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Activation of the Drosophila innate immune system accelerates growth in cooperation with oncogenic Ras

  • Fabienne Brutscher,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    ⨯
  • Federico Germani,

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

    Current address: Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland

    Affiliation Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    ⨯
  • George Hausmann,

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

    Affiliation Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    ⨯
  • Lena Jutz,

    Roles Investigation

    Current address: Center for Molecular Neurology, University of Antwerp, Antwerp, Belgium

    Affiliation Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    ⨯
  • Konrad Basler

    Roles Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing

    konrad.basler@mls.uzh.ch

    Affiliation Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    ⨯

Abstract

Innate immunity in Drosophila acts as an organismal surveillance system for external stimuli or cellular fitness and triggers context-specific responses to fight infections and maintain tissue homeostasis. However, uncontrolled activation of innate immune pathways can be detrimental. In mammals, innate immune signaling is often overactivated in malignant cells and contributes to tumor progression. Drosophila tumor models have been instrumental in the discovery of interactions between pathways that promote tumorigenesis, but little is known about whether and how the Toll innate immune pathway interacts with oncogenes. Here we use a Drosophila epithelial in vivo model to investigate the interplay between Toll signaling and oncogenic Ras. In the absence of oncogenic Ras (RasV12), Toll signaling suppresses differentiation and induces apoptosis. In contrast, in the context of RasV12, cells are protected from cell death and Dorsal promotes cell survival and proliferation to drive hyperplasia. Taken together, we show that the tissue-protective functions of innate immune activity can be hijacked by pre-malignant cells to induce tumorous overgrowth.

Citation: Brutscher F, Germani F, Hausmann G, Jutz L, Basler K (2025) Activation of the Drosophila innate immune system accelerates growth in cooperation with oncogenic Ras. PLoS Biol 23(4): e3003068. https://doi.org/10.1371/journal.pbio.3003068

Academic Editor: Harmit S. Malik, Fred Hutchinson Cancer Research Center, UNITED STATES OF AMERICA

Received: April 23, 2024; Accepted: February 13, 2025; Published: April 28, 2025

Copyright: © 2025 Brutscher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: RNA sequencing data generated in this study are deposited in the GEO database (accession number: GSE266879). All other relevant data are within the paper and its Supporting Information files.

Funding: This work (FB) was supported by the research grant Swiss Cancer Research Foundation & Swiss Cancer League (no. KFS-4835-08-2019, https://www.krebsliga.ch/) and the Swiss National Science Foundation (no. 207594, http://www.snf.ch/de/Seiten/default.aspx). FB was supported by the Candoc Forschungskredit (no. FK-20-085, https://www.research.uzh.ch/de/funding/phd/uzhcandoc.html). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AED, after egg deposition;; ANOVA, analysis of variance;; BDSC, Bloomington Drosophila Stock Center;; Bsk, Basket;; Cact, Cactus;; Cat. No, catalog number;; Dcp1, Cleaved Drosophila caspase-1;; Dl, Dorsal; EAD, Eye-antennal disc;; EdU, 5-ethynyl-2′-deoxyuridine;; Egr, Eiger;; ElaV, Embryonic lethal abnormal vision;; Ets21c, Ets at 21c;; Eya, Eyes absent;; eyFlp, Eyeless promoter-driven Flippase;; Gal4 (DBD), Gal4 DNA binding domain;; GMR, glass multiple reporter;; GO:BP, Gene Ontology, Biological Process;; GSEA, Gene Set Enrichment Analysis;; HSD, Honestly Significant Difference;; ID, Identifier;; IκB, Inhibitor of κB;; JNK, Jun-N-terminal Kinase pathway;; Mmp1, Matrix metalloproteinase 1;; n/a, not applicable;; NF-κB, Nuclear factor ‘kappa-light-chain-enhancer’ of activated B-cells;; ORF, Open Reading Frame;; PGRP-SA, Peptidoglycan recognition protein SA;; Puc, Puckered;; qRT-PCR, quantitative real-time PCR;; Rel, Relish;; Rpr, Reaper;; RT, real-time;; Scaf, Scarface;; TLR, Toll-like receptor pathway;; VDRC, The Vienna Drosophila Resource Center VDRC;; WD, wing disc;; wntD, wnt inhibitor of Dorsal.

Introduction

One of the main Drosophila innate immune signaling pathways is the Toll pathway [1]. Toll signaling was initially discovered in the Nobel prize winning screen for genes involved in early Drosophila embryonic patterning [2]. In subsequent research, Toll signaling was also found to be required for the innate immune response to infection [3]. The signaling cascade is conserved from invertebrates to mammals and leads to the nuclear translocation of the NF-κB (nuclear factor ‘kappa-light-chain-enhancer’ of activated B-cells)/ Rel-like [4] transcription factors, Dorsal and Dif. In the nucleus they regulate diverse genes, including the production of antimicrobial peptides [1,5].

The Toll pathway also acts as an organismal surveillance system that measures cellular fitness during development [6] and during cell competition [7,8]. Less fit cells with elevated Toll activity are eliminated via apoptosis; this prevents abnormal cells from contributing to the structure of the tissue and thus helps maintaining tissue homeostasis [6–8].

In mammals, innate immunity provides the first line of defense against infection, but aberrant activation can be detrimental [9]. The NF-κB transcription factors of the Toll-like receptor pathway (TLR; homologous to the Drosophila Toll pathway) are frequently overactivated in different malignant cell types, as for example in the context of oncogenic Ras [10], and contribute to tumor progression by affecting processes such as cell survival and proliferation, invasion, metastasis and angiogenesis [11]. Their role in cancer, whether it is pro- or anti-tumorigenic, appears to vary depending on the context [12,13]. Ras is frequently mutated in cancer [14], but accelerated growth and progression towards malignancy requires multiple additional steps of oncogene activation and inactivation of tumor suppressor genes [15]. Drosophila tumor models have been instrumental in the discovery of factors involved in such cooperative oncogenesis (reviewed in [16,17]). If and how the Toll pathway functions in the initial stages of Ras-induced oncogenesis needs further investigation.

