1. Cell Biology
  2. Developmental Biology and Stem Cells
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Precise let-7 expression levels balance organ regeneration against tumor suppression

  1. Linwei Wu
  2. Liem H Nguyen
  3. Kejin Zhou
  4. T Yvanka de Soysa
  5. Lin Li
  6. Jason B Miller
  7. Jianmin Tian
  8. Joseph Locker
  9. Shuyuan Zhang
  10. Gen Shinoda
  11. Marc T Seligson
  12. Lauren R Zeitels
  13. Asha Acharya
  14. Sam C Wang
  15. Joshua T Mendell
  16. Xiaoshun He
  17. Jinsuke Nishino
  18. Sean J Morrison
  19. Daniel J Siegwart
  20. George Q Daley
  21. Ng Shyh-Chang
  22. Hao Zhu Is a corresponding author
  1. University of Texas Southwestern Medical Center, United States
  2. The First Affiliated Hospital of Sun Yat-Sen University, China
  3. Boston Children's Hospital and Dana Farber Cancer Institute, United States
  4. Harvard University, United States
  5. Harvard Medical School, United States
  6. Howard Hughes Medical Institute, Boston Children’s Hospital, United States
  7. University of Pittsburg, United States
  8. Genome Institute of Singapore, Singapore
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Cite as: eLife 2015;4:e09431 doi: 10.7554/eLife.09431

Abstract

The in vivo roles for even the most intensely studied microRNAs remain poorly defined. Here, analysis of mouse models revealed that let-7, a large and ancient microRNA family, performs tumor suppressive roles at the expense of regeneration. Too little or too much let-7 resulted in compromised protection against cancer or tissue damage, respectively. Modest let-7 overexpression abrogated MYC-driven liver cancer by antagonizing multiple let-7 sensitive oncogenes. However, the same level of overexpression blocked liver regeneration, while let-7 deletion enhanced it, demonstrating that distinct let-7 levels can mediate desirable phenotypes. let-7 dependent regeneration phenotypes resulted from influences on the insulin-PI3K-mTOR pathway. We found that chronic high-dose let-7 overexpression caused liver damage and degeneration, paradoxically leading to tumorigenesis. These dose-dependent roles for let-7 in tissue repair and tumorigenesis rationalize the tight regulation of this microRNA in development, and have important implications for let-7 based therapeutics.

https://doi.org/10.7554/eLife.09431.001

eLife digest

The development of animals is guided by the expression of certain genes at critical moments. Many different mechanisms control development; in one of them, the expression of genes can be decreased by molecules called microRNAs. In particular, the group of microRNAs called let-7 has been intensively studied in roundworms and fruit flies. Although mammals have extremely similar let-7 microRNAs they seem to be more important during adulthood.

Previous studies using cells grown in the laboratory have shown that mammalian let-7 microRNAs decrease cell proliferation and cell growth. Furthermore, in mouse models of various cancers, let-7 microRNAs often reduce tumour growth when they are supplied to adult mice. Therefore, overall the let-7 group has been classified as genes that act to suppress tumors, and thus protect mice (and most likely humans too) from cancers. However, in-depth analysis of let-7 microRNAs was still missing.

Wu and Nguyen et al. have now studied mice with liver cancer using strains where they were able to regulate the levels of let-7. These mice overproduce a strong cancer-inducing gene in the liver; half were used as controls and the other half were further engineered to have moderately elevated levels of let-7 expression. Most of the control mice got large cancerous tumors, but only a few mice in the other group developed cancers and the tumors were smaller. This confirmed that let-7 hinders tumor formation.

Wu and Nguyen et al. also observed that the protected mice were less able to regenerate their liver tissues. Further experiments showed that deleting just two out of ten let-7 microRNAs enhanced the mice’s ability to regenerate liver tissue after injury. These findings indicate that let-7 microRNAs slow down the growth of both cancerous and normal cells. Lastly, when let-7 levels were raised to very high levels for a prolonged amount of time this actually led to liver damage and subsequent tumor formation.

This last observation may have important consequences for possible cancer therapies. Some scientists have shown that providing extra let-7 can slow or even reverse tumour growth, but the findings here clearly point out that too much let-7 could actually worsen the situation. Since the let-7 family comprises a handful of microRNAs in mammals, in the future it will also be important to find out to what extent these molecules play overlapping roles and how much they differ.

https://doi.org/10.7554/eLife.09431.002

Introduction

MicroRNAs are thought to control cellular responses to stresses such as tissue damage and transformation (Leung and Sharp, 2010; Chivukula et al., 2014), but the impact of this idea is unclear because microRNAs have been understudied in vivo. let-7 is one of the most ancient and omnipresent microRNAs, yet relatively little is known about its functional roles in mammalian development and physiology. let-7 was first identified as a gene that regulates the timing of developmental milestones in a C. elegans screen (Reinhart et al., 2000). In mammals, mature let-7 is undetectable in early embryos and embryonic stem cells, but becomes highly expressed in most adult tissues (Schulman et al., 2005; Thomson et al., 2006). A handful of previous studies have implicated let-7 in body size regulation, metabolism, stem cell self-renewal, and colon carcinogenesis (Zhu et al., 2011; Frost and Olson, 2011Shyh-Chang, et al., 2013Nishino et al., 2013; Madison, et al., 2013, but the core functions of let-7 in regeneration and disease remain incompletely understood.

In addition to questions about what let-7 does, it is unknown why so many let-7s are expressed at such high levels. In mice and humans, the let-7 family is comprised of 10 to 12 members who are thought to share a common set of mRNA targets. It has been thought that deep redundancy might make it difficult to discern any phenotypes that individual let-7s might have. Essential unanswered questions regarding let-7 biology include whether let-7 members are redundant, have unique functions, or are regulated to maintain a specific total dose. Our previous study of Lin28a, which inhibits the biogenesis of each let-7 member similarly (Heo et al., 2008; Nam et al., 2011), suggests that total let-7 dose alterations, rather than regulation of specific members, is important. In transgenic mice, modest increase in Lin28a and consequent 40% suppression of total let-7 levels promote increased glucose uptake and an overgrowth syndrome (Zhu et al., 2010).

In this study we examined the consequences of let-7 dose disruption in cancer and organ regeneration in genetic mouse models. While let-7s have been implicated as a tumor suppressor, this has predominantly been shown in cell lines and xenograft assays (Guo et al., 2006; Chang et al., 2009; Iliopoulos et al., 2009; Viswanathan et al., 2009; Wang et al., 2010; Lan et al., 2011), as well as using exogenous let-7 delivery to mouse cancer models (Esquela-Kerscher et al., 2008; Trang et al., 2010; Trang et al., 2011). Here, we confirmed the tumor suppressor activity of an endogenous transgenic let-7 in a MYC-driven hepatoblastoma model. However, we found that this same level of let-7 overexpression impaired liver regeneration after partial hepatectomy (PHx). Furthermore, chronic high-dose let-7 resulted in severe liver damage and paradoxical liver cancer development. Overall, we provide in vivo evidence that let-7 expression levels have been developmentally constrained to balance the need for regenerative proliferation against the need to antagonize malignant proliferation, findings with implications for let-7 based therapies.

