4EHP and NELF-E regulate physiological ATF4 induction and proteostasis in disease models of Drosophila

Domain: biology
Published: 2025-12-23
Editorial outcome: major_revision

Abstract
Cells adapt to proteostatic and metabolic stresses, in part, through stress activated eIF2α kinases that stimulate the translation of ATF4. Stress-induced ATF4 translation is regulated through elements at ATF4 mRNA’s 5’ leader. In addition to eIF2α kinases, ATF4 induction requires other regulators that remain poorly understood. Here, we report an ATF4 regulatory network consisting of eIF4E-Homologous Protein (4EHP), NELF-E, the 40S ribosome, and eIF3 subunits. Specifically, we found that the mRNA cap-binding protein, 4EHP, was required for ATF4 signaling in the Drosophila larval fat body and in disease models associated with abnormal ATF4 signaling. NELF-E mRNA, encoding a regulator of pol II-mediated transcription, was identified as a top interactor of 4EHP in a TRIBE (Targets of RNA Binding through Editing) screen. Quantitative proteomics analysis revealed that the knockdown of NELF-E or 4EHP commonly reduced several subunits of the 40S ribosome (RpS) and the eIF3 translation initiation factor. Moreover, reduction of NELF-E, 4EHP, RpS12, eIF3l, or eIF3h suppressed the expression of ATF4 and its target genes. These results uncover a previously unrecognized ATF4 regulatory network consisting of 4EHP and NELF-E that impacts proteostasis during normal development and in disease models.

Introduction
Cells adapt to external or physiological stress in part by inducing the expression of stress-responsive genes. Among known adaptive mechanisms is the Integrated Stress Response (ISR), a signaling pathway initiated by stress-activated eIF2α kinases1,2. ATF4 (Activating Transcription Factor 4) is a major transcription factor that mediates ISR, inducing the transcription of various genes involved in proteostasis and amino acid biosynthesis3–8. Reflecting the broad role of ISR in responding to diverse cellular stress, there is an increasing list of metabolic and degenerative diseases associated with abnormal ISR signaling.

Multiple eIF2α kinases responding to diverse conditions of stress can initiate ISR signaling. For example, GCN2 gains activity in response to amino acid deprivation, and PERK is best characterized for its activation by endoplasmic reticulum (ER) stress. In mammals, the eIF2α kinase HRI is activated in response to mitochondrial stress9–11. In Drosophila, certain types of mitochondrial stress also activate PERK, as in the case of parkin mutants that impair mitophagy12–14. Notably, loss of parkin underlies rare forms of familial Parkinson’s Disease in humans15, suggestive of a possible role of ISR in this disease.

The immediate consequence of eIF2α phosphorylation is to transform it into an inhibitor of the guanine exchange factor eIF2B, thereby suppressing the delivery of the ternary complex (TC, consisting of eIF2-GTP-Met-tRNAMet) to the 40S ribosome16. The net effect is a general attenuation of mRNA translation initiation. The mRNAs of metazoan ATF4 and yeast GCN4 evade such suppression, and in fact, undergo preferential translational induction to further mediate ISR’s gene expression program. Such induction of ATF4 and GCN4 relies on the properties of their unique 5’ leaders that contain regulatory upstream Open Reading Frames (uORFs)17–20. uORFs are present in numerous transcripts, and they often reduce the translation of downstream ORFs because of translation termination after uORF translation. However, the 40S ribosomes on the mRNAs of ATF4 and GCN4 can continue to scan and reinitiate translation downstream of uORFs20–22. Re-initiation after termination is possible if the 40S can retain essential translation initiation factors such as eIF323–25. During the translation of standard ORFs, re-initiation doesn’t occur in part because the 40S-eIF3 interaction becomes unstable and eIF3 is eventually lost24. But on short uORFs, such as those found in ATF4 or GCN4, some eIF3 are still retained by the 40S after uORF translation, allowing the 40S-eIF3 complex to recruit a fresh TC and other initiation factors for re-initiation at the downstream main ORF26–29. Supporting this notion, studies have found that the reduction of certain eIF3 subunits can impair uORF-mediated translational regulation of yeast GCN4, plant AtbZip11, and ATF4 in human cell lines23–25,30,31.

Here, we report a previously unrecognized pathway that regulates 40S ribosome subunits, eIF3, and ATF4 expression. In the Drosophila larval fat body with physiological stress and ATF4 signaling, we found that the depletion of 4EHP or NELF-E reduced ATF4 and its target gene expression. Drosophila parkin (park) mutants dramatically stimulated ATF4 signaling, which was also suppressed by 4EHP loss. Consistently, 4EHP affected ATF4-associated pathological phenotypes, including parkin mutants’ lethality and light-dependent retinal degeneration. We profiled mRNAs that bind to 4EHP’s cap-binding domain using TRIBE (Targets of RNA Binding through Editing)32, and among the top interactors was NELF-E mRNA. 4EHP was necessary for NELF-E expression, and NELF-E knockdown reduced ATF4 levels. Among the genes commonly reduced after the depletion of NELF-E or 4EHP were components of the 40S ribosome (RpS genes) and a subunit of the eIF3 translation initiation factor complex. Reduction of RpS12, eIF3l, or eIF3h suppressed physiological ATF4 signaling in the Drosophila fat body without affecting the expression of control transgenes. Together, these findings uncover an ATF4 regulatory network consisting of 4EHP, NELF-E, 40S ribosome, and eIF3 subunits, impacting proteostasis during normal development and in disease models.

Results
As a screen identifies 4EHP as a gene required for physiological ATF4 expression in the fat body
The Drosophila genome encodes a single ATF4 ortholog annotated as cryptocephal (crc)33. A well-established transcriptional target of Drosophila crc is Thor, an ortholog of 4E-BP134–36. The single intron of Thor contains crc/ATF4-binding sites, and this regulatory sequence was previously fused to the coding sequence of DsRed fluorescent protein to generate an ATF4 reporter known as Thorintron-DsRed (also referred to as 4E-BP intron-DsRed)34 (Fig. 1a). During the late third instar larval stage, Thorintron-DsRed expression is induced in the larval fat body (Fig. 1b, c), a metabolic tissue analogous to the mammalian liver and the adipose tissue. We had previously shown that the Thorintron-DsRed signal in the fat body is dependent on GCN2 and ATF4, suggestive of physiological amino acid deprivation stress during normal development34.

We performed a small-scale RNAi screen for their capacity to regulate crc/ATF4 signaling, utilizing the Gal4/UAS method37 to express RNAi transgenes with the fat body specific dcg-Gal4 (Fig. 1b). The 184 RNAi lines targeted either known translation regulators or the Drosophila homologs of ribosome-associated proteins38. The RNAi lines that reduced the Thorintron-DsRed signal comparable to that caused by crc/ATF4 RNAi were marked as hits. Our screen identified 19 hits, and the RNAi line targeting 4EHP (eIF4E-Homologous Protein, also known as eIF4E2) was among those to cause the starkest decreases in DsRed fluorescence (Fig. 1c–j and Supplementary Data 1). The knockdown of 4EHP in the fat body caused a slight delay in development (Supplementary Fig. S1), and to assess any general effect on protein synthesis, we also examined a control transgene with a single ORF, UAS-GFP expression driven by the dcg-Gal4 driver. Neither crc nor 4EHP RNAi reduced this control GFP expression in the fat body, supporting their specific effects on crc/ATF4 signaling (Fig. 1f’–h’, j). To validate the role of 4EHP, we employed a hypomorphic loss-of-function 4EHP mutant allele, 4EHPCP53, with significantly reduced 4EHP expression39. The Thorintron-DsRed signal was also reduced in this 4EHPCP53 background (Supplementary Fig. S2). Going beyond the analysis of the Thor reporter, we validated that Thor transcripts were significantly reduced in 4EHP RNAi and 4EHPCP53 larvae, as measured through RT-qPCR (Fig. 1k).

4EHP is homologous to eIF4E, with a conserved domain that binds the 7-methylguanosine cap (m7GpppN, where N is a nucleotide) of mRNAs. Although eIF4E is required for a bulk of cap-dependent mRNA translation, 4EHP has a weaker cap-binding affinity and associates with other proteins, such as 4E-T, to bind to a more specific set of target mRNAs40–43. Perhaps because 4EHP works with other such factors, overexpression of 4EHP alone was insufficient to induce Thorintron-DsRed, and RNAi lines that target 4E-T reduced this reporter in the larval fat body (Supplementary Fig. S3). We decided to further characterize 4EHP due to its possible specificity in gene expression control.

We assessed Drosophila crc protein levels in the larval fat body through western blots. Overexpression of crc (specifically the RB transcript) in this tissue using the dcg-Gal4 driver generates a clear anti-crc band near the 25 kDa marker (Fig. 1l, lane 3). Control larval fat bodies also show a crc protein band, albeit weaker than that caused by the transgene overexpression (Fig. 1l, lane 1). Knockdown of 4EHP significantly reduced the crc band intensity (Fig. 1l, m), indicating that 4EHP is required for physiological crc/ATF4 expression in the larval fat body.

Loss of 4EHP impacts gene expression related to metabolism and translation
To better understand 4EHP’s role in gene expression, we performed RNA-seq analysis of the larval fat body transcripts with or without 4EHP RNAi. With the criterion of p-adjusted <0.05, 183 transcripts were significantly downregulated and 218 upregulated after 4EHP RNAi (genotype: dcg-Gal4/UAS-4EHP RNAi) as compared to controls (genotype: dcg-Gal4/+) (Fig. 2a and Supplementary Data 2). The Principal Component Analysis (PCA) plot showed a clear separation of the two genotypes (Fig. 2b). crc transcript levels did not change significantly by 4EHP RNAi (Fig. 2a), supporting the idea that crc/ATF4 signaling is impacted through a post-transcriptional mechanism.

As transcriptional changes in stressed cells do not necessarily lead to similar changes in protein levels, we also performed a quantitative proteomics analysis in control and 4EHP RNAi larval fat body (Fig. 2c and Supplementary Data 3). With the criterion of p-adjusted <0.05, 364 proteins were detected at significantly reduced levels and 333 at higher levels in 4EHP RNAi fat body samples.

