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Methionine restriction breaks obligatory coupling of cell proliferation and death by an oncogene Src in Drosophila

  1. Hiroshi Nishida
  2. Morihiro Okada
  3. Lynna Yang
  4. Tomomi Takano
  5. Sho Tabata
  6. Tomoyoshi Soga
  7. Diana M Ho
  8. Jongkyeong Chung
  9. Yasuhiro Minami
  10. Sa Kan Yoo  Is a corresponding author
  1. Division of Cell Physiology, Kobe University, Japan
  2. RIKEN CPR, Japan
  3. RIKEN BDR, Japan
  4. Institute for Advanced Biosciences, Keio University, Japan
  5. Harvard Medical School, United States
  6. Seoul National University, Republic of Korea
  7. Division of Developmental Biology and Regenerative Medicine, Kobe University, Japan
Research Article
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Cite this article as: eLife 2021;10:e59809 doi: 10.7554/eLife.59809

Abstract

Oncogenes often promote cell death as well as proliferation. How oncogenes drive these diametrically opposed phenomena remains to be solved. A key question is whether cell death occurs as a response to aberrant proliferation signals or through a proliferation-independent mechanism. Here, we reveal that Src, the first identified oncogene, simultaneously drives cell proliferation and death in an obligatorily coupled manner through parallel MAPK pathways. The two MAPK pathways diverge from a lynchpin protein Slpr. A MAPK p38 drives proliferation whereas another MAPK JNK drives apoptosis independently of proliferation signals. Src-p38-induced proliferation is regulated by methionine-mediated Tor signaling. Reduction of dietary methionine uncouples the obligatory coupling of cell proliferation and death, suppressing tumorigenesis and tumor-induced lethality. Our findings provide an insight into how cells evolved to have a fail-safe mechanism that thwarts tumorigenesis by the oncogene Src. We also exemplify a diet-based approach to circumvent oncogenesis by exploiting the fail-safe mechanism.

Introduction

Tumorigenesis requires activation and inactivation of not one, but multiple signaling pathways (Hanahan and Weinberg, 2011; Lowe et al., 2004; Shortt and Johnstone, 2012). This is likely because cells have evolutionarily refined fail-safe mechanisms to prevent tumorigenesis by a single oncogene activation. Because of this fail-safe mechanism, the transforming effects of oncogenes are often cancelled by the cell’s intrinsic ability to prevent tumorigenesis (Lowe et al., 2004; Shortt and Johnstone, 2012). Thus, transformation by oncogenic drivers requires inhibition of tumor suppressor signaling, which regulates cell death or senescence.

A well-known example of the fail-safe mechanism is the case of Myc oncogene. Myc is a potent oncogene implicated in most human tumor entities, but it is also a powerful cell death driver (Evan et al., 1992; Hoffman and Liebermann, 1998; Prendergast, 1999). Originally, two models were proposed to explain the Myc-induced cell death (Evan et al., 1994). The ‘conflict’ model suggests that, in an environment where proliferation is not supported, inappropriate growth signals induced by Myc invoke the cell’s intrinsic mechanisms to cease abnormal proliferation, leading to apoptosis. In this model, the main function of Myc is to induce proliferation, and cells undergo apoptosis in response to the aberrant growth signal. In contrast, the ‘dual’ model proposes that apoptosis occurs as a direct result of bona fide signaling of the Myc pathway, rather than as the cell’s response to Myc-invoked aberrant growth signaling. In this model, cell death is a normal, obligate function of Myc, which is intrinsically imprinted in the Myc signaling itself. Although these two models are not mutually exclusive, delineation of Myc signaling supports the dual model: Myc drives p53-mediated apoptosis through transcriptional regulation of the tumor suppressor ARF (Lowe et al., 2004; Shortt and Johnstone, 2012).

In addition to Myc, many other oncogenes are known to induce cell death. This phenomenon is now accepted as an intrinsic tumor suppressive mechanism (Shortt and Johnstone, 2012). However, contrary to the well-studied mechanisms of Myc-induced cell death, how other oncogenes, such as Ras or Src, couple cell proliferation and death remains unclear (Lowe et al., 2004).

In this study, we focus on the Src oncogene. Src is the first oncogene identified (Yeatman, 2004). Src expression and activity is often increased in human cancer, which contributes to oncogenesis (Ishizawar and Parsons, 2004; Summy and Gallick, 2003; Yeatman, 2004). Due to the implication of Src in tumorigenesis, many clinical inhibitors targeting Src family kinases (SFKs) have been developed but their use as therapeutic drugs has been unsuccessful (Gargalionis et al., 2014; Sousa-Victor and Jasper, 2014). The reason for the low efficacy of these Src inhibitors remains unclear because both SFKs and SFK inhibitors have a broad spectrum of targets, making mechanistic analyses difficult. This compels us to better understand how Src regulates signaling pathways.

Drosophila has two SFKs: Src42A and Src64B (Kussick et al., 1993; Pedraza et al., 2004; Takahashi et al., 1996). The endogenous expression patterns of Src42A and Src64B are different, but ectopic expression of either of them induces similar effects. Drosophila Src regulates a variety of signaling pathways, including Notch, MAPKs, Jak-Stat, EGF, Wnt, and Hippo signaling (Cordero et al., 2014; Enomoto and Igaki, 2013; Ho et al., 2015; Read et al., 2004; Tateno et al., 2000). Src activation induces apoptosis as well as tissue growth (Pedraza et al., 2004). However, how cell death and proliferation are coordinated downstream of Src activation still remains elusive. Here, we investigate how the Src oncogene couples cell proliferation and death in epithelia of the wing imaginal disc.

Results

Previous studies have shown that a Drosophila Src, Src42A, simultaneously induces cell death and proliferation in both eye and wing imaginal discs (Pedraza et al., 2004). In this study, we used a vg-Gal4 driver, which promotes local expression in the DV boundary and a part of the hinge region of the wing disc. Indeed, expression of constitutively active (CA) Src42A with an amino acid substitution of Tyr511 to Phe, which is refractory to inactivating phosphorylation by Csk (Tateno et al., 2000), induced both mitosis and apoptosis, which were detected by phospho-histone 3 (pH3) and cleaved caspase DCP1, respectively (Figure 1A–D). This provocation of both cell proliferation and death results in a mild overgrowth in the Src42A CA-expressing region, likely reflecting the mutually cancelling effects of proliferation and death. (Figure 1E).

Src activation induces both cell proliferation and death, resulting in a mild tissue overgrowth.

(A) Src42A constitutively active (CA) expression induces cell proliferation, which was detected by phospho-histone 3 (pH3) staining. (B) Quantification of pH3 staining. The number of pH3+ cells was normalized by the area of GFP+ cells. Two-tailed unpaired t-test. (C) Src42A CA expression induces caspase activation, which was detected by cleaved DCP1 staining. (D) Quantification of percentage of cDCP1+ cells in GFP+ cells. Two-tailed unpaired t-test. (E) Quantification of the total volume of GFP+ cells (µm³). Two-tailed unpaired t-test. Scale bars, 100 µm.

Src signaling mediates a myriad of pathways, but how these signals converge to produce specific phenotypes remains unclear, especially in the context of cell proliferation and death coupling. It has previously been shown that Src-induced cell death cannot be inhibited by p21 overexpression, which suppresses cell proliferation (Pedraza et al., 2004). But, since p21 directly inhibits the cell cycle through inhibition of Cyclin-dependent kinases, the most downstream component of proliferation signals, it still remains unclear whether Src-induced aberrant proliferation signals play a role in cell death.

In order to find how Src signaling drives both cell proliferation and death simultaneously and whether cell death occurs in response to proliferation signals, we reasoned that interrogation of downstream signaling provoked by Src activation will give us clues on how these two opposing phenotypes are driven by Src. To search for an effector downstream of Src, we performed an RNAi screening mainly focusing on cell death-related factors (Supplementary file 1). The screening took advantage of the organismal lethality induced by Src42A CA expression in the wing disc (Figure 2A). We searched for RNAis that could suppress this Src-induced lethality.

Figure 2 with 1 supplement see all
Inhibition of Slpr suppresses the phenotypes induced by Src activation.

(A) Src42A constitutively active (CA) expression in the wing disc induces organismal lethality, which is suppressed by knockdown of slpr. One-way ANOVA with Sidak’s post-test. (B) The small, disheveled wing phenotype of the rare escapers with Src42A CA is suppressed by knockdown of slpr. (C) Src42A CA-mediated JNK activation, which was detected by the TRE-RFP reporter, is suppressed by knockdown of slpr. (D) Quantification of TRE-RFP in C. One-way ANOVA with Sidak’s post-test. Scale bars, 100 µm.

We identified that slipper (slpr), a mixed lineage kinase, regulates Src signaling. Src-induced lethality was suppressed by slpr knockdown (Figure 2A and Figure 2—figure supplement 1A–B). We confirmed knockdown efficiency of the slpr RNAi (Figure 2—figure supplement 1C) by RT-qPCR. Rare escapers of vg>Src42A CA flies have small, disheveled wings, but slpr knockdown completely reversed this phenotype (Figure 2B, Figure 2—figure supplement 1A). This is not an event that is observed only in the wing disc because Slpr inhibition in the eye disc also suppressed organismal lethality and the abnormal eye phenotype of escapers that are induced by the Src expression in the eye disc (Figure 2—figure supplement 1D–E).

The link between Src and Slpr has not been previously known. Slpr was originally identified as a JNKKK, a MAPKKK that regulates JNK signaling (Stronach and Perrimon, 2002). Src has long been known to regulate JNK signaling (Tateno et al., 2000). Some effectors such as ubiquitin E2 complex Bendless (Ma et al., 2013) and F-actin cytoskeleton (Enomoto and Igaki, 2013; Fernández et al., 2014; Rudrapatna et al., 2014) have been shown to link Src and JNK. But how Src exactly regulates JNK signaling still remains elusive. We speculated that Slpr may link Src and JNK signaling. Indeed, slpr RNAi significantly suppressed Src-mediated activation of JNK, which was detected by the JNK activity reporter TRE-RFP (Figure 2C–D). This observation suggests that Slpr is a JNKKK that transduces Src activation to JNK signaling.

Although our findings that Slpr mediates Src-induced JNK activation are consistent with the previous literature showing that Slpr is a MAPKKK upstream of JNK, the effects of slpr knockdown on the wings and organismal survival over Src stress were perplexing. This is because a combination of JNK inhibition and Src activation is known to induce a massive overgrowth phenotype (Enomoto and Igaki, 2013; Ho et al., 2015). Indeed, simultaneously activating Src and suppressing JNK resulted in substantial overgrowth of the wing disc (Figure 3A–D) and complete organismal lethality (Figure 3E). On the other hand, combining Src activation and Slpr inhibition did not induce tissue overgrowth (Figure 3A–D). We found that Slpr inhibition suppressed both cell proliferation and apoptosis induced by Src whereas JNK inhibition suppressed only apoptosis but not cell proliferation (Figure 3A–D). We confirmed the same results with a different RNAi for slpr (Figure 3—figure supplement 1A–D). This suggests that JNK inhibition-mediated suppression of apoptosis is responsible for the tumor overgrowth. Consistent with this idea, a combination of Src activation and apoptosis inhibition by microRNAs for reaper, hid and grim (Siegrist et al., 2010), which inhibits DIAP1, also induced overgrowth, similar to JNK inhibition (Figure 3—figure supplement 1E). On the other hand, a combination of Src activation and cell death induction enhanced survival over the Src tumor (Figure 3—figure supplement 1F). Furthermore, the overgrowth phenotype induced by Src activation and JNK inhibition was suppressed by slpr knockdown (Figure 3—figure supplement 1G–I). Taken together, these findings strongly imply that Slpr, which was originally identified as a MAPKKK upstream of JNK, regulates other signaling pathways along with the JNK pathway.