Results

let-7g inhibits the development of MYC-driven hepatoblastoma

To study the effect of let-7 on carcinogenesis, we employed an inducible MYC-driven hepatoblastoma model (Shachaf et al., 2004). In this model, most let-7s are suppressed by more than 60% (Nguyen et al., 2014). However, MYC affects the expression of many other microRNAs (Chang et al., 2009; Kota et al., 2009). To test if let-7 suppression is specifically required for MYC’s oncogenic program, we simultaneously overexpressed let-7g and MYC using a triple transgenic, liver-specific, tet-off model (Figure 1A: LAP-tTA; TRE-MYC; TRE-let-7S21L transgenic mice). This transgenic form of let-7g is an engineered let-7 species called let-7S21L (let-7g Stem + miR-21 Loop) (Zhu et al., 2011), in which the precursor microRNA loop derives from mir-21 and cannot be bound and inhibited by Lin28b (Figure 1B), which is highly expressed in MYC-driven tumors (Chang et al., 2009; Nguyen et al., 2014).

Figure 1 with 1 supplement see all
let-7g inhibits the development of MYC-driven hepatoblastoma.

(A) Schema of the liver-specific inducible LAP-MYC +/- let-7S21L cancer model. (Blet-7S21L is a chimeric construct containing the let-7g stem, miR-21 loop, and let-7g flanking sequences. (C) Schema showing that LAP-MYC +/- let-7S21L mice were induced at 14 days of age, tissues were collected at 90 days of age, and survival was followed. (D) Ninety-day old mice bearing tumors in the LAP-MYC (87.5%, 7/8) and LAP-MYC + let-7S21L (27.3%, 3/11) mouse models. (E) Livers showing tumors from the above mice. (F) Liver surface area occupied by tumor. (G) Kaplan-Meier curve of LAP-MYC alone and LAP-MYC + let-7S21L mice. (H) Mature let-7 expression levels in as determined by RT-qPCR. (I) Human c-MYC mRNA expressionin tumors as determined by RT-qPCR. (J) Heat map of let-7 target gene expression in WT normal livers, MYC tumors, and MYC + let-7S21L tumors as measured by RT-qPCR. Red is higher and blue is lower relative mRNA expression. (K) Gene expression plotted as bar graphs to show relative changes. (L) Evolutionarily conserved let-7 target sites within 3’UTRs (Targetscan.org). All data in this figure are represented as mean ± SEM. *p < 0.05, **p < 0.01.

https://doi.org/10.7554/eLife.09431.003

Induction of MYC with or without let-7S21L was initiated at 14 days of age (Figure 1C). By 90 days of age, large multifocal tumors had formed in 88% of the MYC alone group, whereas single small tumors appeared in only 27% the MYC + let-7S21L group (Figure 1D–F) and overall survival was dramatically improved (Figure 1G). The level of let-7g was increased more than eightfold in both non-tumor and tumor tissues (Figure 1H). Tumors from both groups showed similar histology (Figure 1—figure supplement 1) and MYC expression (Figure 1I). Gene-expression within tumors showed that previously validated let-7 targets involved in proliferation and growth including Cdc25a (Johnson et al., 2007), Cdc34 (Legesse-Miller et al., 2009), E2f2 (Dong et al., 2010), E2f5 (Kropp et al., 2015), and Map4k4 (Tan et al., 2015) were upregulated in MYC-tumors, but suppressed back down to normal levels in the context of let-7 overexpression (Figure 1J–L), suggesting that the repression of these targets restrains MYC-dependent tumorigenesis. These data indicated that let-7g has potent tumor suppressor activity in the context of MYC-driven hepatoblastoma.

let-7g overexpression inhibits liver regeneration after partial hepatectomy

Since increasing let-7g was extremely effective at suppressing hepatoblastoma without compromising overall health, we asked if this increase in levels would impact tissue homestasis. We examined let-7g overexpression in the setting of liver injuries that drive rapid proliferation and growth. After PHx, let-7s in regenerating tissues fell, but returned to normal after forty hours (Figure 2A), findings consistent with a previous report (Chen et al., 2011). Similarly, let-7s also declined acutely after chemical injury with the xenobiotic TCPOBOP (1,4-bis-[2-[3,5-dichloropyridyloxy]] benzene) (Figure 2—figure supplement 1A). This shows that while let-7 increases in a temporally defined fashion during development (Figure 2—figure supplement 1B), it can transiently fluctuate after environmental perturbations. To test if the observed let-7 suppression is necessary for regeneration, we induced let-7g in LAP-let-7S21L mice and performed PHx (Figure 2B–D). The body weight (Figure 2—figure supplement 1C), liver function (Figure 2—figure supplement 1D), resected liver mass (Figure 2E) and histology (Figure 2—figure supplement 1E) were unaffected in LAP-let-7S21L mice compared to control mice. Forty hours after PHx, there was reduced liver mass and decreased Ki-67 in LAP-let-7S21L mice (Figure 2F–H). Liver mass was no different at four and fourteen days, indicating a kinetic delay but not a permanent impairment (Figure 2—figure supplement 1F,G).

Figure 2 with 1 supplement see all
let-7g overexpression inhibits liver regeneration after partial hepatectomy.

(A) Mature endogenous let-7 expression levels in WT C57Bl/6 mice at different time points after PHx as determined by RT-qPCR (n=4 and 4 for each time point). (B) Schema of the LAP-let-7S21L dox-inducible model. LAP-tTA single transgenic mice served as the controls. (C) Schema showing that let-7S21L control and LAP-let-7S21L mice were induced at 42 days of age, PHx was performed after 14 days of induction, and tissues were collected 40 hr post PHx. (D) Mature let-7 expression levels in let-7S21L and LAP-let-7S21L livers after 14 days of induction (n=4 and 4). (E) Resected liver/body weight ratios of LAP-tTA Control and LAP-let-7S21L mice at the time of PHx (n=4 and 4). (F) Liver/body weight ratios 40 hr after PHx (n=4 and 4). (G) Ki-67 staining on resected and post-PHx liver tissues. (H) Quantification of Ki-67-positive cells (n=2 and 2 mice; ten 20x fields for each mouse were quantified). (I) Resected liver/body weight ratios 2 days after intravenous injection of 0.5 mg/kg negative control or let-7g microRNA mimics packaged in C12-200 LNPs (n=5 and 5). (J) Liver/body weight ratios 40 hr after PHx (n=4 and 4). (K) Mature let-7g expression levels in mimic treated livers (n=5 and 5). All data in this figure are represented as mean ± SEM. *p<0.05, **p<0.01.

https://doi.org/10.7554/eLife.09431.005

To rule out increasing demands on microRNA biogenesis machinery as a mechanism of proliferative suppression, we delivered mature control or let-7g microRNA mimics (0.5 mg/kg) into wild-type mice two days prior to hepatectomy using C12-200 lipidoid nanoparticles (LNPs) (Love et al., 2010). let-7g, but not control mimics, inhibited regeneration (Figure 2I–K). While let-7 overexpression blocked MYC-induced tumorigenesis, these data show that a similar increase in let-7 levels inhibited post-injury organ growth and regeneration.

