Bioelectric and Epigenetic Landscapes in Lateralized Breast Tumors Reveal Distinct Tumor Microenvironment Signatures

Bioelectric and Epigenetic Landscapes in Lateralized Breast Tumors Reveal Distinct Tumor Microenvironment Signatures | 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 Bioelectric and Epigenetic Landscapes in Lateralized Breast Tumors Reveal Distinct Tumor Microenvironment Signatures Sebastián Real , Sergio Laurito , Pablo Gonzalez , Oscar Bello , Joao Carvalho , María Roqué doi: https://doi.org/10.1101/2025.06.30.662430 Sebastián Real 1 National Council of Scientific and Technological Research , Argentina 2 Institute of Physiology, Faculty of Medicine, National University of Cuyo , Mendoza, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sergio Laurito 1 National Council of Scientific and Technological Research , Argentina 3 Faculty of Exact Sciences, National University of Cuyo , Mendoza, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pablo Gonzalez 4 Children’s Hospital “Humberto Notti” , Mendoza, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oscar Bello 1 National Council of Scientific and Technological Research , Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joao Carvalho 5 Center of Physics of the University of Coimbra (CFisUC) , Coimbra, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site María Roqué 1 National Council of Scientific and Technological Research , Argentina 3 Faculty of Exact Sciences, National University of Cuyo , Mendoza, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: mroquemoreno{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Cell communication within the tumor microenvironment (TME) plays a pivotal role in cancer progression, yet little is known about whether this communication differs between anatomically paired organs. Here, we report that breast tumors arising in the left (L) versus right (R) mammary glands exhibit significant asymmetry in composition, bioelectric state, and epigenetic regulation suggesting the existence of a stable, lateralized tumor TME shaped by differential cell communication. By mining TCGA data of invasive ductal carcinomas, we found that R-sided tumors display a higher stromal content, particularly enriched in cancer-associated fibroblasts (CAFs). Further subtype analysis revealed a predominance of inflammatory CAFs (iCAFs) in R tumors and dividing CAFs (dCAFs) in L tumors, suggesting distinct TME profiles. These CAF differences were confirmed in paired L-R xenografts, where R tumors showed increased α-SMA content. Conditioning cell culture experiments with mammary tissue extracts demonstrated that L-sided environments induce greater membrane depolarization in breast cancer cells. These bioelectric differences were as well replicated in paired L-R xenograft tumors, where L tumors consistently exhibited a depolarized membrane potential relative to their R counterparts. Methylome profiling by Nanopore Sequencing revealed that L tumors were hypermethylated at key ion channel genes involved in electrical cell communication-most notably connexins-correlating with their reduced gene expression. A computational model further showed that bistable membrane potential and methylation states can emerge spontaneously, governed by the strength of Gap Junction-mediated communication and the initial state. Taking together, our results reveal a previously unrecognized L-R asymmetry in tumor biology, in which stromal composition and cell communication mechanisms establish stable, lateralized bioelectric and epigenetic states. These findings have broad implications for understanding tumor heterogeneity, TME conditioning, and the possibility of side-specific therapeutic strategies. INTRODUCTION Breast cancer is the most frequently diagnosed malignancy and a leading cause of cancer-related mortality in women worldwide 1 . According to the International Agency for Research on Cancer of the World Health Organization (WHO), over 2.3 million new cases were diagnosed in 2020, accounting for 11,7% of all cancer diagnoses globally ( https://gco.iarc.fr/en ). The disease exhibits significant geographical and demographic variations in incidence and mortality, influenced by diverse factors including genetic predisposition, lifestyle factors, and access to medical care. And although advances in early detection and targeted therapies are undeniable, breast cancer remains a major global health burden 1 . A key feature of breast cancer is that it is not a uniform disease, which adds complexity to the treatment strategies, requiring therapeutic approaches based on the specific tumor biology. The heterogeneity manifests at multiple levels 2 , primarily reflected on molecular profiling characterization 3 , and furthermore in the stromal composition, immune cell infiltration, and metabolic profiles, affecting tumor behavior and treatment response 4 . However, while significant progress has been made in classifying intrinsic tumor subtypes, the role of extrinsic factors remains underexplored. It is within this latter category that tumor laterality differences ‒left (L) vs. right (R)‒ fall, which are of interest to our study. Emerging evidence ‒including our own previous findings 5 , 6 , 7 as well as studies from other groups 8 , 9 , 10 , 11 ‒ suggests that tumor laterality may contribute to breast cancer heterogeneity, potentially influencing tumor development, progression, and treatment response. It is well described that L-sided breast cancer incidence is higher than that of its R-sided counterpart 8 , and even though the difference is subtle (L/R=1,15), this is repeatedly observed across different epidemiologic studies in diverse populations 12 . L- and R-sided breast tumors exhibit distinct biological characteristics 13 . More recently the L-R tumor asymmetry has gained clinical relevance in breast cancer, when it was revealed that L-sided tumors present a worse outcome and a more aggressive biology 14 . The different incidence ratio of cancer on other bilateral organs has also been described, such as for lung, testes and ovary 8 , which suggest that bilateral organs do not share identical processes regarding tumorigenesis. Supporting this concept, embryological studies in different bilaterian animal models show how normal sides of the body differ at diverse multiple biological levels 15 . In humans, L-R asymmetry is established early during embryonic development through conserved signaling pathways (Nodal, Lefty, Pitx2), which regulate organ positioning and function 16 , 17 , 18 . These asymmetries manifest in different aspects. For example the vascular and lymphatic differences, since the L thoracic duct drains the most lymphatic fluid, potentially influencing immune responses and inflammation 19 ; or the neural lateralization, given that the brain exhibits hemispheric specialization, influencing peripheral nervous system control of visceral organs 20 . These biological asymmetries suggest that distinct physiological conditions exist on the L-R sides of the body 21 , 22 . While gross anatomical asymmetries (e.g., heart on the L, liver on the R) are well recognized, subtle physiological and molecular asymmetries can influence disease susceptibility, including for example neurological disorders, where Parkinson’s disease often presents with unilateral motor symptoms, reflecting neural asymmetries 23 . Regarding cancer, as said above, asymmetry is also evident, e.g. in colorectal cancer where R-sided tumors exhibit differential molecular profiles (i.e., microsatellite instability, BRAF mutations) and poorer prognosis 24 ; in lung cancer, differences in lung characteristics have been proposed to contribute to asymmetric tumor progression 25 . Still, for diseases affecting bilateral organs, a knowledge gap persists between the lateral differences of the healthy organ and their association with tumor variations. Our study focuses on breast tumors, since the L-R differences have reached clinical implications: L seems to be a worse disease than its R counterpart. We aim to investigate if/how L-R stroma influences tumorigenesis. As we have shown in our previous work 5 , 6 , 7 L differs from R breast tumors epigenetically (at its DNA methylation profile), bioelectrically (at cell membrane potential level and ion channel gene expression), and proliferatively (at MKI67 expression level). Bioelectric signaling 26 and epigenetic regulation 27 are emerging as key factors influencing tumor heterogeneity. Both are key mechanisms for intra-tumoral cell communication and communication of tumor cells with the microenvironment (TME). The TME consists of stromal components that actively shape tumor progression, such as cancer-associated fibroblasts (CAFs) which remodel the extracellular matrix, secrete growth factors (TGF-β, VEGF), and promote angiogenesis 28 . Despite accumulating evidence, to our knowledge, no comprehensive molecular study systematically compared L-R TME of breast tumors. This study integrates bioinformatic, computational modeling, bioelectric, and epigenetic analyses to compare L- and R-sided breast tumors and address the possible clinical relevance of tumor’s laterality. RESULTS 1. L-R differences in stromal composition of breast tumors To determine whether L-R glands differ in the components surrounding a growing tumor, we analyzed tumors from each side and focused on their stroma. By using the R-based package TCGA-Biolinks, we obtained the RNA-Seq data of primary breast tumors and filtered the samples by primary invasive ductal carcinomas (IDC, n=784). Afterwards, a tumor purity up to 70% was set as cutoff using the CPE score 29 , applying the criterion of targeting non-tumoral cells from the surrounding environment or those infiltrating the tumor, resulting in 276 tumor samples with available laterality data. To avoid possible confounder effects between L-R groups, we checked for absence of differences in age, tumor subtypes, stage, and size, and patient’s menopausal status. The Xcell Rbased tool was used 30 , ‒a gene signature-based method to infer TME cell type composition, by evaluating signatures of 64 immune and stromal cell types, available at https://github.com/dviraran/xCell ‒, to explore the stromal and immune scores, which were calculated as transformed ssGSEA scores of normalized counts of RNA-Seq. The comparison of the L-R scores was performed by R-statistics and by the Xena Functional genomics Explorer Tools from UCSC, USA ( https://xenabrowser.net/ ). In the 276 cohort we observed a significant increase of the stromal score in R tumors, compared to the L-ones (Welch’s corrected t-Test, p=0,006, t=-2,734) ( Figure 1a ). When we expanded the analysis to the 784 IDC cohort, which included samples with higher levels of purity (meaning less presence of stroma), the L-R difference was still detectable at significant levels (Welch’s corrected t-Test, p=0,01, t=-2,398), showing that even in samples with low number of stromal cells, the difference was maintained ( Figure 1b ). On the contrary, no differences were found for the immune score. Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 1. L-R differences in stromal composition of breast IDCs. a. L-R comparison of stromal gene signature score in 276 TCGA IDC cohort with <70% purity. A significant increased score is observed in R-tumors (Welch’s corrected t-Test, p=0,006). b. The same comparison as panel A, expanding the analysis to the full IDC cohort in TCGA (n=784). The increased stromal score in R tumors is maintained, even though tumors with less stromal cells were included in the analysis. c . L-R comparison of fibroblast gene signature score, in 276 TCGA IDC tumors with <70% purity, and d , in 784 TCGA IDC cohort lacking purity restriction. In both cohorts, the CAF score is increased in R-tumors (Welch’s corrected t-Test, p=0,01). To improve data visualization, the Y-axes are shown using a base-10 logarithmic scale. Statistics: unpaired T-Test with Welch’s correction. Plots obtained with the Xena Functional genomics Explorer Tools from UCSC, USA ( https://xenabrowser.net/ ) and performed with Adobe Illustrator 2024. When breaking down the stromal score by using gene expression signatures of cell subtypes (i.e., endothelial cells, smooth muscle cells, fibroblasts, chondrocytes, lymphatic endothelial cells, microvascular endothelial cells, mesangial cells, adipocytes, myocytes, mesenchymal stem cells, osteoblasts, preadipocytes, skeletal muscle cells and pericytes), the fibroblasts appeared to be more abundant in R-sided IDC (cohort 276 with <70% purity tumors, Welch’s corrected t-Test, p<0,05). The CAF-signatures tested were: Fibroblast_FANTOM_1 (126 genes) (increased in R, Welch-corrected t-Test, p=0,01, t=-528) ( Figure 1c ), Fibroblasts_HPCA_ 1 (increased in R, Welch-corrected t-Test, p=0,05) and Fibroblast_HPCA_2 (increased in R, Welch-corrected t-Test, p=0,04) (gene list detail in Supplemental Data 1). The same patterns were observed by analyzing the full IDC cohort (n=784) without purity selection ( Figure 1d ). CAFs are activated fibroblasts that share similarities with fibroblasts stimulated by inflammatory conditions or activated during wound healing 31 . CAF are key players in shaping the tumor TME with functions in tumor progression, inflammation and maintenance of the extracellular matrix. Different CAF phenotypes have been described in the TME. To further investigating whether a specific CAF subtype was increased in R tumors, we used gene signatures obtained by scRNASeq to discriminate CAF subtypes proposed recently by Cords et al 32 . We selected the top 6 genes of the 7 subtypes (gene list detail in Supplemental Data 2) to compare by bioinformatic tools the L-R expression of each signature, i.e.: inflammatory , dividing , vascular , matrix , hsp , rare and tumor CAFs . Interrogating the 784 IDC cohort, we found the inflammatory iCAF as the subtype increased in R-sided tumors (Welch’s corrected t-Test, p=0,02, t=-2,260) and inversely, the dividing dCAFs increased in L-tumors (Welch’s corrected t-Test, p=0,02, t=-2,247) ( Figure 2 ). These differences did not reach significance in the smaller 276 cohort of IDCs (Welch’s corrected t-Test, p>0,05). Download figure Open in new tab Figure 2. CAF subtype composition differs between L-R in IDC tumors. L-R comparison of CAF subtype gene signature expression in the 784 TCGA IDC cohort. iCAF are increased in R-tumors (Unpaired t-Test, p=0,0033), and dCAF are increased in L-tumors (Unpaired t-Test, p=0,0026). No significant differences were found among the remaining subtypes. Data are presented as mean ± standard error of the mean (SEM). Analysis performed with GraphPad Prism v5, figure performed with Adobe Illustrator 2024. We have previously shown 6 , 7 that R-sided tumors are less proliferative. Our new findings showing an enrichment of iCAFs in R-tumors, align with our previous observations and with the characterization reported by Corbs et al. 32 . Specifically, they described a downregulation of E2F targets, G2/M checkpoint, and mitotic spindle gene sets associated to iCAFs, in contrast to the opposite profile observed in dCAFs. We next aimed to compare in-vivo the L-R quantity of CAFs in paired tumors, taking advantage of an ongoing breast PDX animal model. We sacrificed 3 animals with grown tumors (∼1000 mm 3 ) and determined by immunohistochemistry and confocal microscopy the amount of alpha-smooth muscle actin (α-SMA), a myofibroblast marker commonly used in human/mice tumor tissue to detect CAFs. In line with what we had seen by bioinformatics tools, we confirmed in the 3 mice a clear tendency of increased α-SMA content in R-sided tumors ( Figure 3 ). Download figure Open in new tab Figure 3. The CAF marker a-SMA increased in R-sided PDX breast tumors. In 3 mice bearing bilateral PDX tumors, CAF abundance was quantified by confocal microscopy using an Alexa-conjugated anti-α-SMA antibody. Fluorescence intensity was normalized to HOETCH staining and analyzed in a pairwise manner. All 3 mice exhibited higher α-SMA levels in R-sided tumors, with statistical significance reached in mouse 1 (M1; unpaired t-Test, p = 0,02). Data are presented as mean ± standard error of the mean (SEM). Analysis performed with GraphPad Prism v5, figure performed with Adobe Illustrator 2024. Taken together the bioinformatic and the in-vivo results, we can propose that L-R TMEs are asymmetric, and that fibroblasts are the main components that contribute to the differential composition. 