Biology subjects

Salas, L. A.

Publications and source records attributed to Salas, L. A..

17 recordsLinked to original sources

bioRxiv2026

Differential Transcript Usage Reveals Isoform-Level Remodeling of Tumor Biology in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by transcriptional reprogramming driven by hypoxia signaling, metabolic rewiring, and immune modulation. While gene-level analyses have defined key features of ccRCC biology, they do not capture isoform-level variation arising from alternative splicing. Differential transcript usage (DTU) represents an additional regulatory layer that may influence protein function, pathway activity, and clinical outcomes, yet its role in ccRCC biology and prognosis remains incompletely understood. We assessed differential expression in 127 ccRCC tumors and 33 normal-adjacent tissues from the Dartmouth Cancer Center cohort, with external validation in 94 CPTAC tumors, adjusting for cell-type proportions. DTU was identified using DRIMSeq/stageR, followed by limmavoom modeling with clinical and tumor microenvironment covariates. Transcript-based consensus clustering defined tumor subgroups, and Cox proportional hazards modeling integrated transcript-level features with clinical variables. In tumor versus normal comparisons, 1,170 transcripts exhibited significant differential usage, mapping to canonical ccRCC pathways with distinct patterns across functional and non-functional transcript classes. Consensus clustering based on transcript us-age identified two subgroups with distinct angiogenic profiles and significant survival differences. Cluster-level analysis revealed DTU in genes involved in cytoskeletal organization (ACTB), immune processes (B2M), extracellular matrix organization (FN1, APLP2), and iron metabolism (FTH1) with protein domain alterations, including the loss of actin-associated domains in ACTB and immunoglobulin-like domains in B2M. Prognostic modeling identified twelve transcripts consistently retained across bootstraps, improving risk stratification over clinical variables alone. External validation confirmed overlapping prognostic transcripts, including FGFR1 and NUCB1. Isoform-level features may serve as biomarkers and therapeutic targets in ccRCC. Statement of significanceTranscript-level analysis uncovers potential regulatory pathways in ccRCC missed by gene-level approaches, revealing isoform-specific alterations that define survival sub-groups and offer potential biomarkers and therapeutic targets.

cancer biology↗

bioRxiv2026

Transcriptomic subtypes in high-grade serous ovarian cancer are driven by tumor cellular composition

High-grade serous ovarian carcinoma (HGSC) is an aggressive malignancy for which bulk transcriptomic subtypes are used to stratify tumors, interpret biology, and guide biomarker development. The four TCGA-derived subtypes, mesenchymal (C1.MES), immunoreactive (C2.IMM), proliferative (C5.PRO), and differentiated (C4.DIF), are consistently observed across cohorts. However, despite their prominence, these subtypes have not translated into therapeutic utility, and their biological basis remains unresolved. Here, we show that HGSC transcriptomic subtypes are largely determined by tumor cellular composition rather than intrinsic malignant transcriptional programs. By integrating controlled single-cell-derived pseudobulk simulations with deconvolution-based analysis of 1,834 primary HGSC tumors across RNA-seq and microarray cohorts, we demonstrate that subtype probabilities align along a composition-driven axis of stromal and immune variation. Cellular composition alone predicted subtype labels with high accuracy (ROC-AUC = 0.81-0.95) and explained a substantial fraction of subtype-associated transcriptomic variation, with the mesenchymal (C1.MES) subtype representing the most robust and reproducible example of composition-driven signal. Although a secondary, composition-independent expression signal is detectable, it does not define the dominant structure of subtype classification. These findings redefine HGSC transcriptomic subtypes as features of the tumor ecosystem rather than discrete malignant states. This reinterpretation has immediate implications for studies that use subtype labels to infer tumor-intrinsic biology and provides a generalizable framework for separating composition-driven and intrinsic signals in bulk tumor data. Significance StatementHGSC transcriptomic subtypes lack consistent clinical utility and remain biologically ambiguous. We show subtype assignments are largely driven by tumor cellular composition, and less so by distinct intrinsic tumor states.

cancer biology↗

bioRxiv2026

Single-cell RNA sequencing of peripheral blood cells identifies transcriptomic signatures in Parkinson's disease