Loss of let-7b and let-7c2 is sufficient to enhance liver regeneration

To assess the physiological relevance of our gain-of-function experiments, we examined knockout mice to determine if let-7 is a bona fide regeneration suppressor. Both let-7b and let-7c2 were conditionally deleted from the liver by crossing Albumin-Cre into a let-7b/c2 floxed mouse ("let-7b/c2 LKO" mice, Figure 3A). Small RNA-sequencing data from Xie et al. showed that let-7 is one of the most highly expressed microRNA families in the liver and that let-7b and let-7c2 together comprise approximately 18% of the let-7 total (Figure 3B) (Xie et al., 2012). Thus, let-7b/c2 LKO mice have substantial, but far from a complete reduction of total let-7 levels.

Figure 3 with 2 supplements see all
Loss of let-7b and let-7c2 is sufficient to enhance liver regeneration.

(A) Schema of liver-specific let-7b and let-7c2 knockout mice (let-7b/c2 LKO). Albumin-Cre excises loxPs in the embryonic liver of let-7b/c2Fl/Fl mice. Mice without Cre serve as the controls. (B) Small RNA sequencing showing the distribution of 10 let-7s in WT mice (n=2) (Data obtained from Xie et al. 2012). (C) Schema showing that PHx was performed on let-7b/c2Fl/Fl and let-7b/c2 LKO mice at 56 days of age and tissues were collected 40 hr post PHx. (D) Resected liver/body weight ratios at the time of PHx, and (E) Liver to body ratios of let-7b/c2Fl/Fl (n=11) and let-7b/c2 LKO mice (n=10) 40 hr after PHx. (F) Ki-67 staining and (G) Quantification of Ki-67-positive cells on resected and 40 hr post-PHx liver tissues (n=3 and 3 mice; total of five 40x fields/mouse were used for quantification). (H) RT-qPCR on let-7 family members from let-7b/c2Fl/Fl and let-7b/c2 LKO mice pre- and 40 hr post-PHx. (I) Viability of H2.35 immortalized human hepatocytes treated with either scrambled, let-7a, or let-7b antiMiRs, measured at two and three days after transfection (n=10 each). All data in this figure are represented as mean ± SEM. *p<0.05, **p<0.01.

https://doi.org/10.7554/eLife.09431.007

These LKO mice were healthy and showed normal liver/body weight ratios and histology at baseline (Figure 3—figure supplement 1A,B). An identical amount of liver mass was resected from let-7b/c2Fl/Fl control and let-7b/c2 LKO mice (Figure 3C,D), but LKO mice exhibited significant increases in liver mass and proliferation 40 hr after surgery (Figure 3E–G). Four and seven days after PHx, there were no differences in liver weights, indicating that other phases of regeneration were unaffected (Figure 3—figure supplement 1C,D). At fourteen days, the liver weight precisely achieved pre-surgery levels in control and LKO mice, indicating accelerated but not excessive regeneration (Figure 3—figure supplement 1C,D). There was no compensatory upregulation of other let-7s in pre- or post-PHx tissues (Figure 3H), supporting the concept that let-7 is a dose-dependent regeneration suppressor.

Cre under the Albumin promoter is expressed in embryonic hepatoblasts that give rise to both hepatocyte and bile duct compartments (Postic and Magnuson, 1999, 2000; Xu et al., 2006; Weisend et al., 2009; Malato et al., 2011), so developmental influences of let-7 loss could have led to adult phenotypes. To define cell- and temporal-specific roles of let-7b/c2, we used adeno-associated virus expressing Cre (AAV8.TBG.PI.Cre.rBG, hereafter called "AAV-Cre"), known to mediate efficient gene excision in hepatocytes but never in biliary epithelial cells (Yanger et al., 2013) (Figure 3—figure supplement 2A,B). These adult and hepatocyte-specific conditional knockout mice also exhibited significantly enhanced regenerative capacity (Figure 3—figure supplement 2C–F). To test if proliferative effects are specific to particular let-7 species, we knocked-down either let-7a or let-7b in SV40 immortalized hepatocytes (H2.35 cells) and found that both led to increased proliferation (Figure 3I). Collectively, our data shows that physiological let-7 levels regulate the kinetics of adult liver regeneration by hepatocytes.

let-7g suppresses liver regeneration through insulin-PI3K-mTOR

let-7 was previously demonstrated to regulate the insulin-PI3K-mTOR pathway (Zhu et al., 2011; Frost and Olson, 2011), which is also important in liver regeneration (Okano et al., 2003; Chen et al., 2009; Haga et al., 2009; Espeillac et al., 2011). To avoid auto-regulatory feedback and compensation as confounding factors, we focused on liver tissues exposed to acute let-7 gain or loss. In regenerating livers treated with let-7g mimic (Figure 2I–K), we found significant protein suppression of insulin receptor β, Igf1rβ, and Irs2, previously validated let-7 targets at the top of the insulin pathway (Figure 4A,B) (Zhu et al., 2011). In addition to insulin signaling components, the expression of cell cycle genes (Ccnb1, Cdc34, and Cdk8) and Map4k4 were also downregulated (Figure 4C). In mice with acute let-7b/c2 deletion by AAV-Cre (Figure 3—figure supplement 2), there was a small increase in insulin receptor β protein levels (Figure 4D,E). Increased mTOR signaling was also evident in the increased phospho-S6K/Total S6K and phospho-S6/Total S6 ratios (Figure 5E).

let-7g suppresses liver regeneration through insulin-PI3K-mTOR.

