in LOAD. Collectively, our findings underscore that \u003cem\u003eHTT IAs\u003c/em\u003e serve as critical risk/progression biomarkers in AD, offering a practical, non-invasive genetic biomarker for clinical implementation, thereby facilitating patient classification and personalized therapeutic interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSubjects\u003c/h2\u003e\u003cp\u003eThis study included LOAD patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;323) and healthy controls (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;335) from a previously described cohort (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Detailed clinical data (diagnosis, gender, onset age, age at death, \u003cem\u003eHTT\u003c/em\u003e gene genotyping) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Based on caudate postmortem sample availability, a sub-cohort was obtained for LOAD non-carrier patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14), LOAD \u003cem\u003eHTT IAs\u003c/em\u003e carrier patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13) and healthy controls (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) for histological and molecular approaches, matched by demographic and neuropathological variables, which are summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Additionally, details of the subject samples used in each experiment are listed in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic data of cohorts studied.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOAD\u003c/p\u003e\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;323)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female %) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188 (57.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e231 (71.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset age\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN. A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath age\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN. A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal follow-up\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN. A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN. A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHTT IAs\u003c/em\u003e carriers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (4.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (7.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHTT\u003c/em\u003e short allele\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 [16\u0026ndash;17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 [17\u0026ndash;18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHTT\u003c/em\u003e long allele\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 [17\u0026ndash;21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 [18\u0026ndash;22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: LOAD, late-onset Alzheimer\u0026rsquo;s disease; \u003cem\u003eN.A\u003c/em\u003e: not available.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eData are shown as n (%).\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eData are shown as median [IQR].\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Statistical analysis: Fisher's test.\u003c/p\u003e\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Statistical analysis: Mann Whitney.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMaterial and data supply for patients were provided by the HCB-IDIBAPS Biobank (B.0000575), integrated in the platform ISCIII Biobanks and Biomodels, and by the Principado de Asturias BioBank (PT23/0077), member of the Spanish National Biobanks and Biomodels Network. Both materials and data were processed following standard operating procedures with the appropriate approval of the Ethics and Scientific Committees. The neuropathological LOAD diagnosis was performed according to standard international consensus criteria (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). All the participants or legal representatives gave written informed consent to participate in the study. All procedures have been approved by the Research Ethics Committee of the Principality of Asturias (CEImPA n\u0026ordm; 2022.266).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry and immunofluorescence\u003c/h3\u003e\n\u003cp\u003eFor immunohistochemistry, formalin-fixed paraffin-embedded tissue sections (5 \u0026micro;m) were processed using the EnVision FLEX\u0026thinsp;+\u0026thinsp;kit (K8002, Agilent-Dako, Agilent, USA) and the Dako Autostainer. Epitope retrieval was performed at 95 \u0026ordm;C for 20 min in PT-LINK (Dako) with pH 6.0 buffer (K8005) for HTT and tau 3R, or pH 9.0 (58004) for tau 4R. Endogenous peroxidase was blocked and sections were incubated with primary antibodies in FLEX antibody diluent (K8006), for 20 min at room temperature and overnight at 4\u0026deg;C. Signal was developed with diaminobenzidine (DAB; DM827\u0026thinsp;+\u0026thinsp;SM803) and counterstained with hematoxylin reagent (K8008). Negative controls were processed, omitting the primary antibody. All the primary antibodies used are summarized in Supplementary Table S3.\u003c/p\u003e\u003cp\u003eImages were acquired using the NanoZoomer-SQ Digital Slide Scanner (C13140-01, Hamamatsu Photonics, Germany) and analyzed with QuPath (version 0.4.2) and Fiji (ImageJ, version 1.6). All acquisition settings, including magnification (40\u0026times;), were kept constant across experimental groups. For 3R-tau (05-803), 4R-tau (05-804), and HTT clone EM-48 (MAB5374; Merck Millipore, USA) quantification, the number of immunopositive neurons was manually counted in caudate nucleus sections from each patient. Five randomly selected regions of interest (ROI) per case were analyzed, each measuring 1 mm\u0026sup2;. Results were expressed as the mean number of positive neurons/mm\u0026sup2;. For HTT clone EPR5526 (ab109115; Abcam, UK), a fixed intensity threshold was applied using Fiji to quantify positive staining in five randomly selected 1 mm\u0026sup2; ROIs per section. The signal intensity was then averaged across the five ROIs and expressed as mean signal intensity/mm\u0026sup2;.\u003c/p\u003e\u003cp\u003eFor double immunofluorescence, after antigen retrieval, sections were washed with distilled water and subsequently rinsed three times with 0.1M phosphate buffer, followed by a rinse in PBS for 10 min. Tissue sections were then blocked for 45 min at room temperature in a blocking solution consisting of 1% of serum albumin and 1% Triton X-100 in PBS. Primary antibodies (see Supplementary Table S3) were diluted in blocking solution supplemented with normal serum overnight at 4\u0026deg;C. The following day, sections were rinsed with PBS and distilled water. Secondary antibodies (donkey anti-mouse Alexa Fluor 488 and donkey anti-rabbit Alexa Fluor 594, 1:1000; Invitrogen, Thermo Fisher Scientific, USA) were incubated for 2 hours at room temperature. Finally, nuclei were counterstained with DAPI, included in the mounting medium (Fluoroshield\u0026trade;, Sigma-Aldrich), and coverslipped for imaging.\u003c/p\u003e\u003cp\u003eImages were acquired on a Leica TSC-SP8X spectral confocal microscope (Leica DMI8 microscope) with excitation lines between 470\u0026ndash;670 nm and PLA APO 20X/0.75 IMM CORR CS2 or PLA APO 40X /1.30 CS2 oil-immersion objective (Leica Microsystems, Germany), using Leica Application Suite X software (version 1.8.1, Copyright 1997\u0026ndash;2015; Leica Microsystems), and processed with LAS_X_SMALL (version 1.0.0) and Uniovi Fiji Confocal/ImageJ (version 1.6) software.\u003c/p\u003e\n\u003ch3\u003eNuclear colocalization analysis\u003c/h3\u003e\n\u003cp\u003eTo quantitatively assess the colocalization of FUS (A300-293A; Bethyl Laboratories, USA) and SFPQ (WH0006421M2, Sigma Aldrich, USA) within neuronal nuclei, all sections were imaged under identical confocal microscope settings and laser intensities. Random images were acquired from two distinct regions of the caudate nucleus, capturing six Z-planes (1 \u0026micro;m step size) to cover the full nuclear volume at 63\u0026times; magnification. Neuronal nuclei were identified based on size (~\u0026thinsp;10\u0026ndash;12 \u0026micro;m in diameter), and those smaller than 8 \u0026micro;m were excluded from analysis. Colocalization analysis was conducted on 30 randomly selected nuclei per section using the \u003cem\u003eColoc 2\u003c/em\u003e plug-in in FIJI. Thresholded Manders\u0026rsquo; coefficients (tM1 and tM2) [56], along with Pearson\u0026rsquo;s correlation coefficient (R) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), were computed to quantify colocalization and assess the correlation between fluorescence signals.\u003c/p\u003e\n\u003ch3\u003eProximity ligation assay\u003c/h3\u003e\n\u003cp\u003eThe proximity ligation assay (PLA) for protein complexes detection was carried out as previously described (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Briefly, paraffin-embedded brain sections were rehydrated and subjected to antigen retrieval in citrate buffer (pH 6.0) at 95\u0026deg;C for 20 min. Following permeabilization with 0.1% Triton X-100 in PBS for 15 min, sections were blocked for 45 min using the Duolink blocking solution (DUO92009, Sigma-Aldrich). Samples were then incubated overnight at 4\u0026deg;C with primary antibodies diluted in antibody diluent (see Supplementary Table S3).\u003c/p\u003e\u003cp\u003eAfter washing twice in 1\u0026times; buffer A (DUO82049), sections were incubated for 1 h at 37\u0026deg;C with PLA probes PLUS and MINUS (DUO92002 and DUO92004, respectively; Sigma-Aldrich), each diluted 1:5 in antibody diluent. Ligation was carried out using ligase diluted 1:40 in ligation buffer (DUO92014) at 37\u0026deg;C for 30 min. Signal amplification was performed with polymerase diluted 1:80 in amplification buffer, incubated at 37\u0026deg;C for 100 min. Sections were then washed sequentially in 1\u0026times; and 0.01\u0026times; buffer B (DUO82049), mounted with DAPI-containing medium (DUO82040), and stored until imaging.\u003c/p\u003e\u003cp\u003eTo assess PLA signal intensity, all sections were imaged under identical conditions using a confocal microscope (Leica SP8), with fixed laser power and acquisition settings. For each section, five random Z-stacks were acquired at 1 \u0026micro;m intervals, covering the entire nuclear volume at 63\u0026times; magnification. Nuclei were selected based on size (10\u0026ndash;12 \u0026micro;m diameter); nuclei smaller than 8 \u0026micro;m were excluded. Seventeen nuclei were randomly selected per section for quantification.\u003c/p\u003e\u003cp\u003eFor each Z-stack, a maximum intensity projection was generated, and mean fluorescence intensity values (range: 0\u0026ndash;255 grayscale units) were measured using the stack profile tool in LAS X software (Leica Microsystems), by manually outlining each nucleus. Fluorescence values were normalized to the nuclear area, and the average intensity per nucleus was calculated to yield a representative value per subject.\u003c/p\u003e\n\u003ch3\u003eWestern blotting\u003c/h3\u003e\n\u003cp\u003eHuman caudate tissue (30 mg) was homogenized in lysis buffer (20 mM HEPES pH 7.4, 100 mM NaCl, 50 mM NaF, 5 mM EDTA, 1% Triton X-100) with protease inhibitors (cOmplete\u0026trade;, Mini Protease Inhibitor Cocktail; Roche, Switzerland) in a glass homogenizer on ice. Subsequently, the samples were centrifuged for 10 min at 12 000 \u0026times; g at 4\u0026deg;C and the supernatant was collected for analysis. Protein concentration was determined with the NanoDrop One 2000c spectrophotometer (Thermo Fisher Scientific, USA).