Figure 3 with 1 supplement see all
Slpr regulates both cell proliferation and cell death that are induced by Src activation.

(A) Apoptosis induced by Src42A constitutively active (CA) is suppressed by slpr RNAi and JNK DN. Note the aggressive overgrowth phenotype induced by combining Src with JNK DN. (B) JNK DN does not inhibit Src42A CA-mediated proliferation whereas slpr inhibition does. (C) Quantification of the total volume of GFP+ cells (µm³) in B. One-way ANOVA with Sidak’s post-test. (D) Quantification of phospho-histone 3 (pH3) staining in B. The number of pH3+ cells was normalized by the area of GFP+ cells. Src42A CA-induced proliferation is suppressed by knockdown of slpr but not by overexpression of JNK DN. One-way ANOVA with Sidak’s post-test. (E) Inhibition of JNK enhances organismal lethality induced by Src42A CA. One-way ANOVA with Sidak’s post-test. Scale bars, 100 µm.

We investigated what is regulating proliferation downstream of the Src-Slpr axis if JNK only regulates cell death. Although Slpr was originally identified as a JNK regulator (Stronach and Perrimon, 2002), subsequent studies report the existence of situations where Slpr can also activate other MAPKs including Erk and p38 (Chen et al., 2010; Sathyanarayana et al., 2003). Thus, we examined whether p38 and Erk could function as downstream components of Src-Slpr signaling. We found that Src activates both Erk and p38 in the wing disc (Figure 4A–B and Figure 4—figure supplement 1A–C). Activated Erk and p38 were observed not only in the region that expresses Src, but also in the surrounding area together with non-cell autonomous proliferation (Figure 4A–B, Figure 4—figure supplement 1A–D). In fact, there was also non-cell autonomous activation of JNK and cell death, albeit much subtler than Erk and p38 (Figure 4—figure supplement 1E–G). This cell autonomous and non-cell autonomous activation of MAPKs by Src is similar to the activation patterns of Yorkie by Src (Enomoto and Igaki, 2013). Inhibition of Slpr reduced Src-induced activation of both p38 and Erk (Figure 4A–B and Figure 4—figure supplement 1A–B) cell autonomously, indicating that Slpr activates p38 and Erk in addition to JNK. It is of note that Slpr inhibition suppressed Src-induced non-cell autonomous activation of p38 but not Erk or JNK (Figure 4B, Figure 4—figure supplement 1A–C,E), suggesting that there are different mechanisms that regulate non-cell autonomous activation of these MAPKs. In line with persistence of the non-cell autonomous JNK activation with Slpr inhibition, Slpr inhibition did not affect non-cell autonomous activation of caspase ( Figure 4—figure supplement 1E, F, G). This suggests that Src-invoked proliferation does not occur as a compensatory proliferation induced by death of the surrounding cells.

Figure 4 with 2 supplements see all
p38 mediates Src-induced cell proliferation.

(A) Src42A constitutively active (CA) expression induces phosphorylation of p38 both cell autonomously and non-cell autonomously, which is suppressed by slpr knockdown. (B) Quantification of phosphorylated p38 in A. One-way ANOVA with Sidak’s post-test. (C) Src42A CA-induced proliferation with/without JNK inhibition is suppressed by p38 DN. (D) Quantification of the total volume of GFP+ cells (µm³) in C. Two-tailed unpaired t-test. (E) Quantification of phospho-histone 3 (pH3) staining in C. The number of pH3+ cells was normalized by the area of GFP+ cells. Two-tailed unpaired t-test. (F) Inhibition of p38 suppresses organismal lethality induced by Src42A CA. One-way ANOVA with Sidak’s post-test. Scale bars, 100 µm.

Next, we investigated which of p38 or Erk might be involved in Src-induced cell proliferation. When we inhibited Erk using several RNAis that had previously been utilized (Singh et al., 2016), Erk inhibition was unable to reverse Src-induced organismal lethality and proliferation (Figure 4—figure supplement 1H–I). We also verified that the erk RNAi we used could inhibit the rough eye phenotype induced by Ras activation (Figure 4—figure supplement 1J). This indicates that, even though Src-Slpr signaling activates Erk, its functional contribution to proliferation is negligible. In fact, Erk inhibition lowered survival over Src expression, implying that Src-Slpr-Erk signaling is protective for animals through an unknown mechanism. On the other hand, when we inhibited p38 in the Src-expressing region, it suppressed cell proliferation (Figure 4C–E), regardless of the existence of JNK inhibition (Figure 4—figure supplement 2A–C). Furthermore, inhibition of the p38 MAPKK, Licorne (Lic) (Cully et al., 2010), suppressed Src-induced proliferation (Figure 4—figure supplement 2D–F). p38 inhibition also suppressed Src-induced lethality (Figure 4F). Importantly, p38 suppression did not inhibit Src-mediated apoptosis (Figure 4—figure supplement 2G–I). We conclude that, downstream of Src-Slpr, p38 regulates cell proliferation without affecting apoptosis.

Since Src activates both proliferation and cell death through two MAPK signaling pathways emanating from Slpr, we reasoned that inhibition of p38-mediated proliferation will be beneficial for a potential therapeutic purpose, uncoupling the link between cell death and proliferation. However, since p38 itself could still activate multiple signaling pathways, we decided to further delineate how p38 regulates cell proliferation, aiming to find a way to uncouple proliferation and cell death in a specific manner.

First we focused on Mef2 and Atf2, the transcription factors that are known to be regulated by p38 (Adachi-Yamada et al., 1999; Sano et al., 2005; Vrailas-Mortimer et al., 2011). Using the previously published RNAis for mef2 or atf2 (Clark et al., 2013; Terriente-Félix et al., 2017) did not suppress aggressive tumorigenesis induced by simultaneous Src activation and JNK inhibition (Figure 5—figure supplement 1A).

We next shifted our focus to Tor signaling since p38 also regulates cell proliferation through Tor (Cully et al., 2010). Tor is a central growth regulator that transduces nutritional information to cell growth (Kim and Guan, 2019; Sabatini, 2017). We examined whether Src activates Tor signaling. Phosphorylation of 4EBP, a readout of Tor signaling activation, was enhanced by Src expression (Figure 5A–B). Src also promoted phosphorylation of S6, another readout of Tor signaling (Kim et al., 2017Figure 5—figure supplement 1B–C). Src-induced phosphorylation of 4EBP and of S6 could be suppressed by Slpr or p38 inhibition, but not by JNK inhibition (Figure 5A–B, Figure 5—figure supplement 1B–C), indicating that Tor signaling functions downstream of the Src-Slpr-p38 axis. We then investigated whether Tor inhibition can suppress the massive overgrowth induced by Src activation and JNK inhibition. Tor inhibition completely suppressed overgrowth induced by Src activation and JNK inhibition (Figure 5C). Taken together, these findings indicate that p38 regulates cell proliferation through Tor signaling.

Figure 5 with 1 supplement see all
Tor signaling functions downstream of the Src-p38 pathway.

(A) Src42A constitutively active (CA) induces phosphorylation of 4EBP, a readout of Tor activation. Src42A CA-mediated phosphorylation of 4EBP was suppressed by p38 inhibition but not by JNK inhibition. (B) Quantification of phosphorylated 4EBP in A. One-way ANOVA with Sidak’s post-test. (C) Knockdown of Tor suppresses Src42A CA-induced cell proliferation. Scale bars, 100 µm.

Tor functions as a nutrient sensor, which can be regulated by manipulation of diet organisms eat. Since dietary manipulation of nutrients, including sugar and amino acids, affects clinical cancer outcome (Badgley et al., 2020; DeBerardinis and Chandel, 2016; Gao et al., 2019; Goncalves et al., 2019; Gonzalez et al., 2018; Kanarek et al., 2018; Knott et al., 2018; Maddocks et al., 2013; Maddocks et al., 2017; Pavlova and Thompson, 2016), we investigated whether dietary manipulation of nutrition can mimic the phenotype of Tor or p38 inhibition. We found that simple dilution of yeast in the fly food suppressed the tumor overgrowth and Tor activation that are induced by Src activation and JNK inhibition (Figure 6A–D). Yeast dilution did not affect Src-induced p38 activation (Figure 6—figure supplement 1A–B), suggesting that nutrition signaling does not function upstream of p38. Importantly, yeast dilution increased survival over the Src-induced stress (Figure 6E), demonstrating the connection between organismal physiology and tumorigenesis.

Figure 6 with 1 supplement see all
Yeast dilution affects Src-induced Tor signaling, tissue growth, and organismal lethality.

(A-B) Dietary restriction of yeast suppresses both Src42A constitutively active (CA)-induced proliferation and phosphorylation of 4EBP. (C) Quantification of the total volume of GFP+ cells (µm³) in A-B. Mann-Whitney test. (D) Quantification of phosphorylated 4EBP in A-B. Two-tailed unpaired t-test. (E) Dietary restriction of yeast reduces organismal lethality caused by Src42A CA expression in the wing disc in a dose-dependent manner. One-way ANOVA with Sidak’s post-test.

Since yeast contains large amounts of amino acids, which activate Tor signaling (Kim and Guan, 2019; Sabatini, 2017), we investigated which amino acid might be involved in transformation by Src. To narrow down candidate amino acids, we compared the effects of essential amino acids and non-essential amino acids on survival over Src-induced stress. Feeding of essential amino acids, but not non-essential ones, enhanced lethality induced by Src expression (Figure 7A). We further narrowed down the responsible amino acid by repeating the essential amino acid feeding experiment, but this time subtracting each essential amino acid from the mixture one by one (Figure 7B). Out of all the essential amino acids, only methionine subtraction reversed lethality of the Src-activated flies induced by feeding essential amino acids (Figure 7B). This methionine specificity was unexpected because a variety of dietary amino acids, including serine, glycine, histidine, asparagine, cysteine, and methionine, have been shown to affect cancer outcome in mammals (Badgley et al., 2020; Gao et al., 2019; Kanarek et al., 2018; Knott et al., 2018; Maddocks et al., 2013; Maddocks et al., 2017). We found that methionine addition reduces the survival over the Src-induced stress (Figure 7C), whereas it does not affect survival of control animals (Figure 7—figure supplement 1A). Even in a detrimental situation where apoptosis is inhibited by JNK DN, methionine manipulation could affect the organismal survival over the Src-induced stress (Figure 7—figure supplement 1B).

Figure 7 with 2 supplements see all
Methionine regulates Src-induced Tor signaling, tissue growth, and organismal lethality.