(A) Western blots of insulin receptor β, Igf1rβ, Irs2, and β-Actin in negative control or let-7g microRNA mimic treated liver tissues 40 hr after PHx. (B) Quantification of intensity of insulin receptor β, Igf1rβ, Irs2 (Image J). (C) Cell cycle gene expression in let-7S21L alone (n=4) and LAP-let-7S21L (n=4) livers before and 40 hr after PHx as determined by RT-qPCR. (D) Western blots of insulin receptor β, Igf1rβ, p-S6K, total S6K, β-Actin, p-S6 (Ser235/236), and total S6 in AAV-Cre treated let-7b/c2 +/+ and let-7b/c2Fl/Fl livers (n=5 and 5). (E) Quantification of intensity of insulin receptor β/β-Actin, p-S6K/total S6K, and p-S6/total S6, 40 hr after PHx (Image J). (F) Rapamycin treatment during and after PHx in let-7b/c2Fl/Fl control and let-7b/c2 LKO mice. Shown are liver weights 40 hr post PHx. (G) INK128 treatment during and after PHx in let-7b/c2Fl/Fl control and let-7b/c2 LKO mice. Shown are liver weights 40 hr post PHx. (H) Western blots of p-S6K, total S6K, and β-Actin in let-7b/c2Fl/Fl control and let-7b/c2 LKO livers treated with either vehicle or INK128 at 40 hr post PHx. All data in this figure are represented as mean ± SEM. *p<0.05, **p<0.01.

https://doi.org/10.7554/eLife.09431.010
Figure 5 with 1 supplement see all
Chronic high-dose let-7g causes hepatotoxicity and liver carcinogenesis.

(A) Schema showing that let-7S21L control and Rosa-rtTA; let-7S21L mice were induced at 42 days of age and collected at 84 days. (B) Images showing the whole body, extremities, and livers of Rosa-rtTA (n=4) and Rosa-let-7S21L mice (n=3) given 1 mg/mL dox between 42 and 84 days of age. (C) Liver function tests: AST (U/L), ALT (U/L), and total bilirubin (mg/L) in these mice. (D) H&E; staining of livers. (E) RT-qPCR of mature let-7s and other microRNAs in let-7g overexpressing mice (n=4 and 3). (FBody weight 3 days after injection of 2.0 mg/kg negative control or let-7g microRNA mimics packaged in C12-200 LNPs relative to pre-injection weight (n=5 and 4). (G) Liver function tests: AST (U/L) and ALT (U/L) in WT C57Bl/6 mice before and 3 days after mimic injection (n=5 and 4). (H) Mature let-7 levels in wild-type C57Bl/6 mice treated with mimics as determined by RT-qPCR (n=5 and 4). (I) Kaplan-Meier curve for Rosa-let-7S21L induced with 1.0 g/L dox at 6 weeks old (n=15 and 17). (J) Gross images of the liver of Rosa-let-7S21L mice induced for 18 months. All data in this figure are represented as mean ± SEM. *p<0.05, **p<0.01.

https://doi.org/10.7554/eLife.09431.011

To determine if mTOR signaling is functionally relevant in LKO mice, we treated mice with rapamycin two hours prior to and immediately after PHx. Rapamycin abrogated differences in regenerating liver weights between control and LKO mice (Figure 4F), demonstrating that let-7b/c2 loss promotes additional mTOR activation to enhance regeneration. Rapamycin’s allosteric inhibition of mTOR can lead to pleiotropic and unpredictable effects due to cell-type specific and feedback related phenomena (Thoreen et al., 2009). INK128 is a second generation mTOR inhibitor that directly competes with ATP at the catalytic domains of mTORC1/2, leading to more complete abrogation of 4EBP and S6K1 (Hsieh et al., 2012). INK128, similar to rapamycin, completely abrogated the regenerative enhancement associated with let-7b/c2 loss (Figure 4G). Analysis of p-S6K confirmed that mTOR is hyperactivated in LKO livers and that INK128 extinguishes the mTOR dependent phosphorylation of this substrate (Figure 4H). Similar results after rapamycin and INK128 indicated that mTOR and its substrates play an essential role in driving increased regeneration in the context of let-7 suppression.

Chronic high-dose let-7g causes hepatotoxicity and liver carcinogenesis

Since acute let-7g induction interferes with hepatocyte proliferation, we asked what the effects of chronic high-dose let-7g induction might be. To answer this question, we induced let-7g using rtTA under the control of the Rosa promoter, which drives higher expression than the LAP promoter (Figure 5A). These mice lost significant body weight and became jaundiced (Figure 5—figure supplement 1A and Figure 5). Liver function tests indicated severe liver injury (Figure 5C) and histology showed prominent microvesicular steatosis, characterized by intra-cytoplasmic lipid droplets (Figure 5D), a finding associated with drug-induced liver injury, acute fatty liver of pregnancy, or Reye’s syndrome in humans. Using this system, mature let-7g was overexpressed by more than twentyfold, as compared to ∼eightfold induction in the LAP-let-7S21L system (Figure 5E). Liver dysfunction was not seen after low-dose let-7 overexpression in LAP-let-7S21L mice (Figure 2—figure supplement 1) or after high-dose miR-26a-2 overexpression in Rosa-rtTA; TRE-miR-26a-2 mice (Figure 5—figure supplement 1B–D), suggesting a dose and let-7 microRNA specific effect.

Another possibility was that the miR-21 loop of the let-7S21L construct saturated the microRNA biogenesis machinery, thus causing non-specific toxicity independent of the let-7 seed sequence. To address this we again delivered a higher dose (2.0 mg/kg) of mature let-7g and control microRNA mimics, which do not harbor loops or tails, into wild-type mice. Mice receiving let-7g mimic lost significantly more weight (Figure 5F) and suffered hepatocyte destruction leading to increased AST/ALT levels (Figure 5G), while control mice remained healthy. Mimic delivery achieved a twelvefold increase of let-7g (Figure 5H). These results suggest that above certain doses let-7 is incompatible with hepatocyte survival, and that let-7’s anti-proliferative activities would interfere with normal tissue homeostasis.

When the Rosa-let-7S21L mice were induced chronically, approximately 50% of the mice survived the acute liver injury seen after dox induction (Figure 5I). Over the course of 18 months, 5 of 10 (50%) of these surviving mice developed large liver tumors, whereas only 1 of 12 (7.7%) of the non-induced mice had any tumors (Figure 5J). Chronic let-7 overexpression likely caused hepatocyte toxicity and selected for pre-malignant hepatocytes that eventually become cancer. Our long-term experiments revealed the potential dangers of chronic let-7 treatment, and the consequent disruption of the balance between tissue regeneration, degeneration, and cancer risk.

Discussion

The role of let-7s in adult animal physiology is unclear in part because the redundancy of this large microRNA family has made loss of function studies challenging. Deep redundancy of multiple highly conserved genes raises the possibility that dose regulation is important. Despite this, overexpression has been helpful in uncovering physiological functions of let-7 (Zhu et al., 2011; Frost and Olson, 2011; Shyh-Chang et al., 2013Nishino et al., 2013; Madison et al., 2013). Using overexpression tools, we have shown that let-7 suppression is a fundamental requirement for MYC-mediated liver transformation, and that let-7 is capable of counteracting strong oncogenic drivers in vivo. However, one negative consequence of raising the level of let-7 expression is a limitation in the ability to regenerate after major tissue loss. More surprisingly, our knockout mouse model showed that the loss of two out of the ten let-7 members in the liver resulted in improved liver proliferation and regeneration. These data suggest a lack of complete redundancy between let-7 microRNA species, but rather a precisely regulated cumulative dose that when increased or decreased, leads to significant alterations in regenerative capacity.