\u003c/p\u003e\u003cp\u003eFifty micrograms of total protein were loaded onto 4\u0026ndash;12% SDS-polyacrylamide gels (M00653; GeneScript, USA) and transferred to PVDF membranes (0.2 \u0026micro;m, Amersham\u0026trade; Hybond; Cytiva, USA), which were then blocked in TBS-T (Tris-buffered saline, 1% Tween-20) supplemented with 5% bovine serum albumin (BSA). Membranes were incubated with primary antibodies (see Supplementary Table S3), overnight at 4\u0026deg;C, and, after TBS-T washes, with HRP-conjugated goat anti-mouse IgG (1:20000, ab97040, Abcam, UK) or anti-rabbit IgG (1:20000, ab7090, Abcam) for 1h, at room temperature. Protein detection was performed with Amersham\u0026trade; ECL\u0026trade; Prime Western Blotting Detection Reagent (Cytiva). Anti-β-Actin (HRP) conjugated antibody (1:4000, sc-47778, Santa Cruz Biotechnologies, USA) was used as loading control. Western blot images were acquired with a ChemiDoc-It\u0026reg; BioChemi HR Camera (UVP) and quantified using ImageJ (Uniovi Fiji Confocal).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003emRNA expression analysis by RT-qPCR\u003c/h2\u003e\u003cp\u003eFrozen postmortem caudate nucleus samples (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33, approximately 5 mg each) were homogenized under cold conditions using Qiazol lysis reagent (Cat. No. 79306, QIAGEN, Germany). Total RNA was extracted using the miRNeasy Micro Kit (Cat. No 74004, QIAGEN) following the manufacturer\u0026rsquo; protocol. RNA concentration and purity were determined on a NanoDrop One 2000c spectrophotometer (Thermo Fisher Scientific). RNA integrity was evaluated for each sample using the Agilent 2200 TapeStation system (Agilent Technologies) and the RNA integrity number (RIN) are provided in Supplementary Table S4. Samples were stored at -80\u0026deg;C until further processing.\u003c/p\u003e\u003cp\u003eFor cDNA synthesis, 500 ng of total RNA were used for cDNA synthesis (StaRT reverse transcription kit, AnyGenes, France). The conditions used were: 10 min at 25\u0026deg;C, 120 min at 37\u0026deg;C, and 5 min at 85\u0026deg;C. Quantification was performed from cDNA using the Perfect Master Mix SYBR Green Kit (AnyGenes) on the 7900HT rapid real-time PCR system (Applied Biosystem, USA). The primers for the \u003cem\u003eMAPT\u003c/em\u003e, \u003cem\u003eMAPT 4R\u003c/em\u003e, and \u003cem\u003eMAPT 3R\u003c/em\u003e genes analyzed were specifically designed by Eurogentec (Belgium). \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eβ-ACTIN\u003c/em\u003e were used to perform the internal normalization of the results, and the one with the most stable values for our dataset was chosen \u003cem\u003ea posteriori\u003c/em\u003e, using the RefFinder tool (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Primer information and amplification conditions are listed in Supplementary Tables S5 and S6. The mRNA levels are represented as relative quantification (2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e), as described in (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003esmall RNA-sequencing\u003c/h3\u003e\n\u003cp\u003eSmall RNA-Seq was performed by Seqplexing (Sequencing Multiplex S.L., Spain) using 200 ng of caudate nucleus total RNA per sample for miRNA library preparation. Library quality was assessed using the QIAxcel Advanced System (QIAGEN). Sequencing was performed on an Illumina NovaSeq X platform (Illumina, USA), generating paired-end reads (2 x 150bp). The sequencing depth ranged from 8.64 to 144\u0026nbsp;million reads per sample (median\u0026thinsp;=\u0026thinsp;25.89\u0026nbsp;million).\u003c/p\u003e\u003cp\u003eRaw sequencing data were obtained in FASTQ format and quality control was performed using FastQC. Adaptor sequences were trimmed and miRTrace software (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) was used to classify the types of RNA present in each sample, distinguish between miRNAs, rRNA, tRNA and artifacts. miRNA counts were generated using miRDeep2 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and miRNA sequences were mapped to the human genome reference version hg38/GRCh38. Human miRNAs identification was performed using miRBase (v. 22) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). All data generated and used in this study are publicly available in ZENODO (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.15230070\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.15230070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and NCBI GEO DataSets (ID: GSE300433; The following secure access code has been created to allow review of record GSE300433 while it remains in private status: mtwfggcozpalrwn).\u003c/p\u003e\n\u003ch3\u003emiRPM custom R package\u003c/h3\u003e\n\u003cp\u003eA custom R package called miRPM was developed and used, integrating the entire bioinformatics workflow. In this package, the count matrices were normalized using the Reads Per Million (RPM) approach, based on the total number of reads per sample. To further assess the RPM normalization performance, the DANA approach was employed (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). DANA is an R-based method that evaluates the effectiveness of different normalization strategies in miRNA-Seq data by computing two metrics: concordance correlation coefficient (cc), which measures the preservation of biological signals, and mscr, which quantifies the reduction of handling effects. In our analysis, we incorporated the RPM normalization method into the DANA framework and made several modifications to improve the computation of the concordance correlation coefficient. These changes addressed issues like excessive miRNA filtering and strict cluster size constraints in the original DANA implementation, ensuring a more robust and interpretable evaluation of normalization methods while maintaining the biological integrity of the data. R code is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sergio30po/miRNA-RPM-DE-Analysis.git\u003c/span\u003e\u003cspan address=\"https://github.com/sergio30po/miRNA-RPM-DE-Analysis.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDifferential expression of miRNA sequencing data\u003c/h2\u003e\u003cp\u003eFor the differential expression analysis of miRNAs, using the miRPM package, an expression filter with two inclusion criteria, applied consecutively, were considered: 1) more than 50% of valid miRNA data in at least one group; 2) an expression level of \u0026gt;\u0026thinsp;1000 RPM in all subjects of at least one group. Before conducting the statistical analysis, the necessary assumptions were evaluated to determine the suitability of using non-parametric tests. Data normality was assessed using the Shapiro-Wilk test, and homoscedasticity between groups was evaluated using Levene\u0026rsquo;s test. Additionally, the coefficient of variation was calculated to evaluate the relative dispersion of the data. Since the assumptions of normality and homogeneity of variances were not met in most cases, non-parametric statistical tests were chosen. Thus, Kruskal-Walli\u0026rsquo;s test was employed to assess overall differences in the expression levels across the three groups. To control the error arising from multiple comparisons, FDR correction was applied. Only those miRNAs with a significant adjusted \u003cem\u003ep-value\u003c/em\u003e were subsequently subjected to Dunn\u0026rsquo;s post-hoc test, which inherently accounts for the multiple pairwise comparisons, to determine which specific group comparisons were significant. Graphical representation of miRNAs levels in heatmap was performed by normalization through Z-score.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eprediction of target genes and pathway analysis of miRNAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the biological relevance of the differentially expressed miRNAs, enrichment analysis of signaling pathways was performed using DIANA-miRPath v.4, incorporating experimentally validated miRNA-gene interactions from miRTarBase v.8.0 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The analysis was conducted using both Kyoto Encyclopedia of Genes and Genomes (KEGG) and REACTOME repositories, and statistical significance was set at \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with FDR correction.\u003c/p\u003e\u003cp\u003eAmong the enriched pathways, particular attention was paid to the spliceosome, given its relevance to tau isoform regulation. For this pathway, validated gene targets associated with the miRNAs of interest were retrieved from miRTarBase v.8.0. The subset of genes involved in the spliceosome was then subjected to Gene Ontology (GO) analysis using PantherDB v.19.0 tool, to characterize enriched biological processes, molecular functions, and cellular components in which they are predominantly represented. GO terms meeting the significance threshold (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained. \u003cem\u003eIn silico\u003c/em\u003e graphs were created using the SRPlot module (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, gene interaction networks were generated to visualize validated miRNA\u0026ndash;target connections. Interaction data were imported into Cytoscape v3.10.3 for visualization and network analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGeneral statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistic 27.0 software (IBM, USA). Categorical variables were described as frequencies and percentages. For between-group comparisons of the frequencies of \u003cem\u003eHTT IAs\u003c/em\u003e, \u003cem\u003eAPOE\u003c/em\u003e alleles or gender frequency, Chi-square and Fisher tests were performed. To determine the relationship between the presence of \u003cem\u003eHTT IAs\u003c/em\u003e and the probability of survival in the LOAD groups, the Kaplan-Meier estimation method and the log-rank test were used. Survival rate was calculated as the time elapsed from pathology diagnosis to age at death (event\u0026thinsp;=\u0026thinsp;1 per subject). Additionally, multivariate Cox-PH regression models were run to identify predictors of survival risk, such as age or the presence of \u003cem\u003eHTT IAs\u003c/em\u003e. Kolmogorov-Smirnov and Shapiro-Wilks normality tests (for \u003cem\u003eN\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50) were performed and the corresponding parametric or nonparametric tests were then applied. Mean and standard deviation or median and interquartile range, as appropriate, were used to describe quantitative variables. To compare age at onset, age at death, disease duration, immunohistochemistry and immunofluorescence results between controls \u003cem\u003evs\u003c/em\u003e. LOAD group or between LOAD subgroups, Student\u0026rsquo;s t-test or Mann-Whitney U-test was performed, as appropriate. Also, the parametric analysis of variance (ANOVA) test, followed by Tukey's comparison, or the nonparametric Kruskal-Wallis\u0026rsquo; test, followed by Dunn's test, was applied when comparisons were made considering the three groups. To determine whether miRNAs levels correlate with the recording of different clinical variables, a Spearman correlation test was applied. The threshold for statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The exact levels of the \u003cem\u003ep-values\u003c/em\u003e are indicated in each figure or additional table, if applicable. Graphs were created using GraphPad Prism 10.2.0 (GraphPad, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eThe presence of\u003c/b\u003e \u003cb\u003eHTT IAs\u003c/b\u003e \u003cb\u003emodifies LOAD progression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe have previously analyzed the possible influence of the \u003cem\u003eHTT IAs\u003c/em\u003e on the development of tauopathies, including AD, and our results suggested that LOAD patients had a higher frequency of \u003cem\u003eHTT IAs\u003c/em\u003e than patients with early-onset AD (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, we have not established whether this could be affecting disease progression. Thus, to further explore the effect of \u003cem\u003eHTT IAs\u003c/em\u003e on LOAD, we considered for this study only LOAD patients from our previous cohort and a group of healthy subjects (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The age of LOAD onset was 75.