2. L-R environments induce differential Vmem in cells and xenograft tumors In previous publications, we have shown that L-R mammary tumors differ bioelectrically and epigenetically 5 , and that breast cancer cell lines can be conditioned with extracts from healthy human mammary tissue, adopting a differential membrane potential (Vmem) depending on whether the extracts originate from the L or R breast 6 . We also found that this was reproducible in the animal model MMTV-PyMT mice 6 . Consistently, L-sided extracts induce depolarization of the Vmem compared to the effects of R-sided extracts or glands. In this work we continued testing the conditioned cell culture, collecting a new sample of normal human breast tissue from a breast reduction surgery in a 45-year-old woman. It is worth mentioning that breast reductive surgeries are not quite frequent and the maintenance of conditioned cells in culture requires sometimes a large amount of extract volume. So, a challenge with the conditioned cell culture model is the limited availability of healthy human mammary bilateral tissue. Focused on facilitating the experimental in-vitro approaches, we interrogated an alternative model to eventually replace human extracts with bovine tissue, since human cell cultures commonly use fetal bovine serum, which contains growth factors that support cell survival and proliferation. We used L-R bovine udder tissue from nulliparous heifers, collected immediately post-slaughter at the abattoir and compared the effects of bovine L-R tissue extracts with those of the recently collected human sample. We observed that the bovine extracts from the L side induced depolarization compared to R side, like the human extract effect ( Figure 4a and b). So, even though it is worth noting that human extracts produce a greater L-R difference, bovine extracts also generate statistically significant differences and in the same bioelectric direction. Beyond the technical utility of the culture setup, it provides more evidence of the differential L-R environments effect on the cellular Vmem. Download figure Open in new tab Figure 4. Membrane potential in L-R conditioned cells. Cellular Vmem was determined by the ratio between MT and DB fluorescence probes, measured by flow cytometry. Quantifications were first normalized to the mean fluorescence ratio of KCl 65mM treated cells (as a control for maximal depolarization), and results are shown as normalized to the R-treated cells. Cells conditioned with L-side human extracts (panel a ) and bovine extracts (panel b ) exhibit a lower MT/DB ratio (depolarized state) compared to those conditioned with R-extracts. Both extract-types separately induce significant differences (One-way t-Test, p<0,05), even though bovine extracts induce smaller differences. Data of L-R are presented as mean ± standard deviation (SD). Analysis performed with GraphPad Prism v5, figure performed with Adobe Illustrator 2024. We then aimed to test the hypothesis in a more challenging model, using xenograft animals unlike the spontaneous generating model MMTV already studied by us 6 . This new approach ensured an identical bioelectric baseline for both sides, allowing for paired intra-mouse comparisons. We generated 5 xenograft mice using human breast cancer MDA-MB231 cells, injecting the same number of cells into the 4th L and R mammary glands. One mouse died, and another did not develop analyzable bilateral tumors. In the remaining 3, we observed the same bioelectric difference as with conditioned cell culture and as with the MMTV animal model: tumors on the L side displayed a depolarized potential compared to their R-sided counterparts ( Figure 5a ). It is interesting to note that the normal mammary tissue of the mouse (NT) exhibited a Vmem like that of the R-sided xenografted tumors, while treatment of the tissues with KCl (as a control for maximal depolarization) resulted similar to that of the L-sided tumors. This can suggest that R-sided tumors (or cells conditioned with R extracts) adopt a bioelectric state closer to that of normal healthy tissue, whereas L-sided tumors (or cells conditioned with L extracts) shift to a bioelectric state resembling maximal depolarization. Download figure Open in new tab Figure 5. Vmem in L-R xenograft tumors. a. Tumor Vmem was assessed in thin tissue sections from 3 pairs of L- and R-sided tumors using the MT/DB fluorescence ratio, quantified by confocal microscopy through summed Z-stack intensity projections. As observed in conditioned cells, L-sided tumors exhibited significantly lower MT/DB ratios than their R-sided counterparts (paired t-Test, p = 0,01), indicating a more depolarized state. Note how R-sided tumors do not differ from healthy mammary tissue (NT) Vmem (unpaired t-Test, ns), whereas L-sided tumors do (unpaired t-Test, p = 0,02). Additionally, R-sided tumors differ from maximal depolarized control (KCl) (p = 0,0005), while L-sided tumors do not. Panels b and c : Upon reimplantation of the original tumor fragments into the contralateral mammary glands of new host mice (dark blue: original tumor; light blue/pink: reimplanted), the L-R Vmem difference was no longer observed, regardless of the tumor’s side of origin (paired t-Test, ns). All data are presented as mean ± standard deviation (SD). Analysis performed with GraphPad Prism v5, figure performed with Adobe Illustrator 2024. Based on the observed results so far, we have demonstrated that the glandular environment of a human, bovine and mice model, contains the cause of the bioelectric shift in the cells. So, moving further on, we asked whether implanting the excised xenograft tumors into inverted sides would induce a shift in the electric potential. However, the results did not support this. After 4 weeks of inverted growth, we found that the bioelectric difference had been lost. It is likely that the reimplantation of a part of the original tumor no longer allows for a bioelectric adaptation to the new side ( Figure 5b and c). 3. L-R environments induce differential ICH-methylation associated to cell communication Having found that L-R environments differ in their composition and can generate bioelectric variations in tumor cells, we aimed to explore the epigenetic part of the story focusing, in this work, on Ion Channel (ICH) genes. We used the generated xenograft animal model, which, for testing our hypothesis, was more robust than spontaneously tumor developing mice as we previously had shown 6 , since the tumors shared the same starting epigenome of the inoculated cells. This allowed afterwards paired intra-mouse comparisons of the grown tumors. On 3 L-R pairs of tumors we analyzed the DNA methylation using a long-read sequencing approach (Oxford Nanopore MinIon sequencer device) and performed a global methylation analysis focusing the posterior analysis on 228 of the 330 human genes with ICH function (according to HGNC). The results showed no L-R difference in the total amount of methylation ( Figure 6a ). Comparing the methylation levels in ICH of L relative to R tumors, 8 ICH genes were found to be significantly more methylated in L tumors ( Table 1 , Figure 6b ). By enrichment analyses these genes were found to be mainly associated with the biological process CELL COMMUNICATION BY ELECTRICAL COUPLING (Enrichment Ratio > 110, Figure 6c ). Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 6. Methylation differences between L-R xenograft tumors. a. The average global methylation in 3 L-R pairs of xenograft tumors, determined by Nanopore sequencing, showed no significant differences related to the tumor side (paired sample t-Test, p>0,05); b. Volcano plot representing methylation differences of L tumors compared to R-ones. Dots above the horizontal dot-line show genes with significant difference, either negative (less methylated in L) and positive (more methylated in L) fold changes are shown in green and red colored dots respectively, c-d. Biological processes with enrichment of genes more ( c ) and less ( d ) methylated in L tumors. Analysis performed with R-statistic, figures performed with Adobe Illustrator 2024. View this table: View inline View popup Table 1. Ion channel list of differentially methylated genes (p-value <0,05) comparing L-R xenografted tumors. Results are listed as more ( a ) and less ( b ) methylated L tumors, compared to R-ones. In humans, the CELL COMMUNICATION by ELECTRICAL COUPLING process is mainly determined by connexins, coded by Gap Junction genes (GJ), and the FLUID TRANSPORT is mainly regulated by Aquaporins (AQP). To validate the differential methylation observations with gene expression data, we explored by bioinformatics the genes involved in these 2 main GO-Biological processes found more methylated in L tumors. Selecting the genes involved in each process which were expressed in epithelial cells, we ended up with a signature composed of: AQP1, AQP7, AQP8, AQP11, GJA1, GJB1, GJB2, GJC2, GJD3 and GJA9, whose L-R expression level was tested in TCGA tumors with the Xena Browser Tool. A lower expression in L-sided IDC was found (Welch’s corrected t-Test, p=0,04), consistent with the increased methylated findings in L-xenograft tumors ( Figure 6b-c ). We further focused on GJ genes and evaluated the expression of selected connexins by qPCR in xenograft tumor tissue. Based on our results, on expression data from TCGA and on protein expression data from the protein Atlas ( https://www.proteinatlas.org/ ), we choose to test GJA1, GJA4, GJA5, GJB2, GJB3, and GJB6. Reproducible and analyzable expression levels were obtained for GJA1 and GJB2 , the two GJ genes prevalently expressed in breast tissue. The results showed a decreased expression of both in L-sided tumors as compared to R-sided ones ( Figure 7a , one-sample t-Test , p < 0,01). In concordance, by Nanopore Sequencing we also found for GJB2 an increased methylation level in L ( Figure 7b , paired t-Test , p < 0,05), although no methylation reads were seen in GJA1 . Download figure Open in new tab Figure 7. Gene Expression and Methylation of Connexins GJA1 and GJB2 in L-R-Sided Xenograft Tumors. Gene expression and methylation levels of GJA1 and GJB2 were analyzed in 3 pairs of L- and R-sided xenografted tumors. a and b: qPCR determination of GJA1 and GJB2 expressions show a reduction in L-sided tumors (one-sample t-Test, p<0,001, Mice 1, 5, 3.3 and Mice 1, 3, 4, 3.3 respectively); c: In the same samples, methylation was determined by Nanopore-Sequencing. GJB2 methylation was found increased in L-sided tumors (one-sample t-Test, p<0,05), consistent with its reduced expression. Nanopore-Sequencing assays for GJA1 failed and no methylation counts were detected. Data are presented as mean ± standard deviation (SD). Analysis performed with GraphPad Prism v5, figure performed with Adobe Illustrator 2024. Taken together, the results in this section establish the basis to suspect of a L-R differential cell communication. Specifically, L-tumors reveal to have less communicating gaps, which is known to affect tumoral intern communication as well as its communication with TME. 4. A Computational Tumor Model shows that weak Gap Junction coupling induces Depolarized and Methylated States as Stable Outcomes The empirical results align with a computational model developed independently to simulate the evolution of membrane potential and methylation in epithelial tissues. We simulated a 10 × 10 cell lattice over 100 ms (with Δt = 0,01 ms), starting from a uniform hyperpolarized state and a global methylation of ICH of 20% (Vmem≈ –60 mV, M = 0,2). Figure 8a maps each cell’s final Vmem in one representative run: nearly all cells return to the hyperpolarized attractor, with only occasional depolarized “islands”. When we repeat the simulation 1000 times ( Figure 8b ), 2 clear outcomes emerge - either the entire tissue remains hyperpolarized or the whole domain flips to depolarization-while mixed states are exceedingly rare. Looking at a single cell across those 1000 runs ( Figures 8c-d ), its membrane potential clusters tightly around two stable states: Vmem≈–55 mV with M≈0,25 (hyperpolarized), or Vmem≈0 mV with M≈1 (depolarized). This bi-stability reflects how small cell-to-cell properties variability (we sampled conductances from a Gaussian distribution) can occasionally tip a cell into depolarization, which then, in certain circumstances, propagates through Gap Junction coupling. Download figure Open in new tab Figure 8. Computer-model outputs demonstrating bistable membrane potential and methylation states in a 10×10 tissue. a . Final membrane potential (Vmem) for each cell in one simulation run. Cells are color-coded by Vmem (blue ≈ –60 mV; yellow ≈ −20 mV). Most remain hyperpolarized, with a solitary depolarized cell visible in the top-left corner (where it is less connected to neighbor cells). b . Heatmap of Vmem across all 1000 independent runs (rows) and all 100 cells (columns). Uniform blue bands indicate runs where every cell ended hyperpolarized; uniform orange lines indicate full-domain depolarization— mixed states (dashed lines) are rare, reflecting strong gap-junction coupling. c-d . Histograms for a single representative cell over 1000 runs. c . Final Vmem clusters tightly around two attractors (≈–55 mV and ≈0 mV). d. Corresponding methylation levels bifurcate into two stable values (≈0,25 and ≈1), with negligible occurrences of intermediate states. Figure performed with Adobe Illustrator 2024. To validate the model’s assumptions, we performed parameter sweeps ( Table 2 ). Decreasing the initial methylation from M = 0,2 toward 0 (hyperpolarization) sharply reduced the chance of global depolarization, whereas moving it toward M = 1 had the opposite effect. Likewise, increasing the depolarizing-channel conductance G0dep from 2,0 to 2,1 nS doubles the frequency of fully depolarized domains; by contrast, changing the polarization-channel conductance G0pol had little effect until it dropped to 0,9 nS, at which point depolarization again became more likely. Finally, weakening Gap Junction coupling (G0max from 1,5 to 1,3 nS) - mimicking the reduced junctional conductance seen in cancer cells-increased domain-wide depolarization. View this table: View inline View popup Table 2: Distribution of the number of depolarized cells at the end of a simulation run, for 1000 runs, with variations of different cells’ bioelectric