The knowledge that let-7 suppresses both normal and malignant growth will have particular relevance to malignancies that arise from chronically injured tissues. In these tissues, winners of the competition between cancer and host cells might ultimately dictate whether organ failure or tumor progression ensues. It has been thought that one key advantage of using microRNAs therapeutically is that they are already expressed at high levels in normal tissues, thus making increased dosing likely to be safe and tolerable. Surprisingly, we showed that chronic let-7 overexpression caused hepatotoxicity, disrupted tissue homeostasis, ultimately leading to carcinogenesis. This is likely due to the high levels of overexpression achieved in the Rosa-rtTA transgenic system, as opposed to the lower dose in the LAP-tTA system. These high doses are likely to be toxic to hepatocytes, a phenomena compounded by the fact that excess let-7 impairs proliferation in surviving cells that might serve to replenish lost tissues. Eventually, certain clones must epigenetically or genetically evolve to evade let-7 growth inhibition in order to transform.

It is also interesting that let-7 overexpression led to dramatically different outcomes in distinct cancer contexts. While dose is most likely the critical variable between the Rosa-rtTA and LAP-tTA systems, Rosa-rtTA does induce expression in cells other than hepatocytes and bile duct epithelia, leaving the possibility that non-cell autonomous influences of let-7 overexpression play a role in liver injury and cancer development. A more interesting possibility would be if distinct genetic subtypes of cancer respond differently to let-7 overexpression. Since let-7 has been conceptualized as a general tumor suppressor, it is surprising that it can cause opposing phenotypes in distinct cancer models. MYC liver cancers show a dramatic suppression of let-7, rendering it especially sensitive to let-7 replacement. Tumors or tissues with more normal levels of let-7 might not respond to increases in let-7. Alternatively, the growth of other cancer models may not depend on the overproduction of let-7 target genes/proteins. let-7 overexpression in these contexts would probably not elicit growth suppression, but may instead exacerbate tissue injury. It would be interesting to evaluate the effects of let-7 overexpression in hepatocellular carcinomas caused by different driver mutations. Together, our data suggest that let-7 therapy directed at hepatocellular carcinomas could be risky, given that most of these cancers occur in severely compromised, cirrhotic livers (Yang et al., 2011).

We speculate that the total dose of let-7 is evolutionarily determined via regulation of the expression levels of individual let-7 members, and is postnatally maintained at a level that can suppress cancer, but which also allows for adequate levels of mammalian regenerative capacity. Clearly, let-7 levels are not static throughout life, since let-7 levels are dynamic after environmental perturbations. However, when baseline let-7 levels are altered permanently by genetic means, compromises in tumor suppression or tissue regeneration were revealed. Our study underscores the importance of regulating appropriate levels of this small RNA to maintain health during times of regenerative stress.

Materials and methods

Mice

All mice were handled in accordance with the guidelines of the Institutional Animal Care and Use Committee at UTSW. MYC tumor models and the LAP-let-7S21L inducible mice were carried on a 1:1 FVB/C57Bl/6 strain background. Please see (Nishino et al., 2013) for more details about the let-7b/c2 floxed mice, which are on a C57Bl/6 background. The chronically injured let-7 inducible mice were on a mixed B6/129 background. All experiments were done in an age and sex controlled fashion unless otherwise noted in the figure legends.

Partial hepatectomy

Two-thirds of the liver was surgically excised as previously described (Mitchell and Willenbring, 2008).

RNA extraction and RT-qPCR

Total RNA was isolated using Trizol reagent (Invitrogen). For RT-qPCR of mRNAs, cDNA synthesis was performed with 1 ug of total RNA using miScript II Reverse Transcription Kit (Cat. #218161, Qiagen). Gene expression levels were measured using the ΔΔCt method as described previously (Zhu et al., 2010).

Western blot assay

Mouse liver tissues were ground with a pestle and lysed in T-PER Tissue Protein Extraction Reagent (Thermo Scientific Pierce). Western blots were performed in the standard fashion. The following antibodies were used: Anti-Insulin receptor β (Cell Signaling #3025), Anti-Igf1rβ (Cell Signaling #9750), Anti-Irs2 (Cell Signaling #3089), Anti-total S6K (Cell Signaling #9202), Anti-p-S6K (Cell Signaling #9205), Anti-total S6 (Cell Signaling #2217), Anti-p-S6 Ser235/236 (Cell Signaling #2211), Anti-mouse β-Actin (Cell Signaling #4970), Anti-rabbit IgG, HRP-linked Antibody (Cell Signaling #7074) and Anti-mouse IgG, HRP-linked Antibody (Cell Signaling #7076).

Histology and immunohistochemistry

Tissue samples were fixed in 10% neutral buffered formalin or 4% paraformaldehyde (PFA) and embedded in paraffin. In some cases, frozen sections were made. Immunohistochemistry was performed as previously described (Zhu et al., 2010). Primary antibodies used: Ki-67 (Abcam #ab15580). Detection was performed with the Elite ABC Kit and DAB Substrate (Vector Laboratories), followed by Hematoxylin Solution counterstaining (Sigma).

Liver function tests

Blood samples (∼50 ul) were taken retro-orbitally in heparinized tubes. Liver function tests were analyzed by the UTSW Molecular Genetics core.

Viral Cre excision

100 μL of AAV8.TBG.PI.Cre.rBG (University of Pennsylvania Vector Core) was retro-orbitally injected at a titer of 5 x× 1010 genomic particles to mediate 90%-–100% Cre excision.

Cell culture and in vitro antiMiR experiments

The H2.35 cell line was directly obtained from ATCC and has been cultured for less than 6 months. The cells were authenticated by ATCC using Short Tandem Repeat (STR) DNA profiling. Cells were cultured in DMEM with 4% (vol/vol) FBS, 1x Pen/Strep (Thermo Scientific) and 200 nM Dexamethasone (Sigma). Cells were transfected with control (Life Technologies Cat. AM17010), let-7a (Life Technologies Cat. #4464084-Assay ID MH10050), or let-7g (Life Technologies Cat. #4464084-Assay ID MH11050) miRVana antiMiRs. AntiMiRs were packaged by RNAiMAX (Invitrogen) and transfected into H2.35 cells cultured in 96-well plates at a concentration of 50 nM. The number of viable cells in each well was measured at 2 and 3 days after transfection using CellTiter-Glo Luminescent Cell Viability Assay (Promega Cat. #G7570).

In vivo microRNA mimic experiments

For in vivo experiments, formulated C12-200 lipidoid nanoparticles (LNPs) were used to package either Control (Life Technologies Cat. #4464061) or let-7g (Life Technologies Cat. 364 #4464070-Assay ID MC11758) miRVana mimic at either 0.5 or 2 mg/kg and delivered intravenously through the tail vein. LNPs were formulated following the previously reported component ratios (Love et al., 2010) with the aid of a microfluidic rapid mixing instrument (Precision Nanosystems NanoAssemblr) and purified by dialysis in sterile PBS before injection.

