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Adipose Tissue Retains an Epigenetic Memory of Obesity that Persists Weight Loss

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Adipose Tissue Retains an Epigenetic Memory of Obesity After Weight Loss

This respository contains code and files related to the publication: Hinte, L.C. et al. Adipose tissue retains an epigenetic memory of obesity after weight loss. Nature.

DOI

Abstract

Reducing body weight to improve metabolic health and other comorbidities is a primary goal in treating obesity. However, maintaining weight loss is a considerable challenge, especially as the body is believed to retain an obesogenic memory that defends against body weight changes. Yet, overcoming this hurdle to long-term effective treatment is difficult because the molecular mechanisms underpinning this phenomenon remain largely unknown. Here, by using single-nuclei RNA-sequencing, we show that both human and mouse adipose tissue retain cellular transcriptional changes after appreciable weight loss. Furthermore, we observed that the mouse adipocyte epigenome continues to bear obesity-induced alterations, negatively affecting adipocyte function. In mice, adipocytes carrying this obesogenic epigenetic memory respond differently to nutritional stimuli, resulting in accelerated rebound weight gain. We find that the epigenetic memory in mice can explain future transcriptional deregulation in response to further high-fat diet feeding. Together, our data suggests the existence of an obesogenic memory in mouse adipocytes, and likely other cells, largely based on stable epigenetic changes. These changes appear to prime cells to respond in a pathological manner to an obesogenic environment and may contribute to the problematic "yo-yo" effect on body weight observed with dieting. Targeting these changes could potentially improve long-term weight management and health outcomes.

Graphical Abstract

Apps to explore data

App to explore snRNAseq data, cell type specific gene expression analysis and epigenetic analysis

Stand alone human snRNAseq shiny app

Stand alone mouse snRNAseq shiny app

Accession Codes

GEO: GSE236580

Contents of this Repository

1. 📁 Rscripts

  :file_folder: Epigenetics

    :file_folder: 0_PreProcessingPeak QC, annotation, and quantification

    :file_folder: 1_MOFACut&Tag and ATACseq based multi-omics factor analysis

    :file_folder: 2_DifferentialAnalysisDifferential analysis of promoters, enhancers, alluvial plots, and GSEA

  :file_folder: TRAPContains Rscripts for analysis of TRAP-seq data and correlation with snRNAseq data

  :file_folder: snRNAseq_Mouse Contains Rscripts to analyse mouse epiAT snRNA-seq data and generate plots

    :file_folder: 0_PreProcessingQC and reference mapping

    :file_folder: 1_SampleIntegrationIntegration of snRNAseq datasets

    :file_folder: 2_DifferentialAnalysisCell-type specific DE and transcriptional retention analysis

  :file_folder: snRNAseq_Human Contains Rscripts to analyse human snRNA-seq data and generate plots

    :file_folder: 0_PreProcessingQC and reference mapping

    :file_folder: 1_SampleIntegrationIntegration of snRNAseq datasets

    :file_folder: 2_DifferentialAnalysisCell-type specific DE and transcriptional retention analysis

2. 📁 ChromHMM

  :file_folder: ChromHMMCommands and outputs of ChroHMM analysis (enhancers)

    :file_folder: tracksEnhancer bed files for adipocytes

    :file_folder: scriptsCommands for Cut&Tag based ChromHMM analysis

3. 📁 cellSNP_Vireo_demuxContains code to run SNP demultiplexing based on tools cellSNP and vireo

4. 📁 DEGsContains cell type specific DEGs for human and mouse AT (obese vs lean; weight loss vs lean)

5. 📁 SessionInfo

Associated Repositories

  1. NextFlow Pipeline for CUT&Tag
  2. NextFlow Pipeline for PeakCalling
  3. NextFlow Pipeline for RNAseq