Here, we use a Drosophila epithelial model in the eye-antennal disc (EAD) to drive the expression of RasV12, an oncogenic version of Ras and characterize the consequences of Toll pathway activation in this context. The approach allows us to investigate the interaction of oncogenic Ras and abnormal Toll/NF-κB activity in promoting tumor growth, relevant to human pathology. Interestingly, we found that in cells expressing RasV12, Toll signaling potently drives tissue hyperplasia. To elucidate the mechanism underlying this effect, we investigated the transcriptional changes in RasV12-transformed epithelia in response to Toll activation by constitutive expression of NF-κB/ Dorsal. During development, in the absence of oncogenic Ras, overexpression of Dorsal suppresses differentiation and induces apoptosis. In contrast, in the context of oncogenic Ras, cells are protected from cell death and Dorsal accelerates tumor growth. Overall, our results suggest that the tissue-protective functions of innate immune activity can be hijacked by premalignant cells to induce tumorous overgrowth.

Results

Toll signaling cooperates with oncogenic Ras to drive overgrowth

To explore the role of TLR/ NF-κB signaling in the context of oncogene activation, we characterized the effects of Toll pathway activation in a widely used epithelial tumor model in the Drosophila larva [18–20]: Using eyeless promoter-driven Flippase expression (eyFlp), we induced genetic alterations, such as overexpression of an oncogene, into GFP-labeled cells in developing eye-antennal discs (EAD) [21]. The size of EAD tumors was quantified at a late larval stage, at 96 h after egg deposition (AED), by three-dimensional reconstruction of GFP fluorescent signals from confocal image stacks. Consistent with a previous study [19], overexpression of a constitutive active form of Ras (RasV12) alone impaired EAD development, but did not induce hyperplastic growth (Fig 1A and 1B). Next, we examined the effect of altering Toll signaling. Overexpression of the NF-κB/ Rel-like transcription factor Dorsal alone impaired EAD development and reduced EAD size (Fig 1A and 1B). Overexpressing only Cactus, the inhibitor of Dorsal (homologous to mammalian Inhibitor of κB (IκB)), also reduced the size of the EAD (Fig 1A and 1B). Although the phenotypic outcome is similar - smaller discs - the underlying cause is different as illustrated by the different effect on the levels of apoptosis (S4G and S4H Fig). In contrast, in combination with RasV12, activation of the Toll pathway through overexpression of Dorsal induced overgrowth (Fig 1A and 1B). The same result was seen when overexpression of a constitutively active Toll receptor (Toll10b) [22] was used to activate Toll signaling (Fig 1A and 1B).

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Fig 1. Toll signaling cooperates with oncogenic Ras to induce overgrowth.

(A) Confocal images of EADs and EAD tumors at 96-h AED of the indicated genotypes representative for tissue sizes quantified in (B). (B) Quantification of tissue volume of the indicated genotypes. Mean volume and standard deviation (error bar) are shown (***p <  0.001, **p <  0.01, ns: p =  0.06, One-way ANOVA with post-hoc Tukey HSD). (C) and (F) Representative confocal images of regions of wing discs (WDs) of indicated genotypes at 96-h AED (or 168-h AED) stained for Dorsal (yellow). Nuclear Dorsal levels are quantified in (E) and (G). The white line highlights the border between GFP-marked cells of the Dpp stripe and GFP-negative cells of the posterior WD compartment. (D) Graphical representation of a third instar WD with the expression pattern of Dpp (green) illustrating the approximate position (dotted line square) of the region within the WD used to analyze Dorsal nuclear intensity. (E) and (G) Quantification of nuclear Dorsal intensity of the indicated genotypes as a read-out for Toll pathway activity. The mean fluorescence intensity of Dorsal in GFP-positive cells, internally normalized to GFP-negative cells, and standard deviation (error bar) are shown (***p <  0.001, **p <  0.01, ns: p =  0.8, One-way ANOVA with post-hoc Tukey HSD). In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S1 Data. Scale bars represent 100 µm in (A) and 10 µm in (C) and (F). ns, not significant; EAD, eye-antennal disc; WD, wing disc; AED, after egg deposition; A, anterior; P, posterior; D, dorsal; V, ventral; cact, Cactus; Dl, Dorsal; Tl10b, Toll10b; eyFlp, eyeless-driven Flippase; dpp, Decapentaplegic; a.u., arbitrary unit.

https://doi.org/10.1371/journal.pbio.3003068.g001

To explore whether the Toll pathway is active in RasV12-transformed EADs alone, we inhibited Toll signaling by overexpressing Cactus. Inhibition of Toll signaling did not significantly affect tumor size in the context of RasV12 (Fig 1A and 1B).

Consistent with their ability to activate the Toll-pathway, overexpression of either Dorsal or Toll10b in the Dpp (Decapentaplegic) stripe of the wing disc (WD) was sufficient to induce the translocation of Dorsal to the nucleus in control EADs and in the context of RasV12 (Fig 1C–1G). Overexpressing Cactus together with Dorsal reduced the level of nuclear Dorsal and rescues the morphological defects caused by Dorsal overexpression in control EADs (S1A, S1B and S1C Fig). In the context of RasV12 only, levels of nuclear Dorsal were unchanged compared to the control, at 96-h AED and also after prolonged growth (168-h AED) (Fig 1C–1G).

Activating Toll signaling also enhanced the overgrowth of an advanced, neoplastic tumor generated by simultaneously impairing apical-basal cell polarity by knockdown of Discs large (dlg) and overexpressing RasV12 (RasV12, dlgRNAi) [23] (S1D and S1E Fig). Importantly, the effect of Dorsal overexpression on the size of RasV12, dlgRNAi tumors was only apparent later during tumor development, at 120-h AED compared to the effect at 96-h AED in the context of RasV12 alone (S1E and S1F Fig). The size of EADs expressing only dlgRNAi was unaffected by Dorsal overexpression (S1G and S1H Fig). Inhibition of Toll signaling by overexpressing Cactus did not affect the size of RasV12, dlgRNAi-induced tumors (S1D and S1E Fig).

To explore whether Toll signaling was active in RasV12, dlgRNAi tumors, we analyzed nuclear Dorsal levels in RasV12, dlgRNAi-transformed cells in the Dpp stripe of the WD. While, at 96-h AED, nuclear Dorsal levels in RasV12, dlgRNAi -transformed cells were not increased, they were significantly elevated at 168-h AED (S1I and S1J Fig). These results suggest that Toll signaling is activated in advanced tumors.