In vivo drug treatments

Rapamycin (LC Biochem) was dissolved in 25% ethanol/PBS and then injected at 1.5 mg/kg 2 hr prior to and 20 hr after PHx. INK128 (LC Biochem) was formulated in 5% polyvinylpropyline, 15% NMP, 80% water and administered by oral gavage at 1 mg/kg 2 hr prior to and 20 hr after PHx.

MicroRNA sequencing

Female CD1 mice were treated with 3 mg/kg TCPOBOP in DMSO-corn oil by gavage (Tian et al., 2011), sacrificed at 3, 6, 9, 12, and 18 hr after treatment, and compared to untreated controls. Replicate libraries were made from two individual mice for each condition. RNA was purified with the Qiagen miRNeasy Mini kit. Small RNA libraries were constructed using an Illumina Truseq Small RNA Sample Prep Kit. 12 indexed libraries were multiplexed in a single flow cell lane and received 50 base single-end sequencing on an Illumina HiSeq 2500 sequencer. Sequence reads were aligned to mm9 using Tophat and quantified with Cufflinks by the FPKM method (Trapnell et al., 2012). Data for each experimental condition represent the average values from two libraries.

Statistical analysis

The data in most figure panels reflect multiple experiments performed on different days using mice derived from different litters. Variation is always indicated using standard error presented as mean ± SEM. Two-tailed Student's t-tests (two-sample equal variance) were used to test the significance of differences between two groups. Statistical significance is displayed as p<0.05 (*) or p<0.01 (**) unless specified otherwise. In all experiments, no mice were excluded form analysis after the experiment was initiated. Image analysis for the quantification of cell proliferation, cell death, and fibrosis were blinded.

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Decision letter

  1. Amy J Wagers
    Reviewing Editor; Harvard University, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for submitting your work entitled "Precise Let-7 expression levels balance organ regeneration against tumor suppression" for peer review at eLife. Your submission has been favorably evaluated by Fiona Watt (Senior editor), a Reviewing editor, and two reviewers.

The following individual responsible for the peer review of your submission has agreed to reveal his identity: Frank Slack (peer reviewer). A further reviewer remains anonymous.

The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.

Summary:

In the manuscript entitled "Precise Let-7 expression levels balance organ regeneration against tumor suppression" Wu and colleagues utilize transgenic overexpression and liver-specific knockout of Let-7 to assess the consequences of Let-7 dysregulation in the setting of MYC-driven carcinogenesis and during liver regeneration after partial hepatectomy. The authors find that moderate Let-7 overexpression blocks MYC-driven hepatoblastoma formation and inhibits liver regeneration after partial hepatectomy. During liver regeneration, Let-7 overexpression alters steady state levels of various metabolites. Further, Let-7 regulates the insulin-mTOR pathway after regeneration. Finally, extreme Let-7 overexpression causes liver injury and liver carcinogenesis.

All reviewers found this work to be innovative and important, noting particularly the likely impact on the field of demonstrating the functional role of Let-7 regulation during carcinogenesis and liver repair and the potential negative effects of high dose Let-7 on normal liver function. However, two reviewers expressed significant concerns about the rather expansive scope of the manuscript in its current form, which causes the work to appear somewhat superficial and difficult to follow in places. The authors are strongly encouraged to consider how they may address this issue in their revision, perhaps by separating some of the multiple, intertwined stories or by providing clearer connections between the various observations that are reported.

Essential revisions:

1) Please revise the manuscript to provide more focus on the major conclusions and message and a clearer, more streamlined description of the experiments and data.

2) Some conclusions presented appear to be stronger than others, and the paper would benefit from inclusion of relevant experimental caveats. For example, the partial hepatectomy studies only examine one time point, at 40 hours post resection, thereby only investigating the initial phase of hepatocyte proliferation, rather than also the subsequent phase of vascular-driven liver remodeling. It is possible that Let-7 is more dynamically regulated and active in both phases. Similar concerns apply to the metabolomics studies, which, while innovative, are problematic in that they represent a snap shot of the metabolic state at one time point. They provide no assessment of metabolite flux, which would require more extensive labeling studies. Further, the increase in nucleotides, for example, could well be due to increased biosynthesis, but could also be due to a block in nucleotide utilization. If rapid cell proliferation is required for liver regeneration, requiring rapid DNA synthesis, would DNA assembly be the rate-limiting step, causing accumulation of nucleotides? Also, would it be informative to correlate metabolite changes in the glycolysis or nucleotide biosynthesis pathways with expression changes of relevant enzymes? Please address these issues experimentally or by inclusion of relevant discussion and caveats.

3) The role of mTOR in liver regeneration has been described, and the connection to the Let-7 pathway is intriguing, as is the fact that rapamycin decreases liver recovery. However, the pleiotropic effects of rapamycin are also well-described, and should be noted. The use of more specific agents (e.g. Torin) or genetic methods, if available, might also be desirable for a more comprehensive assessment.

4) Please comment on whether differences in cellular context (i.e., oncogenetic background) in addition to or instead of Let7g levels may explain the different outcomes in the myc-driven vs. spontaneous tumor models.

https://doi.org/10.7554/eLife.09431.015

Author response

All reviewers found this work to be innovative and important, noting particularly the likely impact on the field of demonstrating the functional role of Let-7 regulation during carcinogenesis and liver repair and the potential negative effects of high dose Let-7 on normal liver function. However, two reviewers expressed significant concerns about the rather expansive scope of the manuscript in its current form, which causes the work to appear somewhat superficial and difficult to follow in places. The authors are strongly encouraged to consider how they may address this issue in their revision, perhaps by separating some of the multiple, intertwined stories or by providing clearer connections between the various observations that are reported. Essential revisions: 1) Please revise the manuscript to provide more focus on the major conclusions and message and a clearer, more streamlined description of the experiments and data.

We have revised the paper so that it is clearer and more streamlined. Redundant data was removed. For example, the experiment involving MYC +/- let-7S21L induction at 4 weeks as opposed to 2 weeks of age, which revealed similar findings, was removed. The metabolism data was removed as suggested by the editors and reviewers. The language in many sections was also shortened and clarified.

2) Some conclusions presented appear to be stronger than others, and the paper would benefit from inclusion of relevant experimental caveats. For example, the partial hepatectomy studies only examine one time point, at 40 hours post resection, thereby only investigating the initial phase of hepatocyte proliferation, rather than also the subsequent phase of vascular-driven liver remodeling. It is possible that Let-7 is more dynamically regulated and active in both phases.

It is possible that let-7 is dynamically regulated and important during multiple mechanistically independent phases of liver regeneration, although it is unlikely given the let-7 expression data. Figure 2A shows that let-7 was significantly altered only during the period shortly after hepatectomy and reverted back to baseline levels at around 40 hours.

