Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s disease

Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s disease | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s disease Samuel G. Faasen , Ligia A. Papale , Alexander Boruch , Phillip E. Bergmann , Kirk J. Hogan , Reid S. Alisch doi: https://doi.org/10.1101/2025.06.18.660344 Samuel G. Faasen 1 Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health , 600 Highland Ave, Madison, WI 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ligia A. Papale 1 Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health , 600 Highland Ave, Madison, WI 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander Boruch 2 William S. Middleton Memorial Veterans Hospital , 2500 Overlook Terrace, Madison, WI 53705, USA 3 Department of Kinesiology, University of Wisconsin-Madison, 1300 University Avenue , Madison, WI 53706, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Phillip E. Bergmann 1 Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health , 600 Highland Ave, Madison, WI 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kirk J. Hogan 4 Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health , 600 Highland Ave, Madison, WI 53792, USA 5 Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health , 600 Highland Ave, Madison, WI 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: alisch{at}wisc.edu khogan{at}wisc.edu Reid S. Alisch 1 Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health , 600 Highland Ave, Madison, WI 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: alisch{at}wisc.edu khogan{at}wisc.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Background Environmental factors play a role in AD pathology and are mediated by changes in DNA methylation levels. Methods and Results We investigated whole genome methylation sequencing (WGMS) data from the blood of participants with mild cognitive impairment (MCI, N =99), late onset dementia due to Alzheimer’s disease (AD, N =109), and who are cognitively unimpaired (CU, N =174) to test for differential methylation in 812 genes with roles in epigenetic regulation ( e.g. , DNA methylation and demethylation, chromatin remodeling, histone modification, and RNA modification) curated from the EpiFactors 2.1 database. 71/812 genes were differentially methylated comprising 190 unique differentially methylated positions (DMPs) in MCI (MCI vs . CU pairwise comparison). 60/812 genes were differentially methylated comprising 220 DMPs in AD (AD vs . CU pairwise comparison). The majority of differentially methylated genes in both MCI (41/71) and AD (33/60) were histone modification genes and 23 differentially methylated genes were shared in both pairwise comparisons. 96 genes were differentially methylated comprising 243 DMPs between persons with MCI and AD (AD vs . MCI pairwise comparison). 10 differentially methylated genes were shared between the 3 pairwise comparisons, including CUGBP elav-like family member 2 ( CELF2 ), histone deacetylase 9 ( HDAC9 ), RNA binding fox-1 homolog 1 ( RBFOX1 ), TATA-box binding protein associated factor 4 ( TAF4 ), and thymine DNA glycosylase ( TDG ). Conclusion Genes that participate in the epigenetic regulation of gene expression, particularly histone modifications, are differentially methylated in blood between persons with and without MCI and AD, warranting further elucidation of their role in the molecular pathogenesis of cognitive decline. Background Late-onset dementia due to Alzheimer’s disease (AD) is characterized by signs and symptoms of dementia that present after the age of 65. Mild cognitive impairment (MCI) is a prodromal stage of AD characterized by mild symptoms with retained functional independence. Eighty percent of persons with MCI will convert to AD within 6 years of their MCI diagnosis ( 1 – 3 ). Evidence indicates that environmental factors increase risk of AD onset and progression through changes in DNA methylation levels [ 4 , 5 ]. DNA methylation is the covalent addition of a methyl group to a cytosine, primarily in a cytosine-phosphate-guanine dinucleotide (CpG), that contributes to regulation of gene expression. Differential DNA methylation in postmortem human brain tissues are reported in genes associated with AD [ 6 , 7 ], with shared patterns of DNA methylation levels between brain and blood [ 8 – 11 ]. Epigenetic mechanisms, including DNA methylation and demethylation, histone modification, and chromatin remodeling, influence AD onset, progression and severity [ 12 – 14 ]. For example, histone modifications are associated with learning, memory, cognition, and synaptic plasticity changes in AD [ 12 , 13 , 15 , 16 ]. Further, proteins that regulate chromatin structure and remodeling have altered expression and protein levels in the brains of persons with AD [ 16 , 17 ]. We recently reported differential DNA methylation in blood samples [ 18 , 19 ] from participants with MCI, AD and those who are cognitively unimpaired (CU) from the Wisconsin Alzheimer’s Disease Research Center (WADRC) [ 20 , 21 ] and the Wisconsin Registry for Alzheimer’s Prevention (WRAP) [ 22 ]. In the present investigation, we investigated the differentially methylated genes from that data specifically for differentially methylated genes that participate in epigenetic regulation curated from the following 9 categories of the human EpiFactors 2.1 database [ 23 , 24 ]: 1) chromatin remodeling; 2) DNA methylation/demethylation; 3) Histone modifications; 4) Polycomb group [PcG] proteins; 5) protein modification; 6) RNA modification; 7) scaffold proteins; 8) histone subunits; and 9) protein complexes (made up of 234 genes participating in various forms of epigenetic modification). Results Differential methylation in genes that participate in epigenetic regulation in MCI and AD Differential methylation in MCI 190 DMPs were found in genes that participate in epigenetic pathways between CU and MCI participants (MCI vs. CU pairwise comparison; Supplementary Dataset 1). The majority of DMPs were hypermethylated (56%; greater levels of DNA methylation in persons with MCI than in those CU; Fig. 1A ). These DMPs annotated to 71 differentially methylated genes and predominantly resided in intronic regions of genes (54%; Fig. 1B ) and in genes encoding histone and RNA modifications (46% and 23%, respectively; local false discovery rate [LFDR] < 0.01 and at least one DMP; Fig. 1C ). 