(A) Addition of essential amino acids enhances organismal lethality caused by Src42A constitutively active (CA) expression in the wing disc, whereas addition of non-essential amino acids does not. Kruskal-Wallis test with Dunn’s post-test. (B) Only methionine subtraction from the diet improves organismal survival over the Src42A CA stress. One-way ANOVA with Sidak’s post-test. (C) Addition of methionine reduces organismal survival over the Src42A CA stress in a dose-dependent manner. One-way ANOVA with Sidak’s post-test. (D) An amount of methionine in the hemolymph was measured by LC-MS/MS. Expression of Src42A CA in the wing disc decreases the circulating methionine in the hemolymph. Two-tailed unpaired t-test. (E-F) Dietary methionine activates both cell proliferation and phosphorylation of 4EBP that are induced by Src42A CA and JNK DN. (G) Quantification of the total volume of GFP+ cells (µm³) in E-F. Mann-Whitney test. (H) Quantification of phosphorylated 4EBP in E-F. Two-tailed unpaired t-test. (ISamS knockdown suppresses phosphorylation of 4EBP and overgrowth induced by Src42A CA and JNK DN. (J) Quantification of the total volume of GFP+ cells (µm³) in I. One-way ANOVA with Sidak’s post-test. (K) Quantification of phosphorylated 4EBP in I. One-way ANOVA with Sidak’s post-test. Scale bars, 100 µm.

We also investigated whether Src-induced tumor can cause a systemic effect on the nutritional state at the organismal level. Since tumor burden is clinically known to affect the amino acid profiles in the blood (Lai et al., 2005), we performed metabolomics analysis of the hemolymph, the fly blood, from the flies that bear Src-induced tumor in the wing discs. This metabolomics analysis revealed that the methionine concentration in the hemolymph is significantly decreased in flies that bear Src-induced tumors compared to control flies (Figure 7D and Figure 7—figure supplement 1C–D), which is correlated with the effects of dietary manipulation of methionine.

The correlative data on methionine in the amino acid subtraction experiment and the hemolymph analysis prompted us to investigate the role of methionine in Src-mediated oncogenesis. Feeding methionine enhanced Src-induced Tor signaling and overgrowth (Figure 7E–H), indicating that methionine is at least partly responsible for the nutrition-mediated activation of Tor signaling in Src-mediated tumors. Methionine addition did not affect Src-induced caspase activation (Figure 7—figure supplement 2A). Requirement of methionine for cell proliferation was relatively specific to tumor growth, because, under a normal condition without tumor, addition of methionine could not reverse the effects of yeast dilution, which induces smaller adult wings in control (Figure 7—figure supplement 2B–C).

Methionine can activate Tor through its conversion to SAM, a universal methyl donor (Gu et al., 2017). Consistently, inhibition of Sam synthetase, which converts methionine to SAM (Obata and Miura, 2015), suppressed both Src-induced overgrowth and Tor activation (Figure 7I–K). This indicates that methionine regulates Tor signaling in Src tumors in a tissue-autonomous manner. We also investigated a role for Samtor in Src-mediated oncogenesis. Samtor is a recently identified SAM sensor that inhibits Tor, which is released by SAM, in both mammals and Drosophila (Gu et al., 2017). As expected, in a diluted yeast condition, which suppresses Tor activation, samtor knockdown enhanced Tor activation with Src (Figure 7—figure supplement 2D–F). Unexpectedly, however, samtor knockdown suppressed Src-mediated overgrowth in spite of its Tor activation. We speculate that this growth suppression is likely attributed to Samtor’s predicted methyltransferase function, which may not be related to Tor signaling. All together, these data indicate that methionine mediates Tor activation during Src-induced oncogenesis.

Finally, we explored a potential cross talk between Src-p38-Tor signaling and methionine-mediated Tor regulation. First, we investigated whether Src can activate methionine metabolism directly. Since Src expression in the wing disc lowers the methionine concentration in the hemolymph (Figure 7D), we hypothesized that Src tumors may uptake more methionine. Consistent with this idea, Src tumors uptake more methionine than control, which was demonstrated by performing an in vitro culture of the wing disc with a methionine analog homopropargylglycine (HPG) (Figure 8A–B). Furthermore, Src tumors exhibit a higher ratio of SAM to methionine (Figure 8C), indicating enhanced methionine flux to SAM. Collectively, Src enhances methionine uptake and methionine flux. Interestingly, this Src-mediated control of methionine metabolism was independent of p38 (Figure 8D–F, Figure 8—figure supplement 1A), suggesting that Src does activate methionine metabolism but not through p38.

Figure 8 with 1 supplement see all
Cross talk between Src signaling and methionine-Tor signaling.

(A) An in vitro culture of the wing disc with a methionine analog homopropargylglycine (HPG) demonstrates that the tumor disc induced by Src42A constitutively active (CA) and JNK DN uptakes more methionine than the control disc. (B) Quantification of the HPG intensity in A. Two-tailed unpaired t-test. (C) The amounts of methionine and SAM in the wing discs were measured by LC-MS. The tumor disc induced by Src42A CA and JNK DN contains a higher amount of SAM, whereas the amount of methionine is not significantly different. Methionine flux was calculated as a ratio of SAM and methionine. Two-tailed unpaired t-test. (D) The increase of methionine incorporation in the Src tumor is not mediated by p38. (E) Quantification of the HPG intensity in D. One-way ANOVA with Sidak’s post-test. (F) p38 inhibition does not suppress the upregulated methionine flux by Src42A CA and JNK DN. Two-tailed unpaired t-test. (G) A schematic of the Src42A CA-mediated coupling of cell proliferation and death. JNK activates cell death, while p38 activates cell proliferation, which is regulated by methionine-mediated Tor signaling. Scale bars, 100 µm.

Discussion

Here, we elucidated the mechanism by which Src drives cell proliferation and cell death in an obligatory coupled manner. The obligation is mediated by coupling of two MAPK pathways diverging from the lynchpin protein Slpr. Downstream of Slpr, JNK activates cell death signaling, while p38 activates cell proliferation in a methionine-Tor dependent manner (Figure 8G). Src can potentially regulate Tor signaling through both p38-dependent and -independent mechanisms (Figure 8—figure supplement 1B). Our work provides several new insights discussed below.

First, our findings that Slpr mediates Src signaling provide a new molecular insight into regulation of Src signaling. Drosophila Src has been known to regulate various signaling pathways, including Notch, MAPKs, Jak-Stat, EGF, Wnt, and Hippo signaling (Cordero et al., 2014; Enomoto and Igaki, 2013; Ho et al., 2015; Read et al., 2004; Tateno et al., 2000), but Slpr has not previously been implicated in Src signaling. Especially, the mechanism behind Src-mediated JNK activation was elusive in spite of its biological importance in various contexts. Slpr fills in the gap between Src and JNK. In hindsight, it may seem sensible that Slpr, a JNKKK, could link Src and JNK. However, previous studies proposed that ubiquitin E2 complex Bendless (Ma et al., 2013) and F-actin cytoskeleton (Enomoto and Igaki, 2013; Fernández et al., 2014; Rudrapatna et al., 2014) mediate Src-JNK signaling. Thus, it was unclear until now whether a MAPKKK is necessary for Src-mediated activation of JNK. Furthermore, there are five Drosophila JNKKKs, including dTAK1, Mekk1, Ask1, Wnd, and Slpr (Nihalani et al., 2001; Ryabinina et al., 2006; Stronach and Perrimon, 2002; Takatsu et al., 2000; Wassarman et al., 1996), each of which functions uniquely in a context-dependent manner. In our initial RNAi screening that identified Slpr as a Src effector, other MAPKKKs were not identified (Figure 2—figure supplement 1A and Supplementary file 1). Thus, identification of Slpr as a linker between Src and JNK provides a new insight. An urging, next question is how Src regulates Slpr. We speculate that the components that are considered as Src downstream and/or Slpr; upstream, such as Dok, Shark, and Misshapen (Ríos-Barrera and Riesgo-Escovar, 2013), may mediate the signal transduction between them. Interestingly, we also found that Slpr inhibition suppresses the phenotype of CA Ras overexpression (Figure 8—figure supplement 1C, D), which, similar to Src, simultaneously induces apoptosis and proliferation (Karim and Rubin, 1998). This suggests that Slpr could function as a lynchpin hub that integrates inputs from multiple oncogenes.

In this study, we exclusively focused on cell autonomous signaling induced by Src. But we noticed that Src elicits non-cell autonomous activation of MAPKs, cell death, and proliferation (Figure 4A–B, Figure 4—figure supplement 1A–G), which is reminiscent of the non-cell autonomous activation of Yorkie by Src (Enomoto and Igaki, 2013). It will be interesting to elucidate how non-cell autonomous signaling is regulated by Src activation in a future study.

Second, although Src was known to induce apoptosis as well as cell proliferation, how Src accomplishes this was unclear. We elucidated that, diverging from Slpr, p38 accelerates cell proliferation and that JNK induces cell death. This is an obligatory coupling of proliferation and death, likely being accomplished through evolution as an imperative mechanism to prevent tumorigenesis by a single oncogene activation. This type of fail-safe mechanism to prevent facile transformation was previously suggested in a context of Myc oncogene. We propose that, although each oncogene should have its unique fail-safe mechanism, the concept of the intrinsic fail-safe mechanism to prevent oncogenesis by a single oncogene is general.

Third, from a therapeutic perspective, our observation that methionine strongly regulates Src-mediated overgrowth is intriguing. Tumor growth in vitro is metabolically regulated by nutrition (DeBerardinis and Chandel, 2016; Pavlova and Thompson, 2016) and dietary manipulation of serine, glycine, histidine, asparagine, cysteine, or methionine could clinically modulate cancer outcome (Badgley et al., 2020; Gao et al., 2019; Kanarek et al., 2018; Knott et al., 2018; Maddocks et al., 2013; Maddocks et al., 2017). Notably, in our physiological in vivo condition, only subtraction of methionine from diet enhances organismal survival over Src-mediated oncogenic stress. Methionine has been studied in contexts of life span, metabolic health, and cancer together with other amino acids (Gao et al., 2019; Lee et al., 2014; Malloy et al., 2013; Obata and Miura, 2015; Orentreich et al., 1993; Sanderson et al., 2019), but the molecular mechanisms behind methionine-mediated cellular and organismal physiology were often unclear. We demonstrate that methionine regulates Tor activation, which controls cell proliferation induced by Src-p38 signaling.

In this study, we also found that the methionine concentration in the hemolymph is lower in flies that bear tumors in the wing disc, which is reminiscent of the clinical condition where tumor affects the amino acid profiles in the blood (Lai et al., 2005). Of note, local glutamine is known to be consumed in the tumor environment (Sanderson et al., 2019), but at least we did not observe reduction of glutamine in the hemolymph of the flies bearing tumors. We presume that Src-induced increase of methionine uptake in the Src tumor is at least partly responsible for the Src tumor-induced hypomethioninemia, although other tissues may also contribute to it as the case of the fat body during wing disc repair (Kashio et al., 2016).