Hence, we conclude that, during the initial stages of tumorigenesis, Toll signaling is able to promote overgrowth if ectopically activated in the context of RasV12- or RasV12, dlgRNAi. Our results also suggest that early in tumorigenesis Toll signaling is not intrinsically activated in the tumors. How activation of the innate immune system interacts with oncogenes to promote overgrowth is unclear.

Dorsal inhibits retinal differentiation

The overgrowth of RasV12-transformed EADs after Toll pathway activation may be attributed to several factors, including its effects on cell differentiation, proliferation, and apoptosis. To explore which of these processes could have contributed to the overgrowth in RasV12, Dorsal tumors, we first investigated the transcriptional changes in RasV12-expressing EADs in response to Toll activation. By choosing a non-clonal induction system [23], we were able to identify transcriptional changes in a homogeneously transformed cell population, circumventing potential confounding non-autonomous effects from wild-type cells. We sequenced the transcriptomes of control and RasV12-transformed EADs with and without concomitant Toll pathway activation induced by Dorsal overexpression. A high number of genes was significantly differentially expressed (Fig 2A). Confirming we had activated the Toll pathway in RasV12, Dorsal tumors, compared to RasV12, lacZ, we found many innate immune pathway-associated genes, such as cactus (cact), Peptidoglycan recognition protein SA (PGRP-SA) or wnt inhibitor of Dorsal (wntD), amongst the most upregulated genes (Figs 2B and S2A).

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Fig 2. Dorsal inhibits retinal differentiation.

(A) Total number and directionality (blue: downregulated, red: upregulated) of significantly differentially expressed genes between indicated genotypes illustrating similarity relative to each other. (B) Volcano plot of genes significantly differentially expressed between RasV12, Dorsal and RasV12, lacZ tumors. Significantly differentially expressed genes (p <  0.05, absolute log2 fold >  0.5) are shown in black and non-significant genes in gray. A positive fold change indicates higher expression in RasV12, Dorsal compared to RasV12, lacZ tumors. Vertical lines indicate the absolute log2 fold threshold of 0.5 and −0.5. Exemplary upregulated genes of interest are annotated and highlighted in red. (C) Bar chart of the 10 most enriched gene sets for the expression signature of genes downregulated after Dorsal overexpression identified through hypergeometric Gene Set Enrichment Analysis (GSEA) on the gene set databases Gene Ontology, Biological Process (GO:BP). Data labels represent the number of significantly differentially expressed genes within each gene set. Significantly enriched gene sets (p <  0.05, absolute log2 fold >  0.0) are depicted in black. (D) Confocal images of EADs and EAD tumors of the indicated genotypes at 96-h AED labeled for retinal determination and photoreceptor differentiation markers Eya (cyan) and ElaV (magenta). The white line highlights the GFP-marked region. (E) qRT-PCR analysis of eya mRNA levels in EADs expressing indicated UAS-transgenes under the control of an eye-specific Gal4 (eyFlp; act>CD2>Gal4) as confirmation of the downregulation of a retinal determination gene after overexpression of Dorsal. Data is shown as fold changes relative to the control (lacZ). n = 3 biologically independent samples were analyzed. Median fold changes and interquartile ranges (error bars) are shown. The dashed horizontal line illustrates the reference fold change of 1. (F) Confocal images of EAD tumors of indicated genotypes at 72-h AED stained with Click-iT EdU (5-ethynyl-2′-deoxyuridine) Alexa Fluor 647 (magenta) to identify DNA synthesis-based proliferation, representative for EdU intensity quantified in (G); scale bars represent 20 µm. The white line highlights the GFP-marked region. (G) Quantification of EdU mean fluorescence intensity per EAD tumor at 72-h AED. Mean volume and standard deviation (error bar) are shown (**p <  0.01, One-way ANOVA with post-hoc Tukey HSD). (H) Confocal images of wing discs of the indicated genotypes representative for relative sizes quantified in (I). The green outline is based on the pattern of Gal4 immunoreactivity and highlights the dorsal, Apterous-marked compartment. (I) Quantification of the Apterous-marked area relative to overall disc area for indicated genotypes. Mean area and standard deviation (error bar) are shown (***p <  0.001, One-way ANOVA with post-hoc Tukey HSD). In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S3 and S9 Data files. Scale bars represent 100 µm, unless otherwise indicated. a.u., arbitrary unit; EAD: ,ye-antennal disc; AED, after egg deposition; Dl, Dorsal; wntD, wnt inhibitor of Dorsal; PGRP-SA, Peptidoglycan recognition protein SA; scaf, scarface; mmp1, matrix metalloproteinase 1; eyFlp, eyeless-driven Flippase; ap, Apterous; Eya, eyes absent,; ElaV, embryonic lethal abnormal vision.

https://doi.org/10.1371/journal.pbio.3003068.g002

Interestingly, overexpression of Dorsal also affected differentiation. In both the control and RasV12 context, Dorsal overexpression significantly downregulated the expression of genes involved in processes connected to EAD development (Fig 2C). Consistent with an impaired EAD development we found that markers for retinal determination and photoreceptor differentiation, such as Eyes absent (Eya) and Embryonic lethal abnormal vision (ElaV), were downregulated after the overexpression of Dorsal (Fig 2D and 2E).

These results suggest that Dorsal overexpression traps cells in a progenitor-like state, maintaining a proliferative potential throughout the entire EAD. To explore this, we looked at proliferation at various timepoints. Overexpression of Dorsal in RasV12-transformed EADs did increase proliferation. The increase was most pronounced during early tumor development, at 72-h AED (Figs 2F, 2G, S2B and S2C), when both tumors (RasV12, lacZ and RasV12, Dorsal) were still of the same size (S2D Fig). Intriguingly, in contrast to Dorsal overexpression (Fig 2D), activation of the Toll pathway by overexpressing Toll10b (Tl10b) alone did not noticeably suppress retinal determination and photoreceptor differentiation (left panel, S2E Fig). However, in the context of RasV12, Dorsal and Tl10b overexpression both induced overgrowth (Fig 1A and 1B). Thus, while inhibition of retinal differentiation likely contributes to overgrowth induced by Toll activation, it does not seem to be sufficient.

