Towards a Personalized Medicine through Liquid Biopsy in Alzheimer’s disease: Epigenome of cell-free DNA reveals methylation differences linked to APOE status

Towards a Personalized Medicine through Liquid Biopsy in Alzheimer’s disease: Epigenome of cell-free DNA reveals methylation differences linked to APOE status | 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 Towards a Personalized Medicine through Liquid Biopsy in Alzheimer’s disease: Epigenome of cell-free DNA reveals methylation differences linked to APOE status Mónica Macías, Juan José Alba-Linares, Blanca Acha, Idoia Blanco-Luquin, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5358927/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Recent studies show that Alzheimer’s disease (AD) patients harbor specific methylation marks in the brain. However, accessing this epigenetic information “locked in the brain” while patients are alive is challenging. Liquid biopsy technique enables the study of circulating cell-free DNA (cfDNA) fragments originated from cells that have died and released their genetic material into the bloodstream. Methods : Here, we isolated and epigenetically characterized plasma cfDNA from 35 AD patients and 35 cognitively healthy controls. Next, we conducted a genome‑wide methylation analysis using the Infinium® MethylationEPIC BeadChip array to identify differential methylation marks in cfDNA between AD patients and controls. AD core biomarkers were also measured in blood and cerebrospinal fluid samples and correlated with differential methylation marks. Pyrosequencing and bisulfite cloning sequencing techniques were performed as an orthogonal validation for epigenome-wide results. Results : Epigenome-wide cfDNA methylation analysis identified 102 differential methylated positions (DMPs) associated with AD at a nominal significance level, of which 74% were hypomethylated. We found significant correlations between DMPs in our dataset and main cognitive and functional status tests (60% for MMSE, and 80% for GDS), along with correlations with AD biomarkers in CSF and blood. In silico functional analysis linked up to 30 DMPs to neurological processes, identifying key regulators such as SPTBN4 and the APOE gene. We identified several differentially methylated regions linked to APOE status annotated to genes already addressed as differentially methylated in AD condition and mostly in brain tissue ( HKR1 , ZNF154 , HOXA5 , TRIM40 , ATG16L2 , ADAMST2 ). In particular, a DMR in the HKR1 gene previously shown in to be hypermethylated in AD hippocampus was further validated in cfDNA with an orthogonal perspective. Conclusions : The feasibility of blood sampling makes plasma cfDNA a promising source of epigenetic biomarkers for Alzheimer's disease that could be further used in the practice of personalized medicine. Despite pre-analytical and technical challenges, liquid biopsy is emerging as a promising technique to further explore in neurodegenerative diseases. Alzheimer’s disease cell free DNA liquid biopsy EPIC array DNA methylation APOE blood Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Alzheimer’s disease (AD) represents the leading cause of agerelated dementia and the seventh leading cause of mortality globally [ 1 ]. Most AD cases occur sporadically in adults older than 65 years, defined as lateonset AD (LOAD). With the everincreasing aging of the population, this neurodegenerative disease currently affects 1 in 9 people over the age of 65, and its prevalence is expected to reach 152 million people worldwide by 2050 [ 2 , 3 ]. Despite its significant impact, the underlying mechanisms for AD pathogenesis remain unclear. Enhancing the accuracy of AD diagnosis would optimize early therapeutic intervention strategies, thereby reducing costs and the increasing burden that AD represents for our society. Multiple factors, such as environmental, biological, and genetic susceptibility appear to be associated with the development of LOAD. Within genetic factors, APOE ε4 polymorphism has been found to be the most consistently associated with LOAD development [ 4 ]. In recent years, epigenetics has demonstrated to play a role in the pathogenesis of neurodegenerative diseases such as AD [ 5 ]. Among different epigenetic modifications, DNA methylation is the most widely studied modification, which involves the attachment of a methyl group to the 5carbon position of a cytosine residue, usually at cytosineguanine dinucleotides (CpGs). In the case of AD, genecandidate studies, along with the latest application of omics technologies to epigenetics, have revealed new DNA methylation variants in genes biologically relevant to AD in human brain tissue. Our group and others have published epigenomewide studies describing differentially methylated genes across different vulnerable brain regions in postmortem samples. These regions include the prefrontal cortex [ 6 – 14 ], frontal cortex [ 15 ], entorhinal cortex [ 9 – 11 , 14 ], hippocampus [ 14 , 16 ], or superior temporal gyrus and inferior frontal gyrus [ 11 , 13 , 17 , 18 ]. However, a major obstacle hinders the translation of these promising findings as biomarkers to clinical practice: the difficulty of accessing brain tissue from living individuals with AD. As a result, the ADspecific epigenetic information remains "locked" within the brain tissue and, therefore, rather inaccessible while the patient is alive. Studies performed on blood-derived genomic DNA have also identified differentially methylated marks between AD patients and controls [ 19 – 22 ]. Nonetheless, most of these marks do not match those observed in brain tissues. It is widely acknowledged that cells undergo necrosis and apoptosis, among various processes of cell death, resulting in the release of their DNA into the bloodstream. This DNA, characterized by specific molecular alterations, is commonly referred to as cell-free DNA (cfDNA). Liquid biopsy, a noninvasive method consisting of a blood test, facilitates the isolation of cfDNA from plasma [ 23 ]. Under normal conditions, cfDNA originates mostly from the apoptosis of white blood cells [ 24 ]. However, in pathological processes, a significant amount of cfDNA is derived from the affected tissue, as evidenced by the enrichment of tissuespecific methylation marks [ 25 ]. So far, the majority of liquid biopsy applications have primarily focused on the identification of genetic variants, such as tumorspecific somatic mutations. However, beyond the field of oncology and genetics, liquid biopsy is emerging as a valuable tool in neurodegenerative diseases [ 26 – 29 ] where: (i) the bloodbrain barrier is dysfunctional, increasing its permeability [ 30 , 31 ]; and (ii) the DNA of the affected cells does not undergo any genetic sequence change. In these diseases, the analysis of epigenetic modifications in cfDNA specimens arises as a novel source of diagnostic biomarkers. Variants in DNA methylation, in particular, are considered exceptional biomarkers because of their stability, potential reversibility and accessibility in body fluids [ 32 ]. Liquid biopsy technique would provide access to this information "locked" in the brain, enabling the identification of epigenetic biomarkers (specific methylation marks) in cfDNA from patients with AD. This molecular assessment of cfDNA specimens could be thus considered a potential surrogate for pathological studies of postmortem brain tissue, providing a source of candidate epigenetic biomarkers that may aid in the clinical management of AD during the patient's lifetime. Hence, in this study, we conducted a genomewide methylation analysis to identify differential methylation signatures of plasma cfDNA in patients with AD compared to controls. MATERIAL AND METHODS Study design We conducted an observational casecontrol study including 70 subjects (35 AD patients and 35 age and sexmatched cognitively healthy controls) to identify cfDNA methylation differences among AD patients and controls by using liquid biopsy procedures. Subjects’ characterization Patients were prospectively recruited from the Dementia Unit of University Hospital of Navarra (tertiary hospital) from March 2019 to December 2021. AD was diagnosed according to the guidelines of the National Institute on Aging and Alzheimer’s Association (NIAAA 2018) [ 33 ]. The diagnosis was established by neurologists through neurological case history and examination, along with blood tests, neuropsychological testing, and magnetic resonance imaging scans. Cognitive status was assessed by the MiniMental State Examination (MMSE) and the Global Deterioration Scale (GDS) [ 34 ]. Controls were recruited from healthy relatives and volunteers who were matched for age and sex, and exhibited no clinical manifestations of dementia or other neurodegenerative diseases, as confirmed by clinical interviews and the MMSE (score > 27). Given that cfDNA concentrations increase in cancer stages [ 35 ], we exclusively enrolled controls and AD patients who had not experienced any tumor disease within, at least, the last five years. The study was approved by the Ethics Committee, and all participants provided written informed consent prior to their involvement. The sample size was calculated to ensure 80% statistical power to detect a minimum significant difference of 5% in cfDNA methylation levels between AD cases and controls. It was assumed that both distributions are normal with equal variance (σ = 0.15) and that an independent samples ttest would be used at a twosided significance level of α = 0.05. Under these conditions, the required sample size was determined to be 35 AD patients and 35 controls using the epiR library of the R statistical package [ 36 ]. Blood and cerebrospinal fluid (CSF) samples Peripheral blood samples were collected from each subject by venipuncture into 10 mL PAXgene® Blood DNA Tubes (QIAGEN, Redwood City, CA, USA), which contain a leukocyte stabilizer to prevent contamination with genomic DNA. The collected samples were centrifuged at 1,900 x g at room temperature for 15 min within an hour. Plasma was then transferred to plastic tubes, centrifuged again at maximum speed, and stored at -80°C until further analysis. For additional analysis, pTau181 was measured in additional EDTA plasma samples from both AD patients and controls, whenever these samples were available. This measurement was performed using the commercially available pTau181 V2 Advantage kit (Quanterix Corp, Billerica, MA, USA), with singlemolecule array (Simoa) technology at the Sant Pau Memory Unit´s laboratory (Barcelona, Spain). As part of their clinical diagnosis, 21 out of 35 AD patients underwent lumbar puncture for CSF biomarker testing to further classify their amyloid/tau/neurodegeneration (ATN) profile [ 33 ]. CSF samples were obtained by lumbar puncture and then centrifuged at 2,000 x g for 10 min at 4 °C within 4 hours after collection. CSF supernatants were aliquoted into 1.5 mL polypropylene tubes and stored at -80°C until further use. Aβ42, Aβ40, pTau181 and tTau in CSF were measured using a Lumipulse G600II instrument (Fujirebio, Ghent, Belgium), according to manufacturer’s instructions. cfDNA isolation and quantification cfDNA was isolated from 2 mL plasma by using QIAmp Circulating Nucleic Acid Kit (QIAGEN, Redwood City, CA, USA), following the manufacturer’s instructions. Doublestranded cfDNA was quantified with a Qubit 2.0 Fluorometer and the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Gilford, NH, USA), as per the manufacturer's guidelines. The amounts of cfDNA used for the methylation differential analysis array are reported in nanograms (ng). Characterization of cfDNA: fragment size analysis The purity and size distribution of cfDNA fragments were measured using the Fragment Analyzer™ Automated CE with the ProSize software (Agilent, Technologies, Inc., Santa Clara, CA). This analysis was performed with the DNF-477 HS Small Fragment kit and the DNF-464 High Sensitivity Large Fragment 50Kb Analysis Kit (Agilent), following the manufacturer´s instructions. Genomewide cfDNA methylation analysis cfDNA from 70 plasma samples was treated with sodium bisulfite using the Zymo EZ 96 DNA methylation kit (Zymo Research, Irvine, CA, USA), according to the manufacturer’s instructions. Given that cfDNA is highly fragmented, we treated our sample set with the Illumina Infinium FFPE restoration kit (Illumina, San Diego, CA, USA) prior to methylation analysis to preserve the samples during the bisulfite conversion process, as previously described [ 37 ]. Subsequently, a methylome study was conducted on cfDNA samples using the Illumina Infinium® MethylationEPIC BeadChip microarray (850K) and the Illumina HiScan System (Ilumina). This approach allows to quantitatively detect the methylation levels at over 850,000 CpG positions throughout the genome, including more than 90% of the sites covered by the Illumina HumanMethylation450 BeadChip and more than 300,000 methylation sites in enhancers regions identified by the ENCODE and FANTOM5 projects [ 38 , 39 ]. Array data preprocessing The EPIC array methylation data was fully preprocessed using the minfi package (v.1.32.0) [ 40 ] in R software environment (v.4.0.2). After importing IDAT files, methylation data from sex chromosome probes was analyzed to validate selfreported sex. Additionally, SNP/ethnicity probes from the sesame package (v.1.4.0) [ 41 ] were used to identify unwanted sources of variation. Samples that did not meet the specific quality control criteria for intensity signals in both the methylated and unmethylated channels were discarded. After completing the quality control steps, background noise signal was removed from the intensity values using the ssNoob method [ 42 ] in minfi . The extracted βvalues were then normalized using the βmixture quantile normalization (BMIQ) approach [ 43 ] implemented in ChAMP (v.2.16.2) [ 44 ]. Furthermore, to avoid spurious methylation signals, probes were filtered out based on the following criteria: (a) having a detection p value > 0.01 in any sample; (b) being crossreactive or multimapping probes [ 38 , 45 ]; (c) being located on sex chromosomes; and (d) including SNPs with a minor allele frequency (MAF) ≥ 0.01 at their CpG or single base extension (SBE) sites (dbSNP v.147). Finally, experimentspecific conflicting probes ( n = 424) were detected using the clustereddistribution approach implemented in the gaphunter function [ 46 ] of the minfi package (threshold = 0.20, outCutoff = 5/63) and were removed from downstream analysis. The final number of probes that passed all filters for differential methylation analyses was 747,200 (Additional file 1: Fig. S1 ). cfDNA cell‑type deconvolution The Houseman algorithm [ 47 ] implemented in the ENmix package (v.1.28.2) [ 48 ] and the FlowSorted.Blood. EPIC reference dataset [ 49 ] were used to predict blood celltype composition from DNA methylation data. Moreover, the deconvolution algorithm and the reference atlas of Moss et al. [ 37 ] were applied to our methylome array data using the Python software (v. 3.8.13) to reveal the tissular and cellular origins of cfDNA. Surrogate Variable Analysis Surrogate Variable Analysis was performed using the sva package (v.3.36.0) [ 50 ] in order to identify the main sources of variation in the highdimensional data of EPIC array methylation. The surrogate variables identified by the Be method were then correlated with the clinicopathological features of the study individuals. Probelevel differential methylation analyses Differentially methylated positions (DMPs) between AD patients and control subjects were identified through the application of linear regression models, defined in the limma package (v.3.44.3) [ 51 ]. M-values were selected as the dependent variable in the models since this logit transformation of methylation β-values achieve greater homoscedasticity for statistical inference [ 52 ]. Based on surrogate variable analysis, all models included the following fixed covariates: sex, age, percentage of nonsized cfDNA, batch effects (array position), and celltype composition obtained from deconvolution analyses. Finally, empirical Bayesmoderated ttests allowed us to perform contrasts to define DMPs, adjusting p -values for multiple comparisons using the Benjamini–Hochberg method (FDR < 0.05). Differential enrichment of DMPs in relation to their genomic distribution was assessed using hypergeometric tests. Regionlevel differential methylation analyses To detect differentially methylated regions (DMRs), the limma p values were fed in the combp function [ 53 ] of the ENmix package (v.1.28.2) [ 48 ] using the default parameters. This method allowed the detection of spatiallyrelated CpG sites with statistical significance. The initial regions were first selected under an FDR < 0.05, and subsequently, the final DMRs were defined using a Sidakcorrected p value < 0.05. Probe annotation The IlluminaHumanMethylationEPICanno.ilm10b4.hg19 package (v.0.6.0) was used to assign each probe based on its location within CGI (CpG Island) and genes. For the annotation of regions, the probes belonging to each region were first individually annotated, as described above. A single annotation was then assigned to each region according to the following criteria: (1) for CGI status, "Island">"N_Shore">"S_Shore">"N_Shelf">"S_Shelf">"OpenSea"; and (2) for gene locations, "TSS1500">"TSS200">"5'UTR">"1stExon">"Body">"3'UTR">"Intergenic". Functional in silico analysis of DMPs We employed Ingenuity Pathways Analysis (IPA) software from Ingenuity Systems® (QIAGEN, Redwood City, California, USA) to further determine the biological significance of ADrelated DMPs by means of causal analytics algorithms [ 54 ]. IPA identified the most significantly enriched biological functions and/or related diseases by calculating the p value through Fisher’s exact test. In addition, the upstream regulator analysis was utilized to predict upstream molecules that potentially regulate the set of differentially methylated genes and to build gene networks. Simultaneously, we conducted a systematic manual annotation using PubMed to determine whether the differentially methylated genes identified in AD patients were enriched in brain functions, as previously described [ 16 ]. Orthogonal validation Furthermore, pyrosequencing and bisulfite cloning sequencing techniques were performed to validate the methylation results obtained from the microarray analysis. Briefly, 200 ng of extracted cfDNA from each sample underwent bisulfite conversion using the EpiTect Bisulfite Kit (QIAGEN, Redwood City, CA, USA), following the manufacturer’s instructions. For pyrosequencing, primers were designed with PyroMark Assay Design version 2.0.1.15 (QIAGEN, Redwood City, California, USA), and PCR reactions were carried out on a VeritiTM Thermal Cycler (Applied Biosystems, Foster City, CA, USA) Next, 20 µL of biotinylated PCR product was immobilized using streptavidin-coated sepharose beads (GE Healthcare Life Sciences, Piscataway, NJ, USA) and 0.3 µM of sequencing primer was annealed to purified cfDNA strands. Pyrosequencing was performed using the PyroMark Gold Q96 reagents (Qiagen) on a PyroMark™ Q96 ID System (Qiagen). For each CpG studied within the amplicon (CpG1-CpG4), methylation levels were expressed as percentage of methylated cytosines over the sum of total cytosines. The EpiTect PCR Control DNA Set (Qiagen) was used as unmethylated and methylated DNA controls for the pyrosequencing reaction. For bisulfite cloning sequencing, primer pair sequences were designed using MethPrimer [ 55 ]. PCR products were cloned using the TopoTA Cloning System (Invitrogen, Carlsbad, CA, USA), and a minimum of 12 independent clones were sequenced for each examined subject and region by Sanger sequencing. Methylation graphs were obtained using the QUMA software [ 56 ]. Both pyrosequencing and bisulfite cloning sequencing primers are listed in Additional file 1: Table S1 . RESULTS Subjects and samples characterization The EPIC array was used in a set of 35 controls and 35 AD patients. There were no significant differences regarding age or sex between AD subjects and controls. Extended demographic and clinical features of the subjects are summarized in Table 1 . Table 1 Blood samples set analyzed by Illumina EPICBeadChip array. Phenotypical features Controls (n = 35) AD patients (n = 35) p -value Median (IQR) Age (years) 77 (72–80) 79 (76–83) 0.213 MMSE 30 (29–30) 22 (19–26) 0.000 GDS 1 (1–1) 4 (4–4) 0.000 cfDNA amount (ng) 96 (47–212) 81 (34–241) 0.445 N (%) Gender 0.811 Female 17 (49) 18 (51) Male 18 (51) 17 (49) APOE genotype 0.001 ε4 non-carriers 31 (89) 15 (43) ε4 carrriers 3 (9) 20 (57) A + T + N+ pTau 181 (pg/mL) 1.5 (1.2–1.8) 3.0 (2.1–3.9) 0.000 The table shows the phenotypical features of the subjects included in the study. cfDNA concentration and quality We managed to isolate plasma cfDNA from all the subjects included in the study. The amounts of cfDNA did not vary significantly between controls and AD patients (median: 95 ng; IQR = 47–228 vs median: 90 ng; IQR = 34–247; p value = 0.625), respectively. First, we verified cfDNAcorresponding size in our sample set as described in the methods section using the DNF-477 High Small Fragment Analysis Kit (Agilent). The expected cfDNA size was confirmed in all samples. The median cfDNA size was 167 bp (IQR = 158–185) for AD patients and 165 bp (IQR = 159–171) for controls with no significant differences between groups ( p value = 0.451). An example electropherogram of a cfDNA sample is presented in Additional file 1: Fig. S2. Surrogate