2016
DOI: 10.1016/j.celrep.2016.08.090
|View full text |Cite
|
Sign up to set email alerts
|

The Homeobox Transcription Factor RHOX10 Drives Mouse Spermatogonial Stem Cell Establishment

Abstract: Summary The developmental origins of most adult stem cells are poorly understood. Here, we report the identification of a transcription factor—RHOX10—that is critical for the initial establishment of spermatogonial stem cells (SSCs). Conditional loss of the entire 33-gene X-linked homeobox gene cluster that includes Rhox10 causes progressive spermatogenic decline, a phenotype indistinguishable from that caused by loss of only Rhox10. We demonstrate that this phenotype results from dramatically reduced SSC gene… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

6
56
0

Year Published

2017
2017
2018
2018

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 53 publications
(62 citation statements)
references
References 56 publications
(107 reference statements)
6
56
0
Order By: Relevance
“…Thus the arrested mutant cells are similar to embryonic proSGs (McCarrey, 2013). This phenotype is similar in some respects to that of Rhox10 mutants, which also have defects in early postnatal germ cell migration and establishment of SSCs (Song et al, 2016). However the perinatal arrest phenotype is more severe in Dmrt1 mutants, suggesting either that Rhox10 acts downstream of Dmrt1 or that the two genes act at least partially in parallel.…”
Section: Dmrt1 and Male Germ Cell Developmentsupporting
confidence: 55%
“…Thus the arrested mutant cells are similar to embryonic proSGs (McCarrey, 2013). This phenotype is similar in some respects to that of Rhox10 mutants, which also have defects in early postnatal germ cell migration and establishment of SSCs (Song et al, 2016). However the perinatal arrest phenotype is more severe in Dmrt1 mutants, suggesting either that Rhox10 acts downstream of Dmrt1 or that the two genes act at least partially in parallel.…”
Section: Dmrt1 and Male Germ Cell Developmentsupporting
confidence: 55%
“…Surprisingly, the abundance of these mRNAs was not altered by either overexpression or down‐regulation of miR‐100 (Figure S1, Supporting Information). Conversely, expression of genes that were previously suggested to regulate SSC proliferation—such as Plzf (Promyelocytic leukemia zinc finger protein, or Zinc finger and BTB domain‐containing 16) (Costoya et al, ), Stat3 (Oatley, Kaucher, Avarbock, & Brinster, ), Gfra1 (GDNF receptor alpha 1) (Grasso et al, ), Setdb1 (SET domain bifurcated 1) (An et al, ), Fbxw7 (F‐box and WD repeat domain‐containing 7) (Kanatsu‐Shinohara, Onoyama, Nakayama, & Shinohara, ), Kdm1a (Lysine demethylase 1a) (Lambrot, Lafleur, & Kimmins, ), and Rhox10 (Rhox homeobox 10) (Song et al, )—was different among miR‐100 inhibitor‐, miR‐100 mimic‐, and untreated control SSCs. In particular, Stat3 abundance was negatively regulated by miR‐100 (Figure ).…”
Section: Resultsmentioning
confidence: 99%
“…Rhox homeobox 10)(Song et al, 2016)-was different among miR-100 inhibitor-, miR-100 mimic-, and untreated control SSCs. In particular,Stat3 abundance was negatively regulated by miR-100 (Figure 5).Indeed, STAT3 protein was significantly decreased in mouse SSCs transfected with miR-100 mimic compared to cells receiving the negative-control mimic, whereas STAT3 protein was significantly increased in SSCs with miR-100 inhibitor compared to the negative-control cells.…”
mentioning
confidence: 89%
“…For the first step, we tried to collect scRNA-seq data sets for mSSCs and mMSCs, which were generated by different research groups, from the GEO database. In this study, scRNA-seq data sets with accession number GSE82174, which was contained transcriptome data for twelve SSCs (WTpA1-WTpA12) from 7 days postnatal C57BL/6J mouse (Song et al, 2016), and GSE70930, which was contained transcriptome data from 16 mice (C57BL/6J) BM-derived MSCs (mMSC1-mMSC16) (Freeman, Jung, & Ogle, 2015) were used. To investigate the gene expression program in mSSCs in comparing with mMSCs, the transcriptome data for all cells were integrated as a count table and submitted to automated single-cell analysis pipeline (ASAP) as a webbased tool (Gardeux, David, Shajkofci, Schwalie, & Deplancke, 2017).…”
Section: Scrna-seq Data Set Preparation For Analysismentioning
confidence: 99%