parameters. Together, these results support our hypothesis that DNA methylation, by tuning ion-channel conductances, governs the membrane-potential bi-stability that underlie tissue-level L-R asymmetries in breast cancer. They also point to straightforward experimental tests: modulating Gap Junction conductance pharmacologically or genetically to probe tissue resilience or applying controlled electric fields to assess their impact on methylation-writer expression. A decrease in cell-cell communication via Gap Junctions, as seen in L-side breast cancers, increases the probability of cell depolarization and ICH methylation. DISCUSSION Breast cancer is rarely examined through the lens of L-R anatomy; however, our data demonstrates that tumors on opposite sides of the body inhabit distinct biological niches and follow divergent evolutionary paths. In a large cohort of IDCs from TCGA, we have shown here that R-sided tumors consistently contained more stroma than their L-sided counterparts, and this difference persisted even along tumor purity thresholds. Dissection of the stromal compartment showed a selective enrichment of CAFs, particularly the inflammatory iCAF subtype on the R, whereas dividing dCAFs predominated on the L. The asymmetry was reproduced in bilateral xenografts, where R tumors accumulated more α-SMA–positive fibroblasts. The CAFs in the TME have received growing attention in the last years. CAFs are a core component of the TME and interact not only with cancer cells but also orchestrate extracellular matrix remodeling 33 , secrete growth factors, modulate immune infiltration 28 , and play an important role in tumor progression. An increased iCAF burden in R-sided tumors may help establish a TME skewed toward chronic inflammation and tissue remodeling. This pro-inflammatory milieu could influence immune cell recruitment and activation, potentially affecting the response to immunotherapies. Notably, recent studies have shown that CAF subtypes, including iCAFs, can modulate the efficacy of immune checkpoint inhibitors targeting the PD-1/PD-L1 axis 28 , 33 . Further investigation is warranted to determine whether the lateralized CAF composition observed in breast tumors may contribute to differential immunotherapy outcomes. Conversely, L-sided tumors were enriched in dividing CAFs (dCAFs) ‒although to a lesser extent than iCAFs‒, consistent with our previous findings indicating that these tumors are more proliferative 6 . This suggests that dCAFs, rather than iCAFs, may play a more direct role in promoting tumor cell proliferation, potentially through secretion of growth factors, extracellular matrix remodeling, or metabolic support. The divergent stromal profiles of L- and R-sided tumors thus appear to reflect distinct biological programs, with implications for both tumor progression and therapeutic response. Functional readouts of these asymmetric TME came from bioelectric measurements. Conditioned-medium experiments demonstrated that extracts from L mammary glands drive membrane depolarization in breast cancer cells, whereas R-sided extracts preserve a more hyperpolarized potential. The same pattern emerged in vivo: paired xenograft tumors displayed lower MT/DB fluorescence ratios on the L, and this L-side depolarization approached the maximal depolarization elicited by KCl. The loss of the voltage asymmetry upon contralateral re-implantation suggests either a technical limitation (due to the use of intact tumor fragments not stratified by Vmem) or an inherent biological resistance to reprogramming. In any way, together, these experiments argue that the host gland dictates an early bioelectric set-point. Epigenetic profiling revealed a parallel layer of asymmetry: L tumors were hypermethylated at a subset of ICH genes, especially GJ–mediated cell communication. Reduced expression of the connexins GJA1 (Cx43) and GJB2 (Cx26) in L-sided xenografts, corroborated a possible functional silencing of electrical coupling. Because GJ distribute small ions and second messengers 34 , their down-regulation would effectively decouple L tumors from tissue-level voltage averaging, allowing depolarization to persist in the face of neighboring hyperpolarized cells. In contrast, R tumors retained connexin expression and a Vmem close to that of normal mammary tissue, consistent with the idea that intact cell communication confers electrical homeostasis. The computational model, developed independently of the empirical work, captures this logic: introducing small variations in depolarizing conductance or methylation pushes a virtual epithelial sheet toward one of two attractors—global hyperpolarization with low methylation or widespread depolarization with high methylation—and the outcome is strongly modulated by GJ coupling strength. Although the sample size for methylome profiling was limited (n=3 pairs), the consistency of the observed trends and their overlap with transcriptomic data support their biological relevance. The congruence between simulation and experiment suggests that bi-stability in the voltage–methylation circuit could underlie the observed L-R divergence. Mechanistically, several non-exclusive scenarios can explain how anatomical laterality translates into the stromal, electrical, and epigenetic asymmetries we document. Developmentally imprinted differences in lymphatic drainage 19 , vascular tone, or nervous innervation between L and R glands could influence fibroblast recruitment and activation, thereby shaping the cytokine milieu that cells perceive. CAF-derived soluble factors can further reinforce these loops: iCAFs secrete IL-6, CXCLs, and prostaglandins 32 capable of modulating both ICH expression and DNA methylation machinery, whereas dCAFs deliver proliferative cues that may exacerbate depolarization-induced cell-cycle entry on the L. Thus, cell communication ‒via GJ, paracrine signaling, and extracellular-matrix interactions‒ emerges as the common currency linking anatomical position to tumor phenotype. From a clinical standpoint, lateralized biology raises the possibility that prognostic markers and therapeutic vulnerabilities may differ between breasts. Our previous work showed that R tumors proliferate less rapidly, and the current data suggest this may stem from iCAF dominance and preserved electrical coupling ‒features reported to restrain tumor growth or prime immune response‒. Conversely, L tumors combine depolarization, connexin silencing, and dCAF enrichment, hallmarks associated with invasion and chemoresistance. Several limitations temper these conclusions, and the functional roles of iCAFs versus dCAFs in shaping voltage remain to be demonstrated experimentally. If confirmed in larger cohorts with matched survival data, laterality could inform risk stratification, guide surgical margin decisions, or motivate side-specific adjuvant strategies. In fact, emerging evidence indicates that immunotherapy efficacy may be influenced by the CAF composition within the TME 28 . If this is validated, the lateralized differences in CAF profiles observed in L-R breast tumors could offer a novel angle for patient stratification or therapeutic targeting. Moreover, such asymmetry might also relate to differential metastatic organotropism, adding a further layer of clinical relevance to tumor laterality. Furthermore, bioelectric modulation and epigenetic editing emerge as actionable levers. Agents that reopen GJ or hyperpolarize membrane potential might revert L tumors to a R-like, less aggressive state, while CRISPR-based demethylation of connexin promoters could restore electrical communication and sensitize tumors to voltage-targeting drugs. Beyond breast cancer, our findings prompt a reevaluation