Interestingly, 12 of the top 13 enriched GO Terms for those proteins significantly reduced by 4EHP RNAi were related to various metabolic processes (Fig. 2d). To assess changes in metabolism, we compared the steady-state metabolic profiles of homozygous 4EHPCP53 larvae with those from control w1118 samples (3 independent samples for each genotype). Among 147 common cellular metabolites assessed, 107 of them were detected from at least three samples, and 89 were detected from all six samples (Supplementary Data 4). Nine metabolites were significantly lower in the 4EHPCP53 larvae (Supplementary Fig. S4a and Supplementary Data 4). We note that seven of these nine downregulated metabolites are either amino acids or their metabolic products. Six of these seven (excluding L-alanine) were metabolites in either the serine-one-carbon pathway or the urea cycle (Supplementary Fig. S4b, c). Changes in these metabolites correlate with the reduction of serine-one-carbon pathway enzyme transcripts, including aay, Nmdmc, Gnmt (Fig. 2a, e) in the 4EHP RNAi dataset. aay, which encodes an L-phosphoserine phosphatase that mediates serine biosynthesis, was also found significantly reduced in our quantitative proteomics data (Fig. 2c, e).

4EHPCP53 homozygous females had a slightly shorter lifespan compared to control females, but the mutant male flies had lifespans similar to controls when reared under standard conditions of nutrients (Supplementary Fig. S5). We suspected that the amino acids available in standard food were sufficient to sustain the viability of 4EHPCP53 flies beyond 80 days despite indications of decreased crc/ATF4 signaling. To assess the effect of nutritional deprivation, we isolated day-1 adult flies and subjected them to total nutrient starvation. Under these conditions, wild-type control adults retained over 50% of their population for 60 h, followed by a drop in population by 72 h, and a total population loss occurred by 96 h. Under equivalent conditions, 4EHPCP53 homozygotes and 4EHP RNAi adults died at a significantly faster rate (Fig. 2f, g). To test if protein deficiency contributes to a shorter lifespan in these flies, we assessed survival after reintroducing protein (1.6% BSA) into their diet. This protein-only diet made 4EHPCP53 and 4EHP RNAi flies less vulnerable to starvation (Fig. 2i, j). In particular, 4EHPCP53 flies displayed a survival curve similar to control flies when reared with 1.6% BSA (Fig. 2i). Equivalent knockdown of crc in the fat body also made the flies vulnerable to total starvation (Fig. 2h), and adding back 1.6% BSA to the diet abolished the survival difference with control flies (Fig. 2k). Together, these results indicate that the loss of 4EHP reduces the levels of certain amino acids and makes flies vulnerable to starvation.

One of the top 13 enriched GO terms in the proteins reduced by 4EHP RNAi was “translation” (Fig. 2d). Among the established initiation factors, eIF4A, eIF3l, and eIF3k were detected significantly lower in 4EHP RNAi samples (Fig. 2c and Supplementary Data 3). Not all initiation factors were reduced by 4EHP RNAi. For example, three subunits of the eIF2 complex were detected at significantly higher levels in samples with 4EHP knockdown (Fig. 2c). Western blots validated the increase in total eIF2α caused by 4EHP knockdown, but there was not a concomitant increase in phospho-eIF2α. As a result, the ratio of phospho-eIF2α to total eIF2α decreased in 4EHP knockdown samples (Supplementary Fig. S6a–c). The ribosome subunits also showed an interesting pattern: All of the significantly reduced ribosomal proteins in the 4EHP RNAi proteome dataset were subunits of the 40S ribosome (referred to as RpS proteins), while many 60S subunits (RpL proteins) were detected at significantly higher levels (Fig. 2c and Supplementary Fig. S6d). While many RpS proteins were reduced, the transcript of RpS7 was the only 40S subunit component reduced in the RNA-seq dataset (Fig. 2a). Our observation is consistent with published reports that the decrease in one RpS subunit causes the reduction of other RpS subunit proteins while increasing RpL subunits and other ribosome biogenesis factors44,45.

4EHP loss affects proteostasis phenotypes
Since ATF4 signaling is deregulated in many diseases, we examined 4EHP’s possible effect in Drosophila disease models. Parkin (park) loss-of-function variants underlie rare forms of Parkinson’s Disease, and park encodes a protein that helps to clear defective mitochondria46,47. Previous studies had reported that Drosophila park loss of function mutants activate PERK and promote eIF2α phosphorylation12,48. To examine the effect of park loss, we used park25 and parkD21 alleles, which are null alleles with deletions in the coding sequence49,50. As reported previously, park25/parkD21 trans-heterozygotes survived to adulthood as long as the larvae were reared under uncrowded conditions (20 or fewer larvae in a vial).

We found that park25/parkD21 adult flies showed intense Thorintron-DsRed reporter expression indicative of strong crc (ATF4) signaling (Fig. 3a, b).

To assess the role of 4EHP in this disease model, we crossed 4EHPCP53 into the park25/parkD21 background (genotype: Thorintron-DsRed/+; park25, 4EHPCP53/parkD21, 4EHPCP53). These flies expressed significantly reduced Thorintron-DsRed signals compared to flies with park25/parkD21 alone (Fig. 3a, b). As noted by others, a large fraction of the park25/parkD21 adult flies died within the first day after eclosion (Fig. 3c). By contrast, introducing 4EHPCP53 in this background (genotype: Thorintron-DsRed/+; park25, 4EHPCP53/parkD21, 4EHPCP53) significantly suppressed the lethality of park25/parkD21 flies (Fig. 3c). To determine if crc/ATF4 signaling also affects parkin mutants, we employed the crc1 loss-of-function allele. crc1 homozygotes exhibit early developmental lethality, but we found that the crc1/+ heterozygotes moderately but significantly (p < 0.0293) prolonged the lifespan of park25/parkD21 adults (Supplementary Fig. S7). These results indicate that the loss of 4EHP suppresses crc/ATF4 signaling in parkin mutant flies, and excessive crc/ATF4 signaling contributes to the enhanced lethality of parkin mutants.

In another model associated with ATF4 signaling, we examined light-dependent retinal degeneration, a phenotype accelerated by the loss of PERK or crc51. To assess the role of 4EHP in this model, we examined pseudopupils, which are trapezoidal patterns that appear in response to blue light deep in the retina. The pseudopupil forms only when ommatidial clusters maintain regular arrays of aligned photoreceptors and serves as a convenient tool to assess ommatidial integrity. As previously reported51, crcGFSTF hypomorphs rapidly lost pseudopupils when reared under light (Fig. 3d). 4EHPCP53 flies also lost pseudopupils earlier than the control (w1118) flies when reared under light (Fig. 3d). Such loss of pseudopupils did not occur when 4EHPCP53 mutant flies were reared in the dark (Fig. 3d). These results further support the idea that 4EHP regulates crc/ATF4 signaling in disease models.

4EHP binds to NELF-E mRNA and NELF-E is required for ATF4 signaling
To gain mechanistic insights into the regulation of crc/ATF4 signaling by 4EHP, we searched for mRNAs that bind to 4EHP’s cap-binding domain. We specifically used an approach referred to as Targets of RNA-binding proteins Identified By Editing (TRIBE), involving an Adenine Deaminase Acting on RNA (ADAR) fused to an RNA-binding protein of interest32,52. ADAR edits adenine to inosine on the bound mRNAs, and those edited RNAs can be identified through RNA-seq. We generated UAS-transgenes with ADAR fused to wild-type 4EHP. As a negative control, we fused ADAR to the 4EHPW114A mutant, which impairs the mRNA 5’ cap-binding pocket39 (Fig. 4a). We drove the expression of these constructs in the fat body using dcgGal4 and analyzed polyA-containing RNAs from third instar larval fat bodies for RNA editing profiles. The mRNAs of crc or any eIF2α kinases did not score as significantly edited targets (Fig. 4b, Supplementary Data 5).

The preferentially edited mRNAs (binding scores >10) were enriched with diverse GO Terms including cellular developmental process and cell differentiation (Fig. 4c). The mRNA most significantly edited by 4EHP-ADAR was CG18132, encoding an uncharacterized protein disulfide isomerase, predicted to assist in protein folding within the endoplasmic reticulum. The second highest was the mRNA of Negative Elongation Factor E (NELF-E) (Fig. 4b, d). We considered NELF-E a high-confidence hit, as another A to I edit 30 bases downstream of the first had the 7th highest score (Fig. 4b, d). An mRNA encoding another subunit of the NELF complex, NELF-A, was also edited with significance: one site was ranked at 141, and a second NELF-A site was ranked at 1157 (Fig. 4b and Supplementary Data 5). NELF-E transcript levels were comparable between control and 4EHP RNAi conditions (Fig. 4e), but our anti-NELF-E western blots revealed that 4EHP RNAi reduced NELF-E protein levels (Fig. 4f, g). These results indicate that 4EHP binds to NELF-E mRNA to regulate its expression at a post-transcriptional level.

The NELF complex is composed of four subunits, and overexpression of NELF-E alone was neither sufficient to enhance Thorintron-DsRed reporter expression nor rescue the Thorintron DsRed levels in 4EHP RNAi larvae (Supplementary Fig. S8). Late third instar larvae failed to emerge when NELF-E was knocked down with the dcg-Gal4 at 25°C, but we obtained viable larvae at 20°C, albeit with a developmental delay (Supplementary Fig. S8e). Under these conditions, two independent RNAi lines targeting NELF-E reduced the Thorintron-DsRed reporter signal in the fat body but not the control dcg-Gal4>UAS-GFP expression (Fig. 5a–d). We also observed that NELF-E RNAi reduced crc protein levels (Fig. 5e, f), corroborating our observations with Thorintron-DsRed. Consistently, flies with NELF-E RNAi died significantly faster than control lacZ RNAi under total starvation (Fig. 5g). Adding 1.6% of BSA as a protein source to the vial abolished such a difference between NELF-E RNAi and lacZ RNAi (Fig. 5h). These results indicated that, similar to 4EHP, NELF-E is required for physiological crc/ATF4 signaling and survival under nutrient deprivation.

NELF-E regulates metabolic pathways and RpS subunits
To further gain insights into NEFL-E’s role, we performed RNA-seq on NELF-E RNAi fat body tissue samples with lacZ RNAi samples as a control. We found that 312 transcripts were significantly induced, while 890 transcripts were found at significantly reduced levels in NELF-E RNAi samples (Fig. 6a and Supplementary Data 6; p-adjusted <0.05). The PCA plot showed a clear separation between the NELF-E RNAi and control samples (Fig. 6b). Similar to what we had seen with 4EHP RNAi, the three serine biosynthetic enzymes (aay, CG6287, and CG11899) and the one carbon pathway enzyme Nmdmc were detected at significantly reduced levels in NELF-E RNAi fat body samples (Fig. 6a). These enzymes are established targets of ATF4 in Drosophila and mammals5,48,53,54.

In addition to RNA-seq, we subjected these fat bodies to quantitative proteomics analysis (Fig. 6c and Supplementary Data 7). Those peptides that were reduced in NELF-E RNAi fat bodies, as compared to lacZ RNAi controls, were enriched with GO Terms that included serine transport and L-serine biosynthetic process (Fig. 6d). Consistently, the proteomics dataset from NELF-E RNAi samples showed reduced levels of L-serine biosynthetic enzymes such as aay and CG11899 (Fig. 6c).