Parkinsons disease (PD) is a progressive age-related neurodegenerative disorder characterized by both motor and non-motor symptoms. The poorly understood prodromal period, decades-long progression, and disease-phenotype heterogeneity continue to impede the development of preventive and curative therapies. A growing appreciation of immune system changes during the progression of PD suggests that evaluating peripheral immune cells may help identify signatures relevant to disease etiology. We employed single-cell RNA sequencing to profile the transcriptomes of peripheral blood mononuclear cells (PBMCs) from a cohort of 12 patients with PD and 12 healthy controls, equally distributed by sex. Analysis identified gene expression signatures specific to immune cell lineages in PD when compared with healthy controls. Analysis of the dataset indicated that aspects of the PD-related changes were associated with sex, including metabolic and inflammatory changes. Further analysis of myeloid and T cell subsets identified additional pathways and gene expression profiles associated with PD. Trajectory analysis of the myeloid and T cell datasets indicated significant changes in the distribution of cells across states of gene expression in PD compared with controls. This work provides new evidence of peripheral immune cell changes in PD utilizing high-resolution transcriptomics in a cohort powered to analyze sex as a variable. HighlightsTranscriptomic dataset in a cohort powered to analyze immune phenotype in Parkinsons disease Parkinsons disease-specific gene expression signatures in peripheral immune cell lineages Identification of sex differences in the immune cell transcriptome in Parkinsons disease Trajectory analysis identifies changes in immune cell phenotypic distribution in Parkinsons monocytes

immunology↗

bioRxiv2025

Epithelial-Mesenchymal Transition is Associated with Altered Immune Composition and Cytotoxic Function in Triple-Negative Breast Cancer

Epithelial-mesenchymal transition (EMT) is a dynamic process that contributes to breast cancer progression, metastasis, and therapy resistance. EMT also influences the tumor immune microenvironment, shaping immune cell infiltration and function. Although the immunological features of fully epithelial or fully mesenchymal tumor states have been characterized, the immune landscape associated with intermediate or partial EMT states remains poorly understood. To address this gap, we established five single-cell-derived clonal populations from the triple-negative 4T1 mouse mammary tumor cell line, representing a spectrum of EMT phenotypes, and analyzed tumors derived from these clones using single-cell RNA sequencing. We show that tumors derived from these clones retained their relative EMT states in vivo, and that EMT progression is associated with a graded reduction in tumor immunogenicity, accompanied by alterations in immune cell composition and function. Along the EMT spectrum, tumor cells exhibited progressive downregulation of major histocompatibility complex (MHC) class I and II gene expression, along with decreased infiltration of cytotoxic CD8+ T cells, and reduced expression of key effector and trafficking genes (Gzmb, Ccr5, Cxcr6). B cell composition also shifted, with decreased frequencies of IgG1-producing plasma cells and an enrichment of regulatory-like B cells. Natural killer (NK) cells similarly demonstrated progressive functional suppression, marked by reduced expression of cytotoxic molecules and the downregulation of key effector pathways. These findings reveal that EMT is associated with immune cell recruitment and function in a graded manner, providing a rationale for integrating EMT phenotyping into therapeutic strategies to overcome immune resistance in breast cancer.

cancer biology↗

bioRxiv2025

Endothelial cell-specific DNA methylation alterations identified in breast cancer with epigenetic deconvolution

DNA methylation alterations are epigenetic modifications that regulate gene expression and contribute to carcinogenesis. Technical challenges and high costs limit the use of single-cell sequencing approaches to measure and understand cell-specific DNA methylation alterations in the tumor microenvironment (TME). Using cell type deconvolution (HiTIMED) and an interaction testing framework (CellDMC), we identified DNA methylation alterations in tumor endothelial cells (TECs) from measures in bulk biospecimens. Alterations in TECs contribute to abnormal angiogenesis, resulting in disorganized, leaky blood vessels that can hinder drug delivery and immune access. We hypothesize that TECs in the TME exhibit distinct DNA methylation patterns compared to normal endothelial cells, driving phenotypic changes that support tumor growth and vascular dysfunction. We used genome-scale DNA methylation data and a testing and validation set design (tumor, n=1,080; nontumor, n=415) to identify TEC-specific DNA methylation alterations in breast cancer. Between cohorts, we validated 4,562 TEC-specific CpGs (4,163 hypermethylated and 399 hypomethylated), and found that many map to genes regulating angiogenesis and endothelial function. Integration with mRNA data from tumor samples in both discovery and validation sets revealed genes whose expression correlates with TEC-specific DNA methylation patterns and endothelial cell proportions, highlighting potentially novel therapeutic targets. This study demonstrates the utility of cellular deconvolution to uncover cell lineage-specific epigenetic changes in cancer using bulk specimen, and identifies TEC-specific methylation alterations as novel potential targets for therapeutic intervention.