Regarding a cross-talk between Src signaling and nutrition-mediated Tor activation, we found that there are multiple cross-talk points. Src regulates methionine uptake and methionine flux in a p38-independent manner, both of which can potentially feed into Tor activation. Then, a question is how Src-p38 regulates Tor signaling, since Src-p38 clearly activates Tor signaling (Figure 5A–B). Although p38 is known to regulate Tor, its exact molecular mechanism remains unclear (Cully et al., 2010). Using the previously published RNAseq data on Src tumor in the wing disc (Ho et al., 2015), we surveyed expression levels of potential Tor regulators and selected genes that are affected by Src expression, including amino acid transporters and GATOR complexes. GATOR complexes regulate Tor through Rag GTPases (Kim and Guan, 2019; Sabatini, 2017). We examined whether their expression is regulated by Src in a p38-dependent manner using RT-qPCR. We found that among the amino acid transporters and GATOR complex components examined, only pathetic (path), an SLC36 amino acid transporter that can transport multiple amino acids, was significantly induced by Src in a p38-dependent manner (Figure 8—figure supplement 1E, F). Since Path can mediate amino acids-mediated Tor activation (Goberdhan et al., 2005; Newton et al., 2020), we speculate that Src-p38 could regulate Tor potentially through Path-mediated uptake of non-methionine amino acids.

Our findings have significant implications in the field of cancer therapeutics. As described in Introduction, SFK inhibitors have been clinically unsuccessful in spite of SFKs’ contribution to tumorigenesis and metastasis (Gargalionis et al., 2014; Sousa-Victor and Jasper, 2014). We expect that the new insights our study provides on the Src tumorigenesis may help pave the way to cancer treatment. Furthermore, our data imply that nutritional state and tumorigenesis are closely linked. We speculate that, in case of tumors with a high SFK activity, manipulation of dietary methionine may have a clinical benefit.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene Drosophila melanogasterrasFlybaseFLYB: FBgn0003205NA
Gene Drosophila melanogastersrc42AFlybaseFLYB: FBgn0264959NA
Gene Drosophila melanogastervgFlybaseFLYB: FBgn0003975NA
Gene Drosophila melanogasterslprFlybaseFLYB: FBgn0030018NA
Gene Drosophila melanogasterrprFlybaseFLYB: FBgn0011706NA
Gene Drosophila melanogastergrimFlybaseFLYB: FBgn0015946NA
Gene Drosophila melanogasterhidFlybaseFLYB: FBgn0003997NA
Gene Drosophila melanogasterbskFlybaseFLYB: FBgn0000229NA
Gene Drosophila melanogasterlicFlybaseFLYB: FBgn0261524NA
Gene Drosophila melanogastertorFlybaseFLYB: FBgn0021796NA
Gene Drosophila melanogasterrlFlybaseFLYB: FBgn0003256NA
Gene Drosophila melanogasterp38bFlybaseFLYB: FBgn0024846NA
Gene Drosophila melanogastermef2FlybaseFLYB: FBgn0003256NA
Gene Drosophila melanogasteratf2FlybaseFLYB: FBgn0265193NA
Gene Drosophila melanogasterSamSFlybaseFLYB: FBgn0005278NA
Gene Drosophila melanogastersamtorFlybaseFLYB: FBgn0035035NA
Gene Drosophila melanogasterCG13248FlybaseFLYB: FBgn0036984NA
Gene Drosophila melanogastertadrFlybaseFLYB: FBgn0036984NA
Gene Drosophila melanogasterCG9413FlybaseFLYB: FBgn0030574NA
Gene Drosophila melanogasterjhl-21FlybaseFLYB: FBgn0028425NA
Gene Drosophila melanogastersbmFlybaseFLYB: FBgn0030574NA
Gene Drosophila melanogasterCG8757FlybaseFLYB: FBgn0036380NA
Gene Drosophila melanogasterCG16700FlybaseFLYB: FBgn0030816NA
Gene Drosophila melanogasterpathFlybaseFLYB: FBgn0036007NA
Gene Drosophila melanogasternprl3FlybaseFLYB: FBgn0036397NA
Gene Drosophila melanogasternprl2FlybaseFLYB: FBgn0030800NA
Gene Drosophila melanogasteriml1FlybaseFLYB: FBgn0035227NA
Gene Drosophila melanogasterwdr24FlybaseFLYB: FBgn0027518NA
Gene Drosophila melanogasterwdr59FlybaseFLYB: FBgn0032339NA
Gene Drosophila melanogasternup44AFlybaseFLYB: FBgn0033247NA
Gene Drosophila melanogastermioFlybaseFLYB: FBgn0031399NA
Gene Drosophila melanogasterRpL32FlybaseFLYB: FBgn0002626NA
Genetic reagent (Drosophila melanogaster)UAS-RasV12Iswar Hariharan labUAS-RasV12NA
Genetic reagent (Drosophila melanogaster)UAS-Src42A CABloomington Drosophila Stock CenterBDSC: 6410
RRID:BDSC_6410
NA
Genetic reagent (Drosophila melanogaster)UAS-Src42ATian Xu labUAS-Src42ANA
Genetic reagent (Drosophila melanogaster)vg-Gal4Bloomington Drosophila Stock CenterBDSC: 6819
RRID:BDSC_6819
NA
Genetic reagent (Drosophila melanogaster)gmr-Gal4Iswar Hariharan labgmr-Gal4NA
Genetic reagent (Drosophila melanogaster)TRE-RFPBloomington Drosophila Stock CenterBDSC: 59011NA
Genetic reagent (Drosophila melanogaster)UAS-slpr RNAiBloomington Drosophila Stock CenterBDSC: 32948
RRID:BDSC_32948
NA
Genetic reagent (Drosophila melanogaster)UAS-slpr RNAiVienna Drosophila Resource CenterVDRC ID: 33516
RRID:FlyBase_FBst0460140
NA
Genetic reagent (Drosophila melanogaster)UAS-GFPIswar Hariharan labUAS-GFPNA
Genetic reagent (Drosophila melanogaster)UAS-his2B RFPIswar Hariharan labUAS-his2B RFPNA
Genetic reagent (Drosophila melanogaster)UAS-miRNA RGHSiegrist et al., 2010PMID:20346676NA
Genetic reagent (Drosophila melanogaster)UAS-JNK DNIswar Hariharan labUAS-JNK DNNA
Genetic reagent (Drosophila melanogaster)UAS-p38 DNBloomington Drosophila Stock CenterBDSC: 59005
RRID:BDSC_59005
NA
Genetic reagent (Drosophila melanogaster)UAS-lic RNAiBloomington Drosophila Stock CenterBDSC: 31643
RRID:BDSC_31643
NA
Genetic reagent (Drosophila melanogaster)UAS-Tor RNAiBloomington Drosophila Stock CenterBDSC: 34639
RRID:BDSC_34639
NA
Genetic reagent (Drosophila melanogaster)UAS-erk RNAi 1Vienna Drosophila Resource CenterVDRC ID: 35641
RRID:FlyBase_FBst0461260
NA
Genetic reagent (Drosophila melanogaster)UAS-erk RNAi 2Vienna Drosophila Resource CenterVDRC ID: 109573
RRID:FlyBase_FBst0481239
NA
Genetic reagent (Drosophila melanogaster)UAS-mef2 RNAiVienna Drosophila Resource CenterVDRC ID: 15550
RRID:FlyBase_FBst0451917
NA
Genetic reagent (Drosophila melanogaster)UAS-atf2 RNAiBloomington Drosophila Stock CenterBDSC: 60124
RRID:BDSC_60124
NA
Genetic reagent (Drosophila melanogaster)UAS-SamS RNAi 1Vienna Drosophila Resource CenterVDRC ID: 7167
RRID:FlyBase_FBst0470579
NA
Genetic reagent (Drosophila melanogaster)UAS-SamS RNAi 2Vienna Drosophila Resource CenterVDRC ID: 103143
RRID:FlyBase_FBst0475005
NA
Genetic reagent (Drosophila melanogaster)UAS-samtor RNAiBloomington Drosophila Stock CenterBDSC: 54010
RRID:BDSC_54010
NA
Genetic reagent (Drosophila melanogaster)UAS-eigerIswar Hariharan labUAS-eigerNA
Genetic reagent (Drosophila melanogaster)UAS-rpr mtsHerman Steller labPMID:20837774NA
Genetic reagent (Drosophila melanogaster)w¹¹¹⁸Erina Kuranaga labw¹¹¹⁸NA
Genetic reagent (Drosophila melanogaster)Oregon RBloomington Drosophila Stock CenterBDSC: 4269
RRID:BDSC_4269
NA
AntibodyRabbit polyclonal phospho H3 antibodyMerckCat# 06–570
RRID:AB_310177
Immunostaining (1:200)
AntibodyRabbit polyclonal cleaved Drosophila Dcp-1 antibodyCell SignalingCat# 9578
RRID:AB_2721060
Immunostaining (1:100)
AntibodyRabbit monoclonal phospho-p38 MAPK antibodyCell SignalingCat# 4631
RRID:AB_331765
Immunostaining (1:100)
AntibodyMouse monoclonal phospho-Erk MAPK antibodyMerckCat# M8159
RRID:AB_477245
Immunostaining (1:100)
AntibodyRabbit monoclonal phospho-4EBP1 antibodyCell SignalingCat# 2855
RRID:AB_560835
Immunostaining (1:100)
AntibodyRabbit polyclonal phospho-S6 antibodyKim et al., 2017PMID:28829944Immunostaining (1:300)
AntibodyAlexa mouse Fluor 568 secondary antibodyThermo FisherCat# A-11004
RRID:AB_253407
Immunostaining (1:300)
AntibodyAlexa rabbit Fluor 568 secondary antibodyThermo FisherA-11036
RRID:AB_10563566
Immunostaining (1:300)
Sequence-based reagentslpr (primer)This paperNAF: 5'-CTACAAGGGCTTCGATCCGTTG-3
R: 5'-GTTTGCCAGCAGCTCTTCATCAG-3
Sequence-based reagentslpr (primer)This paperNAF: 5'-CAATCATCTGCAGCAGAAGACGC-3'
R: 5'-CATCGGAGAATTTGGAATAGGTGC-3'
Sequence-based reagentSamS (primer)Obata and Miura, 2015PMID:32938923F: 5'-GCCAACGGCGTTCATATC-3'
R: 5'-GGCATATCCAAACATGATACCC-3'
Sequence-based reagentCG13248 (primer)FlyPrimerBankPP18106F: 5'-AAACCGATGCCTCAACACCTT-3'
R: 5'-CAGTCAGCACGTAGATGCCA-3'
Sequence-based reagenttadr (primer)FlyPrimerBankPP20579F: 5'-CAGCCCGCTGTAAAACTAGC-3'
R: 5'-GGCCAGAGCATCTAGCCAG-3'
Sequence-based reagentCG9413 (primer)FlyPrimerBankPP29104F: 5'-TGGGGTGGCTTTAATTGTTGG-3'
R: 5'-CAGTGCGAACCAGTAAACCG-3'
Sequence-based reagentjhl-21 (primer)Newton et al., 2020PMID:32938923F: 5'-TCAAGCGGAAGCTAACACTCA-3'
R: 5'-TTCGGTGTAAATAAAGACTCCCG-3'
Sequence-based reagentsbm (primer)FlyPrimerBankPP3597F: 5'-AATGTGCCAACAAAAACAACGA-3'
R: 5'-GTCCCTGATGAGTCGGTCTC-3'
Sequence-based reagentCG8757 (primer)Newton et al., 2020PMID:32938923F: 5'-AGAAACGATTGGATCGGGCA-3'
R: 5'-ATCTGCCATCTTTTGGACCGA-3'
Sequence-based reagentCG16700 (primer)FlyPrimerBankPP25676F: 5'-CCTACAAGCTATCTGGAGACCA-3'
R: 5'-GAGACCTCCGTTCTTGAAGGC-3'
Sequence-based reagentpath (primer)Newton et al., 2020PMID:32938923F: 5'-TGTTTGATTTGCGCGGCATT-3'
R: 5'-TTCGACCCGCTGTCCACTAT-3'
Sequence-based reagentnprl3 (primer)FlyPrimerBankPP28256F: 5'-GTTAAACCACAGCTATGCAACCA-3'
R: 5'-CAGAGTGGGATGACTGACAAAG-3'
Sequence-based reagentnprl2 (primer)FlyPrimerBankPP27923F: 5'-TTCAACGCTGCATTCTCACC-3'
R: 5'-ATTCCGTGCGTACTTCTGCTG-3'
Sequence-based reagentiml1 (primer)FlyPrimerBankPP8389F: 5'-CGTGGCTGCAACAAATCCTAC-3'
R: 5'-GCCCGATTCTATGCTTATCACA-3'
Sequence-based reagentwdr24 (primer)FlyPrimerBankPP3395F: 5'-GCCCTGGCCCTGAATAAGG-3'
R: 5'-TGAAGCCATTGCTGTTTATGGAG-3'
Sequence-based reagentwdr59 (primer)FlyPrimerBankPP2977F: 5'-GCACCCGAACAAACGTACATC-3'
R: 5'-CCGAGTAATCAACCGACATGG-3'
Sequence-based reagentnup44A (primer)FlyPrimerBankPP28620F: 5'-GAGGAGGTGATTGGCGAAAAG-3''
R: 5'-GCGAGTCTACAAGGGTGGTG-3'
Sequence-based reagentmio (primer)NAPMID:26024590F: 5'-AGCGAGACGAGCTAAACAATTC-3'
R: 5'-GTGTAAGAGGCAAGCAAAGGTT-3'
Sequence-based reagentRpL32 (primer)This paperNAF: 5'-CCAGCATACAGGCCCAAGATCGTG-3'
R: 5'-TCTTGAATCCGGTGGGCAGCATG-3'
Commercial assay or kitMethionine analog homopropargylglycine (HPG)- based on Click-iT HPG Alexa Fluor 488 Protein Synthesis Assay kit Single Cell 3’ Library and Gel Bead Kit v2InvitrogenC10428NA
Commercial assay or kitMaxwell RSC simply RNA Tissue KitPromegaAS1340NA
Commercial assay or kitReverTra Ace qPCR RT KitToyoboFSQ-101NA
Commercial assay or kitBCA protein assay kitThermo Fisher23225NA
Software, algorithmImageJNAhttps://imagej.nih.gov/ij/ RRID:SCR_003070NA
Software, algorithmIMARIS 9.5.1Oxford Instrumenthttps://imaris.oxinst.com/packages
RRID:SCR_007370
NA
OtherDAPISigmaD95421:1000