Previous studies had documented gene expression changes in cells deficient in NELF55,56. Because those studies had not made associations between NELF and ATF4, we re-examined one of the published datasets reported with NELF-B knockout mouse embryonic stem cells56. The transcripts specifically reduced in the knockout cells include the major enzymes that mediate the serine-one-carbon pathway, homologous to the Drosophila enzymes reduced by NELF-E RNAi (Supplementary Fig. S9). Taken together, the gene expression profiling results support the idea that NELF regulates ATF4 target genes across phyla.

We note that mouse NELF-B knockouts had significantly reduced ATF4 transcript levels56. However, analogous changes in the Drosophila crc expression did not occur in Drosophila NELF-E knockdown samples (Fig. 6a). This suggested that Drosophila NELF-E may utilize a post-transcriptional mechanism to regulate crc expression. To gain insight, we examined gene expression changes that were common between 4EHP and NELF-E knockdown samples. There was an overlap between the transcripts impacted by RNAi of 4EHP and NEFL-E (Supplementary Fig. S10a), with 83% of the transcripts significantly reduced in 4EHP RNAi samples also significantly reduced after NELF-E RNAi. Similarly, the proteomics data showed that 63% of the proteins reduced in 4EHP RNAi fat body were also reduced in NELF-E RNAi fat bodies (Supplementary Fig. S10a). The proteins commonly reduced in 4EHP and NELF-E RNAi samples were enriched with GO terms such as metabolic processes and cytoplasmic translation (Supplementary Fig. S10b). Since crc/ATF4 expression is regulated at the level of mRNA translation, we further examined if translation initiation factors changed their levels in 4EHP or NELF-E RNAi samples. eIF3l was the only eIF protein that was significantly reduced in both conditions. In addition, many 40S ribosome subunits (RpS proteins) were detected at significantly reduced levels (Supplementary Fig. S10a). The most strongly reduced peptides mapping to the RpS subunits were RpS10b, Rps27A, RpS20, and Rps12 (Fig. 6e). According to the published human 40S ribosome structure (PDB 7r4x), these subunits cluster together in a specific part of the ribosome (Fig. 6f). Therefore, we speculate that the reduction of RpS12 transcripts in NELF-E RNAi fat body leads to the instability of the other subunit peptides.

The effect of NELF-E RNAi appeared selective as none of the RpL subunits were significantly reduced in the NELF-E RNAi or 4EHP RNAi data (Fig. 6e and Supplementary Fig. S6). In fact, many RpL subunit peptides were detected at higher levels, possibly through a feedback regulatory mechanism against RpS reduction (Fig. 6e). Also detected at higher levels were the three eIF2 subunits, and the increase in the total eIF2α was validated through western blots (Supplementary Fig. S11). Phospho-eIF2α levels didn’t increase together with total eIF2α, resulting in a reduced ratio of phospho-eIF2α to total eIF2α in the NELF-E RNAi samples (Supplementary Fig. S11).

To test if the reduction of RpS subunit contributes to ATF4 signaling impairment, we employed the RpS12S2783, a loss of function allele caused by a transposable P-element insertion. The homozygotes were lethal as expected, but the RpS12S2783/+ heterozygotes were viable and reduced Thorintron-DsRed signals in the larval fat body as compared to controls (Fig. 6g). Similar to the case of 4EHP and NELF-E knockdown, RpS12S2783/+ did not reduce the expression of control dcg-Gal4>UAS-GFP (Fig. 6g, h), indicating that crc/ATF4 signaling is specifically sensitive to the reduction of the RpS12 gene dosage.

crc/ATF4 signaling is selectively impaired by the reduction of eIF3 subunits
We next turned our attention to the eIF3 complex as eIF3l was commonly reduced by the knockdown of 4EHP and NELF-E, and because eIF3 partners with the post-termination 40S subunit for re-initiation downstream of uORFs23,24,30. The knockdown of certain subunits caused larval lethality or severe developmental delay, but fat body-specific expression of RNAi lines targeting eIF3 subunits, eIF3h and eIF3l, and the eIF3-associated factor eIF3j, did not interfere with larval development, with most flies successfully reaching pupal stages (Supplementary Fig. S12). We found that eIF3l knockdown in the fat body reduced Thorintron-DsRed signals in the larval fat body as compared to controls (Fig. 7a–d).

To independently validate the role of eIF3, we knocked down eIF3h. Of note, our previous screen had scored eIF3h RNAi as a suppressor of the crc/ATF4-signaling reporter expression22. Two independent RNAi lines targeting eIF3h (VDRC106189 and BDSC55603) suppressed Thorintron-DsRed expression when expressed in the larval fat body (Fig. 7e–g and Supplementary Fig. S13). These knockdown conditions did not block a control GFP transgene expression (Fig. 7e’, f’, h), further supporting a specific effect of eIF3h knockdown on ATF4 signaling. We also examined an allele of eif3h, k09003, with a transposable element (P-element) inserted in its coding sequence. Homozygote eif3hk09003 did not survive to late larval stages, but the heterozygote 3rd instar larvae had weaker Thorintron-DsRed expression in the fat body (Supplementary Fig. S14).

To gain further insight into eIF3h RNAi’s effect, we generated a UAS line with the crc 5’ leader preceding the DsRed reporter (Fig. 7i). This transgene will not capture potential crc (ATF4) regulation that acts through the protein-coding sequence but is designed to report regulatory inputs at the 5’ leader upstream of the main ORF. We first tested the stress-inducible nature of this reporter in eye imaginal discs. When the reporter was expressed with the eye specific GMR-Gal4, no DsRed expression was detected, indicating that the crc 5’ leader inhibited the main ORF translation in unstressed cells. Expressing Rh1G69D, a missense allele of Rh1 that induces crc/ATF4 when expressed in eye imaginal discs57, robustly induced DsRed expression (Supplementary Fig. S15). The equivalent experiment in the PERK −/− background abolished DsRed induction, indicating that crc 5’ leader-DsRed reports eIF2α kinase-mediated crc induction (Supplementary Fig. S15).

When driven with the fat body-specific Gal4 driver, we detected clear crc 5’ leader-DsRed signals that reported physiological ISR activity (Fig. 7j, k). Such reporter signal was significantly suppressed when eIF3h was knocked down (Fig. 7l, m). eIF3h RNAi did not significantly reduce phospho-eIF2α levels (Supplementary Fig. S16). These results support the idea that depletion of 4EHP or NELF-E reduces eIF3 levels, and physiological ATF4 signaling is suppressed by the reduction of eIF3 subunits.

While our data demonstrates the requirement of eIF3h, overexpression of eIF3h alone was not sufficient to induce Thorintron-DsRed (Supplementary Fig. S17a–c), perhaps because other eIF3 subunits could be limiting. Consistently, overexpressing eIF3h did not rescue the suppression of Thorintron-DsRed caused by NELF-E RNAi (Supplementary Fig. S17d, e).

Finally, we examined if the newly identified regulators are themselves under the control of crc/ATF4 signaling. We found that the transcript levels of 4EHP, NELF-E, and eIF3h did not change significantly in late larval fat bodies as examined through RT-qPCR (Supplementary Fig. S18). These results do not support a feedback regulatory relationship between crc/ATF4 and 4EHP, NELF-E, eIF3h.

Methods
Fly strains
All flies were reared in a standard cornmeal-agar diet supplemented with molasses unless otherwise specified. Due to sexual differences in metabolism and ATF4 signaling, males were analyzed unless otherwise stated. All gene overexpression and RNAi experiments were done using the Gal4/UAS binary expression system37. NELF-E RNAi crosses were maintained at 20°C due to larval lethality at higher temperatures. All other crosses were maintained at 25°C. The following fly stocks were used for this study: w1118 (BDSC #5905), Thor (4E-BP) intron-DsRed34, dcg-Gal4, UAS-crc33, UAS-lacZ RNAi74, UAS-ATF4 (crc) RNAi (VDRC #109014), UAS-4EHP RNAi (VDRC #38399 and BDSC #36876), UAS-NELF-E RNAi (VDRC #21009, BDSC #32835), UAS-eIF3h RNAi (VDRC #106189, BDSC #55603), UAS-eIF3l RNAi (VDRC #107267), UAS-4E-T RNAi (VDRC #101047 and # 34755), 4EHPCP5339, parkD2150, park2549, Perke01744 (BDSC #85557).

Molecular cloning
To generate flies expressing 4EHP-ADAR constructs for TRIBE, fusion protein sequences were subcloned into the pUASTattB vector. The 4EHP-RD CDS (full sequence Flybase ID FBtr0303160) composed the N-terminus of the fusion protein, followed by a 3’ linker joined to the 5’ end of the ADAR-RN catalytic domain sequence. In accordance with previous reports using TRIBE, the ADAR sequence contains a mutation at E488Q to “hyper”-activate ADAR’s function to improve sensitivity52. The ADAR catalytic domain was followed by a second linker, joined by a C-terminal V5 tag. For 4EHPW114A-ADAR, the TGG codon for tryptophan at nucleotides 340–342 was substituted for GCT to encode alanine. The full construct was generated via Invitrogen GeneArt Gene Synthesis Services. EcoR1 and Kpn1 sites were introduced to the construct via High Fidelity PCR amplification using the following primers: ADAR-F-EcoR1 5’- GCGGAATTCATGAGCATGGAGAAAGTAGC-3’; ADAR-R-Kpn1 5’- AGTGGTACCTCACGTAGAATCGAGACCGA-3’. The resulting product was verified to be free of PCR-induced mutations using sanger sequencing with services provided by Genewiz. The pUASTattB plasmids containing the verified complete construct were injected into the line 24482 by BestGene Inc to insert into the 2nd chromosome at a 51C locus.

To generate the crc 5’ UTR-DsRed reporter, the crc RA isoform was used, and its ATF4 main ORF was replaced with the DsRed coding sequence. The resulting crc 5’ UTR fused to DsRed was subcloned into the pUASTattB vector and injected into the VK37 strain to insert into the 2nd chromosome at 22A3.

Metabolic profiling
Late larval stage samples from control (w1118) and 4EHPCP53 homozygous flies were analyzed by the NYU Metabolomics Core Resource Laboratory using the hybrid LC-MS assay. Triplicate samples for each genotype were processed after scaling the metabolite extraction to a measured aliquot (5 larvae/mL). A panel of 147 metabolites were assessed, and 89 metabolites were detected in all 6 samples after background threshold correction. To assess differential metabolite expression, metabolite peak intensities were extracted to a library of m/z values and retention times developed with authentic standards. Intensities were extracted with an in-house script with a 10 ppm tolerance for the theoretical m/z of each metabolite, and a maximum of 30 s retention time window. A cocktail of isotope-labeled amino acid standards was spiked into the metabolite extraction solvent cocktail. Peak intensities were extracted according to a library of m/z values and retention times for the doubly labeled (13C and 15N uniform) amino acids.