44/71 genes are involved in histone modification ( Fig. 1C ). Three differentially methylated genes were histone subunits and 14 were in epigenetic modification complexes, predominantly involved in histone modification (59%; Supplementary Dataset 2). Download figure Open in new tab Figure 1: DMPs in genes that encode proteins that participate in epigenetic modification. Pie charts display the proportions of hypermethylated and hypomethylated DMPs in each comparison: MCI vs . CU [A]; AD vs . CU [D]; and AD vs . MCI [G]. Sankey plots show the locations of DMPs in each pairwise comparison relative to genomic structures comprising the following: 5 kilobases (kb) upstream of the transcription start site (TSS; −5kb to TSS); the 5’ untranslated region (5’ UTR); within a gene exon (exon); within a gene intron (intron); and the 3’ untranslated region (3’ UTR): MCI vs . CU [B]; AD vs . CU [E]; and AD vs . MCI [H]. Bar plots show the number of genes from each pairwise comparison, stratified by their broad epigenetic modification types: MCI vs . CU [C]; AD vs . CU [F]; and AD vs . MCI [I]. Differential methylation in AD 173 DMPs were in genes that participate in epigenetic pathways between CU and AD participants (AD vs. CU pairwise comparison; Supplementary Dataset 1). The majority of DMPs were hypomethylated (78%; greater levels of DNA methylation in persons with CU than in those with AD; Fig. 1D ). These DMPs annotated to 60 differentially methylated genes and predominantly resided in intronic regions of genes (66%; Fig. 1E ) and in genes encoding histone modifications (56%; local false discovery rate [LFDR] < 0.01 and at least one DMP; Fig. 1F ). 35/60 genes are involved in histone modification ( Fig. 1C ). Three differentially methylated genes were histone subunits, and 14 were in epigenetic modification complexes, mostly involved in histone modification (58%) or chromatin remodeling (39%; Supplementary Dataset 2). Differential methylation between MCI and AD 243 DMPs were in genes that participate in epigenetic pathways between MCI and AD participants (AD vs. MCI pairwise comparison; Supplementary Dataset 1). The majority of DMPs were hypomethylated (80%; greater levels of DNA methylation in persons with MCI than in those with AD; Fig. 1G ). These DMPs annotated to 96 differentially methylated genes and predominantly resided in intronic regions of genes (66%; Fig. 1H ) and in genes encoding histone modifications (61%; local false discovery rate [LFDR] < 0.01 and at least one DMP; Fig. 1I ). 71/96 genes are involved in histone modification ( Fig. 1C ). Zero differentially methylated genes encoded for histone subunits and 30 were in epigenetic modification complexes, mostly involved in histone modification (60%) or chromatin remodeling (27%; Supplementary Dataset 2). Shared differential methylation in MCI and AD on genes that participate in epigenetic regulation Shared differentially methylated positions in MCI and AD 23 DMPs were shared between both MCI (MCI vs. CU) and AD (AD vs. CU); all presented with the same methylation polarity in both comparisons. 19/23 DMPs have decreased methylation levels in both MCI and AD (82%; Fig. 2A ). 11 of the shared DMPs were within a single gene, thymine DNA glycosylase ( TDG ). Four of these 11 DMPs were also identified between persons with MCI and AD (AD vs. MCI pairwise comparison). All four were located in an exon or the 5’ untranslated region (UTR) of TDG ( Fig. 2B ) and exhibited decreased DNA methylation levels in MCI and AD compared to CU, and increased DNA methylation levels between MCI and AD ( Fig. 2C ). Download figure Open in new tab Figure 2: Shared DMPs and genes between the 3 pairwise comparisons. 3-way Venn diagrams showing the overlap of DMPs [A] between the MCI vs. CU, AD vs. CU, and AD vs. MCI pairwise comparisons. Four DMPs within TDG are shared between the 3 pairwise comparisons. A gene schematic of the sense strand of TDG is depicted [B]. Alignment to human reference genome hg38 coordinates are shown in basepairs (bp) the below the x-axis. Blue rectangles indicate coding exons, and thin blue lines indicate introns. One in 25 non-significant CpGs are shown (grey vertical line). The significance (y-axis) of hypermethylated DMPs (black fill) or hypomethylated DMPs (white fill) are displayed for MCI vs. CU (diamond), AD vs. CU (triangle), or AD vs. MCI (square) pairwise comparisons. A corrected significance level of LFDR < 0.05 and greater than 2.5% methylation difference was adopted for all comparisons. A zoomed-in section of the 5’ UTR of TDG is inset into the figure to help visualize the position of the DMPs. Example distributions of the estimated DNA methylation values for 2 of the 4 shared DMPs [C]. Estimated DNA methylation values at a CpG dinucleotide are adjusted for estimated white blood cell proportions, the first two principal components, age, sex, and body mass index (BMI). 3-way Venn diagrams showing the overlap of differentially methylated genes [D] between the MCI vs. CU, AD vs. CU, and AD vs. MCI pairwise comparisons. Differentially methylated genes shared in MCI and AD 23 differentially methylated genes were found in both MCI and AD ( Fig. 2D ). 11 differentially methylated genes were shared between all three pairwise comparisons, including CUGBP elav-like family member 2 ( CELF2 ), histone deacetylase 9 ( HDAC9 ), RNA binding fox-1 homolog 1 ( RBFOX1 ), TATA-box binding protein associated factor 4 ( TAF4 ), and TDG ( Fig. 2D ). Differential DNA methylation in enhancer regions interacting with promoters of genes that participate in epigenetic regulation in MCI and AD Differential methylation in enhancers in MCI 41 unique enhancer regions containing 62 DMPs interact with the promoters of 42 genes that participate in epigenetic regulation in MCI (MCI vs. CU pairwise comparison; Supplementary Table 3). The majority of DMPs residing in enhancers were hypermethylated (58%; greater levels of DNA methylation in persons with MCI than in those who are CU). 