To further test if inhibition of differentiation is required for enhanced tumor growth by Toll activation, we examined the consequences of co-overexpressing RasV12 and Dorsal in the dorsal compartment of the wing disc (WD). During the experimental time window, the cells of the dorsal compartment are not yet differentiated and any effect on growth would therefore be independent of the differentiation status [24]. We found that growth of the dorsal compartment, marked by the expression of Apterous, was enhanced when Dorsal was overexpressed together with RasV12 (Fig 2H and 2I). This demonstrated that the growth-promoting effect of Toll signaling on RasV12-driven tumors is not limited to the EAD and can occur independent of changes to the differentiation status.

Dorsal promotes overgrowth independent of JNK signaling

Among the genes differentially expressed following Toll activation were various targets of the JNK pathway. Previous reports have demonstrated that Toll signaling can activate the JNK pathway [25–27]. To investigate if JNK signaling contributes to the Toll signaling-induced overgrowth in our system, we focused on the transcriptional changes after Toll activation that are associated with the JNK pathway. Common transcriptional target genes of the JNK pathway were strongly upregulated after Dorsal overexpression (Fig 3A). For example, the expression of JNK-regulated genes, such as scarface (scaf) [28], matrix metalloproteinase 1 (mmp1) [29–31] and Ets at 21c (Ets21c) [32,33] was elevated in RasV12, Dorsal tumors (Figs 3A, 3B, 3C and S3A).

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Fig 3. Dorsal promotes overgrowth independent of JNK signaling.

(A) Heatmap showing the expression pattern of genes commonly known as transcriptional targets of the JNK pathway across all four conditions. Color intensity represents the expression level (lowest to highest from blue to red) scaled into z-scores. Three target genes of interest, used in downstream analyses, ets21c, mmp1 and scaf, are depicted in bold. (B) Confocal images of EADs and EAD tumors of indicated genotypes at 96-h AED, stained for Mmp1 confirming the upregulation of this JNK-regulated gene. The white line highlights the GFP-marked region. (C) qRT-PCR analysis of mmp1 and ets21c mRNA levels in EADs expressing indicated UAS-transgenes under the control of an eye-specific Gal4 (eyFlp; act>CD2 > Gal4) as confirmation of the JNK-dependent upregulation of target genes after overexpression of Dorsal. Data is shown as fold changes relative to the control (lacZ or lacZ,bskDN). n = 3 biologically independent samples were analyzed. Median fold changes and interquartile ranges (error bars) are shown. The dashed horizontal line illustrates the reference fold change of 1. (D) Confocal images of EADs tumors of indicated genotypes at 96-h AED representative for tissue sizes quantified in (E). (E) Quantification of tissue volume of indicated genotypes at 96-h AED before and after JNK pathway inhibition. Mean volume and standard deviation (error bar) are shown (ns: p = 0.6, ns*: p = 0.07), One-way ANOVA with post-hoc Tukey HSD). In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S5 and S9 Data files. Scale bars represent 100 µm. ns, not significant; EAD, eye-antennal disc; AED, after egg deposition; Dl, Dorsal; mmp1, matrix metalloproteinase 1; Ets21c, Ets at 21c; bskDN, dominant-negative Basket; JNK, Jun-N-terminal Kinase pathway; eyFlp, eyeless-driven Flippase.

https://doi.org/10.1371/journal.pbio.3003068.g003

Previous studies had suggested that JNK is required for malignancy (invasiveness), but not overgrowth of RasV12,scribble-mutant tumors [18,30]. To inhibit JNK signaling we either co-expressed a dominant-negative form of the Drosophila JNK Basket (BskDN) or the negative JNK pathway regulator Puckered (Puc). The transcription of JNK target genes was markedly decreased by these manipulations (Figs 3C and S3A). Further confirming the effectiveness of the manipulations, both BskDN and Puc reverted the effect of activating the JNK pathway by egr overexpression (S3B Fig). However, inhibiting JNK signaling in RasV12, Dorsal tumors did not markedly reduce tissue overgrowth (Figs 3D, 3E and S3C). Moreover, also the increase in the size of RasV12, dlgRNAi tumors caused by the overexpression of Dorsal was independent of JNK signaling (S3D and S3E Fig). These results are consistent with the notion that JNK drives malignancy but not overgrowth [18,30].

Taken together, these results suggest that factors other than enhanced JNK signaling mediate the growth stimulatory effect that Toll activation exerts on RasV12 signaling.

Dorsal-induced caspase-dependent cell death is suppressed by RasV12

We found that Dorsal affected differentiation and proliferation in RasV12-transformed EADs. A third feature that could have contributed to the increased growth of these tumors is changes in cell death rate. Therefore, we asked if apoptosis rates changed in the different conditions. In EADs, Toll activation by Dorsal overexpression led to increased levels of activated effector caspase, cleaved Drosophila caspase-1 (Dcp1) (Fig 4A and 4B). Consistent with this, the expression of the pro-apoptotic gene, reaper, was upregulated (Fig 4C). Overexpression of Cactus alone had no effect on the level of cell death (S4G and S4H Fig). The Toll-dependent increase in cell death was independent of JNK signaling as expressing BskDN did not abolish either Dcp1 or rpr expression (S4A, S4B and S4C Fig).

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Fig 4. Dorsal-induced caspase-dependent cell death is suppressed by RasV12.