Vascular-driven liver remodeling was previously shown to be influential 4-7 days after partial hepatectomy (Ding et al. Nature 2010). To completely exclude the possibility that this mechanism is influenced by let-7, we assessed the liver regeneration at 4, 7 and 14 days post partial hepatectomy. In both loss and gain of function models, there were no significant differences in regenerated liver mass (Figure 2–figure supplement 1E,F and Figure 3–figure supplement 1C,D). These results support the idea that let-7 expression changes lead to alterations in the initial, but not subsequent phases of liver regeneration after partial hepatectomy.

Similar concerns apply to the metabolomics studies, which, while innovative, are problematic in that they represent a snap shot of the metabolic state at one time point. They provide no assessment of metabolite flux, which would require more extensive labeling studies. Further, the increase in nucleotides, for example, could well be due to increased biosynthesis, but could also be due to a block in nucleotide utilization. If rapid cell proliferation is required for liver regeneration, requiring rapid DNA synthesis, would DNA assembly be the rate-limiting step, causing accumulation of nucleotides? Also, would it be informative to correlate metabolite changes in the glycolysis or nucleotide biosynthesis pathways with expression changes of relevant enzymes? Please address these issues experimentally or by inclusion of relevant discussion and caveats.

As suggested by the editors and reviewers, we excluded the metabolomics data from this manuscript in an effort to focus on the major findings of the paper.

3) The role of mTOR in liver regeneration has been described, and the connection to the Let-7 pathway is intriguing, as is the fact that rapamycin decreases liver recovery. However, the pleiotropic effects of rapamycin are also well-described, and should be noted. The use of more specific agents (e.g. Torin) or genetic methods, if available, might also be desirable for a more comprehensive assessment.

Rapamycin has pleiotropic and cell-type dependent effects. This is partly because it acts allosterically through FKBP12 rather than mTOR itself, and thus does not completely inhibit mTORC1 and mTORC2. As a result, variable inhibition of mTORC1 substrates can lead to incomplete protein synthesis inhibition via p-4EBP. In contrast, second generation ATP-competitive mTOR kinase inhibitors such as Torin and INK128 directly antagonize mTORC1/2 complexes and thus are more potent and specific inhibitors (Hsieh et al., 2012).

To confirm the rapamycin results with a second compound, and to examine the impact of more potent mTOR blockade, we performed partial hepatectomy in let-7b/c LKO mice treated with INK128. Torin1 was not used in part because of poor mouse microsome stability and a short in vivo half-life (Liu and Gray et al., Bioorg Med Chem Lett. 2011), and because we have had previous successes using INK128 in vivo.

INK128, similar to rapamycin, completely abrogated the regenerative enhancement associated with let-7 loss (Figure 4G). p-S6K western blots confirmed that mTOR is hyperactivated in LKO livers and that INK128 extinguishes mTOR activity (Figure 4H). Identical results after rapamycin and INK128 indicated that the mTOR signaling pathway and its substrates (especially S6K) play an essential role in driving increased regeneration in the context of let-7 suppression.

4) Please comment on whether differences in cellular context (i.e., oncogenetic background) in addition to or instead of Let7g levels may explain the different outcomes in the myc-driven vs. spontaneous tumor models.

Additional comments/clarification from the reviewers:

The authors use 2 different tumor models to assess the effects of let-7 overexpression on tumorigenesis in the liver, and they observe two different outcomes. In the current manuscript, they attribute the difference in outcome solely to differences in expression levels of let-7 (8 fold in one model and 20-fold in the other). However, there are many other differences in the oncogenetic backgrounds of the models that also could contribute to or account for the differences. For instance, in one case where let-7 reduces tumor burden, tumors arise due to strong induction of oncogenic Myc; these tumors have a 3-5 month latency. In the other case, tumors arise spontaneously, with unknown oncogenic mutations, and show an 18 mo. latency. Thus, an alternative interpretation of the data is that let-7 overexpression in Myc overexpressing tumors is tumor suppressive whereas let-7 overexpression in tumors lacking Myc overexpression is tumor promoting.

This would be analogous to observations in Drosophila indicating that oncogenic Ras converts TNF signaling from anti- to pro-oncogenic (see, e.g., Cordero et al. Dev. Cell 18:999, 2010). Or, it may be that let-7 overexpression in young mice (<6 mo) is tumor suppressive whereas let-7 overexpression in aged mice (>12mo.), which have acquired additional age-associated genetic, epigenetic and microenvironmental alterations, is tumor promoting. One could test this experimentally, by using the RosaLet7S21L system in the LAP-tTA; TRE-Myc background, but as this would be a rather time-consuming experiment, the authors may instead wish simply to discuss more clearly the other differences in the models that might contribute to the different outcomes, rather than attributing outcomes only to let-7 expression levels.

We agree that cellular context or oncogenetic background in addition to let-7 dose may indeed contribute to the different cancer outcomes. We did not to make the claim that let-7 overexpression is effective in MYC cancers solely because it is expressed at low doses. We only stated that let-7 dose is a critically important variable in liver injury (in the absence of MYC). As a key piece of evidence, we directly compared low and high dose let-7 overexpression in normal livers. Long-term low dose let-7 overexpression in LAP-let-7S21L mice (Figure 2 and Figure 2—figure supplement 1) never resulted in liver dysfunction or tumor formation. This was emphasized and clarified in the text.

Age should also be excluded as a possibility, since high dose let-7 was induced by Rosa-rtTa from 2 or 4 weeks onward, mirroring the MYC + let-7 induction times. There are however caveats to consider. The LAP and Rosa promoters induce expression in different cells at different levels, resulting in complex non-cell autonomous influences that might play a role in liver cancer development. We have commented on this possibility in the revised paper.

Certainly, MYC tumors may be particularly sensitive to let-7 overexpression because of its specific oncogenetic context. MYC-driven liver cancers show a dramatic suppression of let-7; other tumor or tissue contexts may not share this extent of let-7 suppression. It would make sense that this context is especially sensitive to let-7 replacement, whereas tumors or tissues with normal levels of let-7 might not respond to let-7 increases. Alternatively, other cancer models may not depend on the overproduction of let-7 target genes/proteins. Overexpressing let-7 in those contexts would probably not suppress growth, but may instead exacerbate tissue injury. We have not had time to thoroughly evaluate other mouse models of liver cancer, although this would clearly be interesting.

Unfortunately, we have not had time to test high and low dose let-7 expression in the context of MYC liver cancers. This is a difficult experiment for our transgenic systems because dose titration of let-7 will also influence the dose of MYC (both are controlled by tet sensitive promoters), which is known to also influence tumor biology.