cancer biology↗

bioRxiv2024

The oncometabolite D-2-hydroxyglutarate promotes DNA hypermethylation at lineage-specific enhancers controlling microglial activation in IDHmut gliomas

Tumor-associated microglia and macrophages (TAMs), the most abundant myeloid populations in gliomas, shape immune responses through transcriptional programs influenced by the tumor microenvironment. Although these programs differ according to tumor IDH status, the underlying epigenetic mechanisms remain poorly understood. Here, we uncover widespread DNA hypermethylation in the myeloid compartment of IDH-mutant gliomas, predominantly at distal enhancers enriched for motifs of core microglial transcription factors (TFs). This remodeled enhancer landscape strongly correlated with reduced activity of TF regulons and coordinated repression of immunomodulatory programs that normally support microglial activation. Using primary human microglia, we show that prolonged exposure to the oncometabolite D-2-hydroxyglutarate (D-2HG) reduces TET activity and increases 5mC/5hmC ratios near TF-binding motifs within enhancers affected ex vivo. Consistent with these epigenetic alterations, D-2HG-treated microglia exhibited transcriptional signatures compatible with blunted proinflammatory responses, whereas pharmacological inhibition of mutant IDH in patients partially restored microglial immune reactivity. Altogether, our findings reveal a chronic D-2HG-driven epigenetic priming mechanism that promotes a hyporesponsive microglial state, providing a rationale for the immunologically cold phenotype of IDH-mutant gliomas and offering insight into how IDH-targeted therapies may reshape microglial immune responses.

cancer biology↗

bioRxiv2023

Molecular subtypes of high grade serous ovarian cancer across racial groups and gene expression platforms

IntroductionHigh-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals. MethodsWe included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. ResultsCluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]). ConclusionsA single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. Statement of SignificanceA single pipeline was used to subtype ovarian high-grade serous carcinoma (HGSC) with array-based or RNA-Seq gene expression data. Subtype distributions differed by race, but subtype-specific survival was similar across racial groups.

cancer biology↗

bioRxiv2023

Normal gastric tissue Helicobacter pylori infection is associated with epigenetic age acceleration, increased mitotic tick rate, tissue cell composition, and Natural Killer cell methylation alterations

BackgroundGastric adenocarcinomas are a leading cause of global mortality, associated with chronic infection with Helicobacter pylori. The mechanisms by which infection with H. pylori contributes to carcinogenesis are not well understood. Recent studies from subjects with and without gastric cancer have identified significant DNA methylation alterations in normal gastric mucosa associated with H. pylori infection and gastric cancer risk. Here we further investigated DNA methylation alterations in normal gastric mucosa in gastric cancer cases (n = 42) and control subjects (n = 42) with H. pylori infection data. We assessed tissue cell type composition, DNA methylation alterations within cell populations, epigenetic aging, and repetitive element methylation. ResultsIn normal gastric mucosa of both gastric cancer cases and control subjects, we observed increased epigenetic age acceleration associated with H. pylori infection. We also observed an increased mitotic tick rate associated with H. pylori infection in both gastric cancer cases and controls. Significant differences in immune cell populations associated with H. pylori infection in normal tissue from cancer cases and controls were identified using DNA methylation cell type deconvolution. We also found natural killer cell-specific methylation alterations in normal mucosa from gastric cancer patients with H. pylori infection. ConclusionsOur findings from normal gastric mucosa provide insight into underlying cellular composition and epigenetic aspects of H. pylori associated gastric cancer etiology.

cancer biology↗

bioRxiv2022

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50,000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially-resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected and preprocessed Visium spatial transcriptomics data (17,943 genes at up to 5,000 spots per patient sampled in a honeycomb pattern) from tissue across four stage-III matched colorectal cancer patients. We compare and prototype several convolutional, Transformer, and graph convolutional neural networks to predict spatial RNA patterns under the hypothesis that the transformer and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the models ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, results indicate that the transformer and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications.

pathology↗

bioRxiv2021

Development of Biologically Interpretable Multimodal Deep Learning Model for Cancer Prognosis Prediction