36/42 genes in the EpiFactors database that have promoter-enhancer interactions comprising one or more DMPs have no differential methylation in their gene body (5 kb upstream of TSS to TTS; Supplementary Table 3). Most of these 42 genes encode histone modifications (48%) and chromatin remodeling (34%; Supplementary Table 3). Differential methylation in enhancers in AD 40 unique enhancer regions containing 52 DMPs interact with the promoters of 42 genes that participate in epigenetic regulation in AD (AD vs. CU pairwise comparison; Supplementary Table 3). The majority of DMPs residing in enhancers were hypomethylated (70%; greater levels of DNA methylation in persons who are CU than in those with AD). 33/42 genes in the EpiFactors database that have promoter-enhancer interactions comprising one or more DMPs have no differential methylation in their gene body (5 kb upstream of TSS to TTS; Supplementary Table 3). Most of these 42 genes encode histone modifications (47%) or histone subunits (23%; Supplementary Table 3). Differential methylation in enhancers between MCI and AD 69 unique enhancer regions containing 81 DMPs interact with the promoters of 59 genes that participate in epigenetic regulation between persons with MCI and AD (AD vs. MCI pairwise comparison; Supplementary Table 3). The majority of DMPs residing in enhancers were hypomethylated (90%; greater levels of methylation in persons with MCI than AD). 41/59 genes in the EpiFactors database that have promoter-enhancer interactions comprising one or more DMPs have no differential methylation in their gene body (5 kb upstream of TSS to TTS; Supplementary Table 3). Most of these 59 genes encode proteins involved in histone modifications (65%; Supplementary Table 3). Shared differential methylation in enhancers in MCI and AD 8 DMPs residing in enhancers were shared between MCI and AD compared to CU, but none were shared between all 3 comparisons ( Fig. 3A ). 8 genes interacting with enhancer regions containing one or more DMPs were shared between MCI and AD ( Fig. 3B ). 5 genes interacting with enhancer regions containing one or more DMPs were shared between the 3 comparisons: Bromodomain containing 7 ( BRD7 ), Chromodomain helicase DNA binding protein 1 ( CHD1 ), IKAROS family zinc finger 1 ( IKZF1 ), NIMA related kinase 9 ( NEK9 ), and TATA-box binding protein associated factor 9 ( TAF9; Fig. 3C - 3F , Supplementary Fig. 1). Download figure Open in new tab Figure 3: Enhancers comprising unique and shared DMPs between the 3 pairwise comparisons. Three-way Venn diagrams showing the number of unique and shared DMPs within differentially methylated enhancers [A] and unique and shared genes interacting with differentially methylated enhancers [B]. Gene schematic diagrams of 4 genes with interactions with differentially methylated enhancers shared in the 3 pairwise comparisons are shown with gene promoter interactions (arched loops): BRD7 [C], CHD1 [D], NEK9 [E], and IKZF1 [F]. Alignment to human reference genome hg38 coordinates are displayed in basepairs (bp) below the x-axis. Orange rectangles represent gene bodies, with white arrows inset if the gene body is large enough, to indicate the direction of transcription. The location of the gene name corresponds to the location of the promoter for each gene. Significance (y-axis) of hypermethylated DMPs (black fill) or hypomethylated DMPs (white fill) are displayed for MCI vs. CU (diamond), AD vs. CU (triangle), or AD vs. MCI (square) pairwise comparisons. One in 25 non-significant CpGs are shown for comparison (grey vertical line). Enhancer (E) and promoter (P) regions are designated by black horizontal lines above the grey vertical lines indicating relative positions. Enhancer-promoter interactions are depicted by arched loops for the MCI vs. CU (solid light grey), AD vs. CU (dashed blue), and the AD vs. MCI (solid black) pairwise comparisons. The height of the arched loops is for clarity and does not indicate the significance of each enhancer-promoter interaction. A corrected significance level of LFDR < 0.05 and methylation difference greater than 2.5% was adopted for all comparisons. Discussion We investigated published WGMS data that provided a panel of differentially methylated positions and genes associated with MCI and AD (18,19] for differential methylation in 812 genes that participate in epigenetic regulation curated from the EpiFactor database. 56% of DMPs in genes involved in epigenetic modification were more methylated in MCI than in CU participants; in contrast, 22% of DMPs in genes were more methylated in AD than in CU participants. The majority of DMPs found between MCI and AD participants in genes involved in epigenetic modifications also had lower DNA methylation levels in AD, suggesting a trend of increasing DNA methylation levels from CU to MCI, and decreasing DNA methylation levels from MCI to AD in genes involved in epigenetic modifications. Since gains of DNA methylation are linked to decreased gene expression [ 25 ], these findings suggest the Epifactors database comprises genes with broad transcriptional repression in MCI participants compared to those with AD. Histone modifications comprise the most differentially methylated genes in each of the 3 pairwise comparisons. Changes in the acetylation and/or methylation at diverse lysine and threonine histone residues have been previously reported in blood and postmortem AD brain tissues [ 12 , 13 , 15 , 26 ]. 