Nutrient starvation experiments
For full nutrient starvation experiments, day-1 adult male flies were collected and relocated to vials containing medium composed of 1.5% agarose in PBS, which were incubated at room temperature. At twelve-hour intervals, deaths were recorded. The experiment was run until all populations perished from starvation. For protein-only diet experiments, a medium composed of 1.5% agarose and 1.6% BSA in PBS was used.

Photoreceptor degeneration assay
We collected 0-1 day AE flies from each genotype (Light: w1118, n=44. 4EHPCP53, n=37. crcGFSTF, n=57. Dark: w1118, n=37. 4EHPCP53, n=15. crcGFSTF, n=38.) and kept in regular cornmeal vials covered by parafilm made holes for a gas exchange. These vials were put into two cardboard boxes (11.5 cm × 11.5 cm × 14 cm) which are with or without a lid. The boxes are put in a 25°C incubator during the assay. For the light source, we put an LED light pad (B4 Tracing Light Box with Internal Cord + Foldable Stand, 14.2 * 10.6 Inches Light Board for Tracing, VKTEKLAB) on the boxes and adjusted the intensity to be 211-259 lux in the no-lid box. The deep pseudopupil (Dpp) in living flies was observed under SMZ1500 (Nikon) under blue light. The observation was done from day 10 to day 28 AE. The survival data was analyzed using “GraphPad Prism 10” (GraphPad Software).

Unless otherwise stated, each biological replicate for RT-qPCR and RNA-seq experiments was composed of RNA derived from 10 male Drosophila larval fat bodies for all genotypes. For sample homogenization, pestles for 1.5 mL microcentrifuge tubes (USA Scientific, 1415–5390) were used. Maxima H Minus Reverse Transcriptase was used to generate cDNA from 500 ng RNA, which was subsequently used to perform qPCR with Green Power SYBR® Green PCR Master Mix (Invitrogen). Relative mRNA levels were determined by using the delta delta CT method to measure cycle threshold, normalized to products generated using primers for Rp49 or Rpl15. The primers for Thor and ATF4 are previously recorded elsewhere. Other primers as follows: Rp49-F 5’- AGATCGTGAAGAAGCGCACCAAG-3’; Rp49-R 5’- CACCAGGAACTTCTTGAATCCGG-3’; Rpl15-F 5’- AGGATGCACTTATGGCAAGC-3’; Rpl15-R 5’- GCGCAATCCAATACGAGTTC-3’; 4EHP-F 5’- CAGCGATGTGGATAATCAG-3’; 4EHP-R 5’- GAGAACCAGAGGCAGTAT-3’; NELFE-F 5’- TTCATGGCAAGAATGTGAATGG-3’; NELFE-R 5’- GATTTGCTGGGCAATAG-3’. eIF3h-F 5’- CTTCAAGCAGGATACGGAGAAG-3’; eIF3h-R 5’- GTTTGATCACTTCCTCCTCTGG-3’.

RNA-seq and data analysis
RNA from the Drosophila larval fat body was isolated with TRIzol following the manufacturer’s instructions. The extracted RNA was then treated with DNase (Turbo DNA free kit) to remove contaminant genomic DNA, and then ethanol precipitated. The samples were vacuum dried, resuspended in dH2O and sent to the NYU Genome Technology Center (RRID: SCR_017929) for RNA sequencing using the Illumina NovaSeq 6000 platform using Sp 100 Flow Cell v1.5. Triplicate samples for each genotype were analyzed. The cDNA library was prepared from polyA-containing mRNA. We followed Igor Dolgalev’s Seq-N-Slide automated workflow (https://github.com/igordot/sns) to process the fastq files. The DESeq2 package (R v3.6.1) was used for differential gene expression analysis between groups of samples. We filtered the data to analyze transcripts with baseMean values above 350. Normalized cpm counts were used to generate heatmaps.

Identification of 4EHP-binding RNAs through TRIBE
Targets of RNA-binding proteins discovered by editing (TRIBE) experiments were performed following published protocols75. Specifically, we generated an in-frame fusion between 4EHP’s coding sequence and the catalytic domain of ADAR through gene synthesis. As a negative control, we generated an equivalent construct with the W114A mutation of 4EHP that disrupts its mRNA cap-binding domain. These sequences were subcloned into pUAST-attB, which were injected to generate targeted insertion lines on the 2nd chromosome. The transgenes were expressed in the larval fat body using the dcg-Gal4 driver, and the RNAs were sequenced through the NYU Genome Technology Core (see above) to generate fastq files. Preprocessing/adapter trimming was performed using FLEXBAR (https://github.com/seqan/flexbar). Read mapping was done using HiSAT2 version 2.1.0. JACUSA was used to identify genomic positions where base frequency distributions differ substantially in RNA-DNA (RDD) or RNA-RNA (RRD) comparisons. For the identification of ADAR-edited sites, we followed the Dieterich lab TRIBE workflow (https://github.com/dieterich-lab/tribe-workflow). We specifically considered as 4EHP targets those that show preferential A to G conversion by wild-type 4EHP-ADAR as compared to the negative control (4EHPW114A mutant-ADAR). The binding score was defined as a log-likelihood ratio of A to G change on specific nucleotides between the two conditions.

Antibodies and western blots
Protein extracts were prepared from larval fat bodies with either RIPA buffer or the TRIzol reagent according to the manufacturer’s instructions. Protein was resuspended in 1% SDS and quantitated using Pierce BCA Protein Assay. SDS-PAGE was run on approximately 3-microgram samples and transferred onto PVDF (Immobilon-P, IPVH00010) or nitrocellulose (Bio-rad, 1620115) membranes. Membranes were blocked using 5% milk in TBS-T or using SuperBlock blocking buffer in TBS (thermoscientific, 37535). Primary antibodies used include guinea pig anti-crc (ATF4)34, anti-phospho-eIF2α (1:500, Cell Signaling 9721S), anti-beta tubulin (Covance #MMS-410P), anti-NELF-E70, anti-DsRed (Takara), anti-Rh1 (DSHB). Secondary antibodies containing HRP (1:5000-1:10000 dilution) were used to image bands, with captured via ChemiDoc or developed using autoradiography sheets. Band intensity was then measured using ImageJ.