Robust cancer prognostication can enable more effective patient care and management, which may potentially improve health outcomes. Deep learning has proven to be a powerful tool to extract meaningful information from cancer patient data. In recent years it has displayed promise in quantifying prognostication by predicting patient risk. However, most current deep learning-based cancer prognosis prediction methods use only a single data source and miss out on learning from potentially rich relationships across modalities. Existing multimodal approaches are challenging to interpret in a biological or medical context, limiting real-world clinical integration as a trustworthy prognostic decision aid. Here, we developed a multimodal modeling approach that can integrate information from the central modalities of gene expression, DNA methylation, and histopathological imaging with clinical information for cancer prognosis prediction. Our multimodal modeling approach combines pathway and gene-based sparsely coded layers with patch-based graph convolutional networks to facilitate biological interpretation of the model results. We present a preliminary analysis that compares the potential applicability of combining all modalities to uni- or bi-modal approaches. Leveraging data from four cancer subtypes from the Cancer Genome Atlas, results demonstrate the encouraging performance of our multimodal approach (C-index=0.660 without clinical features; C-index=0.665 with clinical features) across four cancer subtypes versus unimodal approaches and existing state-of-the-art approaches. This work brings insight to the development of interpretable multimodal methods of applying AI to biomedical data and can potentially serve as a foundation for clinical implementations of such software. We plan to follow up this preliminary analysis with an in-depth exploration of factors to improve multimodal modeling approaches on an in-house dataset.

pathology↗

bioRxiv2021

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods{dagger} may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.

pathology↗

bioRxiv2021

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ naive and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.

bioinformatics↗

bioRxiv2020

Investigation of Capsule-Inspired Neural Network Approaches for DNA Methylation

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules - such as gene promoter context, CpG island relationship, or user-defined groupings - and relate them to diagnostic and prognostic outcomes. We demonstrate these models utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep learning analyses interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.

bioinformatics↗

bioRxiv2019

MethylNet: A Modular Deep Learning Approach to Methylation Prediction

BackgroundDNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. ResultsThe results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. ConclusionThe ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.

bioinformatics↗

bioRxiv2019

PyMethylProcess - highly parallelized preprocessing for DNA methylation array data

SummaryThe ability to perform high-throughput preprocessing of methylation array data is essential in large scale methylation studies. While R is a convenient language for methylation analyses, performing highly parallelized preprocessing using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. Here, we present a methylation data preprocessing pipeline called PyMethylProcess that is highly reproducible, scalable, and that can be quickly set-up and deployed through Docker and PIP.\n\nAvailability and ImplementationProject Name: PyMethylProcess\n\nProject Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI as pymethylprocess.\n\nAvailable on DockerHub via joshualevy44/pymethylprocess.\n\nHelp Documentation: https://christensen-lab-dartmouth.github.io/PyMethylProcess/\n\nOperating Systems: Linux, MacOS, Windows (Docker)\n\nProgramming Language: Python, R\n\nOther Requirements: Python 3.6, R 3.5.1, Docker (optional) License: MIT\n\nContactjoshua.j.levy.gr@dartmouth.edu

epidemiology↗

bioRxiv2019

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

BackgroundUmbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age appropriate cell-specific DNA-methylation profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450K and 850K EPIC arrays. These datasets are biologically and technically different, and currently there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution.\n\nResultsWe first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n=191) with matched FACS measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes.\n\nConclusionThese results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

bioinformatics↗

bioRxiv2017

Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: Findings from the Pregnancy and Childhood Epigenetics (PACE) consortium.

Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Epigenetic modifications such as DNA methylation could contribute, but data are scarce.\n\nWithin the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817-mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents.\n\nIn newborns, maternal BMI was associated with modest (<0.2% per BMI unit (1kg/m2), P<1.06*10-7) methylation variation at 9,044 sites throughout the genome. Adjustment for estimated cell proportions attenuated the number of significant CpGs to 104, including 86 sites common to the unadjusted model. These 86 sites map to several genes reported to be associated with adiposity-related and/or neuropsychiatric traits. At 72/86 sites, the direction of association was the same in newborns and adolescents, suggesting persistence of signals. However, we found evidence for a causal intrauterine effect of maternal BMI on newborn methylation at just 8/86 sites.\n\nIn conclusion, maternal adiposity is associated with modest variations in newborn blood DNA methylation, but the potential biological consequences of these variations are currently unclear.

epidemiology↗