8/10 histone modification writers differentially methylated in AD (AD vs. CU) have amino acid targets that also show altered acetylation, methylation, or phosphorylation levels in postmortem brain tissue from persons with AD [ 12 ]. Histone modification complexes contained the most differentially methylated genes of all types of complexes in the 3 pairwise comparisons. Validation of these results is necessary. Ten genes are shared between all 3 pairwise comparisons, including CUGBP elav-like family member 2 ( CELF2 ), histone deacetylase 9 ( HDAC9 ), RNA binding fox-1 homolog 1 ( RBFOX1 ), TATA-box binding protein associated factor 4 ( TAF4 ), and Thymine DNA glycosylase ( TDG ). CELF2 , as a member of the CELF family, plays various roles in co-transcriptional and post-transcriptional RNA processing, stability, and translation. This gene is a GWAS identified risk factor for AD [ 27 ] and has a direct role in the metabolism of Tau proteins [ 28 ]. HDAC9 is a histone deacetylase. HDAC9 function in humans induces memory impairment, increases amyloid-β deposition, promotes tau hyperphosphorylation, increases neuroinflammation and mitochondrial dysfunction in AD and with age [ 29 – 31 ]. RBFOX1 participates in tissue-specific splicing of mRNA precursors. RBFOX1 is a key susceptibility gene in the balance between resilient and phenotype-presenting AD populations [ 32 , 33 ] and has loci associated with a higher AD incidence level in older Black adults [ 34 ], increased somatic mutations with age [ 35 ], and amyloidosis [ 36 ]. Reduced expression of RBFOX1 is correlated to higher amyloid-β burden and worse cognition during life [ 36 ]. TAF4 is a core subunit of the transcription factor II D (TFIID) complex, is required for the formation of the RNA polymerase II pre-initiation complex, and is essential for embryogenesis and embryonic stem cell differentiation [ 37 ]. TDG controls the base excision repair of guanine/thymine DNA mismatches, created by the deamination of 5-methylcytosine, back to guanine/cytosine base pairs, and is essential to the development and maintenance of chromatin states by recruiting chromatin-modifying enzymes and protecting CpG-rich promoter sequences from hypermethylation [ 38 ]. Alterations to TDG would have global consequences due to its major role in protecting the stability of the genome and epigenome [ 39 ]. Differential methylation in these genes may cause vast alterations to the epigenome and transcriptome. Certain gene families were not differentially methylated in the present data, including the DNA methyltransferases (DNMT) and ten-eleven translocase (TET) families of DNA methylation and demethylation enzymes, except for DNMT1 in MCI and AD, DNMT3B in AD, and TET2 in MCI. In turn, most histone deacetylases assessed using the EpiFactors dataset are not differentially methylated in the three pairwise comparisons (AD vs. CU, 7/9; MCI vs. CU, 7/9; AD vs. MCI, 6/9). The lack of differential methylation in these families of genes suggests that the DNA methylation changes we observe are gene and pathway specific. We identified DMPs in enhancer regions known to interact with the promoters of genes that participate in epigenetic regulation, including genes not found to be differentially methylated in any pairwise comparisons, thereby increasing the aggregate number of genes with altered DNA methylation levels in persons with MCI and AD. The promoters of genes encoding histone modifications represented the most targeted by enhancer regions containing one or more DMPs (47-65%). Five genes shared the interaction with enhancer regions containing one or more DMPs in all three pairwise comparisons: Bromodomain containing 7 ( BRD7 ), Chromodomain helicase DNA binding protein 1 ( CHD1 ), IKAROS family zinc finger 1 ( IKZF1 ), NIMA related kinase 9 ( NEK9 ), and TATA-box binding protein associated factor 9 ( TAF9 ). BRD7 acts as a transcriptional coactivator or corepressor as a component of chromatin remodeling complexes [ 40 ], is involved in synapse formation and oligodendrocyte differentiation and myelination [ 41 , 42 ] and has been linked to apoptosis in AD [ 43 ]. CHD1 encodes a protein that regulates the opening of chromatin and plays a role in transcription [ 44 ] and has been identified as a key transcriptional regulator of switch genes in the brains of AD patients [ 45 ]. IKZF1 encodes a hematopoietic zinc finger transcription factor participating in gene expression via chromatin remodeling [ 46 , 47 ]. IKZF1 is specifically expressed in adult microglia in the human cortex and hippocampus, and its loss has been found to induce spatial learning deficits, impaired hippocampal long-term potentiation, abnormal microglial morphology, astrogliosis, and composition changes to the inflammasome and synaptosome [ 48 ]. IKZF1 has increased expression in postmortem hippocampal samples from AD patients [ 48 ]. NEK9 is a serine-threonine protein kinase heavily involved in mitosis and has been identified as a potential therapeutic target in AD due to its roles in tauopathy and tau phosphorylation [ 49 ]. TAF9 is one member of the family of TAFs required for activated transcription. Studies have identified TAF9 as a top novel target for diagnosis and therapy in AD [ 50 ]. Together, these data identify DMPs in enhancer regions known to interact with the promoters of key genes that participate in epigenetic regulation in MCI and AD. Future work must validate these findings, investigate more genes involved in epigenetic modification not currently listed in EpiFactors, and integrate these results with other omics data to improve the molecular resolution of AD pathology. Conclusions Genes that participate in the epigenetic regulation of gene expression are themselves differentially methylated in blood between persons with and without MCI and AD. Differentially methylated positions (DMPs) within these genes stand to further elucidate the molecular pathogenesis of MCI and AD and may enhance the utility and accuracy of contemporary plasma biomarkers. Methods Details of the cohorts, sample acquisition and handling, DNA methylation sequencing and data analysis have been previously published [ 18 , 19 ]. Sections describing both study participants and original differential DNA methylation analysis are summarized below. Study participants This research was conducted in accord with the Declaration of Helsinki. The experimental protocol was approved by the institutional review board (IRB) of the University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin. All participants signed an IRB-approved informed consent. Participants were enrolled in the Wisconsin Alzheimer’s Disease Research Center (WADRC) [ 20 , 21 ], or the Wisconsin Registry for Alzheimer’s Prevention (WRAP) [ 22 ]. Participants were classified