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.02 years and disease duration until death was 9.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86 years, with a higher percentage of women in the LOAD group versus the control (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The distribution frequency of the different \u003cem\u003eAPOE\u003c/em\u003e gene isoforms was consistent with previous reports (Table S7) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The presence of subjects carrying \u003cem\u003eHTT IAs\u003c/em\u003e was higher in the case of LOAD patients \u003cem\u003eversus\u003c/em\u003e healthy controls, with a frequency similar to that observed in previous studies (7.12% \u003cem\u003evs.\u003c/em\u003e 4.16%; \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.102; Fisher's test, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Moreover, the number of CAG repeats was significantly higher in the case of long \u003cem\u003eHTT\u003c/em\u003e allele of LOAD subjects with respect to healthy controls (20 [18\u0026ndash;22] \u003cem\u003evs.\u003c/em\u003e 18 [17\u0026ndash;21], respectively; \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Mann Whitney; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Importantly, within the range of \u003cem\u003eIAs\u003c/em\u003e in the LOAD group, the most frequent repeat was 27 CAGs, with subjects presenting even 35 CAGs (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo study the effect of the presence of \u003cem\u003eHTT IAs\u003c/em\u003e specifically in LOAD donors and clinical variables, we divided this group into non-carrier (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;300) and carrier (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;23) patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of CAG repeats in the long \u003cem\u003eHTT\u003c/em\u003e allele was higher in carriers \u003cem\u003eversus\u003c/em\u003e non-carriers (28 [27\u0026ndash;30] \u003cem\u003evs.\u003c/em\u003e 19 [18\u0026ndash;22]; \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Mann Whitney; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant differences were found in the percentage of females, onset age and death age between the two groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics data of LOAD cohort studied.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLOAD non-HTT IAs\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLOAD HTT IAs\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female %)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216 (66.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (65.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.488 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset age\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.855 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath age\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.492\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHTT\u003c/em\u003e short allele\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 [17\u0026ndash;17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 [13\u0026ndash;18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHTT\u003c/em\u003e long allele\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 [18\u0026ndash;22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 [27\u0026ndash;30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: LOAD, late-onset Alzheimer\u0026rsquo;s disease\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eData are shown as n (%).\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eData are shown as median [IQR].\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eStatistical analysis: Fisher's test.\u003c/p\u003e\u003cp\u003e\u003csup\u003ee\u003c/sup\u003eStatistical analysis: Student\u0026rsquo;s t-test.\u003c/p\u003e\u003cp\u003e\u003csup\u003ef\u003c/sup\u003eStatistical analysis: Mann-Whitney.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHowever, the disease duration, after diagnosis, was shorter in carrier subjects with respect to non-carrier (7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89 years \u003cem\u003evs.\u003c/em\u003e 9.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96 years, respectively; \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053, Student\u0026rsquo;s t-test; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In fact, the survival rate after diagnosis showed a clear reduction of this rate in LOAD donors with \u003cem\u003eHTT IA\u003c/em\u003es (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017; Kaplan-Meier estimator; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The disease duration was estimated based on the available clinical records of age at disease onset and age at death, allowing us to calculate survival time from onset to death. To verify this result, logistic regression models were used. The model proposed revealed a significant fit (Cox-Snell R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01) and a significant association between disease duration and the presence of \u003cem\u003eHTT IAs\u003c/em\u003e (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). These results suggest that the \u003cem\u003eHTT IAs\u003c/em\u003e modifies LOAD progression, decreasing patient survival after disease onset.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreased diffuse HTT protein levels in caudate neurons of LOAD patients are further exacerbated by\u003c/b\u003e \u003cb\u003eHTT IAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIt has been previously described that HTT levels are increased in the neurons of hippocampus and frontal cortex in LOAD (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). To determine whether \u003cem\u003eHTT IAs\u003c/em\u003e affect HTT total protein burden and its distribution in LOAD patients, a histopathological analysis was performed on caudate nucleus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). First, we explored an antibody that recognizes both wild-type and mutant HTT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, EPR5526 clone). The results show significantly higher HTT intensity signal in the cytoplasm of caudate neurons in both LOAD \u003cem\u003eHTT IAs\u003c/em\u003e non-carriers and carriers compared to healthy controls (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002 and \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 respectively, ANOVA followed by Tuckey\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), as reported by Axenhus \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Notably, LOAD group carrying \u003cem\u003eHTT IAs\u003c/em\u003e had an even higher intensity signal in HTT-positive neurons, compared to non-carriers (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ANOVA followed by Tuckey\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), which has not been described before. Given this, we next sought to ascertain the presence of intranuclear inclusions, a typical hallmark of HD pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, EM-48 clone). However, HTT inclusions were only detected in HD samples used as positive controls. Interestingly, we observed cytoplasmic HTT EM-48 labeling in a subset of neurons, exhibiting a cytoplasmic pattern consistent with our previous analysis with HTT EPR5226. Quantitatively, both LOAD \u003cem\u003eHTT IAs\u003c/em\u003e non-carrier and carrier groups showed a significant increase in these HTT-positive neurons compared to control subjects (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ANOVA followed by Tuckey\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Furthermore, LOAD \u003cem\u003eHTT IA\u003c/em\u003es carriers displayed a higher number of HTT-positive neurons than non-carriers (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ANOVA followed by Tuckey\u0026rsquo;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and HD subjects revealed a significantly greater increase compared to all three other groups (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ANOVA followed by Tuckey\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results were consistent with previously published data (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), extending the regions in which there is an increase of HTT-positive neurons in the brains of LOAD patients. Moreover, this finding was further exacerbated in \u003cem\u003eHTT IAs\u003c/em\u003e carriers, although no sign of HTT aggregation or inclusion formation was observed. All in all, the increased HTT levels observed in LOAD patients represent another component in the co-occurrence of proteinopathies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) at the brain level. Furthermore, the presence of \u003cem\u003eHTT IAs\u003c/em\u003e appears to amplify this phenomenon, potentially promoting a more disturbed neuronal environment that detrimentally modifies disease progression in these subjects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHTT IAs\u003c/b\u003e \u003cb\u003eincrease tau 3R and 4R isoforms in the caudate nucleus of LOAD patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn imbalance in tau 3R and 4R isoforms has been previously reported in the striatum and cerebral cortex of HD patients (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Thus, we explored whether a disruption of the 3R/4R balance of tau isoforms could also be present in LOAD patients with \u003cem\u003eHTT IAs\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eWe first analyzed mRNA expression levels of total \u003cem\u003eMAPT\u003c/em\u003e and its \u003cem\u003eMAPT\u003c/em\u003e 3R and \u003cem\u003eMAPT\u003c/em\u003e 4R transcripts in the caudate nucleus of healthy controls and LOAD patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). No significant differences in total \u003cem\u003eMAPT\u003c/em\u003e and \u003cem\u003eMAPT 3R\u003c/em\u003e mRNA levels were observed among the three groups. However, \u003cem\u003eMATP 4R\u003c/em\u003e mRNA levels were significantly higher in the non-carrier LOAD group compared to controls (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, Kruskal-Wallis followed by Dunn\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Next, total tau protein and its 3R and 4R isoforms were analyzed by Western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and Figure S3). Due to comprised integrity, control samples could not be reliable included in this protein analysis, thus comparisons were restricted to the LOAD groups. Total tau levels were significantly higher in LOAD carrier patients (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006, Student\u0026acute;s t-test; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), with a trend toward higher levels of the 3R isoform in the \u003cem\u003eHTT IAs\u003c/em\u003e carrier group (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, Student\u0026acute;s t-test; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). No changes in tau 4R levels were detected between the LOAD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further evaluate the distribution and abundance of tau 3R and 4R isoforms, we performed immunohistochemical analysis of tau 3R- and 4R-positive neurons across different regions of the caudate nucleus in healthy subjects and in both LOAD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Tau 4R quantification revealed a higher number of tau 4R-positive neurons in LOAD patients compared to controls, although this increase was only statistically significant in the \u003cem\u003eHTT IAs\u003c/em\u003e carriers (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017, Kruskal-Wallis followed by Dunn\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). Interestingly, both LOAD non-carrier and carrier groups presented a