Drosophila husbandry

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Flies were maintained as previously described (Yoo et al., 2016). The fly food is composed of the following ingredients: 0.8% agar, 10% glucose, 4.5% corn flour, 3.72% dry yeast, 0.4% propionic acid, 0.3% butyl p-hydroxybenzoate.

The composition of the food for yeast restriction and amino acid addition/subtraction is described in Supplementary file 1 and Supplementary file 2. The amino acid concentrations are based on the previously described holidic medium for Drosophila melanogaster (Piper et al., 2014).

Drosophila stocks

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Flies were crossed and raised at 25°C unless otherwise noted. For wild-type controls, Oregon-R (Bloomington stock center [BL] 4269) was used. The following fly stocks were used in this study:

  • UAS-Src42A CA (BL6410)

  • UAS-Src42A (a gift from Dr Tian Xu) vg-Gal4 (BL6819) gmr-Gal4 (a gift from Dr Iswar Hariharan)

  • TRE-RFP (a gift from Dr Dirk Bohmann)

  • UAS-slpr RNAi (BL32948, v33516)

  • UAS-GFP (a gift from Dr Iswar Hariharan)

  • UAS-his2B RFP (a gift from Dr Iswar Hariharan)

  • UAS-miRNA RGH (a gift from Dr Iswar Hariharan)

  • UAS-JNK DN (a gift from Dr Iswar Hariharan)

  • UAS-p38 DN (BL59005)

  • UAS-lic RNAi (BL31643)

  • UAS-Tor RNAi (BL34639)

  • UAS-erk RNAi (RNAi 1: v35641, RNAi 2: v109573)

  • UAS-RasV12 (a gift from Dr Iswar Hariharan)

  • UAS-mef2 RNAi (v15550)

  • UAS-atf2 RNAi (BL60124)

  • UAS-SamS RNAi (RNAi 1: v7167, RNAi 2: v103143)

  • UAS-samtor RNAi (BL54010)

  • UAS-rprmts (a gift from Hermann Steller) (Sandu et al., 2010)

  • UAS-eiger (a gift from Dr Iswar Hariharan) w1118 (a gift from Dr Erina Kuranaga)

Immunofluorescence and confocal imaging

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Wing discs were dissected in PBS, fixed with paraformaldehyde in PBS, and washed in PBS with 0.1% Triton X-100. We used the following antibodies and fluorescent dyes: rabbit phospho H3 antibody (1:200, 06–570, Merck), rabbit cleaved Drosophila Dcp-1 antibody (1:100, #9578, Cell Signaling), rabbit phospho-p38 MAPK antibody (1:100, #4631, Cell Signaling), mouse phospho-Erk MAPK antibody (1:100, M8159, Merck), rabbit phosphor-4EBP one antibody (1:100, #2855, Cell Signaling), rabbit phospho-S6 antibody (1:300) (Kim et al., 2017), DAPI (D9542, Sigma), and Alexa Fluor secondary antibodies (A11004, A11008). Fluorescent images were acquired with confocal microscopes (Zeiss LSM 880,780). Quantification of the cell volume and the intensity measurement of fluorescent signals were performed by using IMARIS 9.5.1. ImageJ was used to measure the wing size.

Immunostaining quantification

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For quantification of the fluorescent intensity for TRE-RFP, p-p38, pERK, p4EBP, and pS6 in GFP+ cells, GFP+ cell volume and sum intensity of fluorescent signals in GFP+ cells were used. Samples were analyzed with IMARIS 9.5.1. The GFP intensity was measured to identify GFP+ cells. The volume and the sum intensity of fluorescent signals were quantified by surface function. The mean intensity within GFP+ cells was calculated by dividing the sum intensity of fluorescent signals by GFP+ cell volume. For quantification of the fluorescent intensity in GFP- cells, the total disc size (μm3) and the sum intensity of fluorescent signals within the whole disc were measured. DAPI intensity was used to determine the whole disc region. Volume of GFP- cells and sum intensity in GFP- cells were quantified by subtracting each information (volume, sum intensity) in GFP+ cells from ones in whole discs. The mean intensity of fluorescence within GFP- cells was calculated as performed in GFP+ cells.

Quantification of proliferating cells

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For quantification of proliferation rate, the number of pH3-positive cells and GFP+ cell area were measured by using ImageJ. The density of pH3+ cells was calculated by dividing the number of pH3+ cells by GFP+ cell area.

Quantification of dying cells

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To determine the dying cells, the intensity of cDCP1 antibody staining was used. Percentage of DCP1-positive cells was measured in GFP+/- cells respectively by using ImageJ.

Measurement of survival rate

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For measuring the survival rate, mated females were allowed to lay eggs on a grape agar plate for 24 hr at 25°C. First instar larvae were collected from the grape agar plate and placed into treatment vials with different food conditions. Each vial contains 50 larvae. The number of adult flies of each genotype that were able to eclose was recorded. Survival rates were calculated as the number of adult flies that eclosed divided by the expected number of larvae of each genotype placed in each vial. Most experiments were performed at 25°C, except the ones performed at 23°C to increase the sensitivity of the assays in Figures 2A, 3E, 4F, 6E and 7A–C, Figure 4—figure supplement 1H and Figure 7—figure supplement 1A–B.

Measurement of amino acids in the larval hemolymph

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We teared 20 L3 larvae on an iced block to collect 5 µl of the hemolymph. Extraction and quantification of metabolites in the hemolymph were performed as described previously with capillary electrophoresis time-of-flight mass spectrometry (Soga et al., 2006; Soga et al., 2003; Tabata et al., 2017).

Measurement of methionine and SAM in the larval wing discs and hemolymph by LC-MS/MS

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Twenty wing discs or 1.5 µl hemolymph were used per sample to measure the amounts of methionine and/or SAM. Frozen samples in 1.5 ml plastic tubes were homogenized in 300 µl of cold methanol with 1× f3-mm zirconia beads using an automill (Tokken Inc) at 41.6 Hz for 2 min. The homogenates were mixed with 200 µl of methanol, 200 µl of H2O, and 200 µl of CHCl3 and then vortexed for 20 min at RT. The samples were centrifuged at 20,000 g for 15 min at 4°C. The supernatant was mixed with 350 µl of H2O and vortexed for 10 min at RT. The aqueous phase was collected after centrifugation and dried in a vacuum concentrator. The samples were redissolved by 50% acetonitrile, transferred to autosampler vial, and kept at 5.0°C. The insoluble pellets were heat-denatured with 0.2 N NaOH and used to quantify total protein using a BCA protein assay kit (Thermo). Chromatographic separations in an Acquity UPLC H-Class System (Waters) were carried out under reverse-phase conditions using an Acquity UPLC HSS T3 column (2.1 × 100 mm) in infusion. A mobile phase consists of solvent A (10 mM ammonium bicarbonate, pH 7.5) and solvent B (acetonitrile). The flow rate was 0.5 ml/min at 30.0°C. Compounds were separated by gradient elution, in turn ionized and detected using a Xevo TQ-S triple quadrupole mass spectrometer coupled with an electro-spray ionization source (Waters). Precursor ion was scanned at m/z (MH+: 399.143 > 250.092 for SAM, and 150.057 > 104.053 for methionine) by multiple reaction monitoring and established methods using individual authentic compounds and biological samples. The peak area of a target metabolite was analyzed using MassLynx 4.1 software (Waters). Metabolite signals were then normalized to the total protein level of the corresponding sample after subtracting the values from the blank sample. A two-tailed unpaired t-test was used to test between samples.

RT-qPCR

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For RT-qPCR, the total RNA was extracted from 30 discs per sample by using the Maxwell RSC simplyRNA Tissue Kit (Promega). Total RNA (250 ng) was subjected to DNase digestion, followed by reverse transcription using the ReverTra Ace qPCR RT Kit (Toyobo). qPCR was performed using the FastStart Essential DNA Green Master Mix (Roche). Rpl32 was used as an internal control. Error bars represent the SE. Primers used for qPCR are shown in Supplementary file 4.

HPG incorporation assay

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Methionine incorporation was monitored by the methionine analog HPG-based on Click-iT HPG Alexa Fluor Protein Synthesis Assay kit (Invitrogen). Wing discs were dissected in S2 medium. After dissection, discs were incubated with the S2 medium containing 5 mM HPG for 30 min. After washing with PBS, the discs were fixed by 4% PFA for 30 min. The discs were then washed by PBS three times and permeabilized with PBS with 0.1% Triton X-100 for 20 min, followed by 15 min incubation with freshly prepared Click-iT reaction cocktail. The discs were then washed by PBS three times and PBS with 0.1% Triton X-100 once, and incubated with DAPI for 30 min. Fluorescent images were acquired with the confocal microscope (Zeiss LSM 880). The mean intensity of HPG in Src42A CA expressing cells were quantified by using ImageJ.

