Lipolysis of bone marrow adipocytes is required to fuel bone and the marrow niche during energy deficits

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Abstract

To investigate roles for bone marrow adipocyte (BMAd) lipolysis in bone homeostasis, we created a BMAd-specific Cre mouse model in which we knocked out adipose triglyceride lipase (ATGL, Pnpla2 gene). BMAd- Pnpla2 ^-/- mice have impaired BMAd lipolysis, and increased size and number of BMAds at baseline. Although energy from BMAd lipid stores is largely dispensable when mice are fed ad libitum, BMAd lipolysis is necessary to maintain myelopoiesis and bone mass under caloric restriction. BMAd-specific Pnpla2 deficiency compounds the effects of caloric restriction on loss of trabecular bone in male mice, likely due to impaired osteoblast expression of collagen genes and reduced osteoid synthesis. RNA sequencing analysis of bone marrow adipose tissue reveals that caloric restriction induces dramatic elevations in extracellular matrix organization and skeletal development genes, and energy from BMAd is required for these adaptations. BMAd-derived energy supply is also required for bone regeneration upon injury, and maintenance of bone mass with cold exposure.

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Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Author and article information

Contributors

Ormond A MacDougald:

ORCID: https://orcid.org/0000-0001-6907-7960

Mone Zaidi: Role: Reviewing Editor

Mone Zaidi: Role: Senior Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 22 June 2022

Publication date Collection: 2022

Volume: 11

Electronic Location Identifier: e78496

Affiliations

[1 ] University of Michigan Medical School, Department of Molecular & Integrative Physiology ( https://ror.org/00jmfr291) Ann Arbor United States

[2 ] University of Michigan Medical School, Department of Pediatrics ( https://ror.org/00jmfr291) Ann Arbor United States

[3 ] Department of Orthopedic Surgery, University of Michigan Medical School ( https://ror.org/00jmfr291) Ann Arbor United States

[4 ] Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine ( https://ror.org/059cjpv64) Hangzhou China

[5 ] Maine Medical Center Research Institute ( https://ror.org/03d1wq758) Scarborough United States

[6 ] University of Michigan Medical School, Department of Internal Medicine ( https://ror.org/00jmfr291) Ann Arbor United States

Icahn School of Medicine at Mount Sinai ( https://ror.org/04a9tmd77) United States

The University of Edinburgh ( https://ror.org/01nrxwf90) United Kingdom

Author information

Ziru Li https://orcid.org/0000-0002-2755-9299

Hui Yu https://orcid.org/0000-0001-5249-0193

Kanakadurga Singer https://orcid.org/0000-0001-8278-3800

Clifford J Rosen https://orcid.org/0000-0003-3436-8199

Ormond A MacDougald https://orcid.org/0000-0001-6907-7960

Article

Publisher ID: 78496

DOI: 10.7554/eLife.78496

PMC ID: 9273217

PubMed ID: 35731039

SO-VID: 73315f62-5fc8-433e-9b75-591c8dbaf5e8

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.