(A) Confocal images of EADs and tumors of indicated genotypes at 96-h AED labeled for Dcp1 illustrating the enrichment of cell death after Dorsal overexpression in control EADs as quantified in (B) and demonstrating the loss of cell death after inhibition of apoptosis through overexpression of RasV12 or p35. For simplicity, all genotypes with overexpression of p35 are only shown with a nuclear marker (gray); a quantification of the Dcp1-positive area can, however, be found in S4F Fig. The white outline highlights the GFP-marked region. (B) Quantification of the Dcp1 positive area relative to whole disc size at 96-h AED. Mean area and standard deviation (error bar) are shown (***p <  0.001, One-way ANOVA with post-hoc Tukey HSD). (C) qRT-PCR analysis of rpr mRNA levels in EADs expressing indicated UAS-transgenes under the control of an eye-specific Gal4 (eyFlp; act>CD2>Gal4) illustrating the increase in pro-apoptotic gene expression after overexpression of Dorsal in control EADs. Data is shown as fold changes relative to the control (lacZ). n = 3 or n = 4 biologically independent samples were analyzed. Median fold changes and interquartile ranges (error bars) are shown. The dashed horizontal line illustrates the reference fold change of 1. (D) Quantification of tissue size of indicated genotypes at 96-h AED to illustrate changes to the effects of Dorsal on tissue growth after inhibition of apoptosis through co-expression of p35. Mean area and standard deviation (error bar) are shown (***p <  0.001, One-way ANOVA with post-hoc Tukey HSD). (E) Graphical model illustrating the transition of the function of NF-κB/ Dorsal from growth-suppressive during development to growth-promoting during RasV12-mediated oncogenesis. The transition is mediated by the combined effects of Dorsal-induced changes in cell death, cell survival, proliferation and differentiation. In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S7 Data. Scale bars represent 100 µm. EAD, eye-antennal disc; AED, after egg deposition; Dl, Dorsal; Dcp1, cleaved Drosophila caspase 1; rpr, reaper; NF-κB, nuclear factor ‘kappa-light-chain-enhancer’ of activated B-cells.

https://doi.org/10.1371/journal.pbio.3003068.g004

In the context of RasV12, Dorsal-induced cell death was inhibited: the levels of cleaved Dcp1 and expression of rpr were significantly reduced (Fig 4A, 4B and 4C). This suggested that inhibition of cell death, as mediated by RasV12, may enable Dorsal to promote tissue overgrowth.

To further explore if inhibiting apoptosis could explain the tissue growth in the context of RasV12, Dorsal overexpression, we co-expressed the baculoviral anti-apoptosis protein p35, an inhibitor of effector caspases, such as Dcp1 [34,35]. Co-expression of p35 and Dorsal abrogated the increased caspase activation seen when overexpressing only Dorsal (Figs 4A and S4F). Consequently, the EAD reached a similar size to the control EAD (Fig 4D). Since Dorsal still impaired retinal differentiation, the EAD was malformed (S4D Fig).

Interestingly, in the context of RasV12, p35-mediated inhibition of apoptosis seemed to impede tissue growth (Fig 4A and 4D). In the context of RasV12, overexpression of p35 impaired growth and reduced tumor size, both in the control (lacZ) background and after concomitant Dorsal overexpression (Fig 4A and 4D). Notably, however, RasV12, Dorsal, p35 EADs were larger than RasV12, lacZ, p35 EADs (Figs 4D and S4E).

Based on the reduced rpr expression and the absence of detectable levels of Dcp1, we contend that RasV12 can prevent programmed cell death, thus driving Dorsal-induced overgrowth.

Discussion

Using Drosophila imaginal epithelia as a model we have explored how the Toll pathway cooperates with oncogenic Ras to drive tumor growth in the initial stages of tumorigenesis. Although Toll activation restricts growth during EAD development, we found that when activated in the context of oncogenic Ras, Toll signaling induces overgrowth. We then evaluated three potential explanations for the enhanced growth: differentiation, proliferation and apoptosis.

Based on a transcriptome analysis we found that the activation of the Toll pathway impaired retinal differentiation, trapping cells in a progenitor-like state (Fig 2). Increased cell death rates prevent EADs from overgrowing after Toll activation (Fig 4). However, the undifferentiated cells likely maintain a proliferative potential throughout the entire EAD. Hence, overexpressing Dorsal in the context of RasV12, which renders cells resistant to cell death, results in increased proliferation and accelerated tissue growth. However, two key lines of evidence indicated that impaired differentiation in combination with RasV12 is not a prerequisite to induce tissue overgrowth: (1) Activation of the Toll pathway via Toll10b induced overgrowth even though it did not impair differentiation. (2) In the WD, in which cells do not differentiate during the experimental time window, activating Toll signaling also induced overgrowth in cooperation with RasV12.

We suggest that the differential effects of overexpression of Dorsal or Toll10b on retinal differentiation and the less potent induction of RasV12-mediated overgrowth after overexpression of Toll10b may reflect different degrees of Toll pathway activity (Fig 1F).

We also found that increased proliferation was most pronounced early after tumor induction, demonstrating the importance of careful assessment of tumor development over multiple time points. The contribution of other processes that we have studied may change as the tumor develops.

Consistent with previous reports in Drosophila [6,7,26,27,36], we found that Dorsal activation is pro-apoptotic and induces cell death during development (Figs 4 and S4). In the context of oncogenic Ras, however, apoptosis is suppressed [37,38], and the Dorsal-mediated outputs were tumor-promoting. The shift from anti- to pro-tumorigenic function is often associated with the ambivalent nature of JNK signaling [39]. This has, for example, been demonstrated in the context of polarity-deficient (scribble mutant) cell clones co-expressing RasV12. In this context, rather than promoting apoptosis, JNK has a pro-tumorigenic function and drives tumor cell invasion [30,31,40–42]. JNK has been reported to be a target of the Toll pathway in different contexts [26,27]. It was thus not unexpected that we found JNK signaling upregulated upon Dorsal overexpression. However, blocking JNK signaling did not significantly affect Dorsal-induced overgrowth in the context of RasV12. Since the expression of the common JNK target genes, mmp1 and Ets21c, was not completely suppressed despite JNK inhibition (Fig 3C), we cannot exclude that they continue contributing to the Dorsal-induced effects. Especially, the transcription factor Ets21c was previously shown to drive tumor growth downstream of JNK in cooperation with RasV12 and is predicted to also be regulated by inputs other than JNK [32,33]. Ets21c may thus still have contributed to the observed overgrowth of RasV12, Dorsal discs. We focus on processes regulated by Toll signaling that contribute to early tumor development in cooperation with oncogenic Ras. However, JNK signaling may have differential effects on advanced tumors, in which for example systemically produced Egr (homologous to TNFα in mammals) largely contributes to JNK signaling activity [41].