of paired organs in oncogenesis and support a broader view in which bioelectric and epigenetic landscapes are sculpted by positional information that persists beyond embryogenesis. In summary, we uncover a coherent network of stromal, electrical, and epigenetic asymmetries that segregate breast tumors according to laterality. R tumors inhabit a TME dominated by inflammatory fibroblasts and maintain GJ–mediated electrical homeostasis, whereas L tumors lose electrical coupling, become depolarized, and engage proliferative fibroblasts, with DNA methylation acting as a molecular lock on these states. This work establishes cell communication at the center of a lateralized tumor ecosystem and underscores the need for spatially resolved diagnostic and therapeutic strategies. MATERIALS and METHODS Extracts from human and bovine mammary tissue Healthy L-R human breast glands were obtained from plastic surgeries, provided by Dr. Cataneo from the Clinic of Plastic Surgery of Mendoza, after patients signed an informed consent previously approved by the Ethics Committee of the Medical School of the National University of Cuyo. Tissues were first disaggregated with a scalpel and the pieces were suspended in 25 mL of DMEM medium with Penicillin/Streptomycin 1% and incubated in a shaker for 24 h at 37 °C. Next, samples were centrifuged to remove solid fat, and the remaining suspension was filtered with cell strainers of first, 100 μm and afterwards 40 μm, to eliminate residual tissue parts. The obtained liquid phase extracts were L-R labeled and stored for further experiments at − 20 °C. L-R bovine udder samples were obtained from nulliparous heifers immediately after slaughter at the Frigorífico María del Carmen abattoir ( https://frigorificomariadelcarmen.com.ar/home/ , certified by SENASA). From the collected tissues, the udder was separated, sectioned, and stored in sterile containers at −20°C until processing. Tissues were mechanically disaggregated using surgical scissors or an Ultraturrax (high-speed rotor) until reaching a weight of 22 g. The material was then transferred to a 50 ml Falcon tube and mixed with 25 ml of DMEM/F-12 culture medium supplemented with 100 U/ml penicillin and 100 µg/ml streptomycin. The resulting extract was incubated at 37°C on a horizontal shaker for 24 hours. Subsequently, the solid and liquid components were separated using a cell strainer and cellulose acetate filters of 5 µm, 0,45 µm, and 0,22 µm pore sizes (the final filter step performed under a biosafety cabinet to ensure sterility). The resulting liquid-phase extract was stored at −20°C until use. Conditioned cell culture Human breast cancer cell line MDA-MB231 (ATCC, RRID:CVCL_0062) was obtained from the Tissue and cell bank from CIQUIBIC Institute (UNC-CONICET), Cordoba, Argentina, and passages up to 15 were used for this work. In general, cells were cultured in DMEM medium (Gibco by Life Technologies, Grand Island, NY, USA, # 112800-058) supplemented with 10% fetal bovine serum (Internegocios S.A, Mercedes, BA, Argentina), 100 U/mL of penicillin and 100 μg/mL streptomycin (Gibco by Life Technologies, Grand Island, NY, USA, #1796440), at 37 °C in a humidified atmosphere containing 5% CO2. For the extract-conditioned cultures, fetal bovine serum was reduced to 1%. MDA-MB-231 were conditioned with a cocktail of different proportions depending on the tissue origin: cocktails from human source consisted of 49% DMEM with Penicillin/Streptomycin, 1% Serum Fetal Bovine and 50% L/R liquid-phase extract; while cocktails from bovine source had 69% DMEM with Penicillin/Streptomicycin, 1% Serum Fetal Bovine and 30% L/R liquid-phase extract. Xenograft tumors All animal procedures were reviewed and approved by the CICUAL committee of the National University of Cuyo, Mendoza, Argentina. 8-10 weeks of age NodScidGama (NSG) female mice (Jackson Laboratory, Bar Harbor, ME, USA) were housed together in the same cage in an on-site housing facility, with access to standard food, water, and a 12/12 light cycle. For the xenograft assay, 10 6 MDA-MB231 cells were inoculated in the 4 th L-R mammary glands of each NSG mice. When the tumors developed and reached 1000mm 3 size, the mice were euthanized using CO2, and paired L-R breast tumors were excised. Tumors were fractionated into 5 1,5-2mm slides for measuring membrane potential by confocal microscopy and for DNA/RNA extraction. The patient-derivate xenograft (PDX) BC-AR474 model was kindly provided by Dr. Claudia Lanari (IBYME-CONICET, BsAs, Argentina) 35 . The PDX tumor was grown and maintained in NGS mice by fractionating and retransplanting 2mm 3 tumor portions into the L-R 4th mammary glands. When the PDX tumor reached 1000 mm 3 , the mice were sacrificed with CO₂ and the paired mammary tumors L-R were fixed with 4% formalin (ANOO6739PI, Anedra, BsAs, Argentina) 10% sucrose (Sigma Aldrich) for 24 h at 4 °C. Subsequently, the tumors were mounted in OCT compound (Cellpath 050226) and maintained at −20 °C until use. Immunofluorescence OCT-prefixed PDX tumors were sectioned at 20 µm wide using a microtome at −22°C and mounted on positively charged slides at room temperature. The tissue was permeabilized with 3% Triton 20 in 100% methanol, blocked with 5% fetal bovine serum in PBS, and incubated overnight with a 1/100 dilution of the primary antibody against alpha-smooth muscle actin (α-Sma, DACO M0851) in blocking solution. Subsequently, it was incubated for 3 h with 1/750 dilution of secondary antibody Goat Anti-Mouse IgG H&L (Alexa Fluor® 647, ab150115) in blocking solution, and the nucleus was stained for 10 minutes with HOETCH 33342 (Thermo Scientific 62249) at a 1/1000 dilution in PBS. Five images of each tumor were captured with a 20X objective and examined by fluorescence confocal microscopy (Olympus FV1000-EVA® confocal microscope). For the analysis, the total α-Sma signal were normalized to total HOETCH signal for each image capture, using Image J software analysis (ImageJ 1.53t/Java 1.8.0_322, National Institutes of Health, USA, RRID: SCR_003070). Paired values were analyzed using a two-tailed paired Student’s t-Test. Membrane potential analysis Membrane Potential Measured by Flow Cytometry As previously described by us 6 , MDA-MB-231 cells were plated and conditioned with L-R extracts for 5 days, trypsinized and then incubated for 30 min with 1 μM DiBAC4(3) (Bis-1,3-Dibutylbarbituric AcidTrimethine Oxonol, an Alexa-fluorescent probe negatively charged) (Invitrogen by Thermofisher Scientific, Cat. No. B438) and MTRed (an APC-fluorescent probe positively charged) at 37 °C and 5% CO2. Afterwards, cell fluorescence was measured by flow cytometry (FACSARIA-III, BD-Biosciences®) with a BP 530/30 emission filter. Results were analyzed using FlowJo v X.0.7® software (RRID:SCR_008520). To provoke maximum depolarization, cells were first treated with 65 mM KCl for 5 min at 37 °C and afterwards incubated for 30 min with the fluorescent probe DiBAC4(3) and MTRed as described above. For Vmem comparisons of cultured cells, 5000-10.000 events per sample were acquired by flow cytometry in each biological replicate. Mitotracker/DiBAC fluorescence ratios were normalized to the mean value of 65 mM KCl-treated cells. L vs R comparisons were performed using an unpaired two-tailed Student’s t-Test, with Welch’s correction applied when variances were unequal. Membrane Potential Measured by Confocal Microscopy Thin slices of the paired L-R 4th gland tumors were cut and placed in Petri dishes with DMEM FluoroBrite (Gibco, #A1896701, Life Technologies, Carlsbad, CA, USA) plus 2% FBS. For measuring membrane potential, 2–3 slices per tumor were used, incubating 15 min at 37 °C with media containing DB 2 mM and MT 500 nm