Variable Analysis Surrogate Variable Analysis revealed one confounding variable of unknown significance (SV1) as the major source of variability affecting our series (Additional file 1: Fig. S3). To ascertain the nature of this biological or technical variable, we decided to further characterize the cfDNA fragmentation pattern employing the Fragment Analyzer technology. We found several samples with a carryover of nonsized cfDNA (Additional file 1: Fig. S4a). To specifically quantify this cfDNA fraction and evaluate its impact, we decided to perform a more indepth characterization of the isolated cfDNA using the DNF-464 High Sensitivity Large Fragment 50Kb Analysis Kit. This kit allows to track the cfDNA fragmentation pattern from 75 bp — 48,500 bp, covering both the expected cfDNAexpected fragment size and an extended region with other potential noncfDNA fragments. Interestingly, we noticed that nonsized cfDNA fragments were present in several samples, thus contributing to the total concentration. An example is shown in Additional file 1: Fig. S4b. We observed SV1 was negatively correlated with non-sized cfDNA. Global evaluation of nonsized cfDNA revealed a presence of 42.24% in controls and of 54.89% in AD patients, with no significant differences between groups ( p value = 0.07) (Additional file 1: Fig. S5). Nevertheless, upon further examination of the influence of nonsized cfDNA on methylation levels, we observed a strong positive correlation between nonsized cfDNA and median βmethylation values (r = 0.445; p < 0.001). Therefore, non-sized cfDNA percentage was used to adjust all the subsequent analyses. Differential methylated positions After quality control and sample tracking, seven samples were discarded from downstream analysis. Finally, cfDNA from 30 controls and 33 AD patients was used for differential methylation analysis. We confirmed that the loss of these subjects did not result in any changes leading to differences between AD patients and controls in terms of phenotypical features as illustrated in Additional file 1: Table S2. Genomewide DNA methylation was investigated in the context of both DMPs and DMRs. First, we built mixed linear models adjusting for potential sources of variability, specifically including sex, age, batch, nonsized cfDNA (%) and celltype composition. After adjusting for FDR correction, we detected no significant DMPs associated with AD condition. When looking at nominal significance level, the analysis revealed 102 ADrelated DMPs (absolute βdifference ≥ 0.1 and p value ≤ 0.05) annotated to 58 genes (Table 2 ), with an overrepresentation of hypomethylated positions in AD cases compared to controls (74%). Table 2 Differentially methylated positions (DMPs) in cfDNA from AD patients with respect to controls. DMP Genomic coordinates Gene ID Relation to CpG context Relation to Gene Structure p- value β-difference cg26023019 chr21 31311859 GRIK1 Island 1stExon 0.013 0.155 cg19665696 chr7 949154 ADAP1 Island Body 0.021 0.151 cg25069157 chr6 44102572 TMEM63B OpenSea Body 0.040 0.148 cg13578160 chr7 72813978 OpenSea 0.007 0.139 cg11955641 chr5 151304999 GLRA1 S_Shore TSS1500 0.001 0.139 cg09465533 chr3 32327675 CMTM8 OpenSea Body 0.001 0.137 cg20601028 chr20 22738632 OpenSea 0.006 0.133 cg24245216 chr19 7004657 OpenSea 0.044 0.130 cg23506049 chr12 103228185 OpenSea 0.039 0.129 cg21550804 chr8 74282865 OpenSea 0.035 0.125 cg22597210 chr19 10172841 C3P1 Island Body 0.008 0.125 cg17857094 chr6 30907280 DPCR1 OpenSea TSS1500 0.029 0.119 cg00055434 chr1 2415344 PLCH2 Island Body 0.006 0.117 cg27416647 chr15 96630572 OpenSea 0.027 0.117 cg11646124 chr1 182140416 OpenSea 0.008 0.113 cg22238209 chr19 35800743 MAG Island Body 0.017 0.113 cg07983614 chr16 84587903 OpenSea 0.024 0.111 cg21764456 chr16 10777077 TEKT5 OpenSea Body 0.037 0.110 cg07812827 chr8 74282708 OpenSea 0.010 0.110 cg06572225 chr11 7748353 OpenSea 0.021 0.110 cg03463818 chr8 94766468 TMEM67 N_Shore TSS1500 0.034 0.108 cg26802564 chr1 30446406 OpenSea 0.006 0.107 cg18056749 chr20 55836268 BMP7 N_Shelf Body 0.043 0.106 cg27454064 chr12 64215611 Island 0.017 0.105 cg24699005 chr19 1192342 N_Shelf 0.022 0.104 cg26861034 chr22 26908874 TFIP11 S_Shore TSS1500 0.002 0.104 cg10411590 chr13 21900810 S_Shore 0.032 0.102 cg00796424 chr12 54365966 HOXC11 N_Shore TSS1500 0.004 0.101 cg12906062 chr13 105462162 OpenSea 0.042 -0.100 cg00242341 chr11 72447419 ARAP1 OpenSea 5'UTR 0.039 -0.100 cg18955367 chr19 49002338 LMTK3 Island Body 0.030 -0.100 cg26003334 chr7 100661866 LOC102724094 OpenSea TSS1500 0.004 -0.101 cg16419584 chr10 129947858 Island 0.027 -0.101 cg13063165 chr15 79093076 ADAMTS7 S_Shore Body 0.017 -0.101 cg01495416 chr8 59085270 OpenSea 0.027 -0.101 cg02258724 chr19 53832577 Island 0.020 -0.101 cg04772328 chr2 170549930 C2orf77 N_Shore Body 0.002 -0.101 cg16045681 chr1 31575570 OpenSea 0.002 -0.102 cg21210642 chr9 100881995 TRIM14 S_Shore TSS1500 0.008 -0.102 cg24463437 chr1 28396758 EYA3 OpenSea Body 0.001 -0.102 cg20212912 chr4 147557774 N_Shore 0.007 -0.102 cg06311780 chr18 6633544 OpenSea 0.012 -0.103 cg21010821 chr11 111782679 HSPB2 OpenSea TSS1500 0.004 -0.103 cg14891200 chr2 220197664 RESP18 S_Shore Body 0.001 -0.103 cg14520947 chr1 225942842 OpenSea 0.003 -0.103 cg15086439 chr1 236563070 EDARADD S_Shelf Body 0.040 -0.103 cg04855678 chr3 195946921 OSTalpha OpenSea Body 0.010 -0.104 cg19146301 chr1 235100790 LOC101927851 OpenSea TSS1500 0.004 -0.104 cg13443570 chr8 99098126 ERICH5 OpenSea Body 0.042 -0.105 cg02784823 chr19 49000897 LMTK3 Island Body 0.041 -0.105 cg06398054 chr1 53092881 OpenSea 0.019 -0.106 cg20062681 chr11 94988642 OpenSea 0.001 -0.106 cg09484559 chr17 12692246 RICH2 N_Shore TSS1500 0.018 -0.106 cg08986575 chr5 173235445 OpenSea 0.049 -0.106 cg27159720 chr9 7971612 OpenSea 0.002 -0.106 cg07870920 chr4 121569769 OpenSea 0.049 -0.106 cg02938172 chr17 185152 RPH3AL Island 5'UTR 0.021 -0.106 cg27087112 chr2 114737475 LOC100499194 Island Body 0.000 -0.107 cg03174228 chr9 124658583 TTLL11 N_Shore Body 0.010 -0.107 cg24984452 chr1 95261186 LINC01057 OpenSea Body 0.035 -0.107 cg17566325 chr12 133022423 N_Shore 0.006 -0.107 cg03465894 chr11 106342311 OpenSea 0.022 -0.108 cg15410835 chr8 143125637 OpenSea 0.001 -0.108 cg24760557 chr10 31986724 OpenSea 0.016 -0.108 cg18625538 chr6 87832609 Island 0.045 -0.109 cg21933626 chr3 123026636 ADCY5 OpenSea Body 0.004 -0.109 cg05800368 chr9 124658957 TTLL11 Island Body 0.036 -0.109 cg02774630 chr2 154727554 GALNT13 N_Shore TSS1500 0.002 -0.109 cg06878111 chr10 9999498 OpenSea 0.003 -0.110 cg23213894 chr11 7691961 CYB5R2 N_Shelf Body 0.010 -0.111 cg06957053 chr7 137533035 DGKI S_Shore TSS1500 0.024 -0.111 cg10531073 chr22 38485757 BAIAP2L2 S_Shore Body 0.005 -0.112 cg24104237 chr3 45649408 LIMD1 OpenSea Body 0.010 -0.112 cg10289324 chr18 60710970 OpenSea 0.007 -0.113 cg15243027 chr2 32784469 BIRC6-AS2 OpenSea Body 0.028 -0.113 cg10092377 chr1 200880981 C1orf106 Island Body 0.001 -0.117 cg01583753 chr2 39470725 N_Shore 0.018 -0.117 cg16127514 chr10 29273678 OpenSea 0.042 -0.117 cg26496930 chr14 70186565 OpenSea 0.041 -0.117 cg10305928 chr10 62426219 ANK3 OpenSea Body 0.043 -0.117 cg14310109 chr6 157297510 ARID1B OpenSea Body 0.005 -0.119 cg16520701 chr8 34606956 OpenSea 0.010 -0.120 cg16210088 chr22 31318349 C22orf27 Island Body 0.009 -0.122 cg24448113 chr5 140475611 PCDHB2 Island 1stExon 0.013 -0.124 cg06862049 chr19 49001890 LMTK3 Island Body 0.023 -0.125 cg12172631 chr19 54584915 TARM1 OpenSea TSS1500 0.021 -0.126 cg20548231 chr22 31318373 C22orf27 Island Body 0.001 -0.127 cg09544050 chr8 143580965 BAI1 Island Body 0.003 -0.128 cg18815398 chr20 61506981 N_Shore 0.000 -0.128 cg26651782 chr19 51505779 KLK9 N_Shore 3'UTR 0.005 -0.128 cg24061197 chr2 220108496 GLB1L S_Shore 5'UTR 0.043 -0.128 cg06260707 chr1 42945689 OpenSea 0.022 -0.128 cg15059639 chr2 171220061 MYO3B OpenSea Body 0.022 -0.128 cg24658778 chr6 152897280 SYNE1 OpenSea Body 0.013 -0.130 cg20920357 chr4 116877727 OpenSea 0.002 -0.133 cg02256650 chr22 31317287 MORC2-AS1 N_Shore TSS1500 0.013 -0.133 cg26140120 chr8 124219575 FAM83A Island Body 0.014 -0.135 cg06452258 chr2 60597809 OpenSea 0.049 -0.136 cg08431893 chr21 44864600 Island 0.004 -0.139 cg16467921 chr8 128801108 OpenSea 0.004 -0.139 cg04248279 chr17 184833 RPH3AL N_Shore 5'UTR 0.007 -0.142 cg24135491 chr17 4487099 SMTNL2 N_Shore TSS200 0.015 -0.147 The table shows 102 DMPs with-difference > 0.100, prioritized by beta difference criteria. Each DMP (CpG site) was annotated by UCSC hg19 build. Genomic distribution of ADrelated DMPs was assessed for differential enrichment in relation to CpG context and genomic regions (Fig. 1 ). We observed that DMPs were more likely to be located in CpG islands and shore regions, showing both a 1.3fold enrichment ( p value < 0.05) compared to the random expectation based on all probes included in the analysis. Correlation with AD clinical parameters and biomarkers Subsequently, Spearman’s coefficient was calculated to evaluate the potential correlation between DNA methylation levels of the top ten DMPs, ranked by the highest positive and negative βdifference criteria respectively, and main AD clinical features and biomarkers. Very interestingly, we found significant correlations between DNA methylation levels of DMPs and MMSE score in 12/20 (60%), and with the GDS in 16/20 (80%) (Tables 3 and 4). Regarding biomarkers, we observed a significant correlation with the CSF Aβ42/Aβ40 ratio in 2/20 (10%) and with plasma pTau181 concentration in 7/20 (35%) (Tables 3 and 4). We also observed several correlation trends although not reaching significance for CSF pTau181 in up to 6/20 (30%). Interestingly, three genes significantly correlated with MMSE, GDS and pTau181 levels, namely SMTNL2, GLRA1 and MORC2-AS1 . Functional in silico analysis of DMPs We performed IPA analysis to further expand the biological significance of ADrelated DMPs found in this study. Within the diseases and functions category, the analysis confirmed that up to 30 molecules among our set of ADrelated DMPs were linked to neurological disorders ( p value range = 4.53E-02–1.10E-03) (Additional file 1: Table S3). In the physiological system development and function category, we found that 11 molecules in our dataset were mostly enriched in nervous system development and function ( p value range = 4.95E-02–2.83E-05) (Additional file 1: Table S3). Furthermore, IPA analysis predicted 24 upstream transcriptional regulators directly or indirectly connected to our dataset genes, prioritized by p value. Among these, SPTBN4 (spectrin β nonerythrocytic 4), which encodes a brain cytoskeletal protein, emerged as the most significantly associated regulator (Additional file 1: Table S4). It is also remarkable the presence of the APOE gene among these upstream regulators. Moreover, causal network analysis (CNA) [ 54 ] further connected upstream regulators to our dataset molecules, placing the APP gene (amyloidβ precursor protein), which encodes a membrane protein mainly expressed in neuronal synapses and closely related to AD development, at the forefront of potential relationships (Fig. 2 ). Differential methylated positions according to APOE status As previously mentioned, the APOE genotype is considered the strongest genetic risk factor for LOAD, and DNA methylation has demonstrated to act closely with this factor revealing DNA methylation differences between APOE ε4 carriers and noncarriers [ 57 ]. Therefore, to explore our results in greater depth, we divided our sample set regarding APOE ε4 status in each group of subjects, either controls or AD patients. No comparison was performed with controls APOE ε4 carriers due to the minimum number of APOE ε4 carriers in the control group ( n = 3; 9%). When looking at nominal significance level, major differences were found when comparing AD APOE ε4 carriers and Controls APOE ε4 noncarriers, represented by 980 DMPs (absolute βdifference ≥ 0.1 and p value ≤ 0.05) annotated to 668 genes (Additional file 1: Table S5). When comparing AD APOE ε4 noncarriers and Controls APOE ε4 noncarriers, we found 286 DMPs (absolute βdifference ≥ 0.1 and p value ≤ 0.05) annotated to 169 genes (Additional file 1: Table S6). Differential methylated regions At a regional level, differential analysis revealed one DMR significantly associated with AD status (Sidakcorrected p value < 0.05). This position (chr2:114,737,458 − 114,737,475) was located in a CpG island and annotated to a lncRNA (LOC100499194). When stratifying by APOE , we identified 17 DMRs (12% hypermethylated and 88% hypomethylated) comparing AD APOE ε4 carriers and Controls APOE ε4 noncarriers and 4 hypermethylated DMRs between AD APOE ε4 noncarriers and Controls APOE ε4 noncarriers (Sidak correction p value < 0.05) (Tables 5 and 6). Most interestingly, up to 6 DMRs were annotated to genes already addressed as differentially methylated in AD condition and mostly in brain tissue (Table 7). Orthogonal validation A DMR found between AD APOE ε4 noncarriers and controls noncarriers annotated to the HKR1 gene ( ZNF875 , Zinc Finger Protein 875, also known as ZNF875 ), a gene previously found to be differentially methylated in hippocampus, was selected for further exploration (Fig. 3 a). This DMR consists of 469 bp containing 10 CpGs dinucleotides. Considering that the expected cfDNA size is around 166 bp and further fragmentation is probably occurring during the deamination step in the bisulfite conversion process, special caution was exercised in designing the region to be explored [ 58 ]. For primer design, we selected a CpG assayed in the EPIC array (cg12024906) located in the extreme of the DMR. Therefore, we designed primers to achieve amplicons contained in the DMR and smaller than the estimated cfDNA size. For pyrosequencing, we explored a region of 140 bp spanning 4 CpGs dinucleotides, including cg12024906. We observed that the DNA methylations levels in the selected CpG site (CpG2) and the average for the amplicon were significantly increased in AD patients compared to controls [30.74 ± 14.57% vs 18.92 ± 16.41% and 36.51 ± 11.85% vs 26.02 ± 19.48%, respectively) (Fig. 3 b). For bisulfite cloning sequencing, we explored a region of 86 bp spanning 6 CpG dinucleotides in eight representative samples. We found that the average DNA methylation levels were strongly increased in AD patients compared to controls for the amplicon [82.28 ± 9.83% vs 17.36 ± 10.78%; p value < 0.001] and the selected CpG site (CpG4) [62.50 ± 17.33% vs 14.58 ± 14.22%; p value < 0.001] (Fig. 3 c). Overall results of this orthogonal validation are detailed in Additional file: Table S7. DISCUSSION In this study, we investigated methylation differences in plasma cfDNA in AD patients and controls from an epigenomewide perspective. We hereby show that cfDNA can be readily isolated from plasma through liquid biopsy procedures and used to identify methylation differences between AD patients and cognitively healthy controls. Specific cfDNA methylation differences seem to be APOE genotyperelated. In particular, 4 DMRs were found in APOE ε4 noncarriers when comparing AD versus control subjects. Methodologies to analyze cfDNA in biological fluids are greatly technologically demanding in terms of sensitivity, given the low levels of cfDNA (concentrations in the range of 10 ng/mL). In neurological disorders, this challenge is compounded by the low relative abundance of the expected brainderived fraction among the background cfDNA [ 27 ]. A way to achieve higher starting amounts of cfDNA could be drawing larger volumes of plasma; however, there should be a plasma volume limitation for this technique to be transferable to neurological clinical practice in the near future, if differences in DNA methylation are intended to be used as biomarkers. Another approach could be relying on sample pooling techniques to increase the amount of starting material when using cfDNA, as has been previously suggested [ 59 ]. However, this approach only yields average methylation values. Additionally, cfDNA appears at higher levels in certain pathological conditions, such as cancer, trauma, stroke, autoimmune disorders, or insufficient renal clearance [ 60 ]. In this regard, we used as an exclusion criterion, for both controls and AD patients, the coexistence of another pathological condition that could potentially overestimate plasma ADrelated cfDNA concentrations. In our study cohort, we obtained cfDNA from plasma of all AD patients and controls. Nevertheless, we did not find a significant increase in total plasma cfDNA levels in AD patients compared to controls in our cohort, which adds evidence in this regard, since previous studies showed conflicting results in terms of these differences [ 61 , 62 ]. This could be explained by the low relative abundance of the expected brainderived cfDNA within the background cfDNA, making it challenging to detect any increase in the concentration of the brainderived fraction in the total cfDNA. A critical aspect for cfDNA analysis involves preanalytical factors, such as the type of collection tube, sample centrifugation protocol, and cfDNA extraction method [ 63 ]. Although commercial kit manufacturers usually claim efficient isolation of pure, highquality cfDNA, this study highlights the significant impact of nonsized cfDNA carryover [ 64 , 65 ]. Nonsized cfDNA contamination often goes unnoticed when employing common cfDNA quantification methods, such as fluorometric techniques, which cannot distinguish between cfDNA and genomic DNA (gDNA) [ 65 ]. Our findings indicate that approximately half of the samples contained nonsized cfDNA carryover. Nevertheless, the presence of nonsized cfDNA did not significantly differ between AD patients and controls in our study cohort. Given that nonsized cfDNA was identified as a major source of variability in the surrogate variable analysis, we decided to adjust our statistical models to account for this variable, as a latent source of noise. Several research groups have conducted epigenomewide studies on cfDNA from AD patients using various methods, including targeted bisulfite sequencing [ 66 ], highthroughput sequencing to map 5Hydroxymethylcytosine (5hmC) as another widespread epigenetic marker [ 67 ], and even EPIC array technology [ 68 ]. In our cohort, using this microarray technology, we found no significant DMPs associated with AD condition in cfDNA after applying a FDR < 0.05 correction. This finding aligns with previous epigenome studies performed on cfDNA [ 69 ]. Besides technical factors and analysis methodologies, bioinformatics filtering thresholds could represent a major source of discordance between assays. A recent paper by BahadoSingh et al. [ 68 ] reported significant differences in cfDNA between AD patients and controls after FDRcorrection employing EPIC array technology and Artificial Intelligence algorithms for data analysis. However, in their study only about 41% of the total probes assayed by the EPIC array passed the quality control, whereas in our study, up to 86% of the overall probes were included in the subsequent differential methylation analysis. Anyhow, the analysis identified an interesting set of 102 differential cfDNA methylation marks at a nominal significance level. Among these methylation marks, hypomethylation in AD cases compared to controls was overrepresented. Regarding genomic location, these differential cfDNA methylation marks were predominantly localized in CpG islands and shores, concordant with previous findings in genomic DNA in AD patients [ 17 ]. Moreover, we found strong correlations between the ten top-ranked nominally significant probes in our dataset and main cognitive and functional status indicators (MMSE and GDS), along with mild correlations with AD biomarkers in CSF and blood, such as Aβ42/Aβ40 ratio and pTau181 levels, respectively. In the functional interpretation of results, IPA allows an overall visualization of the effects of a gene dataset within a pathway, displaying it as a network to facilitate a more accurate understanding of the results. The most significant upstream regulator identified the SPTBN4 gene, with ANK3 being its main target molecule in our dataset. SPTBN4 encodes for spectrin β nonerythrocytic 4 and, along with ANK3 , a member of the ankyrin family, forms part of the axon initial segment [ 70 ]. The spectrin/ankyrin complex links the cytoskeleton and voltagegated channels, which are crucial for regulating neuronal polarity and synapsis [ 71 ]. Interestingly, SánchezMut et al. reported hypermethylation of SPTBN4 in the frontal cortex of human AD patients [ 72 ]. It is also remarkable the presence of APOE among this gene list, interacting again with ANK3 and with ADGRB1 (Adhesion G ProteinCoupled Receptor B1, BrainSpecific Angiogenesis Inhibitor, BAI1), which mediates hippocampal spatial learning and memory [ 73 ]. To the best of our knowledge, this is the first APOE stratified study conducted with the Infinium EPIC array on cfDNA obtained from AD patients. It is wellknown that APOE genotype is an important risk factor for AD and may influence the course of the disease, which may be represented in the status of ADassociated DNA methylation marks [ 21 ]. Therefore, we decided to further include the APOE genotype into our linear models to analyze its contribution on the cfDNA methylation differences found in this study. When stratifying AD patients and controls based on the presence of the APOE ε4 genotype, we observed a substantial increase in differentially methylated positions by 10%. This finding aligns with previous methylation studies that have demonstrated distinct and markedly