bioRxivCell BiologyPreprint
A Multiomic Analysis of Cachectic Mice Reveals Cancer Driven Suppression of Muscle Stem Cell Differentiation
Darren M. BlackburniD, Aldo Hernández-CorchadoiD, Korin SahinyaniD, Hedieh Habibi Khorasani, Felicia Lazure, Vincent R. RichardiD, Dianbo QuiD, Shuo Wang, Christoph H. BorchersiD, Arezu Jahani‐AsliD, Hamed Shateri NajafabadiiD, Antonis E. KoromilasiD, Vahab D. SoleimaniiD
Abstract
Cancer cachexia affects a large proportion of cancer patients, inducing a rapid decline in muscle mass. Patients with cachexia have a worse prognosis and are less responsive to cancer therapies. The exact cause of cachexia remains unknown, nor are there any effective treatments for the condition. In this study, we use the C26 adenocarcinoma cell line to determine how cancer cells affect myofiber and muscle stem cell function. We determined that C26 cancer cells adapt to the host environment, in both male and female mice, greatly altering their transcriptome to promote their survival and growth. C26 cells directly communicate with muscle stem cells via GDF15 and MMP9. These circulatory factors cause the muscle stem cells to upregulate the EMT pathway and become less capable of undergoing differentiation and contributing to muscle regeneration. Muscle stem cells from tumor bearing mice are less proliferative and less prone to differentiation, Chromatin accessibility data shows that there are fewer accessible myogenic regulatory binding sites. Cytokine array determined that circulating GDF15 and MMP9 were highly upregulated and were derived form C26 tumor cells. However, blocking tumor derived GDF15 is not sufficient to prevent the onset of cachexia and rescue the loss of muscle stem cell function. Together, these findings establish a new conceptual paradigm in which cancer orchestrates muscle wasting through coordinated transcriptional, metabolic, and epigenetic suppression of muscle stem cell differentiation.
- organism: mouse
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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