into three groups based on cognitive status: 109 participants with Alzheimer’s disease (AD), 99 with mild cognitive impairment (MCI), and 174 who are cognitively unimpaired (CU). Participants are evaluated annually for cognitive status with cognitive performance tests determined by a consensus conference panel based on National Institute on Aging–Alzheimer’s Association (NIA-AA) criteria. [ 51 ] Whole genome methylation sequencing acquisition and analysis High molecular weight genomic DNA was extracted from blood samples acquired on the nearest appointment date following MCI or AD diagnosis. The DNA samples underwent whole genome methylation sequencing (WGMS), and the resulting data was processed using standard procedures. A CpG was defined as a differentially methylated position (DMP) if it has a local false discovery rate (LFDR) 2.5% between groups. Genes were considered differentially methylated if they contained at least one DMP and had a gene-wide LFDR < 0.01. Genomic coordinates for all protein coding genes were obtained from ENSEMBL (v86) [ 52 ]. Genes were defined as sequences spanning 3 kilobases (kb) 5′ of a transcription start site (TSS) to 200 base pairs (bp) 3′ of a transcription termination site (TTS). This approach generated 9,756 DMPs and 1,743 differentially methylated genes between CU and MCI, 14,530 DMPs and 1,847 differentially methylated genes between CU and AD, and 19,759 DMPs and 2,929 differentially methylated genes between MCI and AD participants in the previous publication [ 19 ]. Selection of Genes Encoding Proteins that Participate in Epigenetic Regulation of Gene Expression: The EpiFactors 2.1 Database EpiFactors is a useful database that specifically catalogs human proteins and complexes with a role in epigenetic regulation, generated from reputable external databases, and provides a wealth of information and data on epigenetic factors, including expression levels across human primary cell samples, cancer cell lines, and post-mortem tissues. This database is useful for researchers studying epigenetic contributions to complex human disease [ 23 , 24 ]. The first dataset contained genes encoding proteins directly participating in epigenetic modifications. These genes fell into a number of categories: genes with regulatory roles in chromatin remodeling ( i.e. , chromatin remodeling, chromatin remodeling cofactor), DNA methylation/demethylation ( i.e. , DNA modification, DNA modification cofactor), Histone modification ( i.e. , histone chaperone, histone modification, histone modification cofactor, histone modification erase, histone modification erase cofactor, histone modification read, histone modification read cofactor, histone modification write, histone modification write cofactor), Polycomb group (PcG) protein, protein modification, RNA modification, scaffold protein, and transcription factors. Transcription factors were excluded from this paper for exploration in a future study. In total, 788 genes were obtained for downstream analysis from this dataset. The second dataset contained 101 genes encoding histone subunits. The third dataset contained 73 protein complexes, made up of 368 genes, participating in various forms of epigenetic modification. The genes utilized in these complexes may already exist in the first dataset. Combining all three datasets generated a list of 1,017 genes for differential methylation analysis, 812 of these genes were contained in the Madrid et al. dataset. Identification of Differentially Methylated EpiFactors genes Gene and DMP files from the previous publication [ 19 ] were utilized for EpiFactors analysis, at the same cutoffs (DMP: LFDR 2.5%; gene: gene-wide LFDR < 0.01 and 1+ DMP). DMPs were annotated to genomic structures with annotatr (version 1.28.0) [ 53 ] for comparison overlapping, genic region and methylation polarity analyses. Differentially methylated genes that encode proteins that participate in epigenetic regulation of gene expression (henceforth just called differentially methylated genes) were matched between the EpiFactors 2.1 Database master list and differentially methylated genes identified in the previous publication ( i.e. , MCI vs. CU, AD vs. CU, AD vs. MCI). Differential DNA methylation analysis of blood promoter-enhancer interactions The genomic coordinates obtained from ENSEMBL (v86) [ 53 ] were extended to include 3 kilobases 5’ of a TSS and 200 base pairs 3’ of a transcription termination site, and promoters were updated to be defined as regions 5 kb upstream of a TSS and 200 bases downstream of a TSS. Differential DNA methylation levels at promoter-enhancer loci and their interactions in white blood cells were examined using published Promoter Capture Hi-C (PCHi-C) data [ 54 ]. An interaction was deemed significant if the median ChiCAGO score across all blood cell types was > 5. Interactions were then filtered, false positives ( e.g ., bait-bait) and sex chromosome interactions were removed, and data lifted over to the GRCh38 human genome reference version with the UCSC liftOver tool [ 55 ]. An enhancer was considered differentially methylated if it contained one or more DMPs. This approach yielded 104,554 promoter-enhancer interactions comprising 58,654 distinct enhancers interacting with 9,631 distinct promoters of genes throughout the genome. Differentially methylated enhancers (defined as an enhancer comprising 1+ DMP) were then limited to those interacting with the promoters of genes identified in the EpiFactors master list (above). Software for statistical analysis and data visualization All statistical analyses ( i.e. DMP and differentially methylated gene generation and identification, EpiFactors overlap, and statistical significance analysis) were conducted in R (v4.2.0). Sankey, Venn diagram, and enhancer-promoter figures were generated with ggplot2 (version 3.4.2) [ 56 ], networkD3 (version 0.4) [ 57 ], and stats (version 3.6.2) [ 58 ]. Pie charts, bar plots and line graphs were constructed using GraphPad Prism (version 10.2.3) [ 59 ]. Declarations Ethics approval and consent to participate The experimental protocol was approved by the Institutional Review Board (IRB) of the University of Wisconsin School of Medicine and Public Health, Madison, WI. All participants signed an IRB-approved informed consent. Consent for publication Not applicable. Availability of data and materials The datasets supporting the conclusions of this article are included within the article (and its additional files). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Institutes of Health grant numbers: R01AG066179 and the UW Department of Neurological Surgery. SF was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number T32NS105602. The funding sources had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript. Authors’ Contributions LRC acquired and provided the participants’ blood. LAP extracted the DNA from the blood. SK analyzed the data and interpreted the general trends. SGF further analyzed and interpreted all the data presented, as well as wrote the manuscript. KJH, RSA, and LAP all contributed to the conception and design of the project, as well as the writing of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to acknowledge and thank the participants and study personnel who made this work possible. Funder Information Declared National Institute of Health, USA , R01AG066179 National Institute of Health, USA , T32NS105602 National Institute of Health, USA , R01AI173035 Abbreviations AD Alzheimer’s disease BMI Body mass index BRD7 Bromodomain containing 7 CELF2 CUGBP elav-like family member 2 CHD1 Chromodomain helicase DNA binding protein 1 CpG Cytosine-guanine dinucleotide CU Cognitively unimpaired DMP Differentially methylated position DNA Deoxyribonuleic acid DNMT1 DNA methyltransferase 1 DNMT3A DNA methyltransferase 3A DNMT3B DNA methyltransferase 3B EPI Enhancer-promoter interaction HDAC9 Histone deacetylase 9 IKZF1 IKAROS family zinc finger 1 LFDR Local false discovery rate kb kilobases MCI Mild cognitive impairment NEK9 NIMA related kinase 9 NIA-AA National Institute on Aging-Alzheimer’s Association PcG Polycomb group PCHi-C Promoter Capture Hi-C RBFOX1 RNA binding fox-1 homolog 1 RNA Ribonucleic acid TAF TATA-box binding 379 protein associated factor TAF4 TATA-box binding protein associated factor 4 TAF9 TATA-box binding protein associated factor 9 TDG Thymine DNA glycosylase TET Tet methylcytosine dioxygenase TET2 Tet methylcytosine dioxygenase 2 TSS Transcription start site TTS Transcription termination site WADRC Wisconsin Alzheimer’s disease research center WGMS Whole genome methylation sequencing WRAP Wisconsin registry for Alzheimer’s prevention References 1. ↵ Shigemizu D , Akiyama S , Higaki S , Sugimoto T , Sakurai T , Boroevich KA , et al. Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data . Alzheimers Res Ther [Internet] . 2020 Dec 1 [ cited 2025 Jun 15 ]; 12 ( 1 ): 1 – 12 . Available from: https://alzres.biomedcentral.com/articles/10.1186/s13195-020-00716-0 OpenUrl CrossRef 2. Alzheimer , Association . Alzheimer’s Association 2024 Alzheimer’s Disease Facts and Figures . Alzheimer’s Association . 2024 ; 20 ( 5 ). 3. ↵ Albert MS , DeKosky ST , Dickson D , Dubois B , Feldman HH , Fox NC , et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease . Alzheimer’s & Dementia . 2011 May 22 ; 7 ( 3 ): 270 – 9 . OpenUrl CrossRef 4. ↵ Bennett DA , Yu L , Yang J , Srivastava GP , Aubin C , De Jager PL . Epigenomics of Alzheimer’s disease . Translational Research . 2015 Jan ; 165 ( 1 ): 200 – 20 . OpenUrl CrossRef PubMed 5. ↵ Gao X , Chen Q , Yao H , Tan J , Liu Z , Zhou Y , et al. Epigenetics in Alzheimer’s Disease . Front Aging Neurosci . 2022 ; 14 : 911635 . 6. ↵ Lang AL , Eulalio T , Fox E , Yakabi K , Bukhari SA , Kawas CH , et al. Methylation differences in Alzheimer’s disease neuropathologic change in the aged human brain . Acta Neuropathol Commun . 2022 Nov 29 ; 10 ( 1 ): 174 . OpenUrl CrossRef PubMed 7. ↵ Zhang Y , Shen S . Epigenome-wide DNA methylation analysis of late-stage mild cognitive impairment . Front Cell Dev Biol . 2024 Jan 16 ; 12 . 8. ↵ Alberca CD , Papale LA , Madrid A , Alisch RS . Hippocampal and peripheral blood DNA methylation signatures correlate at the gene and pathway level in a mouse model of autism . Hum Mol Genet . 2023 Dec 1 ; 32 ( 24 ): 3312 – 22 . OpenUrl CrossRef PubMed 9. Horvath S , Zhang Y , Langfelder P , Kahn RS , Boks MP , van Eijk K , et al. Aging effects on DNA methylation modules in human brain and blood tissue . Genome Biol . 2012 ; 13 ( 10 ): R97 . OpenUrl CrossRef PubMed 10. Iturria-Medina Y , Adewale Q , Khan AF , Ducharme S , Rosa-Neto P , O’Donnell K , et al. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity . Sci Adv . 2022 Nov 16 ; 8 ( 46 ). 11. ↵ Iturria-Medina Y , Khan AF , Adewale Q , Shirazi AH . Blood and brain gene expression trajectories mirror neuropathology and clinical deterioration in neurodegeneration . Brain . 2020 Feb 1 ; 143 ( 2 ): 661 – 73 . OpenUrl CrossRef PubMed 12. ↵ Santana DA , Smith M de AC , Chen ES. Histone Modifications in Alzheimer’s Disease . Genes (Basel ). 2023 Jan 29 ; 14 ( 2 ): 347 . OpenUrl PubMed 13. ↵ De Plano LM , Saitta A , Oddo S , Caccamo A . Epigenetic Changes in Alzheimer’s Disease: DNA Methylation and Histone Modification . Cells . 2024 Apr 21 ; 13 ( 8 ): 719 . OpenUrl CrossRef 14. ↵ Nikolac Perkovic M , Videtic Paska A , Konjevod M , Kouter K , Svob Strac D , Nedic Erjavec G , et al. Epigenetics of Alzheimer’s Disease . Biomolecules . 2021 Jan 30 ; 11 ( 2 ): 195 . OpenUrl CrossRef PubMed 15. ↵ Zhang K , Schrag M , Crofton A , Trivedi R , Vinters H , Kirsch W . Targeted proteomics for quantification of histone acetylation in Alzheimer’s disease . Proteomics . 2012 Apr 11 ; 12 ( 8 ): 1261 – 8 . OpenUrl CrossRef PubMed Web of Science 16. ↵ Nativio R , Lan Y , Donahue G , Sidoli S , Berson A , Srinivasan AR , et al. An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease . Nat Genet . 2020 Oct 28 ; 52 ( 10 ): 1024 – 35 . OpenUrl CrossRef PubMed 17. ↵ Montalbano M , Jaworski E , Garcia S , Ellsworth A , McAllen S , Routh A , et al. Tau Modulates mRNA Transcription, Alternative Polyadenylation Profiles of hnRNPs, Chromatin Remodeling and Spliceosome Complexes . Front Mol Neurosci . 2021 Dec 3 ; 14 . 