The inhibition of cell death by oncogenic Ras may have enabled Dorsal to switch from limiting tissue growth by inducing apoptosis to promoting overgrowth by enhancing proliferation. To induce overgrowth, Dorsal may directly regulate the expression of genes that promote apoptosis and proliferation. While in control EADs the induction of apoptosis may outweigh a potential increase in proliferation, the positive effects of Dorsal on proliferation and tissue growth prevail when cell death is inhibited, e.g., via RasV12 or p35 (Fig 4A and 4D). Caspases are known to not only drive cell death, but they can also have non-apoptotic functions [43], such as caspase-induced proliferation leading to tissue hyperplasia [44,45]. We therefore suggest that in the context of RasV12, in which apoptosis is suppressed, Toll signaling may also promote overgrowth through caspase-induced proliferation. Taken together, our data indicates that Toll/ Dorsal accelerates tumor growth in cooperation with oncogenic Ras likely through the combined effects on differentiation and apoptosis.

Even though mammalian TLR/NF-κB signaling is pre-dominantly considered a tumor-promoting pathway [46], in certain contexts, NF-κB can act as a tumor-suppressor [47–51]. Currently, to the best of our knowledge, in vivo studies investigating the relationship between tumor-intrinsic oncogenic Ras and NF-κB are rare [52,53]. In the present study, we used the Drosophila larva as a simple in vivo system to investigate the interplay between NF-κB/Dorsal and oncogenic Ras, focusing on the initial stages of tumor development. In the EAD we found that activation of Toll signaling suppressed retinal differentiation and induced caspase-activation. In the context of oncogenic Ras, the latter in particular seemed to contribute to tumorous overgrowth.

Materials and methods

Drosophila stocks and husbandry

Flies were reared and maintained at 25 °C (12:12 h light/dark cycle) on standard corn-meal food containing, per liter, 100 g fresh yeast, 55 g corn meal powder, 10 g organic wheat flour, 8 g agar, 75 g white sugar and 15 ml nipagin. The fly strains used in this study and listed in Table 1 were mainly obtained from the Bloomington Drosophila Stock Center (BDSC), the Vienna Drosophila Resource Center (VDRC) or, for UAS-ORF constructs inserted at position 86FB [56], from the FlyORF Injection Service. Crosses were raised at 25 °C, except those involving tub-Gal80ts, which were raised at 18 °C and moved to 29 °C, according to the experimental set up. To induce the expression of transgenes in the EAD, virgin flies of eyFlp;; act>CD2>Gal4,UAS-GFP or eyFlp; UAS-RasV12,UAS-dlgRNAi/CyO,tub-Gal80; act> CD2> Gal4, UAS-GFP were crossed to respective UAS-constructs. To induce transgene expression in the WD, virgin flies of dpp-Gal4 or ap-Gal4,tub-Gal80ts were crossed to respective UAS-constructs.

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Table 1. Drosophila stocks used in this study.

https://doi.org/10.1371/journal.pbio.3003068.t001

Immunohistochemistry

Micro-dissected imaginal disc tissues were fixed in 4% formaldehyde in 1 x phosphate-buffered saline (PBS) for 20 min and washed three times for 5 min in PBS at room temperature (RT). Samples were permeabilized for 10 min in 0.1% Triton X-100 in 1 × PBS (PBT) and blocked for 30 min in 2% HINGS. Samples were incubated with primary antibody solutions listed in Table 2 at 4 °C overnight, washed three times for 5 min in PBS and then incubated with host-specific Alexa Fluor secondary antibodies (Alexa Fluor 555, 647) and DAPI (1:200) at RT. The samples were washed three times for 5 min before mounting in Vectashield. To reduce tissue compression, for volume measurements, all EADs and EAD tumors were mounted on Poly-L-lysine coated glass bottom dishes without coverslips. Click-iT Plus EdU Alexa Fluor 647 Imaging Kit was used to quantify DNA synthesis (S-Phase) in vivo, following the manufacture’s protocol. EADs and EAD tumors were incubated in EdU labeling solution for 45 min at RT.

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Table 2. Primary antibodies used in this study.

https://doi.org/10.1371/journal.pbio.3003068.t002

Microscopy

Confocal images were acquired using an inverted laser scanning confocal microscope (Leica SP8 Inverse, HC PL APO CS2 20 ×/0.75 NA objectives, 405 nm (50 mW), 488 nm (20 mW), 552 nm (20 mW) and 638 nm (30 mW) lasers). Single confocal planes were imaged and processed using the Fiji package of ImageJ [57]. Brightfield images of adult eyes were acquired on Axio Zoom V16 (Zeiss) and were processed using Fiji.

Quantification and statistical analysis of tumor growth

Tumor- and control EAD bearing larvae were dissected 72, 96 or 120-h AED as indicated for each experiment. Typically, we measured the size of tumors and control EADs using three-dimensional (3D) reconstruction of GFP-labeled tissue from confocal image stacks as previously described in [58]. The confocal planes were imaged with the 10 ×/0.3 NA objective (Leica Sp8 Inverse, HC PL APO) at 15 µm intervals until the entire sample was captured from top to bottom. 3D reconstruction and volume measurements were done using IMARIS image analysis software (Bitplane). To compare the sizes of control and tumor EADs with and without co-expression of p35, two-dimensional measurements were done using FIJI’s polygon tool. Tumor- and control WD bearing larvae were raised and maintained at 18 °C until moved to 29 °C three days AED (approximate equivalent developmental age at 25 °C: 36-h AED) until dissected 3 days later during wandering larval stage. Tumor and control area in the dorsal compartment of the WD was measured based on the region marked by Apterous using FIJI’s polygon tool. Data visualization and statistical analysis were carried out using R, version 4.2.3 [59]. One-way ANOVA (analysis of variance) with post-hoc Tukey HSD (honestly significant difference) was used to compare the mean value between multiple groups.

RNA isolation, cDNA synthesis and quantitative real-time PCR

Total RNA was extracted from approximately 50 control or 30 tumor EADs per sample from larvae of the respective genotypes at 96-h AED. Per genotype three biologically independent replicates were sampled. The samples were then snap-frozen in dry ice. RNA was isolated using the RNeasy Micro Kit following the manufacture’s protocol. cDNA was synthesized using the PrimeScript RT Master Mix. Quantitative real-time PCR reactions were performed in technical triplicates using the PowerUp SYBR Master Mix and analyzed using the QuantStudio3 system (Applied Biosystems). The sequences of the primers used in this study are listed in Table 3.