higher number of tau 3R-positive neurons compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Furthermore, LOAD subjects carrying \u003cem\u003eHTT IAs\u003c/em\u003e exhibited a significantly more pronounced increase in tau 3R-positive neurons compared to LOAD non-carriers (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ANOVA followed by Tuckey\u0026acute;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Collectively, our immunohistochemical findings demonstrate a complex modulation of tau 3R and 4R isoform levels and distribution within the caudate nucleus of LOAD patients. While both isoforms show elevated neuronal counts in LOAD patient groups, the significantly more pronounced increase of the 3R isoform in LOAD patients carrying \u003cem\u003eHTT IAs\u003c/em\u003e strongly suggest that the presence of these alleles distinctively influences the tau 3R/4R balance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFinal stage neurofibrillary tangles predominate in LOAD patients with\u003c/b\u003e \u003cb\u003eHTT IAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn AD, both 3R and 4R tau isoforms are present in neurofibrillary tangles (NFTs), key elements in AD pathophysiology. This contrasts with other tauopathies like Pick\u0026rsquo;s disease, which predominantly features 3R tau, or Progressive Supranuclear Palsy (PSP), which mainly involves tau 4R (\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNFTs begin as fibrillar bundles in neurons, evolve into mature tangles, and are externalized after neuronal death (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This progression appears to be unidirectional and correlates with the sequential predominance of tau isoforms. Ghost tangles, representing the final stage of NFTs degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), are primarily composed of the 3R isoform, suggesting a temporal shift from 4R to 3R tau during the course of AD pathology (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Accordingly, we hypothesized that the increased number of 3R-positive neurons observed in LOAD patients carrying \u003cem\u003eHTT IAs\u003c/em\u003e may reflect a more advanced stage of NFT maturation in these individuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe first analyzed the proportion of each stage of NFT maturation intra-group. Within non-carrier subjects, we observed a significantly higher number of pretangle-positive cells and mature tangles compared to ghost tangles (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024 and \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, respectively; Kruskal-Wallis followed by Dunn\u0026rsquo;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Conversely, within \u003cem\u003eHTT IAs\u003c/em\u003e carrier patients, there was a significantly less pretangles regarding mature tangles (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005; Kruskal-Wallis followed by Dunn\u0026rsquo;s test), with no significant differences observed in ghost tangles compared to other stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eWhen comparing NFTs structures between LOAD groups, the primary differences emerged at the early and late stages of maturation. Specifically, LOAD patients with \u003cem\u003eHTT IAs\u003c/em\u003e displayed significantly fewer pretangles (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Mann Whitney U-test) and a greater number of ghost tangles than the non-carrier group (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Student\u0026rsquo;s t-test; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These findings suggest that the presence of \u003cem\u003eHTT IAs\u003c/em\u003e is associated with faster neuropathological progression, affecting tau aggregation and indicating a more accelerated neurodegenerative process in LOAD patients carrying \u003cem\u003eHTT IAs.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSRSF6 splicing factor is decreased in caudate nucleus of LOAD patients with\u003c/b\u003e \u003cb\u003eHTT IAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the observed increase in tau 3R isoform levels in LOAD \u003cem\u003eHTT IAs\u003c/em\u003e patients, we explored the possibility of disturbances in the spliceosome pathway, as it has been well documented in AD and other neurodegenerative pathologies (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Within the factors involved in \u003cem\u003eMAPT\u003c/em\u003e splicing, the serine/arginine splicing factor family (SRSF) stands out (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In fact, increased levels of SRSF6 protein, which is involved in the inclusion of exon 10 of the \u003cem\u003eMAPT\u003c/em\u003e gene, and potentially related to increased tau 4R isoform, have been previously described in HD patients (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we found no changes in \u003cem\u003eSRSF6\u003c/em\u003e mRNA expression between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). However, SRSF6 immunoblot analysis in LOAD patients showed that this protein is decreased in \u003cem\u003eHTT IAs\u003c/em\u003e carriers compared to non-carriers (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017; Student\u0026rsquo;s t-test; Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C; Figure S4A, B). These results suggest that downregulation of SRSF6 protein could be one of the factors contributing to the observed increase in tau 3R in LOAD \u003cem\u003eHTT IAs\u003c/em\u003e patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also conducted an exploratory analysis of mRNA expression levels for other members of the SRSF family known to be involved in \u003cem\u003eMAPT\u003c/em\u003e splicing (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The results showed that \u003cem\u003eSRSF1\u003c/em\u003e and \u003cem\u003eSRSF9\u003c/em\u003e, both implicated in the inclusion of \u003cem\u003eMAPT\u003c/em\u003e exon 10, had lower expression levels in LOAD patients with \u003cem\u003eHTT IAs\u003c/em\u003e compared to controls (\u003cem\u003ep-value\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.01 and \u003cem\u003ep-value\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.02, respectively; Kruskal-Wallis followed by Dunn\u0026rsquo;s test; Figure S4B). Lower mRNA expression levels were also observed in two other family members involved in \u003cem\u003eMAPT\u003c/em\u003e exon 10 exclusion, such as \u003cem\u003eSRSF3\u003c/em\u003e (although not significantly) and \u003cem\u003eSRSF4\u003c/em\u003e (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Kruskal-Wallis followed by Dunn\u0026rsquo;s test) in carrier patients. However, due to the low RIN levels of the RNA samples and the absence of the corresponding protein levels analyses for the specific splicing factors, these mRNA findings should be interpreted with caution.\u003c/p\u003e\u003cp\u003eIn summary, our findings demonstrate a significant decrease in SRSF6 protein levels in LOAD patients with \u003cem\u003eHTT IAs\u003c/em\u003e, which potentially contributes to the observed tau 3R isoform imbalance. While exploratory mRNA analyses suggest broader alterations in other SRSF family members, these observations require further validation, particularly at the protein level, to definitively ascertain whether the presence of \u003cem\u003eHTT IAs\u003c/em\u003e induces a widespread alteration within the SRSF family.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreased formation of nuclear FUS-SFPQ complexes in caudate neurons of\u003c/b\u003e \u003cb\u003eHTT IAs\u003c/b\u003e \u003cb\u003ecarrier LOAD patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that the formation of a nuclear complex between fused in sarcoma (FUS) and the proline/glutamine splicing factor (SFPQ) plays a critical role in the regulation of \u003cem\u003eMAPT\u003c/em\u003e pre-mRNA splicing, facilitating exon 10 (E10) exclusion through the assembly of an intranuclear dimer (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). This interaction is disrupted in several neurodegenerative diseases, including tauopathies, where it contributes to splicing defects and the aberrant expression of tau isoforms (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo elucidate the role of the FUS-SFPQ complex, and its possible modulation by the presence of \u003cem\u003eHTT IAs\u003c/em\u003e, we assessed the levels of FUS and SFPQ in the caudate nucleus. While no differences were detected at the mRNA expression level (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B), protein levels of both FUS and SFPQ were significantly elevated in LOAD patients carrying \u003cem\u003eHTT IAs\u003c/em\u003e compared to non-carriers (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03 and \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, respectively; Student\u0026rsquo;s t-test; Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u0026ndash;E; Figure S5). This upregulation of FUS and SFPQ protein levels in \u003cem\u003eHTT IAs\u003c/em\u003e carriers suggested enhanced assembly and/or stability of the FUS\u0026ndash;SFPQ complex, which, given its crucial role in \u003cem\u003eMAPT\u003c/em\u003e splicing and E10 inclusion/exclusion (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), could profoundly influence LOAD pathogenesis. Therefore, we explored whether this elevation involved a change in FUS-SFPQ complex formation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess complex formation, we investigated nuclear localization and colocalization of FUS and SFPQ (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Quantitative analysis using Pearson\u0026rsquo;s correlation coefficient (R) revealed increased nuclear colocalization in LOAD \u003cem\u003eHTT IA\u003c/em\u003e carriers, with no differences observed between controls and non-carrier patients (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 carriers \u003cem\u003evs\u003c/em\u003e. other groups; Kruskal-Wallis followed by Dunn\u0026rsquo;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). These findings were further supported by Mander\u0026rsquo;s threshold colocalization coefficients (tM1 and tM2; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). For more validation at protein\u0026ndash;protein interaction level, we employed proximity ligation assays (PLA; Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI\u0026ndash;J). Quantification of the PLA signal demonstrated significantly higher interaction levels in LOAD \u003cem\u003eHTT IA\u003c/em\u003e carriers than in the other groups (\u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 carriers \u003cem\u003evs\u003c/em\u003e. other groups; Kruskal-Wallis followed by Dunn\u0026rsquo;s test; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ), consistent with enhanced FUS\u0026ndash;SFPQ complex formation.\u003c/p\u003e\u003cp\u003eCollectively, these results provide compelling evidence that the presence of \u003cem\u003eHTT IAs\u003c/em\u003e in LOAD patients promotes aberrant assembly of splicing regulatory complexes in the caudate nucleus. This mechanism may underlie the observed shift in tau isoform expression, reinforcing the emerging role of RNA-binding proteins in modulating tau pathology through alternative splicing dysregulation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHTT\u003c/b\u003e \u003cb\u003eCAG repeat size modulates caudate nucleus miRNA profiles in LOAD patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur previous findings have revealed that LOAD patients with \u003cem\u003eHTT IAs\u003c/em\u003e presented a shift in tau isoform balance towards tau 3R and an increased burden of ghost tangles in the caudate nucleus, consistent with the lower survival rate observed in these patients. These histopathological changes could be due to alterations in splicing factors dynamics, as evidenced by reduced SRSF6 levels and increased FUS/SFPQ complex formation, providing a potential mechanistic link to the observed increase of tau 3R. Beyond alterations in splicing factor activity, post-transcriptional gene regulation by small non-coding RNAs, such as miRNAs, plays a critical role in the intricate molecular landscape of neurodegenerative diseases, including \u003cem\u003eMAPT\u003c/em\u003e alternative splicing (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Interestingly, impaired miRNA expression as a function of the number of CAG repeats in the \u003cem\u003eHTT\u003c/em\u003e gene was observed in different brain regions in HD mouse models, with the striatum the most vulnerable area (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Therefore, we hypothesized that the caudate nucleus of LOAD patients carrying \u003cem\u003eHTT IAs\u003c/em\u003e might exhibit an altered miRNA profile.