different methylation marks between AD patients and controls when stratifying by the APOE genotype [ 57 , 74 , 75 ]. However, further research is needed to elucidate the mechanisms linking DNA methylation and the presence of the ε4 allele. Nevertheless, the most significant differences were found when exploring results at a regional level, probably because positionlevel differences are often too subtle to be detectable [ 19 ]. In this regard, we identified several cfDNA methylation differences in regions associated with genes known to be important in AD. For instance, we found a hypermethylated region in AD APOE ε4 noncarriers annotated to the SLCO2A1 gene (Solute Carrier Organic Anion Transporter Family Member 2A1). This gene encodes for prostaglandin transporter, which is reported to be localized in neurons, microglia, and astrocytes, and is poorly expressed in AD human brain. This transporter has been postulated as a possible modulator of prostaglandinmediated neuroinflammation associated with AD [ 76 ]. In addition, we detected a hypomethylated region in AD APOE ε4 carriers spanning the DNMT3B gene, which encodes DNA Methyltransferase 3 Β, an enzyme that catalyzes de novo DNA methylation in mammalian cells [ 77 ]. It would be interesting to check whether DNMT3B hypomethylation identified in AD APOE ε4 carriers may be related to the higher expression of these DNA methyltransferases with increasing age [ 78 ]. This deregulation in DNMTmediated de novo methylation by its own methylation state may contribute to explain the AD epigenetic misbalance [ 79 ]. Notably, we identified this DMR when comparing AD APOE ε4 carriers with control APOE ε4 noncarriers. The association of DNMT3B deregulation and APOE ε4 genotype have been addressed previously, indicating synergistic effects on AD onset [ 80 ]. However, studies elsewhere have found no significant correlation between DNMT3B methylation and APOE status [ 81 ]. These findings within our dataset may help to uncover new underlying molecular changes linked to AD pathology. Liquid biopsy has emerged as a noninvasive tool for reflecting molecular changes occurring in tissues. Interestingly, we observed how some of the ADrelated regional changes identified in plasma cfDNA in our study cohort were consistent with changes previously reported in other studies performed on brain samples (Table 7). In particular, several DMRs identified in this study were associated with genes previously reported as differentially methylated in the hippocampus of AD patients, such as HKR1 and ATG16L2 . The HKR1 gene ( ZNF875 , Zinc Finger Protein 875) gene is a transcriptional regulator and its methylation levels have been proposed as an aging biomarker [ 82 ]. In this study, we observed that HKR1 methylation changes remained significant in AD patients after adjusting for age, which may indicate underlying agerelated epigenetic changes contributing to neurodegeneration in AD [ 83 , 84 ]. Moreover, both HKR1 and ATG16L2 methylation levels were found to correlate significantly with pTau burden in the AD hippocampus [ 16 ]. In this respect, we found no correlation between cfDNA methylation levels of these DMRs and tTau/pTau181 and pTau181 as surrogate biomarkers for Tau deposition in CSF and blood, respectively. Our study adds evidence to using cfDNA to characterize methylation changes in neurological diseases, such as AD. Plasma cfDNA emerges as a novel source of epigenetic biomarkers that may help to improve AD diagnosis in the clinical setting. Precision medicine based on liquid biopsy procedures is an innovative approach that merits further research. Validation of these candidate biomarkers in larger and multicentric cohorts will contribute to design panels of composite biomarkers from different origins that may significantly improve clinical tools in the dementia field. The primary limitations of our study are those related to the nature of the cfDNA. In this work we have demonstrated that nonsized cfDNA carryover in standard cfDNA isolation protocols can go unnoticed, which should be considered when analyzing results [ 85 ]. A possible approach to overcome nonsized cfDNA carryover could be the further use of methods based on capillary electrophoresis to selectively elute DNA according to its base pair length [ 86 ]. CONCLUSIONS In summary, our study demonstrates that cfDNA is present in the plasma of AD patients and can be readily isolated during their lifetime. The availability of blood sampling makes the analysis of epigenetic alterations in cfDNA a promising source of biomarkers in AD to be used in the practice of personalized medicine. Some candidate epigenetic biomarkers seem to be related to the APOE genotype. Exploring the potential of liquid biopsy can enhance our understanding of this complex disorder. Moreover, this study highlights the critical importance of preanalytical factors, bioinformatics workflows, and thresholds when analyzing the cfDNA from an epigenome wide perspective. Abbreviations AD = Alzheimer Disease APOE = Apolipoprotein E cfDNA = Cell‑free DNA CpG = cytosine-guanine dinucleotide CSF = cerebrospinal fluid DMP = differential methylated position DMR = differential methylated region LOAD = late-onset Alzheimer Disease Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE The Navarra Ethics Research Committee approved this study and written informed consent was obtained from all subjects included in the study. Procedures were in accordance with the revised Helsinki Declaration. CONSENT FOR PUBLICATION Not applicable. AVAILABILITY OF DATA AND MATERIALS All data generated and/or analyzed during this study are either included in this article or are available from the corresponding author on reasonable request. COMPETING INTERESTS D.A. participated in advisory boards from Fujirebio-Europe, Roche Diagnostics, Grifols S.A. and Lilly, and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmacéutica S.L., Zambon S.A.U. and Esteve Pharmaceuticals S.A. D.A. declares a filed patent application (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease). FUNDING The authors sincerely appreciate the funding support from the Government of Navarra [GºNa 36/18], and the Spanish Government through grants from the Institute of Health Carlos III (FIS PI20/01701), co‑funded by the European Regional Development Fund (ERDF), European Union, A way of shaping Europe. The project leading to these results has received funding from La Caixa Banking Foundation (ID 100010434) and Fundación Luzón (HR20‑01109_BIOP‑ALS) under agreement LCF/PR/PR15/51100006. In addition, B.A. (Blanca Acha) is supported by a PFIS fellowship from the Spanish Government (FI18/00150). M.M. (Mónica Macías) is beneficiary of a Río Hortega grant from the Spanish Government (CM20/00240) and a Navarrabiomed postdoctoral research grant (2022). J.A.-J. (Johana Álvarez-Jiménez) has received a Doctorandos industriales grant for 2023–2026. A.U.‑C. (Amaya Urdánoz‑Casado) received a Doctorandos industriales grant for 2018–2020 and a predoctoral research grant (2019) founded by the Department of Industry and Health of the Government of Navarra. D.A. was supported by research grants from Institute of Health Carlos III (ISCIII), Spain INT19/00016 and INT23/00048. M.M. (Maite Mendioroz) received a Contrato de intensificación grant from the Institute of Health Carlos III (INT19/00029) and a grant (LCF/PR/PR15/51100006) founded by La Caixa Banking Foundation and Caja Navarra Banking Foundation. AUTHOR’S CONTRIBUTIONS M.M (Mónica Macías participated in the study concept, design and manuscript preparation; J.S.R-G., J.C, C.C, M.E.E. and I.J. contributed to recruitment; B.A. E.C. participated in data and samples collection; M.R. (Miren Roldan) and M.R. (Maitane Robles) isolated and characterized cfDNA; D.A. analyzed ptau181 in plasma; J.J.A-L., A.F-F. and M.F-F. performed bioinformatics analysis, assisted in data interpretation and contributed to manuscript preparation and revision; M.M (Mónica Macías), I.B-L., J.A-J. and A.U-C. performed analysis and interpretation of data, figure design and drawing and drafting/review of the manuscript for content; M.M. (Maite Mendioroz) had a major role in the design of the work, supervised the research and finalized the manuscript for publication. ACKNOWLEDGEMENTS We would like to thank all the subjects who participated in this study for their generous contribution. We would also like to thank Alba Leiza Dávila for English language editing. References Alzheimer's disease facts and figures. Alzheimers Dement. 2024;20(5):3708-3821. Alzheimers Disease International. (2018). World Alzheimer Report 2018. Available from: https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf. . Alzheimers Disease International. (2021). World Alzheimer Report 2021. Available from: https://www.alzint.org/u/World-Alzheimer-Report-2021.pdf. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45(12):1452-1458. Sanchez-Mut JV, Gräff J. Epigenetic Alterations in Alzheimer's Disease. Front Behav Neurosci. 2015;9:347. Zhang L, Silva TC, Young JI, Gomez L, Schmidt MA, Hamilton-Nelson KL, et al. Epigenome-wide meta-analysis of DNA methylation differences in prefrontal cortex implicates the immune processes in Alzheimer's disease. Nat Commun. 2020;11(1):6114. Zhang L, Young JI, Gomez L, Silva TC, Schmidt MA, Cai J, et al. Sex-specific DNA methylation differences in Alzheimer's disease pathology. Acta Neuropathol Commun. 2021;9(1):77. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, et al. Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci. 2014;17(9):1156-1163. Lunnon K, Smith R, Hannon E, De Jager PL, Srivastava G, Volta M, et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease. Nat Neurosci. 2014;17(9):1164-1170. Gasparoni G, Bultmann S, Lutsik P, Kraus TFJ, Sordon S, Vlcek J, et al. DNA methylation analysis on purified neurons and glia dissects age and Alzheimer's disease-specific changes in the human cortex. Epigenetics Chromatin. 2018;11(1):41. Smith RG, Pishva E, Shireby G, Smith AR, Roubroeks JAY, Hannon E, et al. A meta-analysis of epigenome-wide association studies in Alzheimer's disease highlights novel differentially methylated loci across cortex. Nat Commun. 2021;12(1):3517. Sanchez-Mut JV, Heyn H, Vidal E, Delgado-Morales R, Moran S, Sayols S, et al. Whole genome grey and white matter DNA methylation profiles in dorsolateral prefrontal cortex. Synapse. 2017;71(6). Smith RG, Hannon E, De Jager PL, Chibnik L, Lott SJ, Condliffe D, et al. Elevated DNA methylation across a 48-kb region spanning the HOXA gene cluster is associated with Alzheimer's disease neuropathology. Alzheimers Dement. 2018;14(12):1580-1588. Semick SA, Bharadwaj RA, Collado-Torres L, Tao R, Shin JH, Deep-Soboslay A, et al. Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer's disease. Acta Neuropathol. 2019;137(4):557-569. Rao JS, Keleshian VL, Klein S, Rapoport SI. Epigenetic modifications in frontal cortex from Alzheimer's disease and bipolar disorder patients. Transl Psychiatry. 2012;2(7):e132. Altuna M, Urdanoz-Casado A, Sanchez-Ruiz de Gordoa J, Zelaya MV, Labarga A, Lepesant JMJ, et al. DNA methylation signature of human hippocampus in Alzheimer's disease is linked to neurogenesis. Clin Epigenetics. 2019;11(1):91. Li QS, Sun Y, Wang T. Epigenome-wide association study of Alzheimer's disease replicates 22 differentially methylated positions and 30 differentially methylated regions. Clin Epigenetics. 2020;12(1):149. Watson CT, Roussos P, Garg P, Ho DJ, Azam N, Katsel PL, et al. Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer's disease. Genome Med. 2016;8(1):5. Perez RF, Alba-Linares JJ, Tejedor JR, Fernandez AF, Calero M, Roman-Dominguez A, et al. Blood DNA Methylation Patterns in Older Adults With Evolving Dementia. J Gerontol A Biol Sci Med Sci. 2022;77(9):1743-1749. Lardenoije R, Roubroeks JAY, Pishva E, Leber M, Wagner H, Iatrou A, et al. Alzheimer’s disease-associated (hydroxy)methylomic changes in the brain and blood. Clin Epigenetics. 2019;11(1):164. Konki M, Malonzo M, Karlsson IK, Lindgren N, Ghimire B, Smolander J, et al. Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer's disease. Clin Epigenetics. 2019;11(1):130. Chang L, Wang Y, Ji H, Dai D, Xu X, Jiang D, et al. Elevation of peripheral BDNF promoter methylation links to the risk of Alzheimer's disease. PLoS One. 2014;9(11):e110773. Macías M, Alegre E, Díaz-Lagares A, Patiño A, Pérez-Gracia JL, Sanmamed M, et al. Liquid Biopsy: From Basic Research to Clinical Practice. Adv Clin Chem. 2018;83:73-119. Sun K, Jiang P, Chan KC, Wong J, Cheng YK, Liang RH, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci U S A. 2015;112(40):E5503-5512. Lehmann-Werman R, Neiman D, Zemmour H, Moss J, Magenheim J, Vaknin-Dembinsky A, et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U S A. 2016;113(13):E1826-1834. Gaitsch H, Franklin RJM, Reich DS. Cell-free DNA-based liquid biopsies in neurology. Brain. 2022;146(5):1758-1774. Southwood D, Singh S, Chatterton Z. Brain-derived cell-free DNA. Neural Regeneration Research. 2022;17(10):2213-2214. Khemka S, Sehar U, Manna PR, Kshirsagar S, Reddy PH. Cell-Free DNA As Peripheral Biomarker of Alzheimer's Disease. Aging Dis. 2024. Pollard C, Aston K, Emery BR, Hill J, Jenkins T. Detection of neuron-derived cfDNA in blood plasma: a new diagnostic approach for neurodegenerative conditions. Front Neurol. 2023;14:1272960. Noe CR, Noe-Letschnig M, Handschuh P, Noe CA, Lanzenberger R. Dysfunction of the Blood-Brain Barrier-A Key Step in Neurodegeneration and Dementia. Front Aging Neurosci. 2020;12:185. Zenaro E, Piacentino G, Constantin G. The blood-brain barrier in Alzheimer's disease. Neurobiol Dis. 2017;107:41-56. Costa-Pinheiro P, Montezuma D, Henrique R, Jerónimo C. Diagnostic and prognostic epigenetic biomarkers in cancer. Epigenomics. 2015;7(6):1003-1015. Jack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535-562. Reisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry. 1982;139(9):1136-1139. Leon SA, Shapiro B, Sklaroff DM, Yaros MJ. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 1977;37(3):646-650. Stevenson M, Nunes T, Sanchez J, Thornton R, Reiczigel J, Robison-Cox J, et al. EpiR: An R package for the analysis of epidemiological data. 2013:9-43. Moss J, Magenheim J, Neiman D, Zemmour H, Loyfer N, Korach A, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun. 2018;9(1):5068. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208. Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2017;45(4):e22. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363-1369. Zhou W, Triche TJ, Jr., Laird PW, Shen H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 2018;46(20):e123. Triche TJ, Jr., Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41(7):e90. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189-196. Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33(24):3982-3984. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203-209. Andrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD. "Gap hunting" to characterize clustered probe signals in Illumina methylation array data. Epigenetics Chromatin. 2016;9(1):56. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86. Xu Z, Niu L, Li L, Taylor JA. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44(3):e20. Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 2018;19(1):64. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882-883. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11(1):587. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28(22):2986-2988. Krämer A, Green J, Pollard J, Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30(4):523-530. Li LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics. 2002;18(11):1427-1431. Kumaki Y, Oda M, Okano M. QUMA: quantification tool for methylation analysis. Nucleic Acids Res. 2008;36(Web Server issue):W170-175. Walker RM, Vaher K, Bermingham ML, Morris SW, Bretherick AD, Zeng Y, et al. Identification of epigenome-wide DNA methylation differences between carriers of APOE epsilon4 and APOE epsilon2 alleles. Genome Med. 2021;13(1):1. Darst RP, Pardo CE, Ai L, Brown KD, Kladde MP. Bisulfite sequencing of DNA. Curr Protoc Mol Biol. 2010;Chapter 7(1):Unit 7 9 1-17. Gallardo-Gomez M, Moran S, Paez de la Cadena M, Martinez-Zorzano VS, Rodriguez-Berrocal FJ, Rodriguez-Girondo M, et al. A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples. Clin Epigenetics. 2018;10(1):53. Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol. 2017;14(9):531-548. Pai MC, Kuo YM, Wang IF, Chiang PM, Tsai KJ. The Role of Methylated Circulating Nucleic Acids as a Potential Biomarker in Alzheimer's Disease. Mol Neurobiol. 2019;56(4):2440-2449. Mendioroz M, Martínez-Merino L, Blanco-Luquin I, Urdánoz A, Roldán M, Jericó I. Liquid biopsy: a new source of candidate biomarkers in amyotrophic lateral sclerosis. Ann Clin Transl Neurol. 2018;5(6):763-768. Bronkhorst AJ, Aucamp J, Pretorius PJ. Cell-free DNA: Preanalytical variables. Clin Chim Acta. 2015;450:243-253. Kresse SH, Brandt-Winge S, Pharo H, Flatin BTB, Jeanmougin M, Vedeld HM, et al. Evaluation of commercial kits for isolation and bisulfite conversion of circulating cell-free tumor DNA from blood. Clin Epigenetics. 2023;15(1):151. Alcaide M, Cheung M, Hillman J, Rassekh SR, Deyell RJ, Batist G, et al. Evaluating the quantity, quality and size distribution of cell-free DNA by multiplex droplet digital PCR. Sci Rep. 2020;10(1):12564. Guemri J, Pierre-Jean M, Brohard S, Oussada N, Horgues C, Bonnet E, et al. Methylated ccfDNA from plasma biomarkers of Alzheimer's disease using targeted bisulfite sequencing. Epigenomics. 2022;14(8):451-468. Chen L, Shen Q, Xu S, Yu H, Pei S, Zhang Y, et al. 5-Hydroxymethylcytosine Signatures in Circulating Cell-Free DNA as Diagnostic Biomarkers for Late-Onset Alzheimer's Disease. J Alzheimers Dis. 2022;85(2):573-585. Bahado-Singh RO, Radhakrishna U, Gordevicius J, Aydas B, Yilmaz A, Jafar F, et al. Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease. Cells. 2022;11(11). Konki M, Lindgren N, Kylaniemi M, Venho R, Laajala E, Ghimire B, et al. Plasma cell-free DNA methylation marks for episodic memory impairment: a pilot twin study. Sci Rep. 2020;10(1):14192. Huang CY, Rasband MN. Axon initial segments: structure, function, and disease. Ann N Y Acad Sci. 2018;1420(1):46-61. Grubb MS, Burrone J. Building and maintaining the axon initial segment. Curr Opin Neurobiol. 2010;20(4):481-488. Sanchez-Mut JV, Aso E, Panayotis N, Lott I, Dierssen M, Rabano A, et al. DNA methylation map of mouse and human brain identifies target genes in Alzheimer's disease. Brain. 2013;136(Pt 10):3018-3027. Zhu D, Li C, Swanson AM, Villalba RM, Guo J, Zhang Z, et al. BAI1 regulates spatial learning and synaptic plasticity in the hippocampus. J Clin Invest. 2015;125(4):1497-1508. Foraker J, Millard SP, Leong L, Thomson Z, Chen S, Keene CD, et al. The APOE Gene is Differentially Methylated in Alzheimer's Disease. J Alzheimers Dis. 2015;48(3):745-755. Panitch R, Sahelijo N, Hu J, Nho K, Bennett DA, Lunetta KL, et al. APOE genotype-specific methylation patterns are linked to Alzheimer disease pathology and estrogen response. Transl Psychiatry. 2024;14(1):129. Choi K, Zhuang H, Crain B, Doré S. Expression and localization of prostaglandin transporter in Alzheimer disease brains and age-matched controls. J Neuroimmunol. 2008;195(1-2):81-87. Gagliardi M, Strazzullo M, Matarazzo MR. DNMT3B Functions: Novel Insights From Human Disease. Front Cell Dev Biol. 2018;6:140. Wang K, Liu H, Hu Q, Wang L, Liu J, Zheng Z, et al. Epigenetic regulation of aging: implications for interventions of aging and diseases. Signal Transduct Target Ther. 2022;7(1):374. Rajendran G, Shanmuganandam K, Bendre A, Muzumdar D, Goel A, Shiras A. Epigenetic regulation of DNA methyltransferases: DNMT1 and DNMT3B in gliomas. J Neurooncol. 2011;104(2):483-494. de Bem CM, Pezzi JC, Borba EM, Chaves ML, de Andrade FM, Fiegenbaum M, et al. The synergistic risk effect of apolipoprotein epsilon4 and DNA (cytosine-5-)-methyltransferase 3 beta (DNMT3B) haplotype for Alzheimer's disease. Mol Biol Rep. 2016;43(7):653-658. Tannorella P, Stoccoro A, Tognoni G, Petrozzi L, Salluzzo MG, Ragalmuto A, et al. Methylation analysis of multiple genes in blood DNA of Alzheimer's disease and healthy individuals. Neurosci Lett. 2015;600:143-147. Zeng Q, Chen X, Ning C, Zhu Q, Yao Y, Zhao Y, et al. Methylation of the genes ROD1, NLRC5, and HKR1 is associated with aging in Hainan centenarians. BMC Med Genomics. 2018;11(1):7. Berson A, Nativio R, Berger SL, Bonini NM. Epigenetic Regulation in Neurodegenerative Diseases. Trends Neurosci. 2018;41(9):587-598. Lardenoije R, Iatrou A, Kenis G, Kompotis K, Steinbusch HW, Mastroeni D, et al. The epigenetics of aging and neurodegeneration. Prog Neurobiol. 