TET3 plays a critical role in white adipose development and diet-induced remodeling

Jung, Byung Chul; You, Dongjoo; Lee, Ikjun; Li, Daofeng; Schill, Rebecca L; Ma, Katherine H. Y.; Pi, Anna; Song, Zehan; Mu, Wei-Chieh; Wang, Ting; Macdougald, Ormond A; Banks, Alexander S; Kang, Sona

doi:10.1016/j.celrep.2023.113196

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TET3 plays a critical role in white adipose development and diet-induced remodeling

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Author(s): Byung Chul Jung ¹ , Dongjoo You ¹ , Ikjun Lee ¹ , Daofeng Li ² ^, ³ , Rebecca L. Schill ⁴ , Katherine Ma ¹ , Anna Pi ¹ , Zehan Song ¹ , Wei-Chieh Mu ¹ , Ting Wang ² ^, ³ , Ormond A. MacDougald ⁴ , Alexander S. Banks ⁵ , Sona Kang ¹ ^, ⁶ ^, ^*

Publication date (Electronic): 30 September 2023

Journal: Cell reports

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SUMMARY

Maintaining healthy adipose tissue is crucial for metabolic health, requiring a deeper understanding of adipocyte development and response to high-calorie diets. This study highlights the importance of TET3 during white adipose tissue (WAT) development and expansion. Selective depletion of Tet3 in adipose precursor cells (APCs) reduces adipogenesis, protects against diet-induced adipose expansion, and enhances whole-body metabolism. Transcriptomic analysis of wild-type and Tet3 knockout (KO) APCs unveiled TET3 target genes, including Pparg and several genes linked to the extracellular matrix, pivotal for adipogenesis and remodeling. DNA methylation profiling and functional studies underscore the importance of DNA demethylation in gene regulation. Remarkably, targeted DNA demethylation at the Pparg promoter restored its transcription. In conclusion, TET3 significantly governs adipogenesis and diet-induced adipose expansion by regulating key target genes in APCs.

In brief

Jung et al. show that Tet3 plays critical role in white adipose tissue (WAT) development and expansion and that Tet3 ablation in adipocyte precursor cells leads to reduced adipogenesis and expansion, consequently contributing to improvement of metabolic health.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2019)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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HTSeq—a Python framework to work with high-throughput sequencing data

Simon Anders, Paul Pyl, Wolfgang Huber (2014)

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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Author and article information

Journal

Journal ID (nlm-journal-id): 101573691

Journal ID (pubmed-jr-id): 39703

Journal ID (nlm-ta): Cell Rep

Journal ID (iso-abbrev): Cell Rep

Title: Cell reports

ISSN (Electronic): 2211-1247

Publication date Nihms-submitted: 12 December 2023

Publication date (Print): 31 October 2023

Publication date (Electronic): 30 September 2023

Publication date PMC-release: 03 January 2024

Volume: 42

Issue: 10

Page: 113196

Affiliations

[1 ]Nutritional Sciences and Toxicology Department, University of California Berkeley, Berkeley, CA, USA

[2 ]Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

[3 ]The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA

[4 ]Department of Molecular & Integrative Physiology, University of Michigan School of Medicine, Ann Arbor, MO, USA

[5 ]Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

[6 ]Lead contact

Author notes

AUTHOR CONTRIBUTIONS

Conceptualization, S.K.; methodology, B.C.J. and S.K.; software, B.C.J., D.L., W.-C.M., T.W., O.A.M., and A.S.B.; validation, B.C.J., D.Y., and I.L.; investigation, B.C.J., D.Y., I.L., K.M., R.L.S., and A.P.; resources, D.L., R.L.S., Z.S., W.-C.M., T.W., O.A.M., and A.S.B.; writing – main text, S.K.; writing – review & proofreading, B.C.J. and S.K.; visualization, B.C.J. and D.Y.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K.

[* ]Correspondence: kangs@ 123456berkeley.edu

Article

Manuscript ID: NIHMS1941886

DOI: 10.1016/j.celrep.2023.113196

PMC ID: 10763978

PubMed ID: 37777963

SO-VID: 65d1e7b7-a5ce-4373-9e5f-bab363ab30fa

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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