\u003c/p\u003e\u003cp\u003eWe performed small RNA-Seq on postmortem caudate nucleus samples from a subset of individuals within the study cohort, carefully selected based on demographic factors such as sex and age at death to minimize potential confounding effects, and Braak stage in the case of LOAD patients to account for disease progression (see Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e for details). Initial analysis identified a total of 1953 miRNAs with at least 1 RPM in at least one subject. To focus on consistently expressed miRNAs, we applied a first inclusion criterion requiring expression in at least 50% of the subjects within at least one of the groups. This reduced the database to 1187 miRNAs. Subsequently, to prioritize highly abundant miRNAs likely to have a greater biological impact, we applied a second inclusion criterion, preserving those miRNAs that exhibited an expression level\u0026thinsp;\u0026gt;\u0026thinsp;1000 RPM in all subjects within at least one of the groups, retaining 39 miRNAs (Figure S6A).\u003c/p\u003e\u003cp\u003eTo further assess the robustness of our normalization strategy and the biological integrity of the data, we employed the DANA approach (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), incorporating RPM normalization method into its framework. This analysis demonstrated that our RPM normalization effectively preserved biological signals and mitigated handling effects, providing robust and interpretable evaluation of the miRNA-Seq data (Figure S6B).\u003c/p\u003e\u003cp\u003eBased on this filtered set, differential expression analysis was performed using our custom R package, miRPM, which integrates the entire bioinformatics workflow for miRNA-Seq data analysis. From the retained 39 miRNAs, we performed a principal component analysis (PCA; Figure S6C), from which PC1 and PC2 components explained 58.9% of the variance observed in the subjects, exhibiting a consistent distribution pattern. In the PCA plot shown in Figure S6C, the control group appeared separated from the LOAD groups along PC1 and PC2, with an observable gradient within the LOAD group, in which \u003cem\u003eHTT IAs\u003c/em\u003e carriers tended to position further along these components compared to non-carriers. However, despite these discernible trends, the overall separation was not sufficient to establish a clear distinction between the three groups solely based on PCA.\u003c/p\u003e\u003cp\u003eThe analysis among the three groups revealed that 26 of the 39 miRNAs that passed the screening process were significantly altered (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Of these 26 miRNAs, all of them exhibited higher expression levels in LOAD \u003cem\u003eHTT IA\u003c/em\u003e carriers compared to control group, while 21 showed significant differences between controls and LOAD non-carriers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, specifically, miR-100-5p, miR-218-5p, miR-27b-3p, miR-487b-3p, and miR-9-3p were differentially expressed regarding controls in LOAD \u003cem\u003eHTT IAs\u003c/em\u003e patients. On the other hand, comparison between the two LOAD groups showed that 14 miRNAs were more overexpressed in \u003cem\u003eHTT IA\u003c/em\u003e carriers than in non-carriers. Collectively, these findings indicate that the miRNA profile is significantly affected by the AD pathology itself, and that \u003cem\u003eHTT IAs\u003c/em\u003e further amplifies these alterations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferentially expressed miRNAs in healthy controls vs. LOAD groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOAD \u003cem\u003enon\u003c/em\u003e-\u003cem\u003eHTT IAs\u003c/em\u003e\u003c/p\u003e\u003cp\u003e[Adjusted \u003cem\u003ep-value vs.\u003c/em\u003e Control]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLOAD \u003cem\u003eHTT IAs\u003c/em\u003e\u003c/p\u003e\u003cp\u003e[Adjusted \u003cem\u003ep-value vs.\u003c/em\u003e Control | LOAD \u003cem\u003enon\u003c/em\u003e-\u003cem\u003eHTT IAs\u003c/em\u003e]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-99b-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e706.99\u0026thinsp;\u0026plusmn;\u0026thinsp;284.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1213.29\u0026thinsp;\u0026plusmn;\u0026thinsp;232.88\u003c/p\u003e\u003cp\u003e[0.0170]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1682.73\u0026thinsp;\u0026plusmn;\u0026thinsp;332.49\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0024]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-9-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16386.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7417.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32643.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5013.56\u003c/p\u003e\u003cp\u003e[0.0096]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39014.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5207.62\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0076]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-30d-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2819.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1657.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5793.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1134.76\u003c/p\u003e\u003cp\u003e[0.0084]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7545.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1484.60\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0102]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-128-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4182.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2453.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7897.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1813.42\u003c/p\u003e\u003cp\u003e[0.0228]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11928.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3091.31\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0026]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-23b-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e626.34\u0026thinsp;\u0026plusmn;\u0026thinsp;269.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1428.61\u0026thinsp;\u0026plusmn;\u0026thinsp;284.35\u003c/p\u003e\u003cp\u003e[0.0068]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1691.89\u0026thinsp;\u0026plusmn;\u0026thinsp;213.97\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0174]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-191-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e746.22\u0026thinsp;\u0026plusmn;\u0026thinsp;424.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1596.30\u0026thinsp;\u0026plusmn;\u0026thinsp;297.25\u003c/p\u003e\u003cp\u003e[0.0083]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1932.93\u0026thinsp;\u0026plusmn;\u0026thinsp;390.83\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0193]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-151a-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e918.83\u0026thinsp;\u0026plusmn;\u0026thinsp;460.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1704.72\u0026thinsp;\u0026plusmn;\u0026thinsp;375.72\u003c/p\u003e\u003cp\u003e[0.0127]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2079.86\u0026thinsp;\u0026plusmn;\u0026thinsp;455.23\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | 0.0168]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-125a-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e837.72\u0026thinsp;\u0026plusmn;\u0026thinsp;400.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2889.29\u0026thinsp;\u0026plusmn;\u0026thinsp;859.72\u003c/p\u003e\u003cp\u003e[0.0021]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4390.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3066.51\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0001 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-30a-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3941.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2223.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7719.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1488.43\u003c/p\u003e\u003cp\u003e[0.0089]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9224.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1615.98\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.001 | 0.0282]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-487b-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e889.57\u0026thinsp;\u0026plusmn;\u0026thinsp;571.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1477.67\u0026thinsp;\u0026plusmn;\u0026thinsp;402.52\u003c/p\u003e\u003cp\u003e[\u003cem\u003en.s\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2171.85\u0026thinsp;\u0026plusmn;\u0026thinsp;648.72\u003c/p\u003e\u003cp\u003e[0.0002 | 0.0044]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-125b-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4169.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2147.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11502.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3279.17\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0029]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13890.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4082.34\u003c/p\u003e\u003cp\u003e[0.0002 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-221-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e946.90\u0026thinsp;\u0026plusmn;\u0026thinsp;466.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1827.74\u0026thinsp;\u0026plusmn;\u0026thinsp;641.08\u003c/p\u003e\u003cp\u003e[0.0052]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2250.74\u0026thinsp;\u0026plusmn;\u0026thinsp;693.33\u003c/p\u003e\u003cp\u003e[\u0026lt;\u0026thinsp;0.0004 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-139-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e669.33\u0026thinsp;\u0026plusmn;\u0026thinsp;319.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1345.48\u0026thinsp;\u0026plusmn;\u0026thinsp;420.42\u003c/p\u003e\u003cp\u003e[0.0043]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1698.56\u0026thinsp;\u0026plusmn;\u0026thinsp;453.94\u003c/p\u003e\u003cp\u003e[0.0006 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-103a-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3531.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2064.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7974.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2293.12\u003c/p\u003e\u003cp\u003e[0.0034]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9017.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2049.48\u003c/p\u003e\u003cp\u003e[0.0008 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-99a-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3093.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1188.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5094.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1597.55\u003c/p\u003e\u003cp\u003e[0.0225]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6288.