We have included a paragraph in the Discussion stating that it is possible that oncogenetic differences underlie the ways in which let-7 impacts cancer biology.

https://doi.org/10.7554/eLife.09431.016

Article and author information

Author details

  1. Linwei Wu

    1. Children's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, United States
    2. Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
    3. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    LW, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Contributed equally with
    Liem H Nguyen
    Competing interests
    No competing interests declared.
  2. Liem H Nguyen

    1. Children's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, United States
    2. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    LHN, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Contributed equally with
    Linwei Wu
    Competing interests
    No competing interests declared.
  3. Kejin Zhou

    1. Simmons Comprehensive Cancer Center, Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    KZ, Conception and design, Acquisition of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  4. T Yvanka de Soysa

    1. Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, United States
    2. Harvard Stem Cell Institute, Harvard University, Boston, United States
    3. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    4. The Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, United States
    Contribution
    TYdeS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  5. Lin Li

    1. Children's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, United States
    2. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    LL, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  6. Jason B Miller

    1. Simmons Comprehensive Cancer Center, Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    JBM, Conception and design, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  7. Jianmin Tian

    1. Department of Pathology, University of Pittsburg, Pittsburg, United States
    Contribution
    JT, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  8. Joseph Locker

    1. Department of Pathology, University of Pittsburg, Pittsburg, United States
    Contribution
    JL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  9. Shuyuan Zhang

    1. Children's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, United States
    2. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    SZ, Conception and design, Acquisition of data, Analysis and interpretation of data
    Competing interests
    No competing interests declared.
  10. Gen Shinoda

    1. Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, United States
    2. Harvard Stem Cell Institute, Harvard University, Boston, United States
    3. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    4. The Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, United States
    Contribution
    GS, Conception and design, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  11. Marc T Seligson

    1. Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, United States
    2. Harvard Stem Cell Institute, Harvard University, Boston, United States
    3. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    4. The Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, United States
    Contribution
    MTS, Conception and design, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  12. Lauren R Zeitels

    1. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    LRZ, Conception and design, Acquisition of data, Analysis and interpretation of data
    Competing interests
    No competing interests declared.
  13. Asha Acharya

    1. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    AA, Conception and design, Acquisition of data, Analysis and interpretation of data
    Competing interests
    No competing interests declared.
  14. Sam C Wang

    1. Children's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, United States
    2. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    3. Department of Surgery, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    SCW, Conception and design, Drafting or revising the article
    Competing interests
    No competing interests declared.
  15. Joshua T Mendell

    1. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    JTM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  16. Xiaoshun He

    1. Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
    Contribution
    XH, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  17. Jinsuke Nishino

    1. Howard Hughes Medical Institute, Children's Research Institute, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    JN, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  18. Sean J Morrison

    1. Howard Hughes Medical Institute, Children's Research Institute, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    SJM, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    SJM: Senior editor, eLife.
  19. Daniel J Siegwart

    1. Simmons Comprehensive Cancer Center, Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    DJS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
    ORCID icon 0000-0003-3823-1931
  20. George Q Daley

    1. Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, United States
    2. Harvard Stem Cell Institute, Harvard University, Boston, United States
    3. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    4. The Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, United States
    Contribution
    GQD, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  21. Ng Shyh-Chang

    1. Stem Cell Transplantation Program, Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana Farber Cancer Institute, Boston, United States
    2. Harvard Stem Cell Institute, Harvard University, Boston, United States
    3. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    4. The Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, United States
    5. Stem cell and Regenerative Biology, Genome Institute of Singapore, Singapore, Singapore
    Contribution
    NSC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  22. Hao Zhu

    1. Children's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, United States
    2. Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    HZ, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    1. Hao.Zhu@utsouthwestern.edu
    Competing interests
    No competing interests declared.

Funding

National Cancer Institute (1K08CA157727-02)

  • Hao Zhu

Howard Hughes Medical Institute

  • Sean J Morrison
  • George Q Daley

Howard Hughes Medical Institute (59108180)

  • Liem H Nguyen

Cancer Prevention and Research Institute of Texas (R1209)

  • Hao Zhu

National Cancer Institute (1R01CA190525-01)

  • Hao Zhu

National Institute of General Medical Sciences (R01GM107536)

  • George Q Daley

The Science and Technology Program of Guangzhou, China (2012J5100031)

  • Linwei Wu

National Institute on Aging (AG024945)

  • Sean J Morrison

Welch Foundation (I-1855)

  • Daniel J Siegwart

Agency for Science, Technology and Research (NSS Scholarship)

  • Ng Shyh-Chang

Youth Teachers Cultivation Project of Sun Yat-Sen University (12ykpy21)

  • Linwei Wu

Burroughs Wellcome Fund (Career Award for Medical Scientists)

  • Hao Zhu

Pollock Foundation

  • Hao Zhu

Cancer Prevention and Research Institute of Texas

  • Joshua T Mendell
  • Sean J Morrison

Cancer Prevention and Research Institute of Texas (R1212)

  • Daniel J Siegwart

American Cancer Society (Postdoctoral Fellowship)

  • Hao Zhu

Cancer Prevention and Research Institute of Texas (R1008)

  • Joshua T Mendell

Cancer Prevention and Research Institute of Texa (R01CA120185)

  • Joshua T Mendell

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

LW was supported by The Science and Technology Program of Guangzhou, China (2012J5100031) and Youth Teachers Cultivation Project of Sun Yat-Sen University (12ykpy21). LHN was supported by the Howard Hughes Medical Institute (HHMI) Pre-doctoral International Student Fellowship. JTM was supported by the Cancer Prevention and Research Institute of Texas (CPRIT grant R1008) and NIH (R01CA120185). SJM is an HHMI Investigator, the Mary McDermott Cook Chair in Pediatric Genetics, the director of the Hamon Laboratory for Stem Cells and Cancer, and a CPRIT Scholar. SJM was also supported by a grant from the National Institute on Aging (AG024945). GQD is an investigator of HHMI and the Manton Center for Orphan Disease Research and was supported by NIGMS R01GM107536. DJS acknowledges support from CPRIT (grant R1212) and the Welch Foundation (I-1855). NSC was supported by a NSS Scholarship from the Agency for Science, Technology and Research, Singapore. HZ was supported by a American Cancer Society Postdoctoral Fellowship, a NIH/NCI K08 grant (5K08CA157727), a NIH/NCI R01 grant (1R01CA190525), the Pollack Foundation, a Burroughs Welcome Career Medical Award, and a CPRIT Recruitment Award (R1209).

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#2012-0143) of the University of Texas Southwestern Medical Center. All surgery was performed under isoflurane anesthesia with appropriate analgesia, and every effort was made to minimize suffering.

Reviewing Editor

  1. Amy J Wagers, Reviewing Editor, Harvard University, United States

Publication history

  1. Received: June 15, 2015
  2. Accepted: October 5, 2015
  3. Accepted Manuscript published: October 7, 2015 (version 1)
  4. Version of Record published: December 10, 2015 (version 2)

Copyright

© 2015, Wu et al.

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.

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