Statistical analysis

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Statistical tests used were indicated in the figure captions. All the data plotted in each graph were summarized in Supplementary file 3. Sample sizes were determined empirically based on the observed effects. All the statistical analyses were performed using Graphpad Prism 9. A two-tailed unpaired t-test was used to test between two samples. The number of samples is shown in Supplementary file 3. One-way ANOVA with Sidak’s multiple comparisons test was used to test among groups. Statistical significance is shown by asterisk; ∗p<0.05, ∗∗p≦0.01, ∗∗∗p≦0.001, ∗∗∗∗p≦0.0001.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

References

    1. Evan G
    2. Harrington E
    3. Fanidi A
    4. Land H
    5. Amati B
    6. Bennett M
    (1994) Integrated control of cell proliferation and cell death by the c-myc oncogene
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 345:269–275.
    https://doi.org/10.1098/rstb.1994.0105
    1. Karim FD
    2. Rubin GM
    (1998)
    Ectopic expression of activated Ras1 induces hyperplastic growth and increased cell death in Drosophila imaginal tissues
    Development 125:1–9.
    1. Kussick SJ
    2. Basler K
    3. Cooper JA
    (1993)
    Ras1-dependent signaling by ectopically-expressed Drosophila src gene product in the embryo and developing eye
    Oncogene 8:2791–2803.

Decision letter

  1. Utpal Banerjee
    Senior and Reviewing Editor; University of California, Los Angeles, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

In this manuscript, the authors address some of the most critical issues regarding the function of upstream components of JNK signaling that have lingered around, unsolved, for quite some time. Although it was known that a key activation point was at the level of a KKKinase, which was almost certain to be slipper, the question of how this pathway controls multiple functions including the apparently contradictory phenotypes of proliferation and cell death has been worked out in some detail in this paper and the study opens, as well, doors for further analysis of how a cell keeps information channelized while also allowing for the possibility of cross talk that maintain homeostatic balance.

Decision letter after peer review:

Thank you for submitting your article "Methionine restriction breaks obligatory coupling of cell proliferation and death by an oncogene Src in Drosophila" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Utpal Banerjee as the Senior Editor. The reviewers have opted to remain 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. Normally, we summarize the reviews at this point to list the specific list of experiments that we expect the authors to conduct. Given the difficulties these days with performing extensive experiments, we provide you with the full reviews so that you can determine the critical issues on which you should focus when carrying out revisions. You will see that the src-slpr aspect is universally lauded as novel. The issues raised have to do with the methionine/TOR angle. It should be easy to determine from the reviews the aspects that are most important to the reviewers. Some parts are easily addressed. If you so wish, please feel free to let us know if there is some aspect that might be particularly difficult to handle experimentally, and I will run this by the reviewers. Please also note the following eLife policy:

The editors have judged that your manuscript is of interest to eLife, but as described below additional experiments are required before it is published. We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162).

First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

This study by Nishida et al. investigates the mechanisms by which the well-known phenomenon of constitutive activation of Src mediates both cell growth and apoptosis during tumorigenesis. In particular, the authors sought to explore whether these two pathways were coupled and through what downstream effectors Src functions. They found that indeed both cell growth and apoptosis were obligately coupled through the "lynchpin" protein Slpr, which mediates cell growth through a Src-p38-Tor pathway and apoptosis through a Src-JNK pathway.

Reviewer #1:

Overall, the authors the authors do a good job rationalizing, designing, explaining, and interpreting most of their results and the important contributions and insights of this paper in both identifying that Src mediates JNK activity, which was not previously understood well, and that it does this through through slpr provide important knowledge to the field. Although the genetic experiments are straightforward, the link with TOR and nutrition in interpreting the phenotype is more complex and difficult to explain as written.

1. The mechanistic link between Src-Slpr-p38 and TOR is not well developed. Although it is clear that TOR inhibition inhibits overgrowth, these experiments do not provide strong support for TOR being a bona fide component of the Src-Slpr-p38 axis. For example, loss of function of TOR is expected to have rather pleiotropic effects. Stronger mechanistic evidence of how p38 activates TOR, would greatly strengthen this line of reasoning. Currently, less direct mechanisms cannot be excluded.

2. The link between the genetic experiments and nutrition, for example the role of methionine, is quite interesting, but is not so well developed and the reasoning behind the experiments is difficult to follow. It seems as though the logic is that diluting amino acids decreases the tumor growth, and that adding the amino acids back suppresses the rescue. Addition of amino acid, without methionine, limits tumor growth to a degree and increases survival compared to EAAs, though the magnitude of the rescue is difficult to interpret (e.g. 7.5 % vs 15 % survival, with quite large variance). Adding methionine alone clearly limits survival of flies with tumor, though it is not clear that block is caused by TOR alone. These latter metabolic experiments are fairly difficult to interpret, and the link between Src-Slpr-p38 and TOR and methionine is not as clear as the work presented earlier in the paper.

3. Are there regional differences within the wing disc (Figure 1) where proliferation (pouch) vs apoptosis (edges) observed more commonly? How would such a difference fit into the model? Clonal analysis could shed light on this issue of autonomy

Reviewer #2:

In this paper, Nishida et al. address a long-standing and intensely-investigated question, namely how are cell proliferation and cell death controlled to modulate tissue growth in animals. They specifically investigate the effects of Src overexpression in imaginal tissues (which leads to both increases cell proliferation and cell death) and they make two key observations: (a) they identify the kinase Slpr as an effector of Src in the control of both cell proliferation (via p38) and cell death (via JNK); (b) they show that the effects of p38 are mediated via p38 and that dietary methionine can control TOR and modulate Src-mediated cell proliferation.

Overall, I liked this paper. I think the two main findings will be of interest to, not just Drosophila researchers, but also cell, developmental and cancer biologists interested in the regulation of tissue growth.

I have two main comments/questions:

1. Are the effects on cell proliferation and cell death strictly autonomous? It's hard to tell clearly from Figure 1, especially since the choice of driver of vg-Gal4 doesn't have clearly demarcated expression boundaries (compared to, say, en-GAL4 or ap-GAL4). This is import since, if the proliferation is a result of cell-death induced compensatory proliferation, then the interpretations of the data in the paper would be very different than if everything was strictly autonomous.

2. The link between p38 and TOR is interesting, but it relies on using measurement of phospho-4EBP as a readout of TOR signaling. These data are certainly very clean, however, it’s important to note that total levels of 4EPB are often induced upon stress signaling. Hence, it's important to be sure that the increase in phospho-4EBP signal does reflect an increase in the amounts of phosphorylated 4EBP (a TOR effect) vs simply an increase in total 4EBP (a TOR-independent, stress effect). A recent paper from the Karpac lab (Cell Reports, 2020) had some nice immunostaining with a non-phospho 4EBP, which would be a useful control in this manuscript. Alternatively, the induction of TOR could be confirmed with a phospho-S6k or phospho-S6 antibody (western on discs or immunostaining). The genetic expts with TOR certainly support a role for TOR in modulating Src function, but they could equally be interpreted as TOR signaling being a parallel pathway or TOR being a downstream Src pathway.

Reviewer #3:

The authors examine how an oncogene, Src, can activate both proliferation and apoptosis during oncogenesis. The answer is surprisingly simple. In the case of Src, it activates both pro-apoptotic (JNK) and proliferative (p38) MAPK pathways and upstream of this, the authors identify the MPKKK slpr as the lynchpin in this pathway. Src also activates Erk-type MAPK, but according to the authors, this has no effect for tumorigenesis. The authors then focus on the role of p38 and found that p38 controls Tor signaling. Interestingly, the nutrition status does influence Src-induced Tor signaling and tissue growth. To further delineate this observation, the authors tested each amino acid and found specifically a requirement of Methionine for Src-induced tumor growth. They also observed that the Methionine concentration in the hemolymph of these Src-bearing larvae is lower compared to controls, presumably because the Src-tumor tissue absorbs more of it. Overall, the authors present a model that combines several signaling pathways and metabolism for Src-induced tumor growth.

This is an interesting manuscript, but I do have some comments and suggestions for improvement.

1. What is the nature of Src42A CA? How does it promote dominant activity to Src?

2. Provide a complete picture of the Src CA suppressor screen in which they found slpr. Were there other suppressors and if so, why did they focus on slpr?

3. Present the results from the screen of the other MPKKK. Did they also screen the MPKK and other upstream components in the MPK pathway (Msn, Traf1, Traf2, Rho1)? This might tell us how Src activates Slpr.

4. How does Src activate Slpr? Is any of the domains of Slpr (SH2, SH3, kinase) required for this activation?

5. How many different slpr RNAi lines were tested? Are the results verified with several independent lines? Do slpr mutants suppress Src42ACA?

6. Verify the Erk-MAPK (rl) RNAi line(s).

7. The data presentation needs improvement. The discs are too small and often the relevant staining (PH3, cDcp1, TRE-RFP, etc.) is hard to judge. This is also particularly important for the non-cell autonomy of Erk and p38 activity in Figure 4A-B. Some convincing close-up pictures could help.

8. There is no comprehensive description of how the quantitation of the staining was conducted. Was the quantitation done in the region of overgrowth? How was the volume of GFP+ tissue determined? Were equivalently sized regions used and how was their level of fluorescence assessed? Was there some internal control to normalize for differences in general staining between regions of interest and region where no change would be expected (ie a part of the disc outside of the vg-Gal4 area)?

9. Figure 1A: quantify the cDcp1 staining.

10. Figure 2C: slpr RNAi seems to suppress TRE-RFP, but not overgrowth. In fact, the slpr RNAi disc (lower panels) is significantly larger than the src42ACA disc (middle panels) including the GFP+ area. That is inconsistent with the authors model. Explain. Please quantify GFP+ areas and overall disc size.

11. Figure 3B: Perform PH3 labeling in vg>Src42ACA JNKDN slprRNAi background. I would like to know if the overgrowth and PH3 labeling in Src42ACA JNKDN is dependent on Slpr. The authors imply this, but don't show it.

12. Figure 5A-B: is the 4EBP phosphorylation in vg>Src42CA JNK DN discs depended on Slpr, i.e. does slpr RNAi block it?

13. In the Discussion, the authors mention that "methionine regulates Tor activation". I don't think this direct link was established in this paper.

14. Is the a SAMTOR homolog in flies? If so, test it in the Src42ACA screen.

https://doi.org/10.7554/eLife.59809.sa1

Author response

Reviewer #1:

Overall, the authors the authors do a good job rationalizing, designing, explaining, and interpreting most of their results and the important contributions and insights of this paper in both identifying that Src mediates JNK activity, which was not previously understood well, and that it does this through through slpr provide important knowledge to the field. Although the genetic experiments are straightforward, the link with TOR and nutrition in interpreting the phenotype is more complex and difficult to explain as written.