Quantitative proteomics
For each genotype, triplicate samples were analyzed. Protein extracts were prepared from ten male larval fat bodies with the Total Protein Extraction Kit for Adipose Tissue/Cultured Adipocytes (AT-022, Invent Biotechnologies Inc) following the manufacturer’s instructions. 10 microliters from each sample was diluted two fold with a buffer consisting of 5% SDS, 20 mM CAA, 20 mM TCEP, 100 mM Tris (pH=8) and incubated 15 minutes at 90°C. 10 microliters of magnetic SP3 beads suspension (5% solids) was added, and the proteins were precipitated on beads by two fold dilution with ethanol. The beads were then washed three times with 150 microliter of 85% ethanol, followed by digestion in 100 microliter of buffer containing 0.2 micrograms of trypsin for 8 h at 37°C (1000 rpm). The resulting peptides were loaded on Evosep Pure C18 tips and analyzed in DIA mode on Q Exactive HF-X coupled to Evosep One LC system (88 min LC gradient). The resulting MS RAW data were analyzed in Spectronaut using directDIA search mode. Quantification was done on the MS2 level.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, the authors discovered novel factors involved in the regulation of ATF4, by genetic screening and TRIBE analysis. It is well established that phosphorylation of eIF2α is critical for ATF4 activation but other regulators for ATF4 induction has been understudied. Understanding these mechanisms could significantly contribute to elucidating the causes of diseases related to ATF4, making the findings in this paper particularly valuable.\nWhile the necessity of 4EHP, NELF-E, and eIF3h for ATF4 induction has been well demonstrated, there are some data and further analyses needed to clarify the authors' claims.\nMajor Points:\nMinor Points:", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Walsh et al. explored previously unknown pathways of ATF4 translational control in Drosophila. They report an ATF4 regulatory network consisting of eIF4E-Homologous Protein (4EHP), NELF-E, and eIF3. In brief, expression of 4EHP, representing a mRNA cap-binding protein, is required for translation of NELF-E mRNA, a negative regulator of pol II-mediated transcription, and these two proteins combined ensure normal ATF4 signaling in the Drosophila larval fat body enabling for example normal production of amino acid metabolites of the Serine-One-Carbon pathway. As a proof of principle, loss of 4EHP, preventing ATF4 induction during stress, suppressed the Drosophila Parkin (Park) mutants that serve as a model for a rare familiar form of Parkinson’s Disease, and that are characterized by high levels of ATF4 expression due to activated PERK. The prospective role of eIF3 in this novel pathway was not clear to me.\nOverall, this work investigates an interesting phenomenon, however, I think it is only half done. In particular, it falls short on the overall proteome analysis (in addition to RNA-seq), does not distinguish whether the observed changes in mRNA levels are due to in/decreased mRNA expression or in/decreased mRNA instability, lacks several important controls, and arrives to several conclusions that, in my humble opinion, are not supported by the data. A detailed, hopefully constructive critique follows.\nMajor:\n1. The introduction, and especially the last paragraph, is very confusing as written. It should be re-written in more logical, simplified way (a schematic of what protein is supposed to do what under what conditions with respect to ATF4 would help a lot too). Also, page 4, line 68; please note that the role of eIF3h in ATF4 translational upregulation in human cell lines was demonstrated in 2017 here (PMID: 28745933).\n2. Westerns are in general of poor quality.\n- Fig. 1j-upper and 5C; anti-crc signal is not visible for the control samples, darker exposures are required, a loading control is in the case of 5C also very problematic\n- Fig. 1j-bottom, 5e-f, and 7h-i; a wrong loading control was picked, the proper one should be anti-eIF2-alpha (or beta or gamma); also, the data are very bumpy and thus inconclusive (it seems that a lot more than 3 replicates will be need to be tested)\n3. The reported changes in mRNA expression profiles (e.g. Fig. 6) should be corroborated by comprehensive proteomics to find out if these changes translate into changes in the overall proteome; this is, in my opinion, a critical experiment that can validate all observations and take this report to a whole new level.\n4. The last chapter of results on NELF-E and eIF3 seems to me like “sewn with a hot needle“. Beginning with figures not referred to in the text, and ending with conclusions that do not seem to be supported by the presented data. I am not saying that eIF3h is not important for ATF4 upregulation, it very likely is, also based on the aforementioned study from human cells, but a lot more should be done to demonstrate it convincingly. Proteomics should be done (see my point 3), all 12 eIF3 subunits should be checked and the resulting data presented. The loss of eIF3h leads to a concomitant loss of 3k and 3l in humans and Neurospora crasa (PMID: 27924037, PMID: 27210288). Therefore, eIF3K or L RNAi could be used as an appropriate specificity control. Does eIF3 expression directly depend on the 4EHP/NELF-E axis? To be able to link these three players into a regulatory network, as you did in the abstract, we should simply know more about their causal relationships.\n5. Fig. 7e-g. I'm not clear on the design of this \"ATF4\" reporter construct. uORF2 overlaps with ATF4, how do you get it to overlap with dsRed without disrupting the sequence? Its sequence (including the overlap) seems to play an important role in the overall control of ATF4 as mentioned above (PMID: 28745933). What is the source of the anti-dsRed double band in 7f?\nMinor:\nThe introduction does not cite all relevant data.\n- page 4, line 57; ATF4 translational regulation is not only about uORFs (see for example PMID: 38507410), which will become relevant further below.\n- page 4, line 62, you can check these studies (PMID: 32589966, PMID: 32589964 PMID: 28119417 PMID: 34352092) that are, in my opinion, equally relevant to this point as those cited.\nFig. 1B. The screen set-up is unclear as depicted.\nPage 13, line 274; what is the P-element?\nPage 17, lines 354-359. This part should be moved to results, I think.\nI thank for the opportunity to review this study.\nLeos Shivaya Valasek", "reviewer_4": "In this manuscript, the authors describe novel factors necessary for the induction of Integrated Stress Response (ISR) signaling through ATF4 (crc in Drosophila). The phosphorylation of eIF2α in response to stress is well-established for the induction of ISR signaling, leading to expression of crc. The expression of crc relies on translation re-initiation on the crc mRNA after translation of an upstream ORF. Though the eIF3 complex has been implicated previously in this re-initiation process, it is still not well understood how crc is expressed as part of ISR signaling. The authors find that 4EHP and NELF-E are necessary for the induction of crc, and that the effect of NELF-E is partially mediated through eIF3h, consistent with a role for the eIF3 complex in translation re-initiation. They also present rigorous genetic, metabolic, and transcriptomic data that provides a resource for better understanding the breadth of ISR signaling beyond the focus of this manuscript. These findings are significant because they identify pathways required for crc expression in ISR signaling that are independent of eIF2α phosphorylation.\nDespite the interest of these findings, we do have concerns with some of the data interpretation in this study – particularly the implication that the induction of crc expression via eIF3h is mediated through both 4EHP and NELF-E, which is not supported by the data presented. In fact, the reduction of many eIF3 subunits is only observed upon knockdown of NELF-E, but not 4EHP, as seen in Table S4. The data is currently presented in a manner that could easily mislead readers to think that all three components are working sequentially, instead of more likely 4EHP and NELF-E being necessary for crc expression through parallel mechanisms. In our opinion, this represents mostly a need for some rewriting and clarification/reconsideration of the findings – rather than a problem with the data presented. For example, the first paragraph of the discussion should be revised to make clear that 4EHP and NELF-E are likely involved in partially overlapping but parallel pathways to induce crc, with only NELF-E acting through eIF3h. In addition, the inclusion of a model would help make the overall mechanism clearer. We note that there are many genes that are impacted similarly by both NELF-E and 4EHP RNAi, not including eIF3h, that warrant further investigation and discussion – and expanding analysis of these overlapping targets could provide some helpful insight into a potential shared mechanism. Altogether, we think the findings presented in this manuscript are impactful and important, if the conclusions are adjusted to more accurately reflect the data shown.\nWe have the following specific comments, which are separated into major and minor points:\nMajor concerns\n1. The heatmaps presented in Fig 6c and 7a without any statistics described are misleading because it is unclear whether the differential expression is significant. If the authors chose to remove the 4EHP RNAi from Fig 7a, it should be made clear in the main text that eIF3 subunit transcripts are not significantly downregulated in the 4EHP RNAi. Specifically, the statement on lines 263-264 “These transcripts were also reduced in 4EHP RNAi samples, although to a lesser extent (Table S4)” is incorrect, as they are not significantly reduced based on the adjusted p values shown.\n2. A PCA of the RNA-seq samples should be included, especially to compare the two controls and replicate samples. As shown in the heatmaps, it seems that replicate control samples are dissimilar, and it is slightly difficult to tell which control was used in each experiment.\n3. Since eIF3h is not reduced by both 4EHP and NELF-E RNAi, a more expansive description (e.g., GO terms) of the overlapping transcripts either reduced or induced (Fig 6a) would be informative. This would be a more balanced approach to describing these data rather than the heatmaps shown of selected genes.\n4. For the degenerative disease models (Fig. 3), it is unclear whether these phenotypes are mediated through crc. Additional experiments or comparison to previous data may bolster this argument. For instance, a 4EHP and crc double mutant in Fig 3d should show a non-compounding effect on ommatidial integrity if they are working in the same cascade. Where possible, comparisons to phenotypes with crc knockdown or overexpression would make it easier to conclude that 4EHP and NELF-E loss phenotypes are due to downstream impacts on crc. Some of these findings have been published elsewhere, but it is difficult to compare directly without inclusion of these data in the same analysis.\n5. The mRNAs identified in the TRIBE experiment should be described more comprehensively. A GO term analysis may be illuminating since there are many interesting metabolic genes identified (Table S2). Since CG18132 may be involved in protein folding in the ER and is the top TRIBE hit, it should be more thoroughly described if mentioned at all.\n6. The Western blots were not very clear in places, and the loading seems uneven with poor intensity for many of the target proteins of interest. In addition, it is unclear how the statistical analysis of band intensity comparison were performed. In some cases, all controls appear to have the same value with no error bar (e.g., Fig 4e), which may be a plotting error. The authors may consider a different method of quantifying crc protein levels (e.g., epitope tagging, alternative protein extraction strategy without using TRIzol) if the antibody does not perform well, as in Fig 5c. Since many conclusions hinge on 4EHP and NELF-E impacting crc protein levels, high quality Western blots are necessary.\n7. The findings from the RNAi screen should be expanded upon, especially given that eIF4EHP is not the top listed suppressor of Thor intron expression in Table S1. A brief description of the other screen hits and a stronger rationale for following 4EHP would be beneficial. Quantitative data should be presented in Table S1 if possible, so the extent to which Thor intron expression is impacted can be assessed. This table (and all others where applicable) should be fully annotated with gene names and FBgn IDs to make these data fully accessible to other researchers.\nMinor concerns\n1. Sequencing data (RNA-seq and TRIBE) should be deposited to an accessible repository like GEO.\n2. A supplemental table describing the metabolic profiling data should be provided.