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Table 3. qRT-PCR primers used in this study.

https://doi.org/10.1371/journal.pbio.3003068.t003

mRNA sequencing and data processing

mRNA sequencing (mRNA seq) libraries were generated from total RNA samples, isolated as stated above, by the Functional Genomics Centre Zurich (FGCZ) according to the TruSeq stranded mRNA library preparation protocol (Illumina). The NovaSeq 6,000 instrument (Illumina) was used by the FGCZ to perform paired-end sequencing at 150 bp read length.

mRNA sequencing (mRNA seq) libraries were generated from total RNA samples, isolated as stated above, by the Functional Genomics Centre Zurich (FGCZ) according to the TruSeq Stranded mRNA library preparation protocol (Illumina). The NovaSeq 6000 instrument (Illumina) was used to perform paired-end sequencing at 150 bp read length for read 1 and read 2.

Raw.fastq files were processed using the RNA-seq analysis pipeline from snakePipes [61] with default parameters. Reads were aligned to the Drosophila melanogaster reference genome and the transcriptome reference annotation (Ensembl assembly dm6, release-94) using HISAT2 [62]. Gene level quantification was performed using featureCounts [63] and for the genes in each pairwise comparison (Dorsal versus lacZ; RasV12, lacZ versus lacZ and RasV12, Dorsal versus RasV12, lacZ), differential expression analysis was performed using DESeq2 [64]. Tables with genes significantly differentially expressed in any of the comparisons can be found in S9 Data. Exploration of differentially expressed genes was done using the RNA-seq data visualization pipeline Searchlight 2 (v2.0.0) [65]. The list of differentially expressed genes was adjusted using the cut-off p.adj < 0.05 and absolute log2fold >  0.5. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) libraries were obtained from FlyEnrichr. RNA sequencing data generated in this study has been deposited in NCBI’s Gene Expression Omnibus (GEO) [66] and is accessible through the GEO Series accession number GSE266879 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE266879).

An extended list of the materials used in this study can be found in Table 4.

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Table 4. Extended list of materials used in this study.

https://doi.org/10.1371/journal.pbio.3003068.t004

Supporting information

S1 Fig. Toll signaling cooperates with oncogenic Ras to induce overgrowth.

(A) Confocal images of EADs of indicated genotypes at 96-h AED stained for Dorsal (yellow) to illustrate the efficiency of overexpressing Cactus to block the morphological defects caused by Toll signaling activation by Dorsal overexpression. The white line highlights the GFP-marked region. (B) Representative confocal images of regions of wing discs (WDs) of indicated genotypes at 96-h AED stained for Dorsal (yellow). Nuclear Dorsal levels are quantified in (C). The white line highlights the border between GFP-marked cells of the Dpp stripe and GFP-negative cells of the posterior WD compartment. (C) Quantification of Dorsal nuclear intensity of the indicated genotypes. The mean fluorescence intensity of Dorsal in GFP-positive cells, internally normalized to GFP-negative cells, and standard deviation (error bar) are shown (**p <  0.01, One-way ANOVA with post-hoc Tukey HSD). (D) Confocal images of RasV12, dlgRNAi-induced EAD tumors of indicated genotypes at 120-h AED representative for tissue sizes quantified in (E). (E) and (F) Quantification of RasV12, dlgRNAi-induced tumor volume of indicated genotypes at 120-h AED (E) and 96-h AED (F). Mean volume and standard deviation (error bar) are shown (***p <  0.001, ns: p =  0.99 (E) or ns: p =  0.41 (F), One-way ANOVA with post-hoc Tukey HSD). (G) Confocal images of dlgRNAi-transformed EADs of indicated genotypes at 120-h AED representative for tissue sizes quantified in (H). Images are extracted singles planes of samples mounted for 3D quantification. (H) Quantification of dlgRNAi-transformed EAD volume of indicated genotypes at 120-h AED. Mean volume and standard deviation (error bar) are shown (ns: p =  0.8, One-way ANOVA with post-hoc Tukey HSD). (I) Representative confocal images of regions of WDs of indicated genotypes at 96-h AED (or 168-h AED) stained for Dorsal (yellow). Nuclear Dorsal levels are quantified in (J). The white line highlights the border between GFP-marked cells of the Dpp stripe and GFP-negative cells of the posterior WD compartment. (J) Quantification of Dorsal nuclear intensity of the indicated genotypes. The mean fluorescence intensity of Dorsal in GFP-positive cells, internally normalized to GFP-negative cells, and standard deviation (error bar) are shown (**p <  0.01, One-way ANOVA with post-hoc Tukey HSD). In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S2 Data. Scale bars represent 100 µm in (A), (D) and (G) and 10 µm in (B) and (I). ns, not significant; EAD, eye-antennal disc; WD, wing disc; AED, after egg deposition; cact, Cactus; Dl, Dorsal; eyFlp, eyeless-driven Flippase; a.u., arbitrary unit.

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S2 Fig. Dorsal inhibits retinal differentiation.

(A) qRT-PCR analysis of cact mRNA levels in EADs expressing indicated UAS-transgenes under the control of an eye-specific Gal4 (eyFlp; act>CD2>Gal4) as a read-out for Toll signaling activity. Data is shown as fold changes relative to the control (lacZ). n = 3 biologically independent samples were analyzed. Median fold changes and interquartile ranges (error bars) are shown. The dashed horizontal line illustrates the reference fold change of 1. (B) Confocal images of EAD tumors of indicated genotypes at 96-h AED stained with Click-iT EdU Alexa Fluor 647 (magenta) to identify DNA synthesis-based proliferation representative for EdU intensity quantified in (C). The white line highlights the GFP-marked region. (C) Quantification of EdU mean fluorescence intensity per EAD tumor at 96-h AED. Mean intensity and standard deviation (error bar) are shown (*p <  0.05, One-way ANOVA with post-hoc Tukey HSD). (D) Quantification of tissue volume of indicated genotypes at 72-h AED illustrating the time-dependency of Dorsal-induced tumor overgrowth. Mean volume and standard deviation (error bar) are shown (*p <  0.05, ns: p =  0.55, One-way ANOVA with post-hoc Tukey HSD. (E) Confocal images of an EAD and tumor of the indicated genotype at 96-h AED labeled for retinal determination and photoreceptor differentiation markers Eya (cyan) and ElaV (magenta). The white line highlights the GFP-marked region. In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S4 Data. Scale bars represent 100 µm. ns, not significant; a.u., arbitrary unit; EAD, eye-antennal disc; AED, after egg deposition; D,: Dorsal; Tl10b, Toll10b; eyFlp, eyeless-driven Flippase; Eya, eyes absent; ElaV, embryonic lethal abnormal vision; EdU, 5-ethynyl-2′-deoxyuridine.