1. The mechanistic link between Src-Slpr-p38 and TOR is not well developed. Although it is clear that TOR inhibition inhibits overgrowth, these experiments do not provide strong support for TOR being a bona fide component of the Src-Slpr-p38 axis. For example, loss of function of TOR is expected to have rather pleiotropic effects. Stronger mechanistic evidence of how p38 activates TOR, would greatly strengthen this line of reasoning. Currently, less direct mechanisms cannot be excluded.

In the original manuscript, we had three pieces of data related to Tor signaling.

1. Src-p38 activates Tor signaling, based on p4EBP.

2. Tor inhibition suppresses Src-induced tumorigenesis.

3. Methionine is important for Tor signaling and Src-induced tumorigenesis.

Based on 1 and 2, we feel it was reasonable to describe that Src-p38-Tor signaling regulates Src-induced tumorigenesis. What was not clear was how Src-p38-Tor signaling crosstalks with methionine-Tor signaling and also how Src-p38 regulates Tor. The link between methionine and Tor was not strong either. As the reviewers see below, we spent considerable time and effort to address these points in this revision. What we eventually elucidated is a rather complicated mechanism, but at least it provides a clearer picture of what’s occurring during Src oncogenesis. The following is what we found.

Regarding methionine-Tor signaling, we found that Sam synthetase, which makes a methyl donor SAM from methionine, is important for Tor activation and Src-induced tumorigenesis (Figure 7 I-K). We also found that Samtor, a Sam sensor that regulates Tor, is important for Src-induced Tor regulation (Figure 7—figure supplement 2 D-F). These findings strengthen the link between methionine and Tor during Src-mediated oncogenesis.

Regarding how Src regulates methionine-Tor signaling, we found that Src promotes uptake of methionine in the tumor and also conversion of methionine to SAM (methionine flux) (Figure 8A-C). However, this was independent of p38 (Figure 8D-F). This indicates that Src could potentially activate methionine-Tor signaling through activation of methionine metabolism (uptake and flux) in a p38-independent manner.

So, a question is how Src-p38 contributes to Tor activation, since clearly Src-p38 signaling activates Tor (Figure 5A-B). Although previously it was shown that p38 activates Tor, its mechanism was not clear 1. To address this point sincerely, we started a new collaboration with Dr Diana Ho at Harvard, who previously performed RNAseq of the vg>Src42A CA wing disc 2. We surveyed expression levels of potential Tor regulators and selected genes that are affected by Src expression, including amino acid transporters and GATOR complexes. GATOR complexes regulate Tor through Rag GTPases 3,4. We examined whether their expression is regulated by Src in a p38-dpendent manner using RT-qPCR. We found that among the amino acid transporters and GATOR complex components examined, only pathetic (path), a SLC36 amino acid transporter that can transport multiple amino acids, was significantly induced by Src in a p38-dependent manner (Figure 8—figure supplement 1D-E). Since Path can mediate amino acids-mediated Tor activation 5,6, we speculate that Src-p38 could regulate Tor potentially through Path-mediated uptake of non-methionine amino acids.

We acknowledge that our data on Path’s role during Src tumorigenesis are indirect and at best suggestive, so we included the data of Path in discussion and carefully discussed this possible mechanism in the manuscript.

Our findings on the role for Src-p38 signaling in Tor activation is consistent with the previous findings that p38 is necessary for Tor activation in response to amino acids 1. Our suggestion of the role for non-methionine amino acids in Tor regulation, at a glance, may seem contradictory from our amino acid subtraction data, which showed that only subtraction of methionine could affect survival over the Src tumor (Figure 7B). But we speculate that because of Path’s function to uptake multiple amino acids, which provides redundancy, single amino acid subtraction experiments elucidated only the function of methionine. In a future study, it will be interesting to investigate effects of subtracting multiple amino acids at the same time.

Here we’d like to emphasize and clarify that the original purpose of this paper was to find the mechanism by which Src induces both cell death and proliferation. But somehow, while we explore the mechanism of Src-mediated Tor regulation and proliferation during revision, this part became unexpectedly and unbalancingly huge. But, we still feel that our original, simple schematic that explains the mechanism of cell death/proliferation is valid (Figure 8G). In this revised manuscript, we also included a schematic showing a detailed possible mechanism by which Src regulates methionine-Tor signaling (Figure 8—figure supplement 1F).

2. The link between the genetic experiments and nutrition, for example the role of methionine, is quite interesting, but is not so well developed and the reasoning behind the experiments is difficult to follow. It seems as though the logic is that diluting amino acids decreases the tumor growth, and that adding the amino acids back suppresses the rescue. Addition of amino acid, without methionine, limits tumor growth to a degree and increases survival compared to EAAs, though the magnitude of the rescue is difficult to interpret (e.g. 7.5 % vs 15 % survival, with quite large variance). Adding methionine alone clearly limits survival of flies with tumor, though it is not clear that block is caused by TOR alone. These latter metabolic experiments are fairly difficult to interpret, and the link between Src-Slpr-p38 and TOR and methionine is not as clear as the work presented earlier in the paper.

Regarding the link between the genetic experiments and nutrition manipulation, please see the response above.

Regarding the variance of the survival rate, we feel it’s an intrinsic property of physiological experiments like ours. Because of the variance in these physiological experiments, we adopted valid statistical analyses. All information of the statistical analyses were included in table 3.

3. Are there regional differences within the wing disc (Figure 1) where proliferation (pouch) vs apoptosis (edges) observed more commonly? How would such a difference fit into the model? Clonal analysis could shed light on this issue of autonomy

Let us clarify where apoptosis and proliferation occur. We quantified both autonomous and non-autonomous proliferation/death in this revised manuscript (Figure 1B, 1D and Figure 4—figure supplement 1D, 1G).

Regarding cell death, it’s almost exclusively cell autonomous, with a slight non-cell autonomous effect, which is consistent with the similar JNK activation pattern (Figure 2C). On the other hand, Src provokes cell proliferation both autonomously and non- autonomously, which is consistent with the similar p38 activation pattern (Figure 4A). We presume why pH3 looked more dominant in the pouch for the reviewer is likely because there are high, basal background signals of pH3 outside of the vg-gal4 region while cell death is relatively restrictive to the vg-gal4 region without basal background signals.

One thing we noticed during experimentation is that when we induced massive overgrowth by combining Src activation and JNK inhibition, the massive growth always occurs at the “side” region. We speculate that this “side region” may correspond to the previously identified “hot spots” 7

Regarding the clonal analysis, Src clone cells induce strong cell competition 8 and clonal analyses are intrinsically unreproducible and unpredictable, thus in this study we mainly stick to vg-gal4. This approach with vg-gal4 and UAS-Src42A CA has been previously adopted by the group of Spyros Artavanis-Tsakonas 2.

Reviewer #2:

In this paper, Nishida et al. address a long-standing and intensely-investigated question, namely how are cell proliferation and cell death controlled to modulate tissue growth in animals. They specifically investigate the effects of Src overexpression in imaginal tissues (which leads to both increases cell proliferation and cell death) and they make two key observations: (a) they identify the kinase Slpr as an effector of Src in the control of both cell proliferation (via p38) and cell death (via JNK), (b) they show that the effects of p38 are mediated via p38 and that dietary methionine can control TOR and modulate Src-mediated cell proliferation.

Overall, I liked this paper. I think the two main findings will be of interest to, not just Drosophila researchers, but also cell, developmental and cancer biologists interested in the regulation of tissue growth.

I have two main comments/questions:

1. Are the effects on cell proliferation and cell death strictly autonomous? It's hard to tell clearly from Figure 1, especially since the choice of driver of vg-Gal4 doesn't have clearly demarcated expression boundaries (compared to, say, en-GAL4 or ap-GAL4). This is import since, if the proliferation is a result of cell-death induced compensatory proliferation, then the interpretations of the data in the paper would be very different than if everything was strictly autonomous.

This has an overlap with the reviewer 1’s comment (Major point 3).

In brief, Src induces cell death and proliferation both cell autonomously and non-cell autonomously. But, cell death is more cell autonomous while cell proliferation is rather both autonomous and non-autonomous. In this paper, we almost exclusively focused on the cell autonomous phenotype and manipulated genes autonomously.

The reviewer is speculating that non-autonomous cell death may lead to more autonomous cell proliferation due to compensatory proliferation. Theoretically that is possible, but we think it is unlikely because of the following reason. We quantified the effect of slpr RNAi to non-cell autonomous JNK activation and cell death. slpr knockdown does not affect JNK or cell death non autonomously (Figure 4—figure supplement 1E-G) but affects cell autonomous proliferation (Figure 3A-D). this strongly suggests cell autonomous proliferation is due to the autonomous signaling events, not due to non-cell autonomous cell death.

We would also like to note that the boundary of vg-gal4 is relatively clear in a normal situation, but Src expression makes the expression boundary less clear likely due to cell death. According to literature, this Src-induced hazy boundary seems to occur even with clonal analyses 8 or another gal4 driver such as ptc-gal410.

2. The link between p38 and TOR is interesting, but it relies on using measurement of phospho-4EBP as a readout of TOR signaling. These data are certainly very clean, however, it’s important to note that total levels of 4EPB are often induced upon stress signaling. Hence, it's important to be sure that the increase in phospho-4EBP signal does reflect an increase in the amounts of phosphorylated 4EBP (a TOR effect) vs simply an increase in total 4EBP (a TOR-independent, stress effect). A recent paper from the Karpac lab (Cell Reports, 2020) had some nice immunostaining with a non-phospho 4EBP, which would be a useful control in this manuscript. Alternatively, the induction of TOR could be confirmed with a phospho-S6k or phospho-S6 antibody (western on discs or immunostaining). The genetic expts with TOR certainly support a role for TOR in modulating Src function, but they could equally be interpreted as TOR signaling being a parallel pathway or TOR being a downstream Src pathway.

We thank this reviewer for this useful comment. By striking a new collaboration with Dr Jongkyeong Chung, we included data of pS6. Src induces phosphorylation of S6 in addition to p4EBP(Figure 5—figure supplement 1B-C).

Reviewer #3:

The authors examine how an oncogene, Src, can activate both proliferation and apoptosis during oncogenesis. The answer is surprisingly simple. In the case of Src, it activates both pro-apoptotic (JNK) and proliferative (p38) MAPK pathways and upstream of this, the authors identify the MPKKK slpr as the lynchpin in this pathway. Src also activates Erk-type MAPK, but according to the authors, this has no effect for tumorigenesis. The authors then focus on the role of p38 and found that p38 controls Tor signaling. Interestingly, the nutrition status does influence Src-induced Tor signaling and tissue growth. To further delineate this observation, the authors tested each amino acid and found specifically a requirement of Methionine for Src-induced tumor growth. They also observed that the Methionine concentration in the hemolymph of these Src-bearing larvae is lower compared to controls, presumably because the Src-tumor tissue absorbs more of it. Overall, the authors present a model that combines several signaling pathways and metabolism for Src-induced tumor growth.

This is an interesting manuscript, but I do have some comments and suggestions for improvement.

1. What is the nature of Src42A CA? How does it promote dominant activity to Src?

We failed to provide sufficient explanation. We apologize for this. We specified Src 42A CA is “Src42A with an amino acid substitution of Tyr511 to Phe , which is refractory to inactivating phosphorylation by Csk 11” in text.