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1970.75\u003c/p\u003e\u003cp\u003e[0.0011| \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-29a-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5212.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2435.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9829.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2232.98\u003c/p\u003e\u003cp\u003e[0.0043]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10515.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3491.47\u003c/p\u003e\u003cp\u003e[0.0016 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-218-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1495.30\u0026thinsp;\u0026plusmn;\u0026thinsp;904.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005.07\u0026thinsp;\u0026plusmn;\u0026thinsp;656.07\u003c/p\u003e\u003cp\u003e[\u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2685.15\u0026thinsp;\u0026plusmn;\u0026thinsp;433.87\u003c/p\u003e\u003cp\u003e[0.0017 | 0.0024]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-126-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1127.99\u0026thinsp;\u0026plusmn;\u0026thinsp;647.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1832.21\u0026thinsp;\u0026plusmn;\u0026thinsp;638.10\u003c/p\u003e\u003cp\u003e[0.0421]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2354.87\u0026thinsp;\u0026plusmn;\u0026thinsp;704.51\u003c/p\u003e\u003cp\u003e[0.0019 | 0.0398]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-100-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1946.82\u0026thinsp;\u0026plusmn;\u0026thinsp;799.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2979.13\u0026thinsp;\u0026plusmn;\u0026thinsp;803.40\u003c/p\u003e\u003cp\u003e[\u003cem\u003en.s\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3582.45\u0026thinsp;\u0026plusmn;\u0026thinsp;951.83\u003c/p\u003e\u003cp\u003e[0.0033 | 0.0289]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-124-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3016.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1393.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6776.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3470.01\u003c/p\u003e\u003cp\u003e[0.0053]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6340.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2815.46\u003c/p\u003e\u003cp\u003e[0.0033 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-24-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1305.32\u0026thinsp;\u0026plusmn;\u0026thinsp;611.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2751.71\u0026thinsp;\u0026plusmn;\u0026thinsp;557.81\u003c/p\u003e\u003cp\u003e[0.0012]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2583.71\u0026thinsp;\u0026plusmn;\u0026thinsp;471.96\u003c/p\u003e\u003cp\u003e[0.0035 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003elet-7a-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5551.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2778.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9428.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1717.47\u003c/p\u003e\u003cp\u003e[0.0084]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10713.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3368.41\u003c/p\u003e\u003cp\u003e[0.0043 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-181a-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3125.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1920.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5420.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1409.52\u003c/p\u003e\u003cp\u003e[0.0428]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7066.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2796.03\u003c/p\u003e\u003cp\u003e[0.0049]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-30c-5p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1855.95\u0026thinsp;\u0026plusmn;\u0026thinsp;964.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3017.19\u0026thinsp;\u0026plusmn;\u0026thinsp;536.48\u003c/p\u003e\u003cp\u003e[0.0148]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3246.25\u0026thinsp;\u0026plusmn;\u0026thinsp;798.72\u003c/p\u003e\u003cp\u003e[0.001 | \u003cem\u003en. s.\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-9-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8417.09\u0026thinsp;\u0026plusmn;\u0026thinsp;6358.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9191.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1803.44\u003c/p\u003e\u003cp\u003e[\u003cem\u003en.s\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12182.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1913.93\u003c/p\u003e\u003cp\u003e[0.0084 | 0.0042]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emiR-27b-3p\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2717.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1559.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3526.19\u0026thinsp;\u0026plusmn;\u0026thinsp;808.69\u003c/p\u003e\u003cp\u003e[\u003cem\u003en.s\u003c/em\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4626.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1250.03\u003c/p\u003e\u003cp\u003e[0.0095 | 0.0265]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Statistical analysis: Kruskal-Wallis (miRNAs selected based on FDR-adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pairwise comparisons were performed using Dunn\u0026rsquo;s post-hoc test, with p-values adjusted for multiple comparisons. Ordered from highest to lowest significance according to LOAD \u003cem\u003eHTT IAs.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo investigate whether altered miRNA expression is associated with clinical features in this genetic context of LOAD, we examined the relationship between the 14 differentially expressed miRNAs between the LOAD groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and key clinical variables. No significant correlations were observed with age at onset, age at death, disease duration, or Braak stage (Table S8). In contrast, CAG repeat size showed a positive, and significant, correlation with the expression of six miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB and Table S8): miR-128-3p (R\u0026thinsp;=\u0026thinsp;0.48, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), miR-99b-5p (R\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), miR-9-5p (R\u0026thinsp;=\u0026thinsp;0.45, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), miR-9-3p (R\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), miR-218-5p (R\u0026thinsp;=\u0026thinsp;0.52, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and miR-27b-3p (R\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). Of these, the last three are among the five miRNAs that we have previously found to be specific to the presence of \u003cem\u003eHTT IAs\u003c/em\u003e. These results suggest that the altered miRNA profile in the caudate nucleus is significantly influenced by \u003cem\u003eHTT\u003c/em\u003e CAG repeat length, potentially contributing to the accelerated disease progression observed in LOAD individuals carrying \u003cem\u003eHTT IAs\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn silico analysis reveals spliceosome pathway as a key target of dysregulated miRNAs in LOAD patients with\u003c/b\u003e \u003cb\u003eHTT IAs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSince the LOAD-associated miRNA profile in caudate is even more impaired by the presence of \u003cem\u003eHTT IAs\u003c/em\u003e, we next explored whether this miRNA pattern could be affecting genes specifically involved in the splicing of the \u003cem\u003eMAPT\u003c/em\u003e gene. As a first approach to our \u003cem\u003ein silico\u003c/em\u003e study, we performed an initial enrichment analysis using validated gene targets. This analysis revealed that the miRNAs differentially expressed between LOAD donors and controls, as well as those distinguishing the LOAD subgroups, were significantly enriched in 151 and 147 biological pathways, respectively. Notably, 13 of these KEGG pathways were directly associated with neurodegenerative processes (Figure S6D-E, Table S9 and S10). To further explore the molecular mechanisms potentially regulated by these miRNAs, we performed an additional enrichment analysis using the REACTOME encyclopedia. This analysis identified over 470 potentially modulated processes, among which ten stood out with highly significant relevance and were all related to RNA biology (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, Table S11), including mRNA splicing and metabolism of non-coding RNAs (such as miRNAs themselves). These findings reinforce our hypothesis of a functional connection between the identified miRNA profile and the spliceosome pathway in this subgroup of patients.\u003c/p\u003e\u003cp\u003eTo explore this pathway more thoroughly, we retrieved the genes included in the spliceosome-related pathway from REACTOME and subjected them to a Gene Ontology (GO) enrichment analysis using PantherDB v.19.0. A total of 246 genes were extracted and categorized according to cellular component, molecular function, and biological process. In terms of cellular component, the nucleus was the most enriched compartment (47%), followed by the cytosol (38%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, Table S12). Regarding molecular function, the majority of genes were associated with nucleic acid-related activities, with DNA binding (31%) and RNA binding (26.14%) being the most prominent (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, Table S13). When analyzing enrichment in biological processes, a large proportion of genes were involved in RNA processing (25.4%), RNA splicing (19.8%), and, more specifically, mRNA splicing (16.07%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, Table S14). These data indicate that the spliceosome machinery is among the most enriched pathways targeted by the miRNAs identified in our analysis.\u003c/p\u003e\u003cp\u003eFinally, experimentally validated miRNA-mRNA interactions were retrieved from miRTarBase, focusing on the 246 genes linked to the spliceosome pathway. Subsequent network analysis using Cytoscape revealed that ten out of the 14 miRNAs differentially expressed between LOAD subgroups shared common target genes with ten members of the SRSF family (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Taken together, these findings provide compelling evidence for a significant functional interplay between the miRNA expression profile identified in the caudate nucleus and the regulation of the spliceosome pathway. This suggests that dysregulation of specific miRNAs may critically influence alternative splicing mechanisms, potentially contributing to the molecular pathology underlying LOAD. These findings underscore the importance of spliceosome as a key regulatory center targeted by miRNAs in this neurodegenerative context, warranting further experimental validation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInter-relationship between dysregulated miRNAs\u003c/b\u003e, \u003cb\u003eHTT\u003c/b\u003e \u003cb\u003eCAG repeat size, and key neuropathological hallmarks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better integrate our findings within a neuropathological framework, we next investigated the associations between miRNA expression profiles, CAG repeat length, and the severity of tau pathology, including both pretangles and ghost tangles, as well as the soluble HTT signal in caudate neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, Table S15). We observed a consistent and statistically significant positive correlation between the intensity of soluble HTT immunoreactivity and the expression levels of eleven miRNAs, with miR-218-5p and miR-27b-3p, included in the group of five miRNAs specific for LOAD \u003cem\u003eHTT IAs\u003c/em\u003e patients, showing the strongest associations. This suggests a transcriptional footprint associated with the presence of an exacerbated HTT protein profile in the caudate nucleus. Only miR-30d-5p, miR-100-5p, and miR-126-3p failed to show significant associations with increased HTT soluble levels.