2015;131:21-64. Nidadavolu LS, Feger D, Wu Y, Grodstein F, Gross AL, Bennett DA, et al. Circulating Cell-Free Genomic DNA Is Associated with an Increased Risk of Dementia and with Change in Cognitive and Physical Function. J Alzheimers Dis. 2022;89(4):1233-1240. Wenz HM, Dailey D, Johnson MD. Development of a high-throughput capillary electrophoresis protocol for DNA fragment analysis. Methods Mol Biol. 2001;163:3-17. Additional Declarations Competing interest reported. D.A. participated in advisory boards from Fujirebio-Europe, Roche Diagnostics, Grifols S.A. and Lilly, and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmacéutica S.L., Zambon S.A.U. and Esteve Pharmaceuticals S.A. D.A. declares a filed patent application (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease). Supplementary Files AdditionalFile1.zip SUPPLEMENTARY INFORMATION Additional file 1: Figure S1: Bioinformatics workflow used in this study: quality control & sample tracking. The diagram shows the bioinformatics pipeline used in this study: procedures for EPIC array methylation data quality control and normalization analysis. (.pptx) Figure S2: Characterization of cfDNA: fragment size analysis. The diagram represents the typical pattern of cfDNA with a peak around 165 bp after performing capillary electrophoresis on a Fragment Analyzer Automated CE System with DNF-477 High Small Fragment Analysis Kit. This kits allows to track cfDNA fragmentation pattern from 50 bp – 1,500 bp. (.pptx) Figure S3: Covariates selection. Heatmap describing the correlations (Pearson’s r) between clinical variables and latent surrogate variables (SV1àSV8) extracted from the data. (.pptx) Figure S4: Characterization of non-sized cfDNA. a) Example of a sample with the presence of non-sized cfDNA. (b) The diagram represents the typical pattern of cfDNA with a peak around 165 bp after performing capillary electrophoresis on a Fragment Analyzer Automated CE System with the DNF-464 High Sensitivity Large Fragment 50Kb Analysis Kit. This kit allows to track cfDNA fragmentation pattern from 75 bp – 48,500 bp. Presence of genomic DNA is shown from 1,500 – 48,500 bp. bp: base pairs. (.pptx) Figure S5: Box-plot showing the percentage of non-sized cfDNA present in controls and AD patients. Horizontal lines represent median methylation values and interquartile range for each group. (.pptx) Table S1: Primer pairs employed for pyrosequencing and bisulfite cloning sequencing. Bp: base pair; Tm: melting temperature. (.xls) Table S2: Blood samples set finally analyzed by Illumina EPICBeadChip array. The table shows the phenotypical features of the subjects included in the study. (.xls) Table S3: Top Diseases and Biofunctions. (.xls) Table S4: Upstream regulators for genes in our dataset. (.xls) Table S5: Differentially methylated positions (DMPs) in cfDNA from AD APOE ε4 carriers and Controls APOE ε4 non-carriers. The table shows 980 DMPs with difference > 0.100, prioritized by beta difference criteria. Each DMP (CpG site) was annotated by UCSC hg19 build. (.xls) Table S6: Differentially methylated positions (DMPs) in cfDNA from AD APOE ε4 non-carriers and Controls APOE ε4 non-carriers. The table shows 286 DMPs with difference > 0.100, prioritized by beta difference criteria. Each DMP (CpG site) was annotated by UCSC hg19 build. AD = Alzheimer disease; ID = identification (.xls) Table S7: EPIC, pyrosequencing and bisulfite cloning sequencing results in HKR1 among AD APOE ε4 non-carriers and Controls APOE ε4 non-carriers. The table shows the results on individualized CpGs within the amplicon and in average. SD=standard deviation (.xls) Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5358927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372442247,"identity":"bc2fb2d9-45e6-4666-950f-101f87def8fc","order_by":0,"name":"Mónica Macías","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Mónica","middleName":"","lastName":"Macías","suffix":""},{"id":372442248,"identity":"616b58d8-8fd5-4a3a-aeb0-aea7871cd86c","order_by":1,"name":"Juan José Alba-Linares","email":"","orcid":"","institution":"Cancer Epigenetics and Nanomedicine Laboratory, Nanomaterials and Nanotechnology Research Center (CINN CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"José","lastName":"Alba-Linares","suffix":""},{"id":372442249,"identity":"bb4aa9f7-7c84-4e82-ad95-3188a3cb0466","order_by":2,"name":"Blanca Acha","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Blanca","middleName":"","lastName":"Acha","suffix":""},{"id":372442250,"identity":"77817105-327f-4068-879c-134de7fc8a33","order_by":3,"name":"Idoia Blanco-Luquin","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Idoia","middleName":"","lastName":"Blanco-Luquin","suffix":""},{"id":372442251,"identity":"302e1317-a33d-4f03-a870-2efd2da7f6d7","order_by":4,"name":"Agustín Fernández Fernández","email":"","orcid":"","institution":"Cancer Epigenetics and Nanomedicine Laboratory, Nanomaterials and Nanotechnology Research Center (CINN CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Agustín","middleName":"Fernández","lastName":"Fernández","suffix":""},{"id":372442252,"identity":"e5b5e32a-eaf9-4908-8845-b07337956236","order_by":5,"name":"Johana Álvarez-Jiménez","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Johana","middleName":"","lastName":"Álvarez-Jiménez","suffix":""},{"id":372442253,"identity":"8d5e2d41-b297-499f-906a-04bb9e05f22d","order_by":6,"name":"Amaya Urdánoz-Casado","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Amaya","middleName":"","lastName":"Urdánoz-Casado","suffix":""},{"id":372442254,"identity":"22cf2d78-abcd-4b54-bdfd-926a09f27f8b","order_by":7,"name":"Miren Roldan","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Miren","middleName":"","lastName":"Roldan","suffix":""},{"id":372442255,"identity":"402e6922-4aab-431e-8e64-3693ad2452a9","order_by":8,"name":"Maitane Robles","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Maitane","middleName":"","lastName":"Robles","suffix":""},{"id":372442256,"identity":"e3d43ef1-1dfc-413d-850c-b3844ca77c1f","order_by":9,"name":"Eneko Cabezon-Arteta","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Eneko","middleName":"","lastName":"Cabezon-Arteta","suffix":""},{"id":372442257,"identity":"eb5da2cf-ee97-474a-83ba-f2507568d166","order_by":10,"name":"Daniel Alcolea","email":"","orcid":"","institution":"Department of Neurology, Institut d'Investigacions Biomèdiques Sant Pau (IIB Sant Pau) Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Alcolea","suffix":""},{"id":372442258,"identity":"d7418f05-e0f3-4b69-a467-abd9a7f1b59e","order_by":11,"name":"Javier Sánchez Ruiz de Gordoa","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"Sánchez Ruiz","lastName":"de Gordoa","suffix":""},{"id":372442259,"identity":"eb02828d-72a9-4191-a6a5-f4d219f46b60","order_by":12,"name":"Jon Corroza","email":"","orcid":"","institution":"Department of Neurology, Hospital Universitario de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"","lastName":"Corroza","suffix":""},{"id":372442260,"identity":"3cb2c356-603d-4872-a8ba-b5d2caf2afc5","order_by":13,"name":"Carolina Cabello","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Cabello","suffix":""},{"id":372442261,"identity":"e8e30028-2728-40fd-a264-1cab11ee3ac3","order_by":14,"name":"María Elena Erro","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Elena","lastName":"Erro","suffix":""},{"id":372442262,"identity":"b65e9975-5ea2-4d9b-9151-4c9ce99f9b43","order_by":15,"name":"Ivonne Jericó","email":"","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":false,"prefix":"","firstName":"Ivonne","middleName":"","lastName":"Jericó","suffix":""},{"id":372442263,"identity":"201c9671-11ef-4b38-b6ee-b063e2fcf974","order_by":16,"name":"Mario F. Fraga","email":"","orcid":"","institution":"Cancer Epigenetics and Nanomedicine Laboratory, Nanomaterials and Nanotechnology Research Center (CINN CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"F.","lastName":"Fraga","suffix":""},{"id":372442264,"identity":"efec8abc-0476-41b0-8242-1e398dd660a3","order_by":17,"name":"Maite Mendioroz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACxgYIfZi5gZmhwgYk0niAgJZGiJ7DjEAtZ9LAhhDQArPmAFALY9thCBufet32w+0PPubY5PEdZ2z8XMB23m5t+2GgLTU20bi0mJ1JbGycuS2tWPIwY7P0DJ7bydvOJAK1HEvLbcCl5UBiYzPvtsOJG4B+keaRuJ0MFGkAOvIwbi3nHzY2/932H6Sl+TePwblkoAgBLTeAtjBuOwDS0ibNk3DADihCSMvDxpm925ITZwK1WM84kJwAFGk4kIDPL+fTH3z4uc0use/84cO3C//Z2QNFHj74UGODUwsGSASrTCBWOQjYk6J4FIyCUTAKRgYAACgPcK3oQryRAAAAAElFTkSuQmCC","orcid":"","institution":"Neuroepigenetics Laboratory, Navarrabiomed-Hospital Universitario de Navarra-Universidad Pública de Navarra, IdiSNA","correspondingAuthor":true,"prefix":"","firstName":"Maite","middleName":"","lastName":"Mendioroz","suffix":""}],"badges":[],"createdAt":"2024-10-30 06:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5358927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5358927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69444573,"identity":"ea2acb72-4d2a-4af2-af35-7a6f7ebeb488","added_by":"auto","created_at":"2024-11-20 11:42:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDMPs AD vs CON. Distribution of DMPs regarding gene structure. \u003c/strong\u003eThe bar graph shows results for the log2 ratios of observed (fraction of differentially methylated probes that overlap a particular region) to expected (fraction of probes selected for analysis that overlap a particular region) for a genomic region. Black boxes represent a significant enrichment (\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05) for a particular feature. TSS=number of nucleotides upstream and downstream the transcription start site.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5358927/v1/65945cd68be9bf090c2ac278.png"},{"id":69443265,"identity":"915589e9-64df-43df-88e2-2352417f9d5a","added_by":"auto","created_at":"2024-11-20 11:34:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2546529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAPP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene plays a central role in the principal network evolving our AD-related DMPs. \u003c/strong\u003eThe graph shows how amyloid precursor protein-encoding gene (\u003cem\u003eAPP\u003c/em\u003e) acts as a core regulator of 12 genes found in our dataset (IPA score = 23). Red and green coloring indicate increased/decreased measurement in our dataset, respectively. Orange and blue coloring indicate predicted activation/inhibition genes participating.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5358927/v1/ba1c86f0dd1fd195f67a1db4.png"},{"id":69443263,"identity":"decc05eb-1552-4b19-9df7-621d91c18c1c","added_by":"auto","created_at":"2024-11-20 11:34:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential methylated region (DMR) annotated to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHKR1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene in plasma cfDNA from Alzheimer’s disease (AD) and control subjects. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) The graph shows the genomic position of the amplicon spanning the DMR within the promoter region of HKR1 gene explored by bisulfite cloning sequencing. At the middle of the graph, predicted functional elements are shown for each of nine human cell lines explored by chromatine imunoprecipitation (ChIP) combined with massively parallel DNA sequencing. The track was obtained from the Chromatin State Segmentation by HMM from ENCODE/Broad track shown at the UCSC Genome Browser. At the bottom, the CpG island is represented by a green box, the DMR by an orange box and the amplicon spanning the DMR is represented in yellow. (\u003cstrong\u003eb\u003c/strong\u003e) \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003eHKR1 \u003c/em\u003ecfDNA methylation levels measured by pyrosequencing. Box-plot charts showing methylation levels for individual CpG within the amplicon and in average between Alzheimer’s disease (AD) patients and controls. Horizontal lines represent median methylation values and interquartile range for each group. *\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05; *** \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.001 (Student’s \u003cem\u003et \u003c/em\u003etest). (\u003cstrong\u003ec\u003c/strong\u003e) Representative examples of bisulfite cloning sequencing validation for the amplicon containing the CpG are shown. Black and white circles denote methylated and unmethylated cytosines, respectively. Black and white circles denote methylated and unmethylated cytosines, respectively. Each column symbolizes a unique CpG site in the examined amplicon and each line represents an individual DNA clone. CpG cytosine-phosphate-guanine.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5358927/v1/3e1f1c348d1780aaff535868.png"},{"id":69445789,"identity":"a737c334-f465-444e-bd37-88e56724159e","added_by":"auto","created_at":"2024-11-20 11:58:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5476740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5358927/v1/60b68106-fca0-4cf4-8aed-a8c9f84bb0ff.pdf"},{"id":69443262,"identity":"3a1d88cb-b022-4cfd-97c7-4b2bb51f579e","added_by":"auto","created_at":"2024-11-20 11:34:45","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1628922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1: Bioinformatics workflow used in this study: quality control \u0026amp; sample tracking.\u003c/strong\u003e The diagram shows the bioinformatics pipeline used in this study: procedures for EPIC array methylation data quality control and normalization analysis. (.pptx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2: Characterization of cfDNA: fragment size analysis.\u003c/strong\u003e The diagram represents the typical pattern of cfDNA with a peak around 165 bp after performing capillary electrophoresis on a Fragment Analyzer Automated CE System with DNF-477 High Small Fragment Analysis Kit. This kits allows to track cfDNA fragmentation pattern from 50 bp – 1,500 bp. \u0026nbsp;(.pptx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3:\u003c/strong\u003e \u003cstrong\u003eCovariates selection.\u003c/strong\u003e Heatmap describing the correlations (Pearson’s r) between clinical variables and latent surrogate variables (SV1àSV8) extracted from the data. (.pptx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e: \u003cstrong\u003eCharacterization of non-sized cfDNA.\u003c/strong\u003e \u003cstrong\u003ea) \u003c/strong\u003eExample of a sample with the presence of non-sized cfDNA. \u003cstrong\u003e(b) \u003c/strong\u003eThe diagram represents the typical pattern of cfDNA with a peak around 165 bp after performing capillary electrophoresis on a Fragment Analyzer Automated CE System with the DNF-464 High Sensitivity Large Fragment 50Kb Analysis Kit. This kit allows to track cfDNA fragmentation pattern from 75 bp – 48,500 bp. Presence of genomic DNA is shown from 1,500 – 48,500 bp. bp: base pairs. (.pptx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e: \u003cstrong\u003eBox-plot showing the percentage of non-sized cfDNA present in controls and AD patients.\u003c/strong\u003e Horizontal lines represent median methylation values and interquartile range for each group. (.pptx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1: Primer pairs employed for pyrosequencing and bisulfite cloning sequencing. \u003c/strong\u003eBp: base pair; Tm: melting temperature.\u003cstrong\u003e \u003c/strong\u003e(.xls)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2: Blood samples set finally analyzed by Illumina EPICBeadChip array. \u003c/strong\u003eThe table shows the phenotypical features of the subjects included in the study.\u003cstrong\u003e \u003c/strong\u003e(.xls)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3: Top Diseases and Biofunctions. \u003c/strong\u003e(.xls)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4: Upstream regulators for genes in our dataset. \u003c/strong\u003e(.xls)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5: Differentially methylated positions (DMPs) in cfDNA from AD \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ε4 carriers and Controls \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ε4 non-carriers. \u003c/strong\u003eThe table shows 980 DMPs with difference \u0026gt; 0.100, prioritized by beta difference criteria. Each DMP (CpG site) was annotated by UCSC hg19 build. (.xls)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6: Differentially methylated positions (DMPs) in cfDNA from AD \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ε4 non-carriers and Controls \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ε4 non-carriers. \u003c/strong\u003eThe table shows 286 DMPs with difference \u0026gt; 0.100, prioritized by beta difference criteria. Each DMP (CpG site) was annotated by UCSC hg19 build. AD = Alzheimer disease; ID = identification\u003cstrong\u003e \u003c/strong\u003e(.xls)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7: EPIC, pyrosequencing and bisulfite cloning sequencing results in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHKR1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e among AD \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ε4 non-carriers and Controls \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ε4 non-carriers.\u003c/strong\u003e The table shows the results on individualized CpGs within the amplicon and in average. SD=standard deviation (.xls)\u003c/p\u003e","description":"","filename":"AdditionalFile1.zip","url":"https://assets-eu.researchsquare.com/files/rs-5358927/v1/980ebce42fd298f3468c5b3c.zip"}],"financialInterests":"Competing interest reported. D.A. participated in advisory boards from Fujirebio-Europe, Roche Diagnostics, Grifols S.A. and Lilly, and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmacéutica S.L., Zambon S.A.U. and Esteve Pharmaceuticals S.A. D.A. declares a filed patent application (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease).","formattedTitle":"Towards a Personalized Medicine through Liquid Biopsy in Alzheimer’s disease: Epigenome of cell-free DNA reveals methylation differences linked to APOE status","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) represents the leading cause of agerelated dementia and the seventh leading cause of mortality globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most AD cases occur sporadically in adults older than 65 years, defined as lateonset AD (LOAD). With the everincreasing aging of the population, this neurodegenerative disease currently affects 1 in 9 people over the age of 65, and its prevalence is expected to reach 152\u0026nbsp;million people worldwide by 2050 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite its significant impact, the underlying mechanisms for AD pathogenesis remain unclear. Enhancing the accuracy of AD diagnosis would optimize early therapeutic intervention strategies, thereby reducing costs and the increasing burden that AD represents for our society.\u003c/p\u003e \u003cp\u003eMultiple factors, such as environmental, biological, and genetic susceptibility appear to be associated with the development of LOAD. Within genetic factors, \u003cem\u003eAPOE\u003c/em\u003e ε4 polymorphism has been found to be the most consistently associated with LOAD development [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, epigenetics has demonstrated to play a role in the pathogenesis of neurodegenerative diseases such as AD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among different epigenetic modifications, DNA methylation is the most widely studied modification, which involves the attachment of a methyl group to the 5carbon position of a cytosine residue, usually at cytosineguanine dinucleotides (CpGs). In the case of AD, genecandidate studies, along with the latest application of omics technologies to epigenetics, have revealed new DNA methylation variants in genes biologically relevant to AD in human brain tissue.\u003c/p\u003e \u003cp\u003eOur group and others have published epigenomewide studies describing differentially methylated genes across different vulnerable brain regions in \u003cem\u003epostmortem\u003c/em\u003e samples. These regions include the prefrontal cortex [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], frontal cortex [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], entorhinal cortex [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], hippocampus [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], or superior temporal gyrus and inferior frontal gyrus [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, a major obstacle hinders the translation of these promising findings as biomarkers to clinical practice: the difficulty of accessing brain tissue from living individuals with AD. As a result, the ADspecific epigenetic information remains \"locked\" within the brain tissue and, therefore, rather inaccessible while the patient is alive. Studies performed on blood-derived genomic DNA have also identified differentially methylated marks between AD patients and controls [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nonetheless, most of these marks do not match those observed in brain tissues.\u003c/p\u003e \u003cp\u003eIt is widely acknowledged that cells undergo necrosis and apoptosis, among various processes of cell death, resulting in the release of their DNA into the bloodstream. This DNA, characterized by specific molecular alterations, is commonly referred to as cell-free DNA (cfDNA). Liquid biopsy, a noninvasive method consisting of a blood test, facilitates the isolation of cfDNA from plasma [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Under normal conditions, cfDNA originates mostly from the apoptosis of white blood cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, in pathological processes, a significant amount of cfDNA is derived from the affected tissue, as evidenced by the enrichment of tissuespecific methylation marks [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. So far, the majority of liquid biopsy applications have primarily focused on the identification of genetic variants, such as tumorspecific somatic mutations. However, beyond the field of oncology and genetics, liquid biopsy is emerging as a valuable tool in neurodegenerative diseases [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] where: (i) the bloodbrain barrier is dysfunctional, increasing its permeability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; and (ii) the DNA of the affected cells does not undergo any genetic sequence change. In these diseases, the analysis of epigenetic modifications in cfDNA specimens arises as a novel source of diagnostic biomarkers. Variants in DNA methylation, in particular, are considered exceptional biomarkers because of their stability, potential reversibility and accessibility in body fluids [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLiquid biopsy technique would provide access to this information \"locked\" in the brain, enabling the identification of epigenetic biomarkers (specific methylation marks) in cfDNA from patients with AD. This molecular assessment of cfDNA specimens could be thus considered a potential surrogate for pathological studies of \u003cem\u003epostmortem\u003c/em\u003e brain tissue, providing a source of candidate epigenetic biomarkers that may aid in the clinical management of AD during the patient's lifetime.