2. Provide a complete picture of the Src CA suppressor screen in which they found slpr. Were there other suppressors and if so, why did they focus on slpr?

Our original screen focused on factors related to cell death, trying to find a mediator of Src-induced cell death. We included a list of RNAis we used (Figure 2—figure supplement 1A-B, table 1). Among RNAis we checked, only slpr RNAi demonstrated both high survival and recovered wings. Some of other RNAis enhanced survival but not the wing phenotype (Figure 2—figure supplement 1A-B), suggesting these should affect non-tissue autonomous event.

3. Present the results from the screen of the other MPKKK. Did they also screen the MPKK and other upstream components in the MPK pathway (Msn, Traf1, Traf2, Rho1)? This might tell us how Src activates Slpr.

At least, based on the survival assay, other JNKKK (dTAK1, Mekk1, Ask1, Wnd) were not hits. Msn, traf1, Traf2, msn or Rho1 were not positive hits either. But again, we’d like to emphasize a caveat of negative data in RNAi screening due to low efficiency of knockdown.

4. How does Src activate Slpr? Is any of the domains of Slpr (SH2, SH3, kinase) required for this activation?

This is related to the point 3 above. Among the potential mediators of Src-Slpr signaling, we found that inhibition of Shark, which is a non-receptor tyrosine kinase of the syk family, could suppress the wing phenotype by Src (Author response image 1). Shark could potentially function downstream of Src 12 To solidify and expand a story on Shark, we feel it’ll require much more experimentation. Importance of each domain of Slpr has been extensively investigated previously 13,14, and we did not focus on these domains in this study. We acknowledge that to clarify what mediates Src-Slpr signaling and which domain of Slpr is involved in the process could be an important future question.

Author response image 1

5. How many different slpr RNAi lines were tested? Are the results verified with several independent lines? Do slpr mutants suppress Src42ACA?

We used two RNAis (Figure 2 and Figure 3—figure supplement 1A-D). We also checked knockdown efficiency of both RNAis by RT-qPCR (Figure 2—figure supplement 1C and Figure 3—figure supplement 1D). Slpr mutant is homozygous lethal, thus we decided not to use it in this manuscript.

6. Verify the Erk-MAPK (rl) RNAi line(s).

The erk RNAis we used had been previously validated (Singh, Liu, Zhao, Zeng, & Hou, 2016). Erk inhibition does not suppress proliferation (Fig 4-figure supplement 1I), which is in a clear contrast to p38 inhibition. We also verified an efficiency of an erk RNAi using the rough eye phenotype induced by Ras activation (Fig4-figure supplement 1J). To the best of our knowledge, there has been no Erk DN generated in Drosophila.

7. The data presentation needs improvement. The discs are too small and often the relevant staining (PH3, cDcp1, TRE-RFP, etc.) is hard to judge. This is also particularly important for the non-cell autonomy of Erk and p38 activity in Figure 4A-B. Some convincing close-up pictures could help.

We included magnified pictures in the revised manuscript. We also included quantification of almost all data.

8. There is no comprehensive description of how the quantitation of the staining was conducted. Was the quantitation done in the region of overgrowth? How was the volume of GFP+ tissue determined? Were equivalently sized regions used and how was their level of fluorescence assessed? Was there some internal control to normalize for differences in general staining between regions of interest and region where no change would be expected (ie a part of the disc outside of the vg-Gal4 area)?

We included this information in the method section.

9. Figure 1A: quantify the cDcp1 staining.

We quantified the data as requested (Figure 1D).

10. Figure 2C: slpr RNAi seems to suppress TRE-RFP, but not overgrowth. In fact, the slpr RNAi disc (lower panels) is significantly larger than the src42ACA disc (middle panels) including the GFP+ area. That is inconsistent with the authors model. Explain. Please quantify GFP+ areas and overall disc size.

As the reviewer rightfully noticed, slpr RNAi suppresses TRE-RFP, that is, cell death. Because of the suppression of cell death, slpr RNAi makes discs bigger than the Src42A CA disc, where death continuously occurs. Compared to the real overgrowth phenotype by Src activation and JNK inhibition, the effect of slpr RNAi to Src disc is much milder because it suppresses both cell death and proliferation. We quantified all data as requested.

11. Figure 3B: Perform PH3 labeling in vg>Src42ACA JNKDN slprRNAi background. I would like to know if the overgrowth and PH3 labeling in Src42ACA JNKDN is dependent on Slpr. The authors imply this, but don't show it.

We performed this experiment as requested (Figure 3—figure supplement 1G-I).

12. Figure 5A-B: is the 4EBP phosphorylation in vg>Src42CA JNK DN discs depended on Slpr, i.e. does slpr RNAi block it?

We now included data showing that slpr RNAi blocks S6 phosphorylation in vg>src42A CA, JNK DN discs (Figure 5—figure supplement 1B-C).

13. In the Discussion, the authors mention that "methionine regulates Tor activation". I don't think this direct link was established in this paper.

We mainly focused on this point in this revision. Please see the response to the reviewer 1.

14. Is the a SAMTOR homolog in flies? If so, test it in the Src42ACA screen.

Yes. The paper by the Sabatini lab demonstrated that SAMTOR works in the fly cells 15. In this revised manuscript, we inhibited samtor. As expected, it enhanced tor (Figure 7—figure supplement 2D-F), but puzzlingly it suppressed proliferation. We speculate that this growth suppression is likely attributed to Samtor’s predicted methyltransferase function, which may not be related to Tor signaling.

References:

1 Cully, M. et al. A role for p38 stress-activated protein kinase in regulation of cell growth via TORC1. Mol Cell Biol 30, 481-495, doi:10.1128/MCB.00688-09 (2010).

2 Ho, D. M., Pallavi, S. K. and Artavanis-Tsakonas, S. The Notch-mediated hyperplasia circuitry in Drosophila reveals a Src-JNK signaling axis. eLife 4, e05996, doi:10.7554/eLife.05996 (2015).

3 Kim, J. and Guan, K. L. mTOR as a central hub of nutrient signalling and cell growth. Nat Cell Biol 21, 63-71, doi:10.1038/s41556-018-0205-1 (2019).

4 Sabatini, D. M. Twenty-five years of mTOR: Uncovering the link from nutrients to growth. Proc Natl Acad Sci U S A 114, 11818-11825, doi:10.1073/pnas.1716173114 (2017).

5 Newton, H. et al. Systemic muscle wasting and coordinated tumour response drive tumourigenesis. Nat Commun 11, 4653, doi:10.1038/s41467-020-18502-9 (2020).

6 Goberdhan, D. C., Meredith, D., Boyd, C. A. and Wilson, C. PAT-related amino acid transporters regulate growth via a novel mechanism that does not require bulk transport of amino acids. Development 132, 2365-2375, doi:10.1242/dev.01821 (2005).

7 Tamori, Y., Suzuki, E. and Deng, W. M. Epithelial Tumors Originate in Tumor Hotspots, a Tissue-Intrinsic Microenvironment. PLoS Biol 14, e1002537, doi:10.1371/journal.pbio.1002537 (2016).

8 Enomoto, M. and Igaki, T. Src controls tumorigenesis via JNK-dependent regulation of the Hippo pathway in Drosophila. EMBO Rep 14, 65-72, doi:10.1038/embor.2012.185 (2013).

9 Singh, S. R., Liu, Y., Zhao, J., Zeng, X. and Hou, S. X. The novel tumour suppressor Madm regulates stem cell competition in the Drosophila testis. Nat Commun 7, 10473, doi:10.1038/ncomms10473 (2016).

10 Vidal, M., Larson, D. E. and Cagan, R. L. Csk-deficient boundary cells are eliminated from normal Drosophila epithelia by exclusion, migration, and apoptosis. Dev Cell 10, 33-44, doi:10.1016/j.devcel.2005.11.007 (2006).

11 Tateno, M., Nishida, Y. and Adachi-Yamada, T. Regulation of JNK by Src during Drosophila development. Science 287, 324-327, doi:10.1126/science.287.5451.324 (2000).

12 Biswas, R., Stein, D. and Stanley, E. R. Drosophila Dok is required for embryonic dorsal closure. Development 133, 217-227, doi:10.1242/dev.02198 (2006).

13 Garlena, R. A., Gonda, R. L., Green, A. B., Pileggi, R. M. and Stronach, B. Regulation of mixed-lineage kinase activation in JNK-dependent morphogenesis. J Cell Sci 123, 3177-3188, doi:10.1242/jcs.063313 (2010).

14 Neisch, A. L., Speck, O., Stronach, B. and Fehon, R. G. Rho1 regulates apoptosis via activation of the JNK signaling pathway at the plasma membrane. J Cell Biol 189, 311-323, doi:10.1083/jcb.200912010 (2010).

15 Gu, X. et al. SAMTOR is an S-adenosylmethionine sensor for the mTORC1 pathway. Science 358, 813-818, doi:10.1126/science.aao3265 (2017).

https://doi.org/10.7554/eLife.59809.sa2

Article and author information

Author details

  1. Hiroshi Nishida

    1. Division of Cell Physiology, Kobe University, Kobe, Japan
    2. RIKEN CPR, Kobe, Japan
    Contribution
    Conceptualization, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0284-5771
  2. Morihiro Okada

    1. RIKEN CPR, Kobe, Japan
    2. RIKEN BDR, Kobe, Japan
    Contribution
    Investigation, Methodology
    Contributed equally with
    Lynna Yang
    Competing interests
    No competing interests declared
  3. Lynna Yang

    RIKEN CPR, Kobe, Japan
    Contribution
    Investigation, Methodology
    Contributed equally with
    Morihiro Okada
    Competing interests
    No competing interests declared
  4. Tomomi Takano

    1. RIKEN CPR, Kobe, Japan
    2. RIKEN BDR, Kobe, Japan
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  5. Sho Tabata

    Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Tomoyoshi Soga

    Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  7. Diana M Ho

    Harvard Medical School, Boston, United States
    Contribution
    Resources, Formal analysis
    Competing interests
    No competing interests declared
  8. Jongkyeong Chung

    Seoul National University, Seoul, Republic of Korea
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5894-7537
  9. Yasuhiro Minami

    Division of Cell Physiology, Kobe University, Kobe, Japan
    Contribution
    Supervision, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Sa Kan Yoo

    1. RIKEN CPR, Kobe, Japan
    2. RIKEN BDR, Kobe, Japan
    3. Division of Developmental Biology and Regenerative Medicine, Kobe University, Kobe, Japan
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - original draft
    For correspondence
    sakan.yoo@riken.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3358-4818

Funding

Japan Society for the Promotion of Science (JP16H0622)

  • Sa Kan Yoo

Japan Agency for Medical Research and Development (17939907)

  • Sa Kan Yoo

National Research Foundation of Korea (NRF-2020R1A5A1018081)

  • Jongkyeong Chung

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

Acknowledgements

This work was supported by AMED-PRIME (17939907) and the JSPS KAKENHI (JP16H0622) to SKY and by National Research Foundation of Korea grant (NRF-2020R1A5A1018081) to JC.

Senior and Reviewing Editor

  1. Utpal Banerjee, University of California, Los Angeles, United States

Publication history

  1. Received: June 9, 2020
  2. Accepted: April 2, 2021
  3. Version of Record published: April 27, 2021 (version 1)

Copyright

© 2021, Nishida 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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