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConversely, the burden of pretangles exhibited a predominantly negative correlation with miRNA levels, reaching statistical significance for 6 out of the 14 miRNAs analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, Table S15). In contrast, ghost tangles, indicative of more advanced tau pathology, displayed a positive correlation with miR-218-5p and miR-30a-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, Table S15). Interestingly, the number of CAG repeats correlated positively with both HTT soluble signal and the abundance of ghost tangles, while showing an inverse correlation with pretangle burden. This finding reinforces the notion that CAG repeat length correlates with HTT protein burden and is associated with a shift toward more mature tau aggregates.\u003c/p\u003e\u003cp\u003eBuilding upon our \u003cem\u003ein silico\u003c/em\u003e target analysis, we examined experimentally validated miRNA-mRNA interactions involving the \u003cem\u003eMAPT\u003c/em\u003e and \u003cem\u003eHTT\u003c/em\u003e genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Network modeling identified three miRNAs (miR-23b-5p, miR-99-5p, and miR-128-3p) that are validated regulators of both \u003cem\u003eMAPT\u003c/em\u003e and \u003cem\u003eHTT\u003c/em\u003e. Additionally, other miRNAs showed gene-specific interactions: miR-191-5p targets \u003cem\u003eMAPT\u003c/em\u003e exclusively, while miR-27b-3p is selectively linked to \u003cem\u003eHTT\u003c/em\u003e. These direct validated interactions provide a molecular basis for the correlations observed, strengthening the evidence that the post-transcriptional effects of \u003cem\u003eHTT\u003c/em\u003e extend beyond its canonical role in HD, reshaping the miRNA scenario in a way that promotes tau pathology in the context of LOAD. Such miRNA-mediated cross-talk between \u003cem\u003eHTT\u003c/em\u003e and \u003cem\u003eMAPT\u003c/em\u003e may contribute to the acceleration of tau-driven neurodegeneration.\u003c/p\u003e\u003cp\u003eCollectively, these data underscore a dual role for \u003cem\u003eHTT IAs\u003c/em\u003e: both as histopathological hallmarks, by increased HTT and ghost tangles, and as modulators of miRNA-mediated gene regulation, synergistically accelerating tau-mediated neurodegeneration. However, further experimental studies are necessary to elucidate the precise molecular mechanisms and the biological significance of these miRNAs in the context of this accelerated neurodegeneration.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLate-onset Alzheimer\u0026acute;s disease (LOAD) patients represent a heterogeneous population. In fact, there is a complex influence of genetic, environmental, and other factors on disease development and progression. Our previous studies showed a higher frequency of intermediate alleles in the \u003cem\u003eHTT\u003c/em\u003e gene among AD patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, in this work, we aimed to investigate whether this genetic characteristic influences LOAD progression and/or its neuropathological hallmarks, with a special focus on \u003cem\u003eMAPT\u003c/em\u003e gene splicing.\u003c/p\u003e\u003cp\u003eA key finding from our study is that LOAD patients carrying \u003cem\u003eHTT IAs\u003c/em\u003e exhibited a lower survival rate after disease onset compared to non-carrier patients, a clinical outcome not previously described. We have analyzed the caudate nucleus, a region particularly sensitive to alterations in \u003cem\u003eHTT\u003c/em\u003e CAG repeat number (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Histopathological and molecular analysis of this brain region revealed that while LOAD non-carriers subjects already display alterations (e.g., increased soluble HTT levels or altered miRNA profiles) compared to healthy controls, these changes were consistently and further exacerbated in \u003cem\u003eHTT IAs\u003c/em\u003e carrier patients. This observed severity in neuropathology included a heightened diffuse HTT immunoreactivity, in a non-aggregated state, distinguishing it from the profound pathology seen with fully mutated HTT in HD, and a more advanced tau pathology. Both factors likely contribute to the compromised survival in \u003cem\u003eHTT IAs\u003c/em\u003e carrier patients by favoring a gain of toxic function of the non-aggregated HTT. The tau 3R predominance observed here, characterized by a tau 3R isoform imbalance and a significant increase in 3R tau-enriched ghost tangles, aligns with its association with axonal cytoskeleton destabilization and reduced neuronal survival (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). This also positions \u003cem\u003eHTT IA\u003c/em\u003e-associated LOAD in later stages of disease, where tau 3R becomes more prevalent (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and may promote the progression of NFTs toward their final, neurotoxic stage (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study elucidates molecular mechanisms underlying this acceleration in neuropathology. It represents the first detailed miRNA profiling in the caudate nucleus of LOAD patients, showing that this profile is already altered in LOAD, and profoundly amplified in \u003cem\u003eHTT IA\u003c/em\u003e carriers. We employed a novel RPM-based analysis method (miRPM) validated by the DANA approach (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This stringent analysis revealed that while 26 miRNAs were generally altered in LOAD \u003cem\u003eversus\u003c/em\u003e controls, \u003cem\u003eHTT IAs\u003c/em\u003e specifically exacerbated these changes, with 14 miRNAs being significantly more overexpressed in \u003cem\u003eHTT IA\u003c/em\u003e carriers compared to non-carriers. Five miRNAs (miR-100-5p, miR-218-5p, miR-27b-3p, miR-487-3p, and miR-9-3p) were uniquely altered in LOAD \u003cem\u003eHTT IAs\u003c/em\u003e patients regarding healthy controls, highlighting their specificity, and emerging as a \u003cem\u003eHTT IA\u003c/em\u003e signature miRNAs. Notably, miR-100-5p has also been linked to disease progression in HD (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), suggesting a shared molecular pathway across HTT-related conditions. Furthermore, a subset of miRNAs showed a positive correlation with CAG repeat length, indicating a CAG-dependent modulation. Importantly, miR-218-5p was the only miRNA analyzed that exhibited positive correlations with CAG repeat length, HTT protein levels, and ghost tangles, and negative with pretangles. This suggests miR-218-5p may play an essential role in the alterations leading to the accelerated disease progression observed in these patients.\u003c/p\u003e\u003cp\u003eOur \u003cem\u003ein silico\u003c/em\u003e studies and network analysis provide mechanistic insights, suggesting that these altered miRNAs directly target key components of the splicing machinery and both \u003cem\u003eMAPT\u003c/em\u003e and \u003cem\u003eHTT\u003c/em\u003e genes. This strongly supports our observations at the histopathological levels regarding tau isoform imbalance and splicing factors dysregulation, such as SRSF6 and FUS-SFPQ, in \u003cem\u003eHTT IAs\u003c/em\u003e carriers. This miRNA-mediated effect suggests that the presence of \u003cem\u003eHTT IAs\u003c/em\u003e significantly modifies the regulatory environment of gene expression to aggravate splicing machinery alterations and accelerate tau-mediated neurodegeneration. This scenario of HTT-tau co-occurrence, where \u003cem\u003eHTT IAs\u003c/em\u003e favor increased HTT and tau 3R levels, driving accelerating NFTs maturation, is in line with studies suggesting HTT pathology as a trigger for multiple proteinopathies, including TDP-43, alpha-synuclein, beta-amyloid, and tau alterations in advanced HD (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While tau isoform shifts differ between HD (4R) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and LOAD (3R), the positive correlation of tau alterations with CAG repeat length in both conditions is striking. This suggests that the final HTT protein state (mutated \u003cem\u003eversus\u003c/em\u003e intermediate) may dictate the precise tau imbalance. Our proposed mechanism integrating these findings is schematically represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese findings collectively underscore the profound impact of \u003cem\u003eHTT IAs\u003c/em\u003e on LOAD pathogenesis, offering another clue to understand the disease\u0026rsquo;s clinical heterogeneity. This comprehensive neuropathological and molecular characterization reveals \u003cem\u003eHTT IAs\u003c/em\u003e as critical risk biomarkers. Crucially, as \u003cem\u003eHTT\u003c/em\u003e genotyping is a widely used and easily accessible technique, our results can contribute to improved clinical practice by enabling a more precise stratification of LOAD patients for clinical trials and facilitating the development of more focused, personalized, therapeutic interventions. The identified miRNA dysregulation, particularly the \u003cem\u003eHTT IA\u003c/em\u003e signature miRNAs, represents a promising avenue for novel therapeutic targets, as their dysregulation offers a precise avenue for future intervention.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eWorking with postmortem human brain tissue, while invaluable for understanding neuropathology, presents unique limitations that can influence study design and sample inclusion. We acknowledge the variability in sample size (\u003cem\u003eN\u003c/em\u003e) across our different experimental assays (e.g., RT-qPCR, Western blot, immunohistochemistry), which is primarily due to the finite nature of our well-characterized donor cohort and the differing quality requirements of each analytical method. For RNA-based analyses, such as RT-qPCR or mRNA expression and miRNA-Seq, we faced challenges with RNA integrity. As indicated by the low RIN values in Table Supplementary 4, RNA is highly susceptible to postmortem degradation. While this significantly affects mRNA expression levels, miRNA sequencing data is less compromised, as several studies indicate that miRNAs, being small nucleotide chains with high resistance to degradation, allow for reliable data acquisition despite low RIN levels (\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Similarly, the integrity of proteins in postmortem brain tissue poses significant challenges for Western blot analysis. We encountered difficulties due to the degradation of protein samples, especially for control subjects, which limited their inclusion. However, it is important to note that the primary objective of our Western blot analyses was to elucidate differences between LOAD \u003cem\u003eHTT IAs\u003c/em\u003e carriers and LOAD non-carrier patients, a critical comparison that remained fully addressed by the available samples. For immunohistochemistry and immunofluorescence, optimal tissue morphology and antigen preservation are critical for accurate high-resolution analysis and cell counting. Despite differences in N across the different techniques, consistent trends and complementary information observed at various molecular levels (e.g., miRNA, mRNA, protein, and IHC profiles) derived from these human samples significantly enhance the overall robustness and interpretability of our conclusions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD: Alzheimer\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e: Apolipoprotein E\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE10: exon 10 of \u003cem\u003eMAPT\u003c/em\u003e gene\u003c/p\u003e\n\u003cp\u003eFDR: false discovery rate\u003c/p\u003e\n\u003cp\u003eFTD: frontotemporal dementia\u003c/p\u003e\n\u003cp\u003eFUS: fused in sarcoma protein\u003c/p\u003e\n\u003cp\u003eGO: gene ontology\u003c/p\u003e\n\u003cp\u003eHD: Huntington\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHTT IAS\u003c/em\u003e: intermediate alleles in \u003cem\u003eHTT\u0026nbsp;\u003c/em\u003egene\u003c/p\u003e\n\u003cp\u003eHTT\u003cem\u003e:\u003c/em\u003e huntingtin protein\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLOAD: Late- onset Alzheimer\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMAPT:\u0026nbsp;\u003c/em\u003emicrotubule associated tau gene\u003c/p\u003e\n\u003cp\u003emiRNAs: microRNAs\u003c/p\u003e\n\u003cp\u003eNDs: neurodegenerative diseases\u003c/p\u003e\n\u003cp\u003eNFTs: neurofibrillary tangles\u003c/p\u003e\n\u003cp\u003ePCA: principal component analysis\u003c/p\u003e\n\u003cp\u003ePLA: proximity ligation assay\u003c/p\u003e\n\u003cp\u003ePMI: \u003cem\u003epostmortem\u0026nbsp;\u003c/em\u003einterval\u003c/p\u003e\n\u003cp\u003ePSP: progressive supranuclear palsy\u003c/p\u003e\n\u003cp\u003eRBP: RNA binding protein\u003c/p\u003e\n\u003cp\u003eRPM: reads per million\u003c/p\u003e\n\u003cp\u003eRIN: RNA integrity number\u003c/p\u003e\n\u003cp\u003eROI: region of interest\u003c/p\u003e\n\u003cp\u003eRPM: read per million\u003c/p\u003e\n\u003cp\u003eSFPQ: Proline/Glutamine rich splicing factor\u003c/p\u003e\n\u003cp\u003eSRSF: Serine/Arginine rich splicing factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were performed after obtained the approval of Ethical Committees of Neurological Tissue Bank (NTB) of the Hospital Clinic-FRCB-IDIBAPS (Barcelona, Spain), the Principado de Asturias BioBank and Research Ethics Committee of the Principality of Asturias (CEImPA n\u0026ordm; 2022.266).