18. ↵ Breen C , Papale LA , Clark LR , Bergmann PE , Madrid A , Asthana S , et al. Whole genome methylation sequencing in blood identifies extensive differential DNA methylation in late onset dementia due to Alzheimer’s disease . Alzheimer’s & Dementia . 2024 Feb 19 ; 20 ( 2 ): 1050 – 62 . OpenUrl CrossRef 19. ↵ Madrid A , Papale LA , Bergmann PE , Breen C , Clark LR , Asthana S , et al. Whole genome methylation sequencing in blood from persons with mild cognitive impairment and dementia due to Alzheimer’s disease identifies cognitive status . Alzheimer’s & Dementia [Internet] . 2025 Jan [ cited 2025 Jan 6 ]; Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/alz.14474 20. ↵ Weintraub S , Besser L , Dodge HH , Teylan M , Ferris S , Goldstein FC , et al. Version 3 of the Alzheimer Disease Centers’ Neuropsychological Test Battery in the Uniform Data Set (UDS) . Alzheimer Dis Assoc Disord [Internet] . 2018 [ cited 2025 Apr 9 ]; 32 ( 1 ): 10 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5821520/ OpenUrl CrossRef 21. ↵ Allison SL , Jonaitis EM , Koscik RL , Hermann BP , Mueller KD , Cary RP , et al. Neurodegeneration, Alzheimer’s disease biomarkers, and longitudinal verbal learning and memory performance in late middle age . Neurobiol Aging . 2021 Jun ; 102 : 151 – 60 . OpenUrl CrossRef PubMed 22. ↵ Johnson SC , Koscik RL , Jonaitis EM , Clark LR , Mueller KD , Berman SE , et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions. Alzheimer’s & Dementia: Diagnosis , Assessment & Disease Monitoring . 2018 Jan 8 ; 10 ( 1 ): 130 – 42 . OpenUrl 23. ↵ Medvedeva YA , Lennartsson A , Ehsani R , Kulakovskiy I V. , Vorontsov IE , Panahandeh P , et al. EpiFactors: a comprehensive database of human epigenetic factors and complexes . Database . 2015 Jul 7 ; 2015 : bav067 . OpenUrl CrossRef PubMed 24. ↵ Marakulina D , Vorontsov IE , Kulakovskiy I V , Lennartsson A , Drabløs F , Medvedeva YA. EpiFactors 2022: expansion and enhancement of a curated database of human epigenetic factors and complexes . Nucleic Acids Res . 2023 Jan 6 ; 51 ( D1 ): D564 – 70 . OpenUrl CrossRef PubMed 25. ↵ Unnikrishnan A , Hadad N , Masser DR , Jackson J , Freeman WM , Richardson A . Revisiting the genomic hypomethylation hypothesis of aging HHS Public Access . 2018 ; 1418 ( 1 ): 69 – 79 . OpenUrl 26. ↵ Raval M , Mishra S , Tiwari AK . Epigenetic regulons in Alzheimer’s disease . In 2023 . p. 185 – 247 . 27. ↵ Li X , Syed I , Zhang Z , Adhikari R , Tang D , Ko S , et al. CELF2 promotes tau exon 10 inclusion via hinge domain-mediated nuclear condensation . bioRxiv [Internet] . 2024 Nov 3 [cited 2025 Apr 9] ; 2024.11.02.621395 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11566031/ 28. ↵ El Bitar F , Al Sudairy N , Qadi N , Al Rajeh S , Alghamdi F , Al Amari H , et al. A Comprehensive Analysis of Unique and Recurrent Copy Number Variations in Alzheimer’s Disease and its Related Disorders . Curr Alzheimer Res [Internet] . 2020 Dec 1 [ cited 2025 Apr 9 ]; 17 ( 10 ): 926 – 38 . Available from: https://www.benthamdirect.com/content/journals/car/10.2174/1567205017666201130111424 OpenUrl 29. ↵ Zhang HL , Hu S , Yang P , Long HC , Ma QH , Yin DM , et al. HDAC9 mediated calmodulin deacetylation induces memory impairment in Alzheimer’s disease . CNS Neurosci Ther [Internet] . 2024 Feb 1 [ cited 2025 Apr 9 ]; 30 ( 2 ): e14573 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10850929/ OpenUrl CrossRef 30. Li T , Yin L , Kang X , Xue W , Wang N , Zhang J , et al. TFEB acetylation promotes lysosome biogenesis and ameliorates Alzheimer’s disease–relevant phenotypes in mice . J Biol Chem [Internet] . 2022 Dec 1 [ cited 2025 Apr 9 ]; 298 ( 12 ): 102649 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9694136/ OpenUrl CrossRef 31. ↵ Shen X , He Y , Ge C . Role of circRNA in pathogenesis of Alzheimer’s disease . 中南大学学报 医学版 [Internet] . 2022 Nov 25 [ cited 2025 Apr 9 ]; 47 ( 7 ): 960 – 6 . Available from: http://xbyxb.csu.edu.cn/thesisDetails#10.11817/j.issn.1672-7347.2022.210729&lang=zh OpenUrl 32. ↵ Castanho I , Yeganeh PN , Boix CA , Morgan SL , Mathys H , Prokopenko D , et al. Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer’s disease . bioRxiv [Internet] . 2025 Jan 15 [ cited 2025 Apr 9 ]; 2025.01.13.632801 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11761133/ 33. ↵ Meng X , Wei Q , Meng L , Liu J , Wu Y , Liu W . Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data . Genes (Basel) [Internet] . 2022 May 1 [ cited 2025 Apr 9 ]; 13 ( 5 ): 837 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9140721/ OpenUrl 34. ↵ Kolpakov S , Yashkin A , Ukraintseva S , Yashin A , Akushevich I. Genome-Related Mechanisms Contributing to Differences in Alzheimer’s Disease Incidence Between White and Black Older US Adults . J Racial Ethn Health Disparities [Internet] . 2024 [cited 2025 Apr 9] ; Available from: https://www.researchgate.net/publication/377706503_Genome-Related_Mechanisms_Contributing_to_Differences_in_Alzheimer's_Disease_Incidence_Between_White_and_Black_Older_US_Adults 35. ↵ Zhang M , Bouland GA , Holstege H , Reinders MJT . Identifying Aging and Alzheimer Disease–Associated Somatic Variations in Excitatory Neurons From the Human Frontal Cortex . Neurol Genet [Internet] . 2023 Jun [ cited 2025 Apr 9 ]; 9 ( 3 ): e200066 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10136684/ OpenUrl CrossRef 36. ↵ Raghavan NS , Dumitrescu L , Mormino E , Mahoney ER , Lee AJ , Gao Y , et al. Association Between Common Variants in RBFOX1, an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer Disease . JAMA Neurol [Internet] . 2020 Oct 1 [ cited 2025 Apr 9 ]; 77 ( 10 ): 1288 – 98 . Available from: https://jamanetwork.com/journals/jamaneurology/fullarticle/2767374 OpenUrl CrossRef 37. ↵ Kazantseva J , Tints K , Neuman T , Palm K . TAF4 Controls Differentiation of Human Neural Progenitor Cells Through hTAF4-TAFH Activity . Journal of Molecular Neuroscience [Internet] . 2015 Jan 1 [ cited 2025 Apr 9 ]; 55 ( 1 ): 160 – 6 . Available from: https://link.springer.com/article/10.1007/s12031-014-0295-6 OpenUrl CrossRef 38. ↵ De Sousa MML , Ye J , Luna L , Hildrestrand G , Bjørås K , Scheffler K , et al. Impact of Oxidative DNA Damage and the Role of DNA Glycosylases in Neurological Dysfunction . Int J Mol Sci [Internet] . 