\n3. Park13 instead of Park25 seems to be referred to in error or the usage is unclear. For instance, “We found that Park13/ParkD21 adult flies showed intense Thorintron-dsRed reporter expression indicative of strong ATF4 signaling (Figure 3a, b)” is written on line 164, but Park13/ParkD21 flies are not shown in the indicated figure.\n4. Supp Fig S6 needs a visible control.\n5. In some cases (e.g., Fig 2a) axis text on plots is not readable.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point-by-point response to reviewer comments\nWe thank the reviewers for the overall positive review. Below is a summary of our revision in response to\nspecific reviewer comments.\nReviewer #1 (Remarks to the Author)\nIn this study, the authors discovered novel factors involved in the regulation of ATF4, by genetic\nscreening and TRIBE analysis. It is well established that phosphorylation of eIF2α is critical for ATF4\nactivation but other regulators for ATF4 induction has been understudied. Understanding these\nmechanisms could significantly contribute to elucidating the causes of diseases related to ATF4, making\nthe findings in this paper particularly valuable.\nThank you for the positive assessment.\nWhile the necessity of 4EHP, NELF-E, and eIF3h for ATF4 induction has been well demonstrated, there\nare some data and further analyses needed to clarify the authors' claims.\nMajor Points:\ninduction, but experimental data is required to rule out the potential impact on overall development and\ntranslation as suppression of translation could decrease ATF4 and the dsRed reporter. The authors need\nto quantify how loss of function of these genes impact developmental timing (e.g., pupation timing) and\ngeneral translation capacity. It is also interesting to quantify the intensity of UAS-driven GFP to\ndemonstrate that the suppression of the dsRed reporter is not merely due to reduced translation (similar\nexperiments conducted for eIF3h in Fig. 7b-d should be performed for 4EHP and NELF-E).\nThank you for the point. To demonstrate that ATF4 reduction is not a consequence of indiscriminate\nsuppression of protein synthesis, we have added control dcg-Gal4>UAS-GFP expression data in fat bodies\nwith 4EHP RNAi (revised Figure 1f’), NELF-E RNAi (revised Figure 5a), and eIF3h RNAi (revised Figure 7f’).\nThe results clearly show that knockdowns of 4EHP, NELF-E, and eIF3h specifically reduces the crc (ATF4)\nreporter but not the control GFP expression. Furthermore, we followed Reviewer #3’s suggestion to\nperform quantitative proteomics in samples with 4EHP RNAi (Figure 2c) and NELF-E RNAi (Figure 6c), and\nthe results do not show a general reduction in the proteome. In fact, many 60S ribosome subunits and\ntranslation initiation factor peptides are found at higher levels in the 4EHP and NELF-E RNAi dataset\n(Figures 2c, S6, 6c, 6e). We now include a Supplemental Figure showing that the knockdown of 4EHP or\nNELF-E delays Drosophila development (Figure S1, S8e), but the results with dcg-Gal4>UAS-GFP and\nquantitative proteomics indicate that the effect of 4EHP and NELF-E RNAi on crc is specific and not due\nto indiscriminate suppression of all protein synthesis.\nregulates NELF-E at the transcriptional level. If NELF-E transcript levels do not change, it would suggest\ntranslational control, but if they do decrease, it may indicate involvement in RNA stability.\nThank you for the point. The revised manuscript now has a new Figure 4e, which shows that NELF-E\ntranscript levels do not change significantly in the 4EHP RNAi condition as revealed in the RNA-seq\ndataset.\nexpression or activity when ATF4 is physiologically activated (in the late third instar larva). Are they\nconsistently expressed, and do they work in combination when eIF2α phosphorylation occurs? Do these\nproteins have any instructive function for ATF4 activation?\nThank you for the point. We have added a new Figure S16 showing that ATF4 (crc) knockdown in the\nthird instar larval fat body does not change the transcript levels of 4EHP, NELF-E, and eIF3h, arguing\nagainst the idea that physiologically activated ATF4 regulates their expression. To determine whether\nthese factors have instructive function for ATF4 activation, we overexpressed 4EHP (Figure S3a, b), NELF-\nE (Figure S8a, b), and eIF3h (Figure S15a, b), but they were not sufficient to increase ATF4 reporter\nexpression. The results are not surprising as these factors operate within a larger protein complex that\nrequires other subunits for their function.\nATF4. Please analyse the data from P7, 4EHP[C53] mutant (or dcg>4EHP RNAi) with those from dcg>crc\nRNAi. Also, simultaneous manipulation of 4EHP and ATF4 may better to provide evidence to show that\nthey are at the same pathway.\nFollowing the suggestion, we performed metabolic profiling in crc (ATF4) RNAi fat body, but we did not\nobtain similar profiles with the 4EHP mutants. We note that metabolic profiles are susceptible to small\ndifferences in inter-organ transport or the metabolic flux. Because of the imperfect data, we deleted\nconclusions attributing the metabolic change to ATF4 signaling (pages 7, 8). The revised manuscript now\nfocuses on similarities in gene expression rather than metabolic profiles. We removed the word “amino\nacid metabolism” from the title, and the metabolomics data have been moved to a Supplemental Figure\n(Fig. S4). The metabolic profiling data in Figure 2 were replaced with gene expression profiling and\nquantitative proteomics data as requested by Reviewer #3. The revised text now centers around\ndescribing the 4EHP loss-of-function phenotype with the following logical flow: We comment that the\nenriched GO terms of proteins reduced by 4EHP RNAi are mostly associated with metabolism (Fig. 2d),\nthat most significant changes in the metabolome are related to the serine-one-carbon pathway (Fig. S4),\nand consistently, the flies are vulnerable to protein starvation (Figure 2).\nchanges in the metabolism driven by ATF4 contribute to alterations in physiology such as starvation\nresistance? In this context, the authors may also perform experiments to manipulate amino acid levels in\nthe diet or in the body.\nTo establish an association with ATF4, we have added new data showing that crc (ATF4) RNAi renders\nflies more vulnerable to starvation, and this vulnerability is suppressed upon providing a protein-only\ndiet (see revised Figure 2h, k).\nMinor Points:\nThe revised text page 5 now states that the 183 RNAi lines targeted either known translation regulators\nor the Drosophila homologs of ribosome-associated proteins (page 5 lines 100 - 101, ref. 38).\nThank you for the point. We now mark dsRed and DAPI with colored fonts in Figures 1c and d.\nas that of controls, as metabolic levels, transcriptomes, starvation resistance, and lifespan can be\nsignificantly influenced by genetic background. If backcrossing was done, this should be stated in the\nmethods; if not, this should be considered in the discussion. The same applies to NELF-E and eIF3h KDs.\nThank you for the point. All flies were in the w1118 background. While they had not been backcrossed to\na common strain, 4EHP RNAi results (Fig. 1c-f, 2g, j) were validated with 4EHPCP53 (Fig. S2, Fig. 2f, i), NELF-\nE RNAi results were validated with two independent RNAi lines (Fig. 5a, d), and eIF3h were also validated\nwith two independent RNAi lines (Fig. 7f, S12). Following the reviewer’s suggestion, we also included a\nstatement in the Discussion that “the Drosophila lines used in this study were not backcrossed to a\ncommon parental strain, but key results were all corroborated with independent RNAi lines or with\nclassical mutant alleles (page 17 lines 390 - 392).\nmutant?\nA strong loss of crc (ATF4) can lead to proteostasis defects and lethality. However, we have added Figure\nS7 showing that the crc -/+ background leads to a moderate (and statistically significant) extension of\nPark mutant lifespan.\nA summary diagram has been added as the new Figure 8.\nshould be included in the results section, with data provided in the supplementary materials.\nThank you for the point. We’ve added the lethality data as the new Figure S11.\nthe text. Additionally, the BDSC #32835 reference for NELF-RNAi used in Fig. 5 appears to be missing.\nWe have now added information regarding the 4EHP RNAi lines in the legends of Figures 1, 2, 4, and the\nreference to NELF-E RNAi #1 (VDRC line) in the revised legends of Figures 4 and 5.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nWalsh et al. explored previously unknown pathways of ATF4 translational control in Drosophila. They\nreport an ATF4 regulatory network consisting of eIF4E-Homologous Protein (4EHP), NELF-E, and eIF3. In\nbrief, expression of 4EHP, representing a mRNA cap-binding protein, is required for translation of NELF-E\nmRNA, a negative regulator of pol II-mediated transcription, and these two proteins combined ensure\nnormal ATF4 signaling in the Drosophila larval fat body enabling for example normal production of amino\nacid metabolites of the Serine-One-Carbon pathway. As a proof of principle, loss of 4EHP, preventing\nATF4 induction during stress, suppressed the Drosophila Parkin (Park) mutants that serve as a model for\na rare familiar form of Parkinson’s Disease, and that are characterized by high levels of ATF4 expression\ndue to activated PERK. The prospective role of eIF3 in this novel pathway was not clear to me.\nOverall, this work investigates an interesting phenomenon, however, I think it is only half done. In\nparticular, it falls short on the overall proteome analysis (in addition to RNA-seq), does not distinguish\nwhether the observed changes in mRNA levels are due to in/decreased mRNA expression or\nin/decreased mRNA instability, lacks several important controls, and arrives to several conclusions that,\nin my humble opinion, are not supported by the data. A detailed, hopefully constructive critique follows.\nMajor:\n1. The introduction, and especially the last paragraph, is very confusing as written. It should be re-\nwritten in more logical, simplified way (a schematic of what protein is supposed to do what under what\nconditions with respect to ATF4 would help a lot too). Also, page 4, line 68; please note that the role of\neIF3h in ATF4 translational upregulation in human cell lines was demonstrated in 2017 here (PMID:\n28745933).\nThank you for the point. We re-wrote the last paragraph of the introduction to enhance clarity. We now\ncite PMID 28745933 (ref. 31) following the reviewer’s suggestion.\n2. Westerns are in general of poor quality.\n- Fig. 1j-upper and 5C; anti-crc signal is not visible for the control samples, darker exposures are required,\na loading control is in the case of 5C also very problematic\nFollowing the suggestion, we replaced the blot in Figure 5e with an improved new blot. We also\nenhanced the contrast of the anti-crc blot in Figure 1. Please note that anti-crc under physiological stress\n(in a normally developing fat body) is naturally very weak as compared to those in other studies that use\nextreme stress imposed by tunicamycin or thapsigargin treatment. Our blot aimed to show this by\ncomparing its weak band to that of crc overexpression (Figure 1j lane 3).\n- Fig. 1j-bottom, 5e-f, and 7h-i; a wrong loading control was picked, the proper one should be anti-eIF2-\nalpha (or beta or gamma); also, the data are very bumpy and thus inconclusive (it seems that a lot more\nthan 3 replicates will be need to be tested)\nWe followed this reviewer’s suggestion to perform quantitative proteomics (see points below), and the\nresults indicate that all three eIF2 subunit levels “increase” in response to the knockdown of 4EHP or\nNELF-E (Figures 2c, 6c, Supplemental Table S3). For example, eIF2alpha nearly doubles (row 341 in the\nTable S3), eIF2beta is detected at 2.6 fold higher (row 342 in Table S3), and eIF2gamma is 2.4 fold higher\nthan controls (row 517 of Table S3) in 4EHP RNAi fat body – a reason why we couldn’t use them as\nloading controls. We replaced the bumpy blot of Figure 5e with a new blot.\nWe performed additional replicate western blots, and this is now indicated in the quantified\ngraphs of Figures 1k, l, and 5f. Please note that our ATF4 reporter, Thor intron-dsRed, provides a robust\nindependent corroboration of our conclusions regarding ATF4.\n3. The reported changes in mRNA expression profiles (e.g. Fig. 6) should be corroborated by\ncomprehensive proteomics to find out if these changes translate into changes in the overall proteome;\nthis is, in my opinion, a critical experiment that can validate all observations and take this report to a\nwhole new level.\nThank you for the point. Following this suggestion, we performed quantitative proteomics with 4EHP and\nNELF-E RNAi fat bodies (Table S3, S7; Figures 2c, S6, 6c-e). The results revealed that serine biosynthesis\nenzymes, RpS subunits, and eIF3l are significantly reduced. We have revamped Figures 2 and 6 to\nincorporate these results.