\u003c/p\u003e \u003cp\u003eHence, in this study, we conducted a genomewide methylation analysis to identify differential methylation signatures of plasma cfDNA in patients with AD compared to controls.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe conducted an observational casecontrol study including 70 subjects (35 AD patients and 35 age and sexmatched cognitively healthy controls) to identify cfDNA methylation differences among AD patients and controls by using liquid biopsy procedures.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjects’ characterization\u003c/h3\u003e\n\u003cp\u003ePatients were prospectively recruited from the Dementia Unit of University Hospital of Navarra (tertiary hospital) from March 2019 to December 2021. AD was diagnosed according to the guidelines of the National Institute on Aging and Alzheimer\u0026rsquo;s Association (NIAAA 2018) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The diagnosis was established by neurologists through neurological case history and examination, along with blood tests, neuropsychological testing, and magnetic resonance imaging scans. Cognitive status was assessed by the MiniMental State Examination (MMSE) and the Global Deterioration Scale (GDS) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Controls were recruited from healthy relatives and volunteers who were matched for age and sex, and exhibited no clinical manifestations of dementia or other neurodegenerative diseases, as confirmed by clinical interviews and the MMSE (score\u0026thinsp;\u0026gt;\u0026thinsp;27). Given that cfDNA concentrations increase in cancer stages [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], we exclusively enrolled controls and AD patients who had not experienced any tumor disease within, at least, the last five years. The study was approved by the Ethics Committee, and all participants provided written informed consent prior to their involvement.\u003c/p\u003e \u003cp\u003eThe sample size was calculated to ensure 80% statistical power to detect a minimum significant difference of 5% in cfDNA methylation levels between AD cases and controls. It was assumed that both distributions are normal with equal variance (σ\u0026thinsp;=\u0026thinsp;0.15) and that an independent samples ttest would be used at a twosided significance level of α\u0026thinsp;=\u0026thinsp;0.05. Under these conditions, the required sample size was determined to be 35 AD patients and 35 controls using the \u003cem\u003eepiR\u003c/em\u003e library of the R statistical package [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBlood and cerebrospinal fluid (CSF) samples\u003c/h3\u003e\n\u003cp\u003ePeripheral blood samples were collected from each subject by venipuncture into 10 mL PAXgene\u0026reg; Blood DNA Tubes (QIAGEN, Redwood City, CA, USA), which contain a leukocyte stabilizer to prevent contamination with genomic DNA. The collected samples were centrifuged at 1,900 \u003cem\u003ex g\u003c/em\u003e at room temperature for 15 min within an hour. Plasma was then transferred to plastic tubes, centrifuged again at maximum speed, and stored at -80\u0026deg;C until further analysis. For additional analysis, pTau181 was measured in additional EDTA plasma samples from both AD patients and controls, whenever these samples were available. This measurement was performed using the commercially available pTau181 V2 Advantage kit (Quanterix Corp, Billerica, MA, USA), with singlemolecule array (Simoa) technology at the Sant Pau Memory Unit\u0026acute;s laboratory (Barcelona, Spain).\u003c/p\u003e \u003cp\u003eAs part of their clinical diagnosis, 21 out of 35 AD patients underwent lumbar puncture for CSF biomarker testing to further classify their amyloid/tau/neurodegeneration (ATN) profile [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. CSF samples were obtained by lumbar puncture and then centrifuged at 2,000 \u003cem\u003ex g\u003c/em\u003e for 10 min at 4 \u0026deg;C within 4 hours after collection. CSF supernatants were aliquoted into 1.5 mL polypropylene tubes and stored at -80\u0026deg;C until further use. Aβ42, Aβ40, pTau181 and tTau in CSF were measured using a Lumipulse G600II instrument (Fujirebio, Ghent, Belgium), according to manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003ecfDNA isolation and quantification\u003c/h3\u003e\n\u003cp\u003ecfDNA was isolated from 2 mL plasma by using QIAmp Circulating Nucleic Acid Kit (QIAGEN, Redwood City, CA, USA), following the manufacturer\u0026rsquo;s instructions. Doublestranded cfDNA was quantified with a Qubit 2.0 Fluorometer and the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Gilford, NH, USA), as per the manufacturer's guidelines. The amounts of cfDNA used for the methylation differential analysis array are reported in nanograms (ng).\u003c/p\u003e\n\u003ch3\u003eCharacterization of cfDNA: fragment size analysis\u003c/h3\u003e\n\u003cp\u003eThe purity and size distribution of cfDNA fragments were measured using the Fragment Analyzer\u0026trade; Automated CE with the ProSize software (Agilent, Technologies, Inc., Santa Clara, CA). This analysis was performed with the DNF-477 HS Small Fragment kit and the DNF-464 High Sensitivity Large Fragment 50Kb Analysis Kit (Agilent), following the manufacturer\u0026acute;s instructions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenomewide cfDNA methylation analysis\u003c/h2\u003e \u003cp\u003e cfDNA from 70 plasma samples was treated with sodium bisulfite using the Zymo EZ 96 DNA methylation kit (Zymo Research, Irvine, CA, USA), according to the manufacturer\u0026rsquo;s instructions. Given that cfDNA is highly fragmented, we treated our sample set with the Illumina Infinium FFPE restoration kit (Illumina, San Diego, CA, USA) prior to methylation analysis to preserve the samples during the bisulfite conversion process, as previously described [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubsequently, a methylome study was conducted on cfDNA samples using the Illumina Infinium\u0026reg; MethylationEPIC BeadChip microarray (850K) and the Illumina HiScan System (Ilumina). This approach allows to quantitatively detect the methylation levels at over 850,000 CpG positions throughout the genome, including more than 90% of the sites covered by the Illumina HumanMethylation450 BeadChip and more than 300,000 methylation sites in enhancers regions identified by the ENCODE and FANTOM5 projects [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eArray data preprocessing\u003c/h3\u003e\n\u003cp\u003eThe EPIC array methylation data was fully preprocessed using the \u003cem\u003eminfi\u003c/em\u003e package (v.1.32.0) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in R software environment (v.4.0.2). After importing IDAT files, methylation data from sex chromosome probes was analyzed to validate selfreported sex. Additionally, SNP/ethnicity probes from the \u003cem\u003esesame\u003c/em\u003e package (v.1.4.0) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] were used to identify unwanted sources of variation. Samples that did not meet the specific quality control criteria for intensity signals in both the methylated and unmethylated channels were discarded.\u003c/p\u003e \u003cp\u003eAfter completing the quality control steps, background noise signal was removed from the intensity values using the ssNoob method [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] in \u003cem\u003eminfi\u003c/em\u003e. The extracted βvalues were then normalized using the βmixture quantile normalization (BMIQ) approach [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] implemented in \u003cem\u003eChAMP\u003c/em\u003e (v.2.16.2) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, to avoid spurious methylation signals, probes were filtered out based on the following criteria: (a) having a detection \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026gt;\u0026thinsp;0.01 in any sample; (b) being crossreactive or multimapping probes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]; (c) being located on sex chromosomes; and (d) including SNPs with a minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.01 at their CpG or single base extension (SBE) sites (dbSNP v.147). Finally, experimentspecific conflicting probes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;424) were detected using the clustereddistribution approach implemented in the \u003cem\u003egaphunter\u003c/em\u003e function [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] of the \u003cem\u003eminfi\u003c/em\u003e package (threshold\u0026thinsp;=\u0026thinsp;0.20, outCutoff\u0026thinsp;=\u0026thinsp;5/63) and were removed from downstream analysis. The final number of probes that passed all filters for differential methylation analyses was 747,200 (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ecfDNA cell‑type deconvolution\u003c/h3\u003e\n\u003cp\u003eThe Houseman algorithm [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] implemented in the \u003cem\u003eENmix\u003c/em\u003e package (v.1.28.2) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and the \u003cem\u003eFlowSorted.Blood. EPIC\u003c/em\u003e reference dataset [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] were used to predict blood celltype composition from DNA methylation data. Moreover, the deconvolution algorithm and the reference atlas of Moss \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] were applied to our methylome array data using the \u003cem\u003ePython\u003c/em\u003e software (v. 3.8.13) to reveal the tissular and cellular origins of cfDNA.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSurrogate Variable Analysis\u003c/h2\u003e \u003cp\u003eSurrogate Variable Analysis was performed using the \u003cem\u003esva\u003c/em\u003e package (v.3.36.0) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] in order to identify the main sources of variation in the highdimensional data of EPIC array methylation. The surrogate variables identified by the Be method were then correlated with the clinicopathological features of the study individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProbelevel differential methylation analyses\u003c/h2\u003e \u003cp\u003eDifferentially methylated positions (DMPs) between AD patients and control subjects were identified through the application of linear regression models, defined in the \u003cem\u003elimma\u003c/em\u003e package (v.3.44.3) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. M-values were selected as the dependent variable in the models since this logit transformation of methylation β-values achieve greater homoscedasticity for statistical inference [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Based on surrogate variable analysis, all models included the following fixed covariates: sex, age, percentage of nonsized cfDNA, batch effects (array position), and celltype composition obtained from deconvolution analyses. Finally, empirical Bayesmoderated ttests allowed us to perform contrasts to define DMPs, adjusting \u003cem\u003ep\u003c/em\u003e-values for multiple comparisons using the Benjamini\u0026ndash;Hochberg method (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Differential enrichment of DMPs in relation to their genomic distribution was assessed using hypergeometric tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRegionlevel differential methylation analyses\u003c/h2\u003e \u003cp\u003eTo detect differentially methylated regions (DMRs), the \u003cem\u003elimma p\u003c/em\u003evalues were fed in the combp function [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] of the \u003cem\u003eENmix\u003c/em\u003e package (v.1.28.2) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] using the default parameters. This method allowed the detection of spatiallyrelated CpG sites with statistical significance. The initial regions were first selected under an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and subsequently, the final DMRs were defined using a Sidakcorrected \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProbe annotation\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eIlluminaHumanMethylationEPICanno.ilm10b4.hg19\u003c/em\u003e package (v.0.6.0) was used to assign each probe based on its location within CGI (CpG Island) and genes. For the annotation of regions, the probes belonging to each region were first individually annotated, as described above. A single annotation was then assigned to each region according to the following criteria: (1) for CGI status, \"Island\"\u0026gt;\"N_Shore\"\u0026gt;\"S_Shore\"\u0026gt;\"N_Shelf\"\u0026gt;\"S_Shelf\"\u0026gt;\"OpenSea\"; and (2) for gene locations, \"TSS1500\"\u0026gt;\"TSS200\"\u0026gt;\"5'UTR\"\u0026gt;\"1stExon\"\u0026gt;\"Body\"\u0026gt;\"3'UTR\"\u0026gt;\"Intergenic\".\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional\u003c/b\u003e \u003cb\u003ein silico\u003c/b\u003e \u003cb\u003eanalysis of DMPs\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe employed Ingenuity Pathways Analysis (IPA) software from Ingenuity Systems\u0026reg; (QIAGEN, Redwood City, California, USA) to further determine the biological significance of ADrelated DMPs by means of causal analytics algorithms [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. IPA identified the most significantly enriched biological functions and/or related diseases by calculating the \u003cem\u003ep\u003c/em\u003evalue through Fisher\u0026rsquo;s exact test. In addition, the upstream regulator analysis was utilized to predict upstream molecules that potentially regulate the set of differentially methylated genes and to build gene networks. Simultaneously, we conducted a systematic manual annotation using PubMed to determine whether the differentially methylated genes identified in AD patients were enriched in brain functions, as previously described [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOrthogonal validation\u003c/h2\u003e \u003cp\u003eFurthermore, pyrosequencing and bisulfite cloning sequencing techniques were performed to validate the methylation results obtained from the microarray analysis. Briefly, 200 ng of extracted cfDNA from each sample underwent bisulfite conversion using the EpiTect Bisulfite Kit (QIAGEN, Redwood City, CA, USA), following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003cp\u003eFor pyrosequencing, primers were designed with PyroMark Assay Design version 2.0.1.15 (QIAGEN, Redwood City, California, USA), and PCR reactions were carried out on a VeritiTM Thermal Cycler (Applied Biosystems, Foster City, CA, USA) Next, 20 \u0026micro;L of biotinylated PCR product was immobilized using streptavidin-coated sepharose beads (GE Healthcare Life Sciences, Piscataway, NJ, USA) and 0.3 \u0026micro;M of sequencing primer was annealed to purified cfDNA strands. Pyrosequencing was performed using the PyroMark Gold Q96 reagents (Qiagen) on a PyroMark\u0026trade; Q96 ID System (Qiagen). For each CpG studied within the amplicon (CpG1-CpG4), methylation levels were expressed as percentage of methylated cytosines over the sum of total cytosines. The EpiTect PCR Control DNA Set (Qiagen) was used as unmethylated and methylated DNA controls for the pyrosequencing reaction.\u003c/p\u003e \u003cp\u003eFor bisulfite cloning sequencing, primer pair sequences were designed using MethPrimer [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. PCR products were cloned using the TopoTA Cloning System (Invitrogen, Carlsbad, CA, USA), and a minimum of 12 independent clones were sequenced for each examined subject and region by Sanger sequencing. Methylation graphs were obtained using the QUMA software [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Both pyrosequencing and bisulfite cloning sequencing primers are listed in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and samples characterization\u003c/h2\u003e \u003cp\u003eThe EPIC array was used in a set of 35 controls and 35 AD patients. There were no significant differences regarding age or sex between AD subjects and controls. Extended demographic and clinical features of the subjects are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eBlood samples set analyzed by Illumina EPICBeadChip array.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotypical \u003c/p\u003e \u003cp\u003efeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (72\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (76\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (29\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (19\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecfDNA amount (ng)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (47\u0026ndash;212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (34\u0026ndash;241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eε4 non-carriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eε4 carrriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026thinsp;+\u0026thinsp;T\u0026thinsp;+\u0026thinsp;N+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epTau 181 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (1.2\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.1\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\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\u003eThe table shows the phenotypical features of the subjects included in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ecfDNA concentration and quality\u003c/h2\u003e \u003cp\u003eWe managed to isolate plasma cfDNA from all the subjects included in the study. The amounts of cfDNA did not vary significantly between controls and AD patients (median: 95 ng; IQR\u0026thinsp;=\u0026thinsp;47\u0026ndash;228 vs median: 90 ng; IQR\u0026thinsp;=\u0026thinsp;34\u0026ndash;247; \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.625), respectively. First, we verified cfDNAcorresponding size in our sample set as described in the methods section using the DNF-477 High Small Fragment Analysis Kit (Agilent). The expected cfDNA size was confirmed in all samples. The median cfDNA size was 167 bp (IQR\u0026thinsp;=\u0026thinsp;158\u0026ndash;185) for AD patients and 165 bp (IQR\u0026thinsp;=\u0026thinsp;159\u0026ndash;171) for controls with no significant differences between groups (\u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.451). An example electropherogram of a cfDNA sample is presented in Additional file 1: Fig. S2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSurrogate Variable Analysis\u003c/h2\u003e \u003cp\u003eSurrogate Variable Analysis revealed one confounding variable of unknown significance (SV1) as the major source of variability affecting our series (Additional file 1: Fig. S3). To ascertain the nature of this biological or technical variable, we decided to further characterize the cfDNA fragmentation pattern employing the Fragment Analyzer technology. We found several samples with a carryover of nonsized cfDNA (Additional file 1: Fig. S4a). To specifically quantify this cfDNA fraction and evaluate its impact, we decided to perform a more indepth characterization of the isolated cfDNA using the DNF-464 High Sensitivity Large Fragment 50Kb Analysis Kit. This kit allows to track the cfDNA fragmentation pattern from 75 bp \u0026mdash; 48,500 bp, covering both the expected cfDNAexpected fragment size and an extended region with other potential noncfDNA fragments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, we noticed that nonsized cfDNA fragments were present in several samples, thus contributing to the total concentration. An example is shown in Additional file 1: Fig. S4b. We observed SV1 was negatively correlated with non-sized cfDNA. Global evaluation of nonsized cfDNA revealed a presence of 42.24% in controls and of 54.89% in AD patients, with no significant differences between groups (\u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.07) (Additional file 1: Fig. S5). Nevertheless, upon further examination of the influence of nonsized cfDNA on methylation levels, we observed a strong positive correlation between nonsized cfDNA and median βmethylation values (r\u0026thinsp;=\u0026thinsp;0.445; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, non-sized cfDNA percentage was used to adjust all the subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDifferential methylated positions\u003c/h2\u003e \u003cp\u003eAfter quality control and sample tracking, seven samples were discarded from downstream analysis. Finally, cfDNA from 30 controls and 33 AD patients was used for differential methylation analysis. We confirmed that the loss of these subjects did not result in any changes leading to differences between AD patients and controls in terms of phenotypical features as illustrated in Additional file 1: Table S2. Genomewide DNA methylation was investigated in the context of both DMPs and DMRs. First, we built mixed linear models adjusting for potential sources of variability, specifically including sex, age, batch, nonsized cfDNA (%) and celltype composition. After adjusting for FDR correction, we detected no significant DMPs associated with AD condition. When looking at nominal significance level, the analysis revealed 102 ADrelated DMPs (absolute βdifference\u0026thinsp;\u0026ge;\u0026thinsp;0.1 and \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026le;\u0026thinsp;0.05) annotated to 58 genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with an overrepresentation of hypomethylated positions in AD cases compared to controls (74%).