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S3 Fig. Overgrowth of RasV12, Dorsal and RasV12, dlgRNAi-induced tumors is independent of JNK signaling.

(A) qRT-PCR analysis of scaf mRNA levels in EADs expressing indicated UAS-transgenes under the control of an eye-specific Gal4 (eyFlp; act>CD2>Gal4) as exemplary confirmation of the JNK-dependent upregulation of target genes after overexpression of Dorsal. Data is shown as fold changes relative to the control (lacZ or lacZ,bskDN). n = 3 biologically independent samples were analyzed. Median fold changes and interquartile ranges (error bars) are shown. The dashed horizontal line illustrates the reference fold change of 1. (B) Images of adult eyes of indicated genotypes showing the efficiency of overexpressing BskDN or Puc to inhibit JNK signaling and rescue Egr induced loss of photoreceptor cells. All flies, except for wild-type control flies expressed GMR-Gal4, UAS-egr. (C) Quantification of tumor sizes of indicated genotypes at 96-h AED confirming the observation of JNK-independent induction of tumor overgrowth after Dorsal overexpression using co-expression of Puc to inhibit JNK signaling. Mean volume and standard deviation (error bar) are shown (***p <  0.001, One-way ANOVA with post-hoc Tukey HSD). (D) Confocal images of of EADs and EAD tumors at 96-h AED of the indicated genotypes representative for tissue sizes quantified in (E). The white outline highlights the GFP-marked region. (E) Quantification of tissue volume of indicated genotypes at 120-h AED illustrating that growth of RasV12, dlgRNAi tumors did not depend on JNK signaling at 120-h AED. Mean volume and standard deviation (error bar) are shown (ns: p = 0.56, ns*: p = 0.92, One-way ANOVA with post-hoc Tukey HSD). In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S6 Data. ns, not significant; EAD, eye-antennal disc; AED, after egg deposition; Dl, Dorsal; mmp1, matrix metalloproteinase 1; ets21c, Ets at 21c; bskDN, dominant-negative Basket; Puc, Puckered; GMR, glass multiple reporter; egr, Eiger; JNK, Jun-N-terminal Kinase pathway; eyFlp, eyeless-driven Flippase.

https://doi.org/10.1371/journal.pbio.3003068.s003

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S4 Fig. Dorsal-induced caspase-dependent cell death is suppressed by RasV12.

(A) Confocal images of EADs of indicated genotypes at 96-h AED labeled for Dcp1 showing the persistence of elevated levels of Dcp1 after JNK inhibition. The white outline highlights the GFP-marked region. (B) Quantification of the Dcp1-positive area relative to whole disc size at 96-h AED comparing control EADs after overexpression of Dorsal with BskDN. Mean area and standard deviation (error bar) are shown (***p <  0.001, One-way ANOVA with post-hoc Tukey HSD). (C) qRT-PCR analysis of rpr mRNA levels in EADs expressing indicated UAS-transgenes under the control of an eye-specific Gal4 (eyFlp; act>CD2>Gal4) illustrating the JNK-independent upregulation of pro-apoptotic gene expression after overexpression of Dorsal in control EADs. Data is shown as fold changes relative to the control (lacZ, bskDN). n = 3 biologically independent samples were analyzed. Median fold changes and interquartile ranges (error bars) are shown. The dashed horizontal line illustrates the reference fold change of 1. (D) Confocal images of EADs of indicated genotypes at 96-h AED labeled for ElaV to highlight the lack of photoreceptor cells after Dorsal and p35 co-expression. (E) Quantification of tissue volume of indicated genotypes at 96-h AED to confirm the increased growth of discs overexpressing Dorsal after inhibition of apoptosis by co-expression of p35 using three-dimensional quantification. Mean volume and standard deviation (error bar) are shown (***p <  0.001, One-way ANOVA with post-hoc Tukey HSD). (F) Quantification of the Dcp1 positive area relative to whole disc size at 96-h AED. Mean area and standard deviation (error bar) are shown. (G) Confocal images of EADs of indicated genotypes at 96-h AED labeled for Dcp1 confirming there is no upregulation of cell death after overexpression of cactus. The white outline highlights the GFP-marked region. (H) Quantification of the Dcp1-positive area relative to whole disc size at 96-h AED comparing cell death levels in control EADs after overexpression of Cactus or Dorsal. Mean area and standard deviation (error bar) are shown (***p <  0.001, ns: p = 0.95, One-way ANOVA with post-hoc Tukey HSD). In all confocal images, DAPI (gray) is used to visualize nuclei and GFP (green) labels cells co-expressing indicated UAS-transgenes. The underlying data sets can be found in S8 Data. Scale bars represent 100 µm. ns, not significant; Dcp1, cleaved Drosophila caspase 1; JNK, Jun-N-terminal Kinase pathway; EAD, eye-antennal disc; AED, after egg deposition; Dl, Dorsal; Dcp1, cleaved Drosophila caspase 1; rpr, reaper; ElaV, embryonic lethal abnormal vision; bskDN, dominant-negative Basket.

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S1 Data. File containing the raw data for Fig 1.

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S2 Data. File containing the raw data for S1 Fig.

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S3 Data. File containing the raw data for Fig 2.

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S4 Data. File containing the raw data for S2 Fig.

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S5 Data. File containing the raw data for Fig 3.

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S6 Data. File containing the raw data for S3 Fig.

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S7 Data. File containing the raw data for Fig 4.

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S8 Data. File containing the raw data for S4 Fig.

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S9 Data. File containing the table of genes significantly differentially expressed in any of the comparisons for data shown in Figs 2A-–2C and 3A.

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Acknowledgments

We would like to thank all current and former members of the Basler group, especially Vivien Fuchs, Jamie Little, Erich Brunner and Giulia Moro, as well as Hugo Stocker for fruitful discussions and advice. We also gratefully acknowledge Tor Erik Rusten and Caroline Dillard for their open communication during the completion of the manuscript.

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