\n4. The last chapter of results on NELF-E and eIF3 seems to me like “sewn with a hot needle“. Beginning\nwith figures not referred to in the text, and ending with conclusions that do not seem to be supported by\nthe presented data. I am not saying that eIF3h is not important for ATF4 upregulation, it very likely is,\nalso based on the aforementioned study from human cells, but a lot more should be done to\ndemonstrate it convincingly. Proteomics should be done (see my point 3), all 12 eIF3 subunits should be\nchecked and the resulting data presented. The loss of eIF3h leads to a concomitant loss of 3k and 3l in\nhumans and Neurospora crasa (PMID: 27924037, PMID: 27210288). Therefore, eIF3K or L RNAi could be\nused as an appropriate specificity control. Does eIF3 expression directly depend on the 4EHP/NELF-E\naxis? To be able to link these three players into a regulatory network, as you did in the abstract, we\nshould simply know more about their causal relationships.\nGreat suggestion. We performed quantitative proteomics, which yielded important insights into the\n4EHP-NELF-E pathway. Unlike the case of humans and Neurospora crassa, eIF3k, l, and h were not\nconcomitantly lost. We found that while NELF-E regulates the transcripts of many eIF3 subunits, at the\nproteome level, only eIF3l was commonly reduced (p adj < 0.05) in the Drosophila fat body with 4EHP or\nNELF-E RNAi.\nWe also noted that many RpS subunits are significantly reduced at the proteome level in both 4EHP and\nNELF-E RNAi fat body. This is notable because a complex between the 40S ribosome and eIF3 is\nnecessary for re-initiation downstream of uORFs (according to Dr. Valasek’s studies). We added new data\non RpS in the revised Figure 6e.\n5. Fig. 7e-g. I'm not clear on the design of this \"ATF4\" reporter construct. uORF2 overlaps with ATF4, how\ndo you get it to overlap with dsRed without disrupting the sequence? Its sequence (including the\noverlap) seems to play an important role in the overall control of ATF4 as mentioned above (PMID:\n28745933). What is the source of the anti-dsRed double band in 7f?\nThe reviewer is correct in that the uORF2 coding sequence changes in the regions that overlap with\ndsRed, and to highlight this, we have changed the color of that coding sequence to gray in Figure 7f.\nHowever, we show in a new Supplemental Figure (Fig. S14) that this reporter is robustly induced by\nstress and is completely abolished when the eIF2a kinase (in this case, PERK) is lost.\nWe were not able to figure out the nature of the double band in 7f. To avoid confusion, we replaced\nthose panels with immunohistochemistry, which show signals that strictly depend on the reporter (see\nFig. 7j-l).\nMinor:\nThe introduction does not cite all relevant data.\n- page 4, line 57; ATF4 translational regulation is not only about uORFs (see for example PMID:\n38507410), which will become relevant further below.\nThe study by Dr. Valasek (PMID 38507410) has determined human ATF4’s regulation by a stem loop\nsequence referred to as SL3. However, that study had investigated SL3 only in mammals, and its\nrelevance in Drosophila had not been examined. Below, we show nucleotide (panels A, B) and amino\nacid sequence alignments (panel C) to highlight that SL3 is not conserved in Drosophila crc. Therefore,\nthe issue of SL3 and the relevance of the uORF2 coding sequence is not relevant to our manuscript.\n- page 4, line 62, you can check these studies (PMID: 32589966, PMID: 32589964 PMID: 28119417 PMID:\n34352092) that are, in my opinion, equally relevant to this point as those cited.\nThank you for pointing this out. We now cite all those papers while introducing the process of re-\ninitiation on page 4.\nFig. 1B. The screen set-up is unclear as depicted.\nTo help readers unfamiliar with gene expression methods in Drosophila, we now cite Brand and Perrimon\n(1993) on page 5 line 99, which describes how the Gal4/UAS system of gene expression works.\nPage 13, line 274; what is the P-element?\nWe now indicate on revised page 16 (line 348) that P-element is a transposable element.\nPage 17, lines 354-359. This part should be moved to results, I think.\nFollowing the suggestion, we have moved that part to the results section with a new figure (Figure S11).\nI thank for the opportunity to review this study.\nLeos Shivaya Valasek\nReviewer #4 (Remarks to the Author):\nIn this manuscript, the authors describe novel factors necessary for the induction of Integrated Stress\nResponse (ISR) signaling through ATF4 (crc in Drosophila). The phosphorylation of eIF2α in response to\nstress is well-established for the induction of ISR signaling, leading to expression of crc. The expression of\ncrc relies on translation re-initiation on the crc mRNA after translation of an upstream ORF. Though the\neIF3 complex has been implicated previously in this re-initiation process, it is still not well understood\nhow crc is expressed as part of ISR signaling. The authors find that 4EHP and NELF-E are necessary for\nthe induction of crc, and that the effect of NELF-E is partially mediated through eIF3h, consistent with a\nrole for the eIF3 complex in translation re-initiation. They also present rigorous genetic, metabolic, and\ntranscriptomic data that provides a resource for better understanding the breadth of ISR signaling\nbeyond the focus of this manuscript. These findings are significant because they identify pathways\nrequired for crc expression in ISR signaling that are independent of eIF2α phosphorylation.\nThank you for the positive assessment.\nDespite the interest of these findings, we do have concerns with some of the data interpretation in this\nstudy – particularly the implication that the induction of crc expression via eIF3h is mediated through\nboth 4EHP and NELF-E, which is not supported by the data presented. In fact, the reduction of many eIF3\nsubunits is only observed upon knockdown of NELF-E, but not 4EHP, as seen in Table S4. The data is\ncurrently presented in a manner that could easily mislead readers to think that all three components are\nworking sequentially, instead of more likely 4EHP and NELF-E being necessary for crc expression through\nparallel mechanisms. In our opinion, this represents mostly a need for some rewriting and\nclarification/reconsideration of the findings – rather than a problem with the data presented. For\nexample, the first paragraph of the discussion should be revised to make clear that 4EHP and NELF-E are\nlikely involved in partially overlapping but parallel pathways to induce crc, with only NELF-E acting\nthrough eIF3h. In addition, the inclusion of a model would help make the overall mechanism clearer. We\nnote that there are many genes that are impacted similarly by both NELF-E and 4EHP RNAi, not including\neIF3h, that warrant further investigation and discussion – and expanding analysis of these overlapping\ntargets could provide some helpful insight into a potential shared mechanism. Altogether, we think the\nfindings presented in this manuscript are impactful and important, if the conclusions are adjusted to\nmore accurately reflect the data shown.\nWe have the following specific comments, which are separated into major and minor points:\nMajor concerns\n1. The heatmaps presented in Fig 6c and 7a without any statistics described are misleading because it is\nunclear whether the differential expression is significant. If the authors chose to remove the 4EHP RNAi\nfrom Fig 7a, it should be made clear in the main text that eIF3 subunit transcripts are not significantly\ndownregulated in the 4EHP RNAi. Specifically, the statement on lines 263-264 “These transcripts were\nalso reduced in 4EHP RNAi samples, although to a lesser extent (Table S4)” is incorrect, as they are not\nsignificantly reduced based on the adjusted p values shown.\nThank you for this point. Following the reviewer’s suggestion, we removed the old heatmap from Figures\n6c and 7a and deleted the comment regarding the reduced expression of specific eIF3 subunits to a\nlesser extent. We replaced this part with the proteins that were commonly reduced in 4EHP and NELF-E\nRNAi samples. Specifically, Reviewer #3 had requested that we perform quantitative proteomics with\nthese samples, and the results indicate that multiple RpS subunits and eIF3l are commonly reduced with\nstatistical significance (p adj < 0.05). Those new results are highlighted in the revised Figure S10.\n2. A PCA of the RNA-seq samples should be included, especially to compare the two controls and\nreplicate samples. As shown in the heatmaps, it seems that replicate control samples are dissimilar, and\nit is slightly difficult to tell which control was used in each experiment.\nFollowing the reviewer’s suggestion, we have now included PCA plots for the RNA-seq results in Figures\n2b and Figure 6b. Please note that we decided to separate the two RNA-seq results because NELF-E RNAi\nwas done at 20 °C, and 4EHP RNAi was performed in 25 °C. The 4EHP RNAi results are now shown in the\nrevised Figure 2, and NELF-E RNAi is shown in the revised Figure 6.\n3. Since eIF3h is not reduced by both 4EHP and NELF-E RNAi, a more expansive description (e.g., GO\nterms) of the overlapping transcripts either reduced or induced (Fig 6a) would be informative. This\nwould be a more balanced approach to describing these data rather than the heatmaps shown of\nselected genes.\nThank you for the excellent suggestion. Since Reviewer #3 has implied that the proteome change is more\nrelevant than transcript changes, we show enriched GO terms for reduced proteins in Figure 2d (4EHP\nRNAi samples), Figure 6d (NELF-E RNAi samples), and Figure S10b (proteins commonly reduced in 4EHP\nand NELF-E RNAi samples). The enriched GO terms in the commonly reduced proteins include\n“cytoplasmic translation” (Figure S10b), which provides a nice segue into our examination of ribosomal\nproteins and initiation factors. Thanks to the reviewer’s suggestion, we feel this part of the revised\nmanuscript now has an improved logical flow.\n4. For the degenerative disease models (Fig. 3), it is unclear whether these phenotypes are mediated\nthrough crc. Additional experiments or comparison to previous data may bolster this argument. For\ninstance, a 4EHP and crc double mutant in Fig 3d should show a non-compounding effect on ommatidial\nintegrity if they are working in the same cascade. Where possible, comparisons to phenotypes with crc\nknockdown or overexpression would make it easier to conclude that 4EHP and NELF-E loss phenotypes\nare due to downstream impacts on crc. Some of these findings have been published elsewhere, but it is\ndifficult to compare directly without inclusion of these data in the same analysis.\nThank you for the suggestion. We have now included a new Figure S7 to show the effect of crc -/+ on the\nParkin mutants. In addition, we include a new Figure 3d that shows accelerated retinal degeneration of\ncrc hypomorphs. We didn’t do the 4EHP and crc double mutant analysis because both are hypomorphs\n(null alleles are lethal), and we expect an additive effect that is difficult to interpret.\n5. The mRNAs identified in the TRIBE experiment should be described more comprehensively. A GO term\nanalysis may be illuminating since there are many interesting metabolic genes identified (Table S2). Since\nCG18132 may be involved in protein folding in the ER and is the top TRIBE hit, it should be more\nthoroughly described if mentioned at all.\nThank you for the point. Following the suggestion, we added a GO Term enrichment graph in the revised\nFigure 4c associated with the TRIBE study. We also introduced CG18132 in the revised manuscript as “an\nuncharacterized protein disulfide isomerase, predicted to assist protein folding within the endoplasmic\nreticulum (page 12 line 249 -250).”\n6. The Western blots were not very clear in places, and the loading seems uneven with poor intensity for\nmany of the target proteins of interest. In addition, it is unclear how the statistical analysis of band\nintensity comparison were performed. In some cases, all controls appear to have the same value with no\nerror bar (e.g., Fig 4e), which may be a plotting error. The authors may consider a different method of\nquantifying crc protein levels (e.g., epitope tagging, alternative protein extraction strategy without using\nTRIzol) if the antibody does not perform well, as in Fig 5c. Since many conclusions hinge on 4EHP and\nNELF-E impacting crc protein levels, high quality Western blots are necessary.\nThank you for the point. Following this suggestion, we used the RIPA buffer to extract additional samples,\nand provide a new Figure 5e (anti-crc), and Figures 1j and 5g (phosphor-eIF2alpha). Additionally, we\npresent new graphs showing normalization to the average intensity of control band loading controls,\nwhich reflects the variability between controls.