\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\u003eDifferentially methylated positions (DMPs) in cfDNA from AD patients with respect to controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGenomic coordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGene\u003c/em\u003e ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelation to CpG context\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelation to Gene Structure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ-difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26023019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31311859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGRIK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1stExon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg19665696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e949154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eADAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg25069157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44102572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTMEM63B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg13578160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72813978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg11955641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151304999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGLRA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg09465533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32327675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCMTM8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg20601028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22738632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24245216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7004657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg23506049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103228185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg21550804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74282865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg22597210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10172841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC3P1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg17857094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30907280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDPCR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg00055434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2415344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePLCH2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg27416647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96630572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg11646124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182140416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg22238209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35800743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMAG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg07983614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84587903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg21764456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10777077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTEKT5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg07812827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74282708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06572225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7748353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg03463818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94766468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTMEM67\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26802564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30446406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg18056749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55836268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBMP7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg27454064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64215611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24699005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1192342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26861034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26908874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTFIP11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg10411590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21900810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg00796424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54365966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHOXC11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg12906062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105462162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg00242341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72447419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eARAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg18955367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49002338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLMTK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26003334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100661866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLOC102724094\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16419584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129947858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg13063165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79093076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eADAMTS7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg01495416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59085270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg02258724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53832577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg04772328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170549930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC2orf77\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16045681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31575570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg21210642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100881995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTRIM14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24463437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28396758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEYA3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg20212912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147557774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06311780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6633544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg21010821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111782679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHSPB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14891200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220197664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRESP18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14520947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225942842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg15086439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236563070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEDARADD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg04855678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e195946921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOSTalpha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg19146301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235100790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLOC101927851\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg13443570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99098126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eERICH5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg02784823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49000897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLMTK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06398054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53092881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg20062681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94988642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg09484559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12692246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRICH2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg08986575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173235445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg27159720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7971612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg07870920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121569769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg02938172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRPH3AL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg27087112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114737475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLOC100499194\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg03174228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124658583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTTLL11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24984452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95261186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLINC01057\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg17566325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133022423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg03465894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106342311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg15410835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143125637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24760557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31986724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg18625538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87832609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg21933626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123026636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eADCY5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg05800368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124658957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTTLL11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg02774630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154727554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGALNT13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06878111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9999498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg23213894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7691961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCYB5R2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06957053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137533035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDGKI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg10531073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38485757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBAIAP2L2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24104237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45649408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLIMD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg10289324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60710970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg15243027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32784469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBIRC6-AS2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg10092377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200880981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC1orf106\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg01583753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39470725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16127514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29273678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26496930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70186565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg10305928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62426219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eANK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14310109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157297510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eARID1B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16520701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34606956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16210088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31318349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC22orf27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24448113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140475611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePCDHB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1stExon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06862049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49001890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLMTK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg12172631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54584915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTARM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg20548231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31318373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC22orf27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg09544050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143580965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBAI1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg18815398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61506981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26651782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51505779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKLK9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24061197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220108496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGLB1L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06260707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42945689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg15059639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171220061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMYO3B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24658778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152897280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSYNE1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg20920357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116877727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg02256650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31317287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMORC2-AS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg26140120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124219575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFAM83A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg06452258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60597809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg08431893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44864600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16467921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128801108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg04248279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRPH3AL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg24135491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4487099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSMTNL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSS200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe table shows 102 DMPs with-difference\u0026thinsp;\u0026gt;\u0026thinsp;0.100, prioritized by beta difference criteria. Each DMP (CpG site) was annotated by UCSC hg19 build.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGenomic distribution of ADrelated DMPs was assessed for differential enrichment in relation to CpG context and genomic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We observed that DMPs were more likely to be located in CpG islands and shore regions, showing both a 1.3fold enrichment (\u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the random expectation based on all probes included in the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with AD clinical parameters and biomarkers\u003c/h2\u003e \u003cp\u003eSubsequently, Spearman\u0026rsquo;s coefficient was calculated to evaluate the potential correlation between DNA methylation levels of the top ten DMPs, ranked by the highest positive and negative βdifference criteria respectively, and main AD clinical features and biomarkers. Very interestingly, we found significant correlations between DNA methylation levels of DMPs and MMSE score in 12/20 (60%), and with the GDS in 16/20 (80%) (Tables\u0026nbsp;3 and 4).\u003c/p\u003e \u003cp\u003eRegarding biomarkers, we observed a significant correlation with the CSF Aβ42/Aβ40 ratio in 2/20 (10%) and with plasma pTau181 concentration in 7/20 (35%) (Tables\u0026nbsp;3 and 4). We also observed several correlation trends although not reaching significance for CSF pTau181 in up to 6/20 (30%). Interestingly, three genes significantly correlated with MMSE, GDS and pTau181 levels, namely \u003cem\u003eSMTNL2, GLRA1\u003c/em\u003e and \u003cem\u003eMORC2-AS1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional\u003c/b\u003e \u003cb\u003ein silico\u003c/b\u003e \u003cb\u003eanalysis of DMPs\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe performed IPA analysis to further expand the biological significance of ADrelated DMPs found in this study. Within the diseases and functions category, the analysis confirmed that up to 30 molecules among our set of ADrelated DMPs were linked to neurological disorders (\u003cem\u003ep\u003c/em\u003evalue range\u0026thinsp;=\u0026thinsp;4.53E-02\u0026ndash;1.10E-03) (Additional file 1: Table S3). In the physiological system development and function category, we found that 11 molecules in our dataset were mostly enriched in nervous system development and function (\u003cem\u003ep\u003c/em\u003evalue range\u0026thinsp;=\u0026thinsp;4.95E-02\u0026ndash;2.83E-05) (Additional file 1: Table S3).\u003c/p\u003e \u003cp\u003eFurthermore, IPA analysis predicted 24 upstream transcriptional regulators directly or indirectly connected to our dataset genes, prioritized by \u003cem\u003ep\u003c/em\u003evalue. Among these, \u003cem\u003eSPTBN4\u003c/em\u003e (spectrin β nonerythrocytic 4), which encodes a brain cytoskeletal protein, emerged as the most significantly associated regulator (Additional file 1: Table S4). It is also remarkable the presence of the \u003cem\u003eAPOE\u003c/em\u003e gene among these upstream regulators. Moreover, causal network analysis (CNA) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] further connected upstream regulators to our dataset molecules, placing the \u003cem\u003eAPP\u003c/em\u003e gene (amyloidβ precursor protein), which encodes a membrane protein mainly expressed in neuronal synapses and closely related to AD development, at the forefront of potential relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential methylated positions according to\u003c/b\u003e \u003cb\u003eAPOE\u003c/b\u003e \u003cb\u003estatus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs previously mentioned, the \u003cem\u003eAPOE\u003c/em\u003e genotype is considered the strongest genetic risk factor for LOAD, and DNA methylation has demonstrated to act closely with this factor revealing DNA methylation differences between \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers and noncarriers [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Therefore, to explore our results in greater depth, we divided our sample set regarding \u003cem\u003eAPOE\u003c/em\u003e ε4 status in each group of subjects, either controls or AD patients. No comparison was performed with controls \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers due to the minimum number of \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers in the control group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3; 9%).