2021 Dec 1 [ cited 2025 Apr 9 ]; 22 ( 23 ): 12924 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8657561/ OpenUrl CrossRef 39. ↵ Onabote O , Hassan HM , Isovic M , Torchia J . The Role of Thymine DNA Glycosylase in Transcription , Active DNA Demethylation, and Cancer. Cancers (Basel) [Internet] . 2022 Feb 1 [ cited 2025 Apr 9 ]; 14 ( 3 ): 765 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8833622/ OpenUrl 40. ↵ Liu T , Zhao M , Liu J , He Z , Zhang Y , You H , et al. Tumor Suppressor Bromodomain-containing Protein 7 Cooperates with Smads to Promote Transforming Growth Factor-β Responses . Oncogene [Internet] . 2016 Jan 19 [ cited 2025 Apr 9 ]; 36 ( 3 ): 362 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5140778/ 41. ↵ Liu Z , Yan M , Liang Y , Liu M , Zhang K , Shao D , et al. Nucleoporin Seh1 Interacts with Olig2/Brd7 to Promote Oligodendrocyte Differentiation and Myelination . Neuron [Internet] . 2019 May 8 [ cited 2025 Apr 9 ]; 102 ( 3 ): 587 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508993/ OpenUrl 42. ↵ Xu Y , Cao W , Zhou M , Li C , Luo Y , Wang H , et al. Inactivation of BRD7 results in impaired cognitive behavior and reduced synaptic plasticity of the medial prefrontal cortex . Behavioural Brain Research . 2015 Jun 1 ; 286 : 1 – 10 . OpenUrl CrossRef PubMed 43. ↵ Du Y , Liu G , Chen D , Yang J , Wang J , Sun Y , et al. NQO1 regulates expression and alternative splicing of apoptotic genes associated with Alzheimer’s disease in PC12 cells . Brain Behav [Internet] . 2023 May 1 [ cited 2025 Apr 9 ]; 13 ( 5 ): e2917 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10175992/ OpenUrl CrossRef 44. ↵ Pilarowski GO , Vernon HJ , Applegate CD , Boukas L , Cho MT , Gurnett CA , et al. Missense variants in the chromatin remodeler CHD1 are associated with neurodevelopmental disability . J Med Genet [Internet] . 2017 Aug 1 [ cited 2025 Apr 9 ]; 55 ( 8 ): 561 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5834353/ OpenUrl 45. ↵ Potashkin JA , Bottero V , Santiago JA , Quinn JP . Computational identification of key genes that may regulate gene expression reprogramming in Alzheimer’s patients . PLoS One [Internet] . 2019 Sep 1 [ cited 2025 Apr 9 ]; 14 ( 9 ): e0222921 . Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222921 OpenUrl 46. ↵ Goldman FD , Gurel Z , Al-Zubeidi D , Fried AJ , Icardi M , Song C , et al. Congenital Pancytopenia and Absence of B Lymphocytes in a Neonate with a Mutation in the Ikaros Gene . Pediatr Blood Cancer [Internet] . 2011 Apr [ cited 2025 Apr 9 ]; 58 ( 4 ): 591 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3161153/ OpenUrl 47. ↵ Kuehn HS , Boisson B , Cunningham-Rundles C , Reichenbach J , Stray-Pedersen A , Gelfand EW , et al. Loss of B Cells in Patients with Heterozygous Mutations in IKAROS . N Engl J Med [Internet] . 2016 Mar 17 [ cited 2025 Apr 9 ]; 374 ( 11 ): 1032 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC4836293/ OpenUrl CrossRef 48. ↵ Ballasch I , García-García E , Vila C , Pérez-González A , Sancho-Balsells A , Fernández J , et al. Ikzf1 as a novel regulator of microglial homeostasis in inflammation and neurodegeneration . Brain Behav Immun . 2023 Mar 1 ; 109 : 144 – 61 . OpenUrl CrossRef PubMed 49. ↵ Rodriguez S , Hug C , Todorov P , Moret N , Boswell SA , Evans K , et al. Machine learning identifies candidates for drug repurposing in Alzheimer’s disease . Nature Communications 2021 12 : 1 [Internet]. 2021 Feb 15 [cited 2025 Apr 9];12(1):1–13 . Available from: https://www.nature.com/articles/s41467-021-21330-0 50. ↵ Vastrad B , Vastrad C , Nilaya C. Identification of candidate biomarkers and signaling pathways associated with Alzheimer’s disease using bioinformatics analysis of next generation sequencing data . bioRxiv [Internet] . 2024 Dec 6 [ cited 2025 Apr 9 ]; 2024.12.03.626535 . Available from: https://www.biorxiv.org/content/10.1101/2024.12.03.626535v1 51. ↵ Jack CR , Albert MS , Knopman DS , McKhann GM , Sperling RA , Carrillo MC , et al. Introduction to Revised Criteria for the Diagnosis of Alzheimer’s Disease: National Institute on Aging and the Alzheimer Association Workgroups . Alzheimers Dement [Internet] . 2011 [ cited 2025 Jan 6 ]; 7 ( 3 ): 257 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3096735/ OpenUrl CrossRef 52. ↵ Howe KL , Achuthan P , Allen J , Allen J , Alvarez-Jarreta J , Amode MR , et al. Ensembl 2021 . Nucleic Acids Res . 2021 Jan 8 ; 49 ( D1 ): D884 – 91 . OpenUrl CrossRef PubMed 53. ↵ Cavalcante RG , Sartor MA. annotatr: genomic regions in context . Bioinformatics . 2017 Aug 1 ; 33 ( 15 ): 2381 – 3 . OpenUrl CrossRef PubMed 54. ↵ Javierre BM , Burren OS , Wilder SP , Kreuzhuber R , Hill SM , Sewitz S , et al. Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters . Cell . 2016 Nov 17 ; 167 ( 5 ): 1369 – 1384.e19 . OpenUrl CrossRef PubMed 55. ↵ Nassar LR , Barber GP , Benet-Pagès A , Casper J , Clawson H , Diekhans M , et al. The UCSC Genome Browser database: 2023 update . Nucleic Acids Res . 2023 Jan 6 ; 51 ( D1 ): D1188 – 95 . OpenUrl CrossRef PubMed 56. ↵ Hadley Wickham . ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York ; 2016 . 57. ↵ J.J. Allaire , Cristopher Gandrud , Kenton Russell , CJ Yetman . networkD3: D3 JavaScript Network Graphs from R . The Comprehensive R Network ; 2017 . 58. ↵ R Core Team . R: a language and environment for statistical computing . Vienna : R Foundation for Statistical Computing ; 2013 . 59. ↵ Dotmatics. GraphPad Prism . 2024 . View the discussion thread. Back to top Previous Next Posted June 24, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s disease Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s disease Samuel G. Faasen , Ligia A. Papale , Alexander Boruch , Phillip E. Bergmann , Kirk J. Hogan , Reid S. Alisch bioRxiv 2025.06.18.660344; doi: https://doi.org/10.1101/2025.06.18.660344 Share This Article: Copy Citation Tools Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s disease Samuel G. Faasen , Ligia A. Papale , Alexander Boruch , Phillip E. Bergmann , Kirk J. Hogan , Reid S. 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