in Fluorobrite DMEM. One slice was used to measure autofluorescence of the cells. Images were captured using a Leica Stellaris Sp8 confocal microscope at 10X magnification, at 37 °C and 3–5 images were taken per tumor, with 3 z-slices per image. As control for normalized measurements, cells were treated with a high concentrated KCl (65 mM) solution as depolarizing agent, for 15 min at 37 °C. Afterwards, slices were incubated with Fluorobrite DMEM containing DB (2 mM) and MT (500 nM). The images were processed using Image J software (ImageJ 1.53t/Java 1.8.0_322, National Institutes of Health, USA, RRID: SCR_003070). For Vmem comparisons in mouse xenograft tumors, confocal Z-stacks from three paired tumors were collapsed to summed-intensity projections. Mean fluorescence intensities were extracted using ImageJ v1.54, and inter-side comparisons were made using a paired two-tailed Student’s t-Test. DNA extraction Cells were collected and tissues were homogenized in PBS using an Ultra turrax homogenizer, following centrifugation, the pellet (cells or tissue) was suspended and washed 1 time with Tris–EDTA (T 10 E 10 Buffer), suspended in cetyltrimethylammonium bromide (CTAB) solution (2 g/l CTAB Sigma-Aldrich®, 100 mM Tris/HCl, 20 mM EDTA and 2% 2-mercaptoethanol) and incubated at 60°C for 1 h for cells or overnight for tissue. Then, chloroform-isoamyl alcohol solution (24:1) was added and the sample was centrifuged. The aqueous phase was collected into a new tube and mixed with 3 volumes of ice-cold 100% ethanol. Precipitated DNA was dissolved in T 10 E 0,1 Buffer. Further the samples were cleaned and purified using QIAamp DNA Micro Kit (Qiagen), according to manufacturer’s instructions. Bioinformatic analysis Clinical and RNA-seq data from 1096 breast primary tumors were acquired from TCGA dataset in Genomic Data Commons repository ( https://portal.gdc.cancer.gov/ ) using TCGAbiolinks R library. Samples were filtered by tumor type, yielding a final cohort of 784 invasive ductal carcinoma (IDC). These were further filtered to retain only those with an estimated consensus purity (CPE) value below 70%, obtained using the tumor.purity function from the TCGAbiolinks v2.30.4 package, resulting in a cohort of 276 samples. The cohorts of 784 and 276 samples, were then classified according to anatomical site (L-R). To assess the presence of stromal and immune cells, the xCell v1.1.0 R package was used, which assigns an immune score and stromal score based either on predefined gene signatures from 64 stromal and immune cell types or immune score signature and stromal signature only (gene list details in Supp Data1). Plots were modelled using ggplot2 v3.5.2 and ggpubr v0.6.0 packages. Data and tables were formatted and modelled using reshape2 v1.4.4, tidyr v1.3.1, dplyr v1.1.4 and stringR v1.5.1 packages. Normalization of RNAseq count by gene-length method and outliers’ analysis were performed with EDAseq package v2.36. Gene annotations were performed via biomaRt v2.58.2 package. Statistical analyses were performed with Rbase v4.3.2. All R scripts used for the analysis are available in the ‘Code Availability’ section. RNA extraction and qPCR gene expression analysis Total RNA from cells and xenograft tumors were extracted using a cell disruptor (Pro200 Proscientific), and TriPure Isolation Reagent (11667165001, Roche), following the manufacturer’s protocol. RNA concentration and purity were determined using a NanoDrop spectrophotometer (Labocon). One microgram of RNA was reverse-transcribed to cDNA using M-MLV Reverse Transcriptase (INBIO Highway). cDNA was diluted to 4 ng/μL and stored at −20 °C. Real-time PCR was conducted with 16 ng cDNA per reaction using the Master qPCR SYBR Green Kit (INBIO Highway). Primer sequences used were: GJA1 forward: 5’-TGAGTGCCTGAACTTGCCTT-3’, GJA1 reverse: 5’-GCCTGGGCACCACTCTTTT-3’; GJB2 forward: 5’-CTCCCGACGCAGAGCAAA-3’, GJB2 reverse: 5’-TCATCTCCCCACACCTCCTT-3’; and ACTB (β-actin) forward: 5’-TGACGTGGACATCCGCAAAG-3’, ACTB reverse: 5’-CTGGAAGGTGGACAGCGAGG-3’. The PCR reaction was performed in Aria Mx-Real time PCR system/Agylent technologies®, and the analysis was performed using AriaMx Software version 2.0 (Agilent, Santa Clara, CA, USA). Relative expression normalization of genes of interest was carried out using β-actin gene expression as endogenous reference control by the ΔΔCq method. Technical triplicates were averaged per sample. Expression ratios between L and R tumors for GJA1 and GJB2 were evaluated using a one-sample two-tailed t-Test against a theoretical ratio of 1. Methylome analysis Library preparation and sequencing ∼200 ng DNA samples from xenograft tumors conditions (L and R, 3 paired tumors) were used as input for Nanopore long-read sequencing. Duplicate libraries were prepared to obtain higher coverage. The libraries were prepared using the Rapid Barcoding Kit from Oxford Nanopore Technologies, following the manufacturer’s instructions. Briefly, this system employs transposome complexes that cleave the DNA and add barcodes to each sample. The samples are then pooled, and the library is purified using magnetic beads. Finally, sequencing-specific adapters are added. Sequencing was performed on a MinION Mk1C device for 48 hours at 37°C, with no real-time basecalling nor alignment, generating POD5 files for subsequent analysis. Raw data processing, global methylation and differential methylated region analysis POD5 files were processed for base calling using Dorado basecaller v0.9.1, including the identification of 5-methylcytosine and 5-hydroxymethylcytosine using the high-accuracy model (HAC). The resulting .bam files were aligned to the reference genome Hg38, obtained from the UCSC Genome Browser ( https://genome.ucsc.edu/ ), using Dorado aligner (minimap2 v2.27). Subsequently, individual .bam files were sorted and indexed, then the duplicated .bam files from each sequencing were accordingly merged, re-sorted and re-indexed using SamTools v1.13. Additionally, for further analyses, bedmethyl files containing the genomic coordinates of all modified cytosines were obtained using ModKit v0.4.4. For the bioinformatics analysis, the NanoMethViz 39 v2.8.1 package was initially used to generate a Tabix files containing the chromosomal coordinates of each detected cytosine modification and its Log-Likelihood Ratio (LLR), which indicates the probability of cytosine modification. For global methylation analysis the methy_to_bsseq() and bsseq_to_log_methy_ratio() functions were used to convert the LLR of each genomic position into a logarithmic methylation ratio (LMR). Prior to statistical analysis and plot generation, the log-methylation ratios were transformed into values ranging from 0 (unmethylated) to 1 (methylated) using a sigmoidal function: 1/1+e -LMR . For differential methylated region analysis (DMR) the methy_to_bsseq() and bsseq_to_edger() functions were used to convert the LLR of each genomic position into an edgeR methylation matrix. Coverage estimation, normalization, experimental design modelling, verification of experimental design, estimation dispersion and Likelihood test were performed using edgeR package v4.0.16. Plots were modelled using ggplot2 v3.5.2 and ggpubr v0.6.0 packages. Data and tables were formatted and modelled using reshape2 v1.4.4, tidyr v1.3.1, dplyr v1.1.4 and stringR v1.5.1 packages. Statistical analyses were performed with Rbase v4.3.2. All R scripts used for the analysis are available in the ‘Code Availability’ section. Computational modeling A simple computer model was developed and run to describe the L-R asymmetries found in breast cancer. It is a cellular automaton, an adaptation of previously published models, as in reference 36 . Cells can