\u003c/p\u003e \u003cp\u003eWhen looking at nominal significance level, major differences were found when comparing AD \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers and Controls \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers, represented by 980 DMPs (absolute βdifference\u0026thinsp;\u0026ge;\u0026thinsp;0.1 and \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026le;\u0026thinsp;0.05) annotated to 668 genes (Additional file 1: Table S5). When comparing AD \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers and Controls \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers, we found 286 DMPs (absolute βdifference\u0026thinsp;\u0026ge;\u0026thinsp;0.1 and \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026le;\u0026thinsp;0.05) annotated to 169 genes (Additional file 1: Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDifferential methylated regions\u003c/h2\u003e \u003cp\u003eAt a regional level, differential analysis revealed one DMR significantly associated with AD status (Sidakcorrected \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This position (chr2:114,737,458\u0026thinsp;\u0026minus;\u0026thinsp;114,737,475) was located in a CpG island and annotated to a lncRNA (LOC100499194).\u003c/p\u003e \u003cp\u003eWhen stratifying by \u003cem\u003eAPOE\u003c/em\u003e, we identified 17 DMRs (12% hypermethylated and 88% hypomethylated) comparing AD \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers and Controls \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers and 4 hypermethylated DMRs between AD \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers and Controls \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers (Sidak correction \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Tables\u0026nbsp;5 and 6). Most interestingly, up to 6 DMRs were annotated to genes already addressed as differentially methylated in AD condition and mostly in brain tissue (Table\u0026nbsp;7).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eOrthogonal validation\u003c/h2\u003e \u003cp\u003eA DMR found between AD \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers and controls noncarriers annotated to the \u003cem\u003eHKR1\u003c/em\u003e gene (\u003cem\u003eZNF875\u003c/em\u003e, Zinc Finger Protein 875, also known as \u003cem\u003eZNF875\u003c/em\u003e), a gene previously found to be differentially methylated in hippocampus, was selected for further exploration (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This DMR consists of 469 bp containing 10 CpGs dinucleotides. Considering that the expected cfDNA size is around 166 bp and further fragmentation is probably occurring during the deamination step in the bisulfite conversion process, special caution was exercised in designing the region to be explored [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For primer design, we selected a CpG assayed in the EPIC array (cg12024906) located in the extreme of the DMR. Therefore, we designed primers to achieve amplicons contained in the DMR and smaller than the estimated cfDNA size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor pyrosequencing, we explored a region of 140 bp spanning 4 CpGs dinucleotides, including cg12024906. We observed that the DNA methylations levels in the selected CpG site (CpG2) and the average for the amplicon were significantly increased in AD patients compared to controls [30.74\u0026thinsp;\u0026plusmn;\u0026thinsp;14.57% vs 18.92\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41% and 36.51\u0026thinsp;\u0026plusmn;\u0026thinsp;11.85% vs 26.02\u0026thinsp;\u0026plusmn;\u0026thinsp;19.48%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eFor bisulfite cloning sequencing, we explored a region of 86 bp spanning 6 CpG dinucleotides in eight representative samples. We found that the average DNA methylation levels were strongly increased in AD patients compared to controls for the amplicon [82.28\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83% vs 17.36\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78%; \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and the selected CpG site (CpG4) [62.50\u0026thinsp;\u0026plusmn;\u0026thinsp;17.33% vs 14.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.22%; \u003cem\u003ep\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001] (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eOverall results of this orthogonal validation are detailed in Additional file: Table S7.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we investigated methylation differences in plasma cfDNA in AD patients and controls from an epigenomewide perspective. We hereby show that cfDNA can be readily isolated from plasma through liquid biopsy procedures and used to identify methylation differences between AD patients and cognitively healthy controls. Specific cfDNA methylation differences seem to be \u003cem\u003eAPOE\u003c/em\u003e genotyperelated. In particular, 4 DMRs were found in \u003cem\u003eAPOE \u003c/em\u003eε4 noncarriers when comparing AD versus control subjects.\u003c/p\u003e \u003cp\u003eMethodologies to analyze cfDNA in biological fluids are greatly technologically demanding in terms of sensitivity, given the low levels of cfDNA (concentrations in the range of 10 ng/mL). In neurological disorders, this challenge is compounded by the low relative abundance of the expected brainderived fraction among the background cfDNA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A way to achieve higher starting amounts of cfDNA could be drawing larger volumes of plasma; however, there should be a plasma volume limitation for this technique to be transferable to neurological clinical practice in the near future, if differences in DNA methylation are intended to be used as biomarkers. Another approach could be relying on sample pooling techniques to increase the amount of starting material when using cfDNA, as has been previously suggested [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, this approach only yields average methylation values. Additionally, cfDNA appears at higher levels in certain pathological conditions, such as cancer, trauma, stroke, autoimmune disorders, or insufficient renal clearance [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In this regard, we used as an exclusion criterion, for both controls and AD patients, the coexistence of another pathological condition that could potentially overestimate plasma ADrelated cfDNA concentrations. In our study cohort, we obtained cfDNA from plasma of all AD patients and controls. Nevertheless, we did not find a significant increase in total plasma cfDNA levels in AD patients compared to controls in our cohort, which adds evidence in this regard, since previous studies showed conflicting results in terms of these differences [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This could be explained by the low relative abundance of the expected brainderived cfDNA within the background cfDNA, making it challenging to detect any increase in the concentration of the brainderived fraction in the total cfDNA.\u003c/p\u003e \u003cp\u003eA critical aspect for cfDNA analysis involves preanalytical factors, such as the type of collection tube, sample centrifugation protocol, and cfDNA extraction method [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Although commercial kit manufacturers usually claim efficient isolation of pure, highquality cfDNA, this study highlights the significant impact of nonsized cfDNA carryover [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Nonsized cfDNA contamination often goes unnoticed when employing common cfDNA quantification methods, such as fluorometric techniques, which cannot distinguish between cfDNA and genomic DNA (gDNA) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Our findings indicate that approximately half of the samples contained nonsized cfDNA carryover. Nevertheless, the presence of nonsized cfDNA did not significantly differ between AD patients and controls in our study cohort. Given that nonsized cfDNA was identified as a major source of variability in the surrogate variable analysis, we decided to adjust our statistical models to account for this variable, as a latent source of noise.\u003c/p\u003e \u003cp\u003eSeveral research groups have conducted epigenomewide studies on cfDNA from AD patients using various methods, including targeted bisulfite sequencing [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], highthroughput sequencing to map 5Hydroxymethylcytosine (5hmC) as another widespread epigenetic marker [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and even EPIC array technology [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In our cohort, using this microarray technology, we found no significant DMPs associated with AD condition in cfDNA after applying a FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 correction. This finding aligns with previous epigenome studies performed on cfDNA [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Besides technical factors and analysis methodologies, bioinformatics filtering thresholds could represent a major source of discordance between assays. A recent paper by BahadoSingh \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] reported significant differences in cfDNA between AD patients and controls after FDRcorrection employing EPIC array technology and Artificial Intelligence algorithms for data analysis. However, in their study only about 41% of the total probes assayed by the EPIC array passed the quality control, whereas in our study, up to 86% of the overall probes were included in the subsequent differential methylation analysis.\u003c/p\u003e \u003cp\u003eAnyhow, the analysis identified an interesting set of 102 differential cfDNA methylation marks at a nominal significance level. Among these methylation marks, hypomethylation in AD cases compared to controls was overrepresented. Regarding genomic location, these differential cfDNA methylation marks were predominantly localized in CpG islands and shores, concordant with previous findings in genomic DNA in AD patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, we found strong correlations between the ten top-ranked nominally significant probes in our dataset and main cognitive and functional status indicators (MMSE and GDS), along with mild correlations with AD biomarkers in CSF and blood, such as Aβ42/Aβ40 ratio and pTau181 levels, respectively.\u003c/p\u003e \u003cp\u003eIn the functional interpretation of results, IPA allows an overall visualization of the effects of a gene dataset within a pathway, displaying it as a network to facilitate a more accurate understanding of the results. The most significant upstream regulator identified the \u003cem\u003eSPTBN4\u003c/em\u003e gene, with \u003cem\u003eANK3\u003c/em\u003e being its main target molecule in our dataset. \u003cem\u003eSPTBN4\u003c/em\u003e encodes for spectrin β nonerythrocytic 4 and, along with \u003cem\u003eANK3\u003c/em\u003e, a member of the ankyrin family, forms part of the axon initial segment [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The spectrin/ankyrin complex links the cytoskeleton and voltagegated channels, which are crucial for regulating neuronal polarity and synapsis [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Interestingly, S\u0026aacute;nchezMut \u003cem\u003eet al.\u003c/em\u003e reported hypermethylation of \u003cem\u003eSPTBN4\u003c/em\u003e in the frontal cortex of human AD patients [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. It is also remarkable the presence of \u003cem\u003eAPOE\u003c/em\u003e among this gene list, interacting again with \u003cem\u003eANK3\u003c/em\u003e and with \u003cem\u003eADGRB1\u003c/em\u003e (Adhesion G ProteinCoupled Receptor B1, BrainSpecific Angiogenesis Inhibitor, BAI1), which mediates hippocampal spatial learning and memory [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first \u003cem\u003eAPOE\u003c/em\u003estratified study conducted with the Infinium EPIC array on cfDNA obtained from AD patients. It is wellknown that \u003cem\u003eAPOE\u003c/em\u003e genotype is an important risk factor for AD and may influence the course of the disease, which may be represented in the status of ADassociated DNA methylation marks [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, we decided to further include the \u003cem\u003eAPOE\u003c/em\u003e genotype into our linear models to analyze its contribution on the cfDNA methylation differences found in this study. When stratifying AD patients and controls based on the presence of the \u003cem\u003eAPOE\u003c/em\u003e ε4 genotype, we observed a substantial increase in differentially methylated positions by 10%. This finding aligns with previous methylation studies that have demonstrated distinct and markedly different methylation marks between AD patients and controls when stratifying by the \u003cem\u003eAPOE\u003c/em\u003e genotype [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. However, further research is needed to elucidate the mechanisms linking DNA methylation and the presence of the ε4 allele.\u003c/p\u003e \u003cp\u003eNevertheless, the most significant differences were found when exploring results at a regional level, probably because positionlevel differences are often too subtle to be detectable [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this regard, we identified several cfDNA methylation differences in regions associated with genes known to be important in AD. For instance, we found a hypermethylated region in AD \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers annotated to the \u003cem\u003eSLCO2A1\u003c/em\u003e gene (Solute Carrier Organic Anion Transporter Family Member 2A1). This gene encodes for prostaglandin transporter, which is reported to be localized in neurons, microglia, and astrocytes, and is poorly expressed in AD human brain. This transporter has been postulated as a possible modulator of prostaglandinmediated neuroinflammation associated with AD [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. In addition, we detected a hypomethylated region in AD \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers spanning the \u003cem\u003eDNMT3B\u003c/em\u003e gene, which encodes DNA Methyltransferase 3 Β, an enzyme that catalyzes \u003cem\u003ede novo\u003c/em\u003e DNA methylation in mammalian cells [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. It would be interesting to check whether \u003cem\u003eDNMT3B\u003c/em\u003e hypomethylation identified in AD \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers may be related to the higher expression of these DNA methyltransferases with increasing age [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This deregulation in DNMTmediated \u003cem\u003ede novo\u003c/em\u003e methylation by its own methylation state may contribute to explain the AD epigenetic misbalance [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Notably, we identified this DMR when comparing AD \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers with control \u003cem\u003eAPOE\u003c/em\u003e ε4 noncarriers. The association of \u003cem\u003eDNMT3B\u003c/em\u003e deregulation and \u003cem\u003eAPOE\u003c/em\u003e ε4 genotype have been addressed previously, indicating synergistic effects on AD onset [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. However, studies elsewhere have found no significant correlation between \u003cem\u003eDNMT3B\u003c/em\u003e methylation and \u003cem\u003eAPOE\u003c/em\u003e status [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. These findings within our dataset may help to uncover new underlying molecular changes linked to AD pathology.\u003c/p\u003e \u003cp\u003eLiquid biopsy has emerged as a noninvasive tool for reflecting molecular changes occurring in tissues. Interestingly, we observed how some of the ADrelated regional changes identified in plasma cfDNA in our study cohort were consistent with changes previously reported in other studies performed on brain samples (Table\u0026nbsp;7). In particular, several DMRs identified in this study were associated with genes previously reported as differentially methylated in the hippocampus of AD patients, such as \u003cem\u003eHKR1\u003c/em\u003e and \u003cem\u003eATG16L2\u003c/em\u003e. The \u003cem\u003eHKR1 gene\u003c/em\u003e (\u003cem\u003eZNF875\u003c/em\u003e, Zinc Finger Protein 875) gene is a transcriptional regulator and its methylation levels have been proposed as an aging biomarker [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. In this study, we observed that \u003cem\u003eHKR1\u003c/em\u003e methylation changes remained significant in AD patients after adjusting for age, which may indicate underlying agerelated epigenetic changes contributing to neurodegeneration in AD [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Moreover, both \u003cem\u003eHKR1\u003c/em\u003e and \u003cem\u003eATG16L2\u003c/em\u003e methylation levels were found to correlate significantly with pTau burden in the AD hippocampus [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this respect, we found no correlation between cfDNA methylation levels of these DMRs and tTau/pTau181 and pTau181 as surrogate biomarkers for Tau deposition in CSF and blood, respectively.\u003c/p\u003e \u003cp\u003eOur study adds evidence to using cfDNA to characterize methylation changes in neurological diseases, such as AD. Plasma cfDNA emerges as a novel source of epigenetic biomarkers that may help to improve AD diagnosis in the clinical setting. Precision medicine based on liquid biopsy procedures is an innovative approach that merits further research. Validation of these candidate biomarkers in larger and multicentric cohorts will contribute to design panels of composite biomarkers from different origins that may significantly improve clinical tools in the dementia field.\u003c/p\u003e \u003cp\u003eThe primary limitations of our study are those related to the nature of the cfDNA. In this work we have demonstrated that nonsized cfDNA carryover in standard cfDNA isolation protocols can go unnoticed, which should be considered when analyzing results [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. A possible approach to overcome nonsized cfDNA carryover could be the further use of methods based on capillary electrophoresis to selectively elute DNA according to its base pair length [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, our study demonstrates that cfDNA is present in the plasma of AD patients and can be readily isolated during their lifetime. The availability of blood sampling makes the analysis of epigenetic alterations in cfDNA a promising source of biomarkers in AD to be used in the practice of personalized medicine. Some candidate epigenetic biomarkers seem to be related to the \u003cem\u003eAPOE\u003c/em\u003e genotype. Exploring the potential of liquid biopsy can enhance our understanding of this complex disorder. Moreover, this study highlights the critical importance of preanalytical factors, bioinformatics workflows, and thresholds when analyzing the cfDNA from an epigenome wide perspective.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD = Alzheimer Disease\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e = Apolipoprotein E\u003c/p\u003e\n\u003cp\u003ecfDNA = Cell‑free DNA\u003c/p\u003e\n\u003cp\u003eCpG = cytosine-guanine dinucleotide\u003c/p\u003e\n\u003cp\u003eCSF = cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003eDMP = differential methylated position\u003c/p\u003e\n\u003cp\u003eDMR = differential methylated region\u003c/p\u003e\n\u003cp\u003eLOAD = late-onset Alzheimer Disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Navarra Ethics Research Committee approved this study and written informed consent was obtained from all subjects included in the study. Procedures were in accordance with the revised Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and/or analyzed during this study are either included in this article or are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.A. participated in advisory boards from Fujirebio-Europe, Roche Diagnostics, Grifols S.A. and Lilly, and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmac\u0026eacute;utica S.L., Zambon S.A.U. and Esteve Pharmaceuticals S.A.\u0026nbsp;D.A. declares a filed patent application (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely appreciate the funding support from the Government of Navarra [G\u0026ordm;Na 36/18], and the Spanish Government through grants from the Institute of Health Carlos III (FIS PI20/01701), co‑funded by the European Regional Development Fund (ERDF), European Union, A way of shaping Europe. The project leading to these results has received funding from La Caixa Banking Foundation (ID 100010434) and Fundaci\u0026oacute;n Luz\u0026oacute;n (HR20‑01109_BIOP‑ALS) under agreement LCF/PR/PR15/51100006. In addition, B.A. (Blanca Acha) is supported by a PFIS fellowship from the Spanish Government (FI18/00150). M.M. (M\u0026oacute;nica Mac\u0026iacute;as) is beneficiary of a R\u0026iacute;o Hortega grant from the Spanish Government (CM20/00240) and a Navarrabiomed postdoctoral research grant (2022). J.A.