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the content of this manuscript and provided consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and used in this study are publicly available in ZENODO (DOI: 10.5281/zenodo.15230070) and NCBI GEO DataSets (ID: GSE300433; The following secure access code has been created to allow review of record GSE300433 while it remains in private status: mtwfggcozpalrwn). R code used to analyze miRNA database is available at https://github.com/sergio30po/miRNA-RPM-DE-Analysis.git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.C-S performed all histological and molecular experiments, conducted data analysis, created the figures and wrote the manuscript. S.P-G performed genotyping the initial samples to establish the studied cohort, developed the miRPM R package, and carried out bionformatic analysis. P.P-H, M.F-S and E.I-G provided guidance on the analysis and interpretation of RNAseq and miRNAs experiments. MD.C-T developed the technical tasks for the preparation of the samples provided by the Biobank of the Principality of Asturias. V.A and M.M-G conceptualized, designed and obtained funding for the study, interpreted the epidemiological data, executed the genetic objective of the study, and supervised the final versions of the manuscript. C.T-Z designed and supervised the molecular and histopathological analysis experiments, interpreted the data, and supervised the different versions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the research staff for their valuable support, including: J. Bermejo-Pampliega (Physiology Area of University of Oviedo; AYUD/2021/5134); M. Alonso-Guervos (Microscopy and Image Processing Unit, Scientific and Technical Services, University of Oviedo); the Molecular Histopathology Service in Animal Cancer Models at Instituto Universitario de Oncolog\u0026iacute;a del Principado de Asturias (IUOPA), and the Molecular Genetics Laboratory of Hospital Universitario Central de Asturias (HUCA). We also acknowledge the collaboration of the Principado de Asturias BioBank (PT23/0077), financed by Servicio de Salud del Principado de Asturias and ISCIII/FEDER, and HCB-IDIBAPS Biobank for sample and data procurement. Finally, we are indebted to Asociaci\u0026oacute;n Parkinson Asturias-Obra Social Cajastur and to all the patients and their families for their invaluable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union (FEDER/FSE) through PI21/00467 (V.A., M. M-M). J.C-S was supported by ISCIII grant AC20/00017, co-founded by EuroNanoMed III (20-0084, M. M-M) and Asociaci\u0026oacute;n Parkinson Asturias-Obra Social Cajastur. S.P-O is supported by Fundaci\u0026oacute;n para la Investigaci\u0026oacute;n e Innovaci\u0026oacute;n Biosanitaria del Principado de Asturias (FINBA). P.P-H was supported by grant AYUD/2021/5134 from Fundaci\u0026oacute;n para el Fomento en Asturias de la Investigaci\u0026oacute;n Cient\u0026iacute;fica y la Tecnolog\u0026iacute;a (FICYT), co-funded by FEDER.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlzheimer\u0026apos;s disease facts and figures. Alzheimers Dement. 2024;20(5):3708-821.\u003c/li\u003e\n\u003cli\u003eRobinson JL, Lee EB, Xie SX, Rennert L, Suh E, Bredenberg C, et al. Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. 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Alzheimers Dement. 2024;20(5):3606-28.\u003c/li\u003e\n\u003cli\u003eFernandez-Nogales M, Santos-Galindo M, Hernandez IH, Cabrera JR, Lucas JJ. Faulty splicing and cytoskeleton abnormalities in Huntington\u0026apos;s disease. Brain Pathol. 2016;26(6):772-8.\u003c/li\u003e\n\u003cli\u003eCherry JD, Esnault CD, Baucom ZH, Tripodis Y, Huber BR, Alvarez VE, et al. Tau isoforms are differentially expressed across the hippocampus in chronic traumatic encephalopathy and Alzheimer\u0026apos;s disease. Acta Neuropathol Commun. 2021;9(1):86.\u003c/li\u003e\n\u003cli\u003eUematsu M, Nakamura A, Ebashi M, Hirokawa K, Takahashi R, Uchihara T. Brainstem tau pathology in Alzheimer\u0026apos;s disease is characterized by increase of three repeat tau and independent of amyloid beta. Acta Neuropathol Commun. 2018;6(1):1.\u003c/li\u003e\n\u003cli\u003eSaroja SR, Sharma A, Hof PR, Pereira AC. Differential expression of tau species and the association with cognitive decline and synaptic loss in Alzheimer\u0026apos;s disease. Alzheimers Dement. 2022;18(9):1602-15.\u003c/li\u003e\n\u003cli\u003eUchihara T. Pretangles and neurofibrillary changes: similarities and differences between AD and CBD based on molecular and morphological evolution. Neuropathology. 2014;34(6):571-7.\u003c/li\u003e\n\u003cli\u003eGallego Romero I, Pai AA, Tung J, Gilad Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biol. 2014;12:42.\u003c/li\u003e\n\u003cli\u003ePotla P, Ali SA, Kapoor M. A bioinformatics approach to microRNA-sequencing analysis. Osteoarthr Cartil Open. 2021;3(1):100131.\u003c/li\u003e\n\u003cli\u003eSonntag KC, Woo TW. Laser microdissection and gene expression profiling in the human postmortem brain. Handb Clin Neurol. 2018;150:263-72.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"proteinopathies, alternative splicing, prognosis, post-transcriptional regulation, clinical heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-7621820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7621820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLate-onset Alzheimer\u0026acute;s disease (LOAD) is a heterogenous disorder influenced by genetic factors. In fact, we have previously described intermediate alleles (\u003cem\u003eIAs\u003c/em\u003e) in the huntingtin (\u003cem\u003eHTT\u003c/em\u003e) gene as potential modifiers in around 6% of AD population. The caudate nucleus, the most affected region in Huntington's disease, is highly sensitive to these \u003cem\u003eHTT\u003c/em\u003e CAG expansions, as they can induce epigenetic changes, including altered microRNA profiles. All this implies a potential source of gene expression deregulation, affecting disease onset and/or progression in LOAD patients with \u003cem\u003eHTT IAs\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe investigated the impact of \u003cem\u003eHTT IAs\u003c/em\u003e on LOAD progression and neuropathology using a comprehensive approach, genotyping \u003cem\u003eHTT\u003c/em\u003e CAG repeats in 323 LOAD patients and 335 healthy controls and further performing histopathological and molecular analyses on caudate nucleus samples in a small subcohort (6 healthy controls, 14 LOAD non-\u003cem\u003eHTT IA\u003c/em\u003e carriers, and 13 LOAD \u003cem\u003eHTT IA\u003c/em\u003e carriers).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e\u003cem\u003eHTT IAs\u003c/em\u003e carriers patients exhibited decreased survival after disease onset, suggesting accelerated progression. Histopathologically, while LOAD patients showed increased soluble HTT levels and altered tau pathology compared to controls, these changes were consistently and markedly exacerbated in \u003cem\u003eHTT IA\u003c/em\u003e carriers, characterized by heightened diffuse HTT immunoreactivity, pronounced tau 3R isoform imbalance, and increased 3R tau-enriched ghost tangles. Interestingly, this pathological exacerbation was supported by alterations in key splicing factors, including decreased SRSF6 and increased FUS-SFPQ complex formation. Analysis of microRNA (miRNA) profiling in the caudate nucleus revealed not only a LOAD-associated miRNA dysregulation that was significantly amplified in \u003cem\u003eHTT IA\u003c/em\u003e carriers, but also five \u003cem\u003eHTT IA\u003c/em\u003e signature miRNAs (miR-100-5p, miR-218-5p, miR-27b-3p, miR-487-3p, and miR-9-3p). \u003cem\u003eIn silico\u003c/em\u003e analyses, supported by network modeling and direct target validation, demonstrated that altered miRNAs target components of the nuclear spliceosome machinery, such as SRSF family, along with \u003cem\u003eMAPT\u003c/em\u003e and \u003cem\u003eHTT\u003c/em\u003e genes, suggesting a direct link to the observed tau 3R/4R imbalance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings underscore that \u003cem\u003eHTT IAs\u003c/em\u003e as critical players in LOAD progression through an intricate network involving miRNA-mediated dysregulation of splicing. Thus, identifying \u003cem\u003eHTT IAs\u003c/em\u003e through routine blood genetic screening offers a practical, non-invasive biomarker for patient stratification, taking a step closer to personalized therapeutic strategies in LOAD.\u003c/p\u003e","manuscriptTitle":"Connecting HTT intermediate alleles and microRNA dysregulation to enhanced tauopathy in Late-Onset Alzheimer's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:36:23","doi":"10.21203/rs.3.rs-7621820/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-30T11:44:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T04:28:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T17:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246764632378774912778846771503463204016","date":"2025-11-09T17:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124630814644782241833568979031502438774","date":"2025-11-07T14:02:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-28T07:40:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T01:25:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T01:24:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Alzheimer's Research \u0026 Therapy","date":"2025-09-15T14:28:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5eb2b5e-7e7b-4b5a-ad8f-6c9f5da77e09","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:06:38+00:00","versionOfRecord":{"articleIdentity":"rs-7621820","link":"https://doi.org/10.1186/s13195-026-02039-y","journal":{"identity":"alzheimers-research-and-therapy","isVorOnly":false,"title":"Alzheimer's Research \u0026 Therapy"},"publishedOn":"2026-04-20 15:58:45","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-10-10 15:36:23","video":"","vorDoi":"10.1186/s13195-026-02039-y","vorDoiUrl":"https://doi.org/10.1186/s13195-026-02039-y","workflowStages":[]},"version":"v1","identity":"rs-7621820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7621820","identity":"rs-7621820","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}