exchange ions with the extracellular medium by general depolarization and polarization ion channels, and the intercellular communication is described by a universal gap junction. The activity of each of these components depends on the membrane voltage, as will be shown in the following equations. The electrical potential (relative to the cell exterior) of cell i , V i , changes due to ionic currents (considering only the movement of positive ions). Its variation with time is determined by the ordinary differential equation, where C i is the cell membrane capacitance, I pol the current through polarization ion channels, I dep the current by depolarization ion channels, and G ij the conductance of ion transfer through gap junctions between contiguous cells i and j . The sum is done over all the neighbor cells, if present, considering a von Neumann neighborhood. The currents and conductances on the expression depend on the membrane electric potential as follows, with where E pol (E dep ) is the polarization (depolarization) membrane electric potential, V T the threshold potential, z the channel gating charge, G 0 pol (G 0 dep ) the polarization (depolarization) ion channels conductance, V 1/2 the value of electrical potential that reduces G i 0 by a factor 2, and V 0 adjusts the gap junction conductance function width 39 . The model parameters were determined, based on experimental results, and presented in reference 39 for a generic non-excitable cell; the values used in the current work are given in Table 3 . As previously shown, G ij describes the gap junction ion conductance between adjacent cells, representing the results of the serial association of cells i and j connexons conductances (G i 0 and G j 0 ). View this table: View inline View popup Download powerpoint Table 3: Values of the different cells’ bioelectric parameters used on the present model. σ=3 is used as the standard deviation on gaussian distributed values around the mean value (stochastic model). Cells are not exactly equal, and in this model, the main individual cell bioelectric parameters magnitude follows a Gaussian distribution. In Table 3 the second column presents the parameter mean value and the third column the respective standard deviation. The effect of an increase of DNA methylation observed in the 6 ion channel-signature described by us previously (the 6-ICH signature 7 ) of depolarized cells, is introduced in the following equation (Hill equation, with Hill coefficient n=2), where the conductance of the polarization channels (G 0 ) decreases with an increase on the level of ICH methylation. This is represented by the variable M , with a value between 0 and 1. The parameter K G gives the inverse of the value of methylation that decreases G 0 change rate by half of the reference value G 0 . τ gives the time scale of change in G 0 . An increase of M brings a decrease of the stable value of G 0 and then to a lower polarization current and a higher probability of cell depolarization. The effect of cell depolarization (increase on the value of membrane potential, Vmem) on the level of its DNA methylation is introduced by the following equation (like the previous one), where the methylation level ( M ) increases in time with the decrease of the absolute value of Vmem (depolarized cells have a less negative, or positive, value of membrane potential when compared with hyperpolarized ones). The parameter K M gives the inverse of the value of Vmem that decreases the change rate of M by 0.5. Again, τ M gives the time scale of change in M . A decrease of |Vmem|, due to cell depolarization, forces an increase on the stable value of M (an increment in ICH methylation). Both previous equations are solved for 100 ms, with time steps of Δt=0,01 ms. Statistics All statistical analyses were performed using R v4.3.2 (R Core Team, Vienna, Austria) and GraphPad Prism version 5.0.3 for Windows (GraphPad Software, San Diego, California, USA; www.graphpad.com ). A two-tailed p < 0.05 was considered statistically significant. Normality of continuous variables was assessed using the Shapiro–Wilk test. Parametric tests like Student’s t-Test were used when the data met assumptions of normality and equal variance; otherwise, non-parametric tests such as the Wilcoxon signed-rank test or Welch’s corrected t-Test were applied. For differential DNA methylation analysis, we used edgeR quasi-likelihood F-tests, and genes with FDR < 0,05 were considered significantly differentially methylated. Volcano plots display FDR values against log2 fold-change (L vs R). For each set of parameters of the computational model, we performed 1000 statistically independent simulation runs. Most of the model parameters values change in each run, following a gaussian distribution about a mean value. Data availability All data generated or analyzed during this study are included in this published article and its supplementary information files. Code availability All scripts used in this project are available in https://github.com/slaurito/EpigeneticsLandscapesInBreast . The matlab code of the computational model is available from the authors upon request. Author contributions MR conceived the study and designed the experiments. SR performed gene expression experiments, generated mice tumors and analysis by IHC and confocal microscopy on tissues. SL performed the R-based experiments and analyzed the data. OB proposed the idea of using bovine extracts and developed the protocols for PG who performed the conditioned cell culture experiments. JC developed the computational models and performed the data analysis and interpretation. MR, JC, SR and SL wrote the manuscript. Parts of the writing and language editing of this manuscript were assisted by the ChatGPT language model (OpenAI), under the supervision of the authors. All authors read and approved the final version. Funding PICT-2020-03959, National Agency of Scientific and Technical Promotion, Argentina. Portugal national funds by FCT - Fundação para a Ciência e Tecnologia, I.P. in the framework of the projects UIDB/04564/2020 and UIDP/04564/2020, with DOI identifiers 10.54499/UIDB/04564/2020 and 10.54499/UIDP/04564/2020, respectively. Competing interest All authors declare non-financial competing interests. Acknowledgements REFERENCES 1. ↵ Trapani , D. et al. Global challenges and policy solutions in breast cancer control . 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Share Bioelectric and Epigenetic Landscapes in Lateralized Breast Tumors Reveal Distinct Tumor Microenvironment Signatures Sebastián Real , Sergio Laurito , Pablo Gonzalez , Oscar Bello , Joao Carvalho , María Roqué bioRxiv 2025.06.30.662430; doi: https://doi.org/10.1101/2025.06.30.662430 Share This Article: Copy Citation Tools Bioelectric and Epigenetic Landscapes in Lateralized Breast Tumors Reveal Distinct Tumor Microenvironment Signatures Sebastián Real , Sergio Laurito , Pablo Gonzalez , Oscar Bello , Joao Carvalho , María Roqué bioRxiv 2025.06.30.662430; doi: https://doi.org/10.1101/2025.06.30.662430 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cancer Biology Subject Areas All Articles Animal Behavior and Cognition (7617) Biochemistry (17633) Bioengineering (13856) Bioinformatics (41841) Biophysics (21399) Cancer Biology (18529) Cell Biology (25422) Clinical Trials (138) Developmental Biology (13352) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24281) Genetics (15582) Genomics (22461) Immunology (17700) Microbiology (40295) Molecular Biology (17140) Neuroscience (88413) Paleontology (666) Pathology (2823) Pharmacology and Toxicology (4813) Physiology (7632) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4284) Systems Biology (9808) Zoology (2267)