-J. (Johana \u0026Aacute;lvarez-Jim\u0026eacute;nez) has received a Doctorandos industriales grant for 2023\u0026ndash;2026. A.U.‑C. (Amaya Urd\u0026aacute;noz‑Casado) received a Doctorandos industriales grant for 2018\u0026ndash;2020 and a predoctoral research grant (2019) founded by the Department of Industry and Health of the Government of Navarra.\u0026nbsp;D.A. was supported by research grants from Institute of Health Carlos III (ISCIII), Spain INT19/00016 and INT23/00048. M.M. (Maite Mendioroz) received a Contrato de intensificaci\u0026oacute;n grant from the Institute of Health Carlos III (INT19/00029) and a grant (LCF/PR/PR15/51100006) founded by La Caixa Banking Foundation and Caja Navarra Banking Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR\u0026rsquo;S CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.M (M\u0026oacute;nica Mac\u0026iacute;as participated in the study concept, design and manuscript preparation; J.S.R-G., J.C, C.C, M.E.E. and I.J. contributed to recruitment; B.A. E.C. participated in data and samples collection; M.R. (Miren Roldan) and M.R. (Maitane Robles) isolated and characterized cfDNA; D.A. analyzed ptau181 in plasma; J.J.A-L., A.F-F. and M.F-F. performed bioinformatics analysis, assisted in data interpretation and contributed to manuscript preparation and revision; M.M (M\u0026oacute;nica Mac\u0026iacute;as), I.B-L., J.A-J. and A.U-C. performed analysis and interpretation of data, figure design and drawing and drafting/review of the manuscript for content; M.M. (Maite Mendioroz) had a major role in the design of the work, supervised the research and finalized the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the subjects who participated in this study for their generous contribution. We would also like to thank Alba Leiza D\u0026aacute;vila for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlzheimer\u0026apos;s disease facts and figures. Alzheimers Dement. 2024;20(5):3708-3821.\u003c/li\u003e\n\u003cli\u003eAlzheimers Disease International. (2018). World Alzheimer Report 2018. Available from: https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf. .\u003c/li\u003e\n\u003cli\u003eAlzheimers Disease International. (2021). World Alzheimer Report 2021. Available from: https://www.alzint.org/u/World-Alzheimer-Report-2021.pdf.\u003c/li\u003e\n\u003cli\u003eLambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer\u0026apos;s disease. Nat Genet. 2013;45(12):1452-1458.\u003c/li\u003e\n\u003cli\u003eSanchez-Mut JV, Gr\u0026auml;ff J. Epigenetic Alterations in Alzheimer\u0026apos;s Disease. Front Behav Neurosci. 2015;9:347.\u003c/li\u003e\n\u003cli\u003eZhang L, Silva TC, Young JI, Gomez L, Schmidt MA, Hamilton-Nelson KL, et al. Epigenome-wide meta-analysis of DNA methylation differences in prefrontal cortex implicates the immune processes in Alzheimer\u0026apos;s disease. Nat Commun. 2020;11(1):6114.\u003c/li\u003e\n\u003cli\u003eZhang L, Young JI, Gomez L, Silva TC, Schmidt MA, Cai J, et al. Sex-specific DNA methylation differences in Alzheimer\u0026apos;s disease pathology. Acta Neuropathol Commun. 2021;9(1):77.\u003c/li\u003e\n\u003cli\u003eDe Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, et al. Alzheimer\u0026apos;s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci. 2014;17(9):1156-1163.\u003c/li\u003e\n\u003cli\u003eLunnon K, Smith R, Hannon E, De Jager PL, Srivastava G, Volta M, et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer\u0026apos;s disease. Nat Neurosci. 2014;17(9):1164-1170.\u003c/li\u003e\n\u003cli\u003eGasparoni G, Bultmann S, Lutsik P, Kraus TFJ, Sordon S, Vlcek J, et al. DNA methylation analysis on purified neurons and glia dissects age and Alzheimer\u0026apos;s disease-specific changes in the human cortex. Epigenetics Chromatin. 2018;11(1):41.\u003c/li\u003e\n\u003cli\u003eSmith RG, Pishva E, Shireby G, Smith AR, Roubroeks JAY, Hannon E, et al. A meta-analysis of epigenome-wide association studies in Alzheimer\u0026apos;s disease highlights novel differentially methylated loci across cortex. Nat Commun. 2021;12(1):3517.\u003c/li\u003e\n\u003cli\u003eSanchez-Mut JV, Heyn H, Vidal E, Delgado-Morales R, Moran S, Sayols S, et al. Whole genome grey and white matter DNA methylation profiles in dorsolateral prefrontal cortex. Synapse. 2017;71(6).\u003c/li\u003e\n\u003cli\u003eSmith RG, Hannon E, De Jager PL, Chibnik L, Lott SJ, Condliffe D, et al. Elevated DNA methylation across a 48-kb region spanning the HOXA gene cluster is associated with Alzheimer\u0026apos;s disease neuropathology. Alzheimers Dement. 2018;14(12):1580-1588.\u003c/li\u003e\n\u003cli\u003eSemick SA, Bharadwaj RA, Collado-Torres L, Tao R, Shin JH, Deep-Soboslay A, et al. Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer\u0026apos;s disease. Acta Neuropathol. 2019;137(4):557-569.\u003c/li\u003e\n\u003cli\u003eRao JS, Keleshian VL, Klein S, Rapoport SI. Epigenetic modifications in frontal cortex from Alzheimer\u0026apos;s disease and bipolar disorder patients. Transl Psychiatry. 2012;2(7):e132.\u003c/li\u003e\n\u003cli\u003eAltuna M, Urdanoz-Casado A, Sanchez-Ruiz de Gordoa J, Zelaya MV, Labarga A, Lepesant JMJ, et al. DNA methylation signature of human hippocampus in Alzheimer\u0026apos;s disease is linked to neurogenesis. Clin Epigenetics. 2019;11(1):91.\u003c/li\u003e\n\u003cli\u003eLi QS, Sun Y, Wang T. Epigenome-wide association study of Alzheimer\u0026apos;s disease replicates 22 differentially methylated positions and 30 differentially methylated regions. Clin Epigenetics. 2020;12(1):149.\u003c/li\u003e\n\u003cli\u003eWatson CT, Roussos P, Garg P, Ho DJ, Azam N, Katsel PL, et al. Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer\u0026apos;s disease. Genome Med. 2016;8(1):5.\u003c/li\u003e\n\u003cli\u003ePerez RF, Alba-Linares JJ, Tejedor JR, Fernandez AF, Calero M, Roman-Dominguez A, et al. Blood DNA Methylation Patterns in Older Adults With Evolving Dementia. J Gerontol A Biol Sci Med Sci. 2022;77(9):1743-1749.\u003c/li\u003e\n\u003cli\u003eLardenoije R, Roubroeks JAY, Pishva E, Leber M, Wagner H, Iatrou A, et al. Alzheimer\u0026rsquo;s disease-associated (hydroxy)methylomic changes in the brain and blood. Clin Epigenetics. 2019;11(1):164.\u003c/li\u003e\n\u003cli\u003eKonki M, Malonzo M, Karlsson IK, Lindgren N, Ghimire B, Smolander J, et al. Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer\u0026apos;s disease. Clin Epigenetics. 2019;11(1):130.\u003c/li\u003e\n\u003cli\u003eChang L, Wang Y, Ji H, Dai D, Xu X, Jiang D, et al. Elevation of peripheral BDNF promoter methylation links to the risk of Alzheimer\u0026apos;s disease. PLoS One. 2014;9(11):e110773.\u003c/li\u003e\n\u003cli\u003eMac\u0026iacute;as M, Alegre E, D\u0026iacute;az-Lagares A, Pati\u0026ntilde;o A, P\u0026eacute;rez-Gracia JL, Sanmamed M, et al. Liquid Biopsy: From Basic Research to Clinical Practice. Adv Clin Chem. 2018;83:73-119.\u003c/li\u003e\n\u003cli\u003eSun K, Jiang P, Chan KC, Wong J, Cheng YK, Liang RH, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci U S A. 2015;112(40):E5503-5512.\u003c/li\u003e\n\u003cli\u003eLehmann-Werman R, Neiman D, Zemmour H, Moss J, Magenheim J, Vaknin-Dembinsky A, et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U S A. 2016;113(13):E1826-1834.\u003c/li\u003e\n\u003cli\u003eGaitsch H, Franklin RJM, Reich DS. Cell-free DNA-based liquid biopsies in neurology. Brain. 2022;146(5):1758-1774.\u003c/li\u003e\n\u003cli\u003eSouthwood D, Singh S, Chatterton Z. Brain-derived cell-free DNA. Neural Regeneration Research. 2022;17(10):2213-2214.\u003c/li\u003e\n\u003cli\u003eKhemka S, Sehar U, Manna PR, Kshirsagar S, Reddy PH. Cell-Free DNA As Peripheral Biomarker of Alzheimer\u0026apos;s Disease. Aging Dis. 2024.\u003c/li\u003e\n\u003cli\u003ePollard C, Aston K, Emery BR, Hill J, Jenkins T. Detection of neuron-derived cfDNA in blood plasma: a new diagnostic approach for neurodegenerative conditions. Front Neurol. 2023;14:1272960.\u003c/li\u003e\n\u003cli\u003eNoe CR, Noe-Letschnig M, Handschuh P, Noe CA, Lanzenberger R. Dysfunction of the Blood-Brain Barrier-A Key Step in Neurodegeneration and Dementia. Front Aging Neurosci. 2020;12:185.\u003c/li\u003e\n\u003cli\u003eZenaro E, Piacentino G, Constantin G. The blood-brain barrier in Alzheimer\u0026apos;s disease. Neurobiol Dis. 2017;107:41-56.\u003c/li\u003e\n\u003cli\u003eCosta-Pinheiro P, Montezuma D, Henrique R, Jer\u0026oacute;nimo C. Diagnostic and prognostic epigenetic biomarkers in cancer. Epigenomics. 2015;7(6):1003-1015.\u003c/li\u003e\n\u003cli\u003eJack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer\u0026apos;s disease. Alzheimers Dement. 2018;14(4):535-562.\u003c/li\u003e\n\u003cli\u003eReisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry. 1982;139(9):1136-1139.\u003c/li\u003e\n\u003cli\u003eLeon SA, Shapiro B, Sklaroff DM, Yaros MJ. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 1977;37(3):646-650.\u003c/li\u003e\n\u003cli\u003eStevenson M, Nunes T, Sanchez J, Thornton R, Reiczigel J, Robison-Cox J, et al. EpiR: An R package for the analysis of epidemiological data. 2013:9-43.\u003c/li\u003e\n\u003cli\u003eMoss J, Magenheim J, Neiman D, Zemmour H, Loyfer N, Korach A, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun. 2018;9(1):5068.\u003c/li\u003e\n\u003cli\u003ePidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208.\u003c/li\u003e\n\u003cli\u003eZhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2017;45(4):e22.\u003c/li\u003e\n\u003cli\u003eAryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363-1369.\u003c/li\u003e\n\u003cli\u003eZhou W, Triche TJ, Jr., Laird PW, Shen H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 2018;46(20):e123.\u003c/li\u003e\n\u003cli\u003eTriche TJ, Jr., Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41(7):e90.\u003c/li\u003e\n\u003cli\u003eTeschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189-196.\u003c/li\u003e\n\u003cli\u003eTian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33(24):3982-3984.\u003c/li\u003e\n\u003cli\u003eChen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203-209.\u003c/li\u003e\n\u003cli\u003eAndrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD. \u0026quot;Gap hunting\u0026quot; to characterize clustered probe signals in Illumina methylation array data. Epigenetics Chromatin. 2016;9(1):56.\u003c/li\u003e\n\u003cli\u003eHouseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.\u003c/li\u003e\n\u003cli\u003eXu Z, Niu L, Li L, Taylor JA. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44(3):e20.\u003c/li\u003e\n\u003cli\u003eSalas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 2018;19(1):64.\u003c/li\u003e\n\u003cli\u003eLeek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882-883.\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.\u003c/li\u003e\n\u003cli\u003eDu P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11(1):587.\u003c/li\u003e\n\u003cli\u003ePedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28(22):2986-2988.\u003c/li\u003e\n\u003cli\u003eKr\u0026auml;mer A, Green J, Pollard J, Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30(4):523-530.\u003c/li\u003e\n\u003cli\u003eLi LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics. 2002;18(11):1427-1431.\u003c/li\u003e\n\u003cli\u003eKumaki Y, Oda M, Okano M. QUMA: quantification tool for methylation analysis. Nucleic Acids Res. 2008;36(Web Server issue):W170-175.\u003c/li\u003e\n\u003cli\u003eWalker RM, Vaher K, Bermingham ML, Morris SW, Bretherick AD, Zeng Y, et al. Identification of epigenome-wide DNA methylation differences between carriers of APOE epsilon4 and APOE epsilon2 alleles. Genome Med. 2021;13(1):1.\u003c/li\u003e\n\u003cli\u003eDarst RP, Pardo CE, Ai L, Brown KD, Kladde MP. Bisulfite sequencing of DNA. Curr Protoc Mol Biol. 2010;Chapter 7(1):Unit 7 9 1-17.\u003c/li\u003e\n\u003cli\u003eGallardo-Gomez M, Moran S, Paez de la Cadena M, Martinez-Zorzano VS, Rodriguez-Berrocal FJ, Rodriguez-Girondo M, et al. A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples. Clin Epigenetics. 2018;10(1):53.\u003c/li\u003e\n\u003cli\u003eSiravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol. 2017;14(9):531-548.\u003c/li\u003e\n\u003cli\u003ePai MC, Kuo YM, Wang IF, Chiang PM, Tsai KJ. The Role of Methylated Circulating Nucleic Acids as a Potential Biomarker in Alzheimer\u0026apos;s Disease. Mol Neurobiol. 2019;56(4):2440-2449.\u003c/li\u003e\n\u003cli\u003eMendioroz M, Mart\u0026iacute;nez-Merino L, Blanco-Luquin I, Urd\u0026aacute;noz A, Rold\u0026aacute;n M, Jeric\u0026oacute; I. Liquid biopsy: a new source of candidate biomarkers in amyotrophic lateral sclerosis. Ann Clin Transl Neurol. 2018;5(6):763-768.\u003c/li\u003e\n\u003cli\u003eBronkhorst AJ, Aucamp J, Pretorius PJ. Cell-free DNA: Preanalytical variables. Clin Chim Acta. 2015;450:243-253.\u003c/li\u003e\n\u003cli\u003eKresse SH, Brandt-Winge S, Pharo H, Flatin BTB, Jeanmougin M, Vedeld HM, et al. Evaluation of commercial kits for isolation and bisulfite conversion of circulating cell-free tumor DNA from blood. Clin Epigenetics. 2023;15(1):151.\u003c/li\u003e\n\u003cli\u003eAlcaide M, Cheung M, Hillman J, Rassekh SR, Deyell RJ, Batist G, et al. Evaluating the quantity, quality and size distribution of cell-free DNA by multiplex droplet digital PCR. Sci Rep. 2020;10(1):12564.\u003c/li\u003e\n\u003cli\u003eGuemri J, Pierre-Jean M, Brohard S, Oussada N, Horgues C, Bonnet E, et al. Methylated ccfDNA from plasma biomarkers of Alzheimer\u0026apos;s disease using targeted bisulfite sequencing. Epigenomics. 2022;14(8):451-468.\u003c/li\u003e\n\u003cli\u003eChen L, Shen Q, Xu S, Yu H, Pei S, Zhang Y, et al. 5-Hydroxymethylcytosine Signatures in Circulating Cell-Free DNA as Diagnostic Biomarkers for Late-Onset Alzheimer\u0026apos;s Disease. J Alzheimers Dis. 2022;85(2):573-585.\u003c/li\u003e\n\u003cli\u003eBahado-Singh RO, Radhakrishna U, Gordevicius J, Aydas B, Yilmaz A, Jafar F, et al. Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer\u0026apos;s Disease. Cells. 2022;11(11).\u003c/li\u003e\n\u003cli\u003eKonki M, Lindgren N, Kylaniemi M, Venho R, Laajala E, Ghimire B, et al. Plasma cell-free DNA methylation marks for episodic memory impairment: a pilot twin study. Sci Rep. 2020;10(1):14192.\u003c/li\u003e\n\u003cli\u003eHuang CY, Rasband MN. Axon initial segments: structure, function, and disease. Ann N Y Acad Sci. 2018;1420(1):46-61.\u003c/li\u003e\n\u003cli\u003eGrubb MS, Burrone J. Building and maintaining the axon initial segment. Curr Opin Neurobiol. 2010;20(4):481-488.\u003c/li\u003e\n\u003cli\u003eSanchez-Mut JV, Aso E, Panayotis N, Lott I, Dierssen M, Rabano A, et al. DNA methylation map of mouse and human brain identifies target genes in Alzheimer\u0026apos;s disease. Brain. 2013;136(Pt 10):3018-3027.\u003c/li\u003e\n\u003cli\u003eZhu D, Li C, Swanson AM, Villalba RM, Guo J, Zhang Z, et al. BAI1 regulates spatial learning and synaptic plasticity in the hippocampus. J Clin Invest. 2015;125(4):1497-1508.\u003c/li\u003e\n\u003cli\u003eForaker J, Millard SP, Leong L, Thomson Z, Chen S, Keene CD, et al. The APOE Gene is Differentially Methylated in Alzheimer\u0026apos;s Disease. J Alzheimers Dis. 2015;48(3):745-755.\u003c/li\u003e\n\u003cli\u003ePanitch R, Sahelijo N, Hu J, Nho K, Bennett DA, Lunetta KL, et al. APOE genotype-specific methylation patterns are linked to Alzheimer disease pathology and estrogen response. Transl Psychiatry. 2024;14(1):129.\u003c/li\u003e\n\u003cli\u003eChoi K, Zhuang H, Crain B, Dor\u0026eacute; S. Expression and localization of prostaglandin transporter in Alzheimer disease brains and age-matched controls. J Neuroimmunol. 2008;195(1-2):81-87.\u003c/li\u003e\n\u003cli\u003eGagliardi M, Strazzullo M, Matarazzo MR. DNMT3B Functions: Novel Insights From Human Disease. Front Cell Dev Biol. 2018;6:140.\u003c/li\u003e\n\u003cli\u003eWang K, Liu H, Hu Q, Wang L, Liu J, Zheng Z, et al. Epigenetic regulation of aging: implications for interventions of aging and diseases. Signal Transduct Target Ther. 2022;7(1):374.\u003c/li\u003e\n\u003cli\u003eRajendran G, Shanmuganandam K, Bendre A, Muzumdar D, Goel A, Shiras A. Epigenetic regulation of DNA methyltransferases: DNMT1 and DNMT3B in gliomas. J Neurooncol. 2011;104(2):483-494.\u003c/li\u003e\n\u003cli\u003ede Bem CM, Pezzi JC, Borba EM, Chaves ML, de Andrade FM, Fiegenbaum M, et al. The synergistic risk effect of apolipoprotein epsilon4 and DNA (cytosine-5-)-methyltransferase 3 beta (DNMT3B) haplotype for Alzheimer\u0026apos;s disease. Mol Biol Rep. 2016;43(7):653-658.\u003c/li\u003e\n\u003cli\u003eTannorella P, Stoccoro A, Tognoni G, Petrozzi L, Salluzzo MG, Ragalmuto A, et al. Methylation analysis of multiple genes in blood DNA of Alzheimer\u0026apos;s disease and healthy individuals. Neurosci Lett. 2015;600:143-147.\u003c/li\u003e\n\u003cli\u003eZeng Q, Chen X, Ning C, Zhu Q, Yao Y, Zhao Y, et al. Methylation of the genes ROD1, NLRC5, and HKR1 is associated with aging in Hainan centenarians. BMC Med Genomics. 2018;11(1):7.\u003c/li\u003e\n\u003cli\u003eBerson A, Nativio R, Berger SL, Bonini NM. Epigenetic Regulation in Neurodegenerative Diseases. Trends Neurosci. 2018;41(9):587-598.\u003c/li\u003e\n\u003cli\u003eLardenoije R, Iatrou A, Kenis G, Kompotis K, Steinbusch HW, Mastroeni D, et al. The epigenetics of aging and neurodegeneration. Prog Neurobiol. 2015;131:21-64.\u003c/li\u003e\n\u003cli\u003eNidadavolu LS, Feger D, Wu Y, Grodstein F, Gross AL, Bennett DA, et al. Circulating Cell-Free Genomic DNA Is Associated with an Increased Risk of Dementia and with Change in Cognitive and Physical Function. J Alzheimers Dis. 2022;89(4):1233-1240.\u003c/li\u003e\n\u003cli\u003eWenz HM, Dailey D, Johnson MD. Development of a high-throughput capillary electrophoresis protocol for DNA fragment analysis. Methods Mol Biol. 2001;163:3-17.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, cell free DNA, liquid biopsy, EPIC array, DNA methylation, APOE, blood","lastPublishedDoi":"10.21203/rs.3.rs-5358927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5358927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Recent studies show that Alzheimer’s disease (AD) patients harbor specific methylation marks in the brain. However, accessing this epigenetic information “locked in the brain” while patients are alive is challenging. Liquid biopsy technique enables the study of circulating cell-free DNA (cfDNA) fragments originated from cells that have died and released their genetic material into the bloodstream.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Here, we isolated and epigenetically characterized plasma cfDNA from 35 AD patients and 35 cognitively healthy controls. Next, we conducted a genome‑wide methylation analysis using the Infinium® MethylationEPIC BeadChip array to identify differential methylation marks in cfDNA between AD patients and controls. AD core biomarkers were also measured in blood and cerebrospinal fluid samples and correlated with differential methylation marks. Pyrosequencing and bisulfite cloning sequencing techniques were performed as an orthogonal validation for epigenome-wide results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Epigenome-wide cfDNA methylation analysis identified 102 differential methylated positions (DMPs) associated with AD at a nominal significance level, of which 74% were hypomethylated. We found significant correlations between DMPs in our dataset and main cognitive and functional status tests (60% for MMSE, and 80% for GDS), along with correlations with AD biomarkers in CSF and blood. \u003cem\u003eIn silico\u003c/em\u003e functional analysis linked up to 30 DMPs to neurological processes, identifying key regulators such as \u003cem\u003eSPTBN4\u003c/em\u003eand the \u003cem\u003eAPOE\u003c/em\u003e gene. We identified several differentially methylated regions linked to \u003cem\u003eAPOE\u003c/em\u003e status annotated to genes already addressed as differentially methylated in AD condition and mostly in brain tissue (\u003cem\u003eHKR1\u003c/em\u003e, \u003cem\u003eZNF154\u003c/em\u003e, \u003cem\u003eHOXA5\u003c/em\u003e, \u003cem\u003eTRIM40\u003c/em\u003e, \u003cem\u003eATG16L2\u003c/em\u003e, \u003cem\u003eADAMST2\u003c/em\u003e). In particular, a DMR in the \u003cem\u003eHKR1\u003c/em\u003e gene previously shown in to be hypermethylated in AD hippocampus was further validated in cfDNA with an orthogonal perspective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eThe feasibility of blood sampling makes plasma cfDNA a promising source of epigenetic biomarkers for Alzheimer's disease that could be further used in the practice of personalized medicine. Despite pre-analytical and technical challenges, liquid biopsy is emerging as a promising technique to further explore in neurodegenerative diseases.\u003c/p\u003e","manuscriptTitle":"Towards a Personalized Medicine through Liquid Biopsy in Alzheimer’s disease: Epigenome of cell-free DNA reveals methylation differences linked to APOE status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 11:34:40","doi":"10.21203/rs.3.rs-5358927/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf65a1ad-c8d1-4076-a0c1-09ed70b20a03","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-20T11:34:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 11:34:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5358927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5358927","identity":"rs-5358927","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}