\n7. The findings from the RNAi screen should be expanded upon, especially given that eIF4EHP is not the\ntop listed suppressor of Thor intron expression in Table S1. A brief description of the other screen hits\nand a stronger rationale for following 4EHP would be beneficial. Quantitative data should be presented\nin Table S1 if possible, so the extent to which Thor intron expression is impacted can be assessed. This\ntable (and all others where applicable) should be fully annotated with gene names and FBgn IDs to make\nthese data fully accessible to other researchers.\nFollowing the suggestion, we annotated Table S1 with gene names and FBgn IDs. The initial hits were not\nbased on quantitative measurements and, therefore, should be considered as preliminary data. We now\ninclude that comment in the Table S1 legend. We comment that 4EHP’s cap-binding domain may have a\n“more specific set of target mRNAs.” (page 6, line 122). We further add in the revised manuscript that\n“we decided to further characterize 4EHP due to its possible specificity in gene expression control” (page\n6, line 125).\nMinor concerns\n1. Sequencing data (RNA-seq and TRIBE) should be deposited to an accessible repository like GEO.\nFollowing the suggestion, we have deposited the data in NIH GEO and added the accession number in\nthe Data Availability section.\n2. A supplemental table describing the metabolic profiling data should be provided.\nFollowing the suggestion, we have added the raw metabolic profiling data as the new Table S4.\n3. Park13 instead of Park25 seems to be referred to in error or the usage is unclear. For instance, “We\nfound that Park13/ParkD21 adult flies showed intense Thorintron-dsRed reporter expression indicative\nof strong ATF4 signaling (Figure 3a, b)” is written on line 164, but Park13/ParkD21 flies are not shown in\nthe indicated figure.\nThank you for catching this. We fixed the error.\n4. Supp Fig S6 needs a visible control.\nWe adjusted the control image to make the background visible.\n5. In some cases (e.g., Fig 2a) axis text on plots is not readable.\nFigure 2 is now the revised Figure S4. Following the suggestion, the size of the fonts has been increased.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nPoint-by-point response to reviewer comments\nWe thank Reviewers #1, #2, #4, and #5 for accep:ng our revisions. Below is a summary of our response\nto the addi:onal remarks by Reviewer #3.\nReviewer #3\nThe authors did a great job and the manuscript improved considerably. Thank you for that. However, I\ns:ll have a few issues that, IMHO, must be resolved before the manuscript can be considered suitable for\npublica:on in this journal.\n1) Figs 1j, 5g, 7n, etc. As I noted in my original comment, to use tubulin as a normaliza:on control for the\nan:-P-eIF2 signal is technically very wrong and any conclusion driven from these experiments concerning\nthe eIF2-independence of the observed effects is dangerously misleading (e.g. lines 347-348; but mainly\nthe closing paragraph 433 – 439!). Moreover, taking into account that the newly added proteomics\nrevealed the RNAi analysis of 4EHP and NELF-E increased levels of all three eIF2 subunits! To examine if\nthe P-status of eIF2-alpha has changed or not under given stress/condi:on, the an:-P-eIF2 signal has to\nbe normalized only to an:-eIF2alpha (in the worst case to other eIF2 subunits, although they do not\nalways occur in the equimolar ra:o). Please check the old scholarly papers by Hinnebusch, Wek, Ron,\nSonenberg… It really is a must.\nTo avoid making any misleading conclusions about eIF2-independence, we deleted all references to\n“eIF2-independent” regula:on in this revised version. This includes lines 347-348 and the closing\nparagraph. The phospho-eIF2 data was peripheral to our primary focus on 40S and eIF3, and therefore,\nwe moved the eIF2a blots to Supplemental Figures S6, S11, and S17. In those Figures, the total eIF2a\nblots corroborate the proteomic data indica:ng increases in total eIF2a. Those Figures also include\nphospho-eIF2a blots and new graphs showing phospho-eIF2a normalized to total eIF2a.\n2) I understand that the human SL3 might not be present everywhere. We have seen it ourselves. What\nmy comment was supposed to imply is the following (and I really do not care if the authors cite our most\nrecent paper describing SL3 or not – it is not about this!). Ignoring this uORF2/ATF4 overlap region in the\npast generated confusing/conflic:ng results. One way or the other, it is a conserved region, because it is\na coding region. Therefore, it is likely, maybe even highly likely, that it contains some mRNA features\nac:ng in-trans even in D.m. Have you checked its poten:al to form stable sec. structures? Your construct\nin Fig. 7 is responding as expected, but can you guarantee that you are monitoring the physiological\nresponse? Perhaps this overlap makes it weaker or stronger? Maybe not, we do not know, but why to\nrepeat the same “mistake”? If anything, the authors should state clearly in the main text that the fact\nthat their construct lacks this region represents a possible limita:on to its ability to respond at the full\nphysiological scale. And again, is it perfectly fine to ignore us :)\nWe do not argue that our reporter monitors ALL physiological response. Using a reporter that responds a\nlimited aspect of crc (ATF4) regula:on has its u:lity, because it can provide specific mechanis:c insights.\nFollowing the reviewer sugges:on, we now add a line in the main text sta:ng that “The transgene will\nnot capture poten:al crc (ATF4) regula:on that acts through the protein-coding sequence but is\ndesigned to report regulatory inputs at the 5’ leader upstream of the main ORF” (lines 340 – 342).\nThis thinking brought me back to the :tle. Considering these and other differences, I guess the :tle\nshould make a reader aware of the fact that this work explored D.m.; e.g. “4EHP and NELF-E regulate\nphysiological 1 ATF4 induc:on and proteostasis in disease models of Drosophila melanogaster.” Or\nsomething like this.\nFollowing the sugges:on, we changed the :tle.\n3) A few notes to consider:\n- there is literature showing that eIF3l and 3k are considered to be inhibitory (not in:a:on-s:mulatory)\nsubunits\n- lines 301 – 303; what do you mean by “a compensatory mechanism”? Specific reduc:on in biogenesis\nof one ribosomal subunit will naturally lead to an increase of the other subunit.\nTo clarify, we revised the sentence to “feedback regulatory mechanism against RpS reduc:on” (lines 307,\n308).\n- 303 – 307; this is really interes:ng; It seems that the 40S bio defect lies in an impaired forma:on of the\n40S beak. Can you check in Ramakrishan’s Science paper (2020) where exactly the posi:ons of 3l and 3k\nin the 48S PIC were mapped? They can exist as a dimer out of eIF3 and thereby they might be needed for\nfinal matura:on of the 40S, which would explain a lot.\nThe below image shows our Figure 6f (lem) juxtaposed with the 48S structure reported by Ramakrishan\nand colleagues (Science 2020, PMID 32882864). The orienta:on of the 40S is similar in both images, with\nthe beak region at the upper right corner. RpS10, RpS27A, and RpS12 cons:tute the beak region the 40S\nstructure (lem). On the other hand, eIF3l and eIF3k bind the opposite end of the 40S (right).\nAlso, there is an EMBO J. paper from D. Wolf’s lab (2022?) on eIF3k and ribosomal proteins with respect\nto 40S biogenesis that could help too.\nThank you for leong us know. We will consider this in our next study when we examine the rela:onship\nbetween eIF3 and ribosome biogenesis (which is not the main subject of the current work).\n- lines 310 – 314; If my assump:on is correct, I would be very curious to see if the levels of RPS go down\nand those of RPL go up in RPS12-S2383/+ in a similar fashion as observed with 4EHP and NELF-E siRNAs.\nThis could nail the mol mech down.\nIn page 9, we had cited other reports that “reduc:on of one RpS subunit causes the reduc:on of other\nRpS subunit proteins while increasing RpL subunits (ref. 44, 45)” (lines 186, 187). Refs 44 and 45 had\nexamined RpS3 -/+ (ref. 44) and RpS23 -/+. Both proteomic studies show reduc:on of other RpS and a\nconcomitant increase in most RpLs. So, the reviewer’s assump:on is supported by published data.\n- 318; I would be more accurate: “the post-termina:on 40S subunits”.\nThank you. We revised it accordingly in line 322.\n- 321; eIF3j is not a bona fide eIF3 subunit, it is only an eIF3-associated factor\nThank you. We changed the sentence to “RNAi lines targe:ng eIF3 subunits eIF3h and eIF3l, and the\neIF3-associated factor eIF3j, did not interfere with larval development” (line 325).\n- 419; for the sake of collegiality, please also include 26 and 29; this is important to us.\nThe references have been added.\n- 425 – 426; for same reason, it would be fair to add that a similar observa:on was made in humans (31).\nThe reference has been added."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors to have addressed the concerns and I believe the manuscript is suitable for publication.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors did a great job and the manuscript improved considerably. Thank you for that. However, I still have a few issues that, IMHO, must be resolved before the manuscript can be considered suitable for publication in this journal.\n1) Figs 1j, 5g, 7n, etc. As I noted in my original comment, to use tubulin as a normalization control for the anti-P-eIF2 signal is technically very wrong and any conclusion driven from these experiments concerning the eIF2-independence of the observed effects is dangerously misleading (e.g. lines 347-348; but mainly the closing paragraph 433 – 439!). Moreover, taking into account that the newly added proteomics revealed the RNAi analysis of 4EHP and NELF-E increased levels of all three eIF2 subunits! To examine if the P-status of eIF2-alpha has changed or not under given stress/condition, the anti-P-eIF2 signal has to be normalized only to anti-eIF2alpha (in the worst case to other eIF2 subunits, although they do not always occur in the equimolar ratio). Please check the old scholarly papers by Hinnebusch, Wek, Ron, Sonenberg… It really is a must.\n2) I understand that the human SL3 might not be present everywhere. We have seen it ourselves. What my comment was supposed to imply is the following (and I really do not care if the authors cite our most recent paper describing SL3 or not – it is not about this!). Ignoring this uORF2/ATF4 overlap region in the past generated confusing/conflicting results. One way or the other, it is a conserved region, because it is a coding region. Therefore, it is likely, maybe even highly likely, that it contains some mRNA features acting in-trans even in D.m. Have you checked its potential to form stable sec. structures? Your construct in Fig. 7 is responding as expected, but can you guarantee that you are monitoring the physiological response? Perhaps this overlap makes it weaker or stronger? Maybe not, we do not know, but why to repeat the same “mistake”? If anything, the authors should state clearly in the main text that the fact that their construct lacks this region represents a possible limitation to its ability to respond at the full physiological scale. And again, is it perfectly fine to ignore us :)\nThis thinking brought me back to the title. Considering these and other differences, I guess the title should make a reader aware of the fact that this work explored D.m.; e.g. “4EHP and NELF-E regulate physiological 1 ATF4 induction and proteostasis in disease models of Drosophila melanogaster.” Or something like this.\n3) A few notes to consider:\n- there is literature showing that eIF3l and 3k are considered to be inhibitory (not intiation-stimulatory) subunits\n- lines 301 – 303; what do you mean by “a compensatory mechanism”? Specific reduction in biogenesis of one ribosomal subunit will naturally lead to an increase of the other subunit.\n- 303 – 307; this is really interesting; It seems that the 40S bio defect lies in an impaired formation of the 40S beak. Can you check in Ramakrishan’s Science paper (2020) where exactly the positions of 3l and 3k in the 48S PIC were mapped? They can exist as a dimer out of eIF3 and thereby they might be needed for final maturation of the 40S, which would explain a lot. Also, there is an EMBO J. paper from D. Wolf’s lab (2022?) on eIF3k and ribosomal proteins with respect to 40S biogenesis that could help too.\n- lines 310 – 314; If my assumption is correct, I would be very curious to see if the levels of RPS go down and those of RPL go up in RPS12-S2383/+ in a similar fashion as observed with 4EHP and NELF-E siRNAs. This could nail the mol mech down.\n- 318; I would be more accurate: “the post-termination 40S subunits”.\n- 321; eIF3j is not a bona fide eIF3 subunit, it is only an eIF3-associated factor\n- 419; for the sake of collegiality, please also include 26 and 29; this is important to us.\n- 425 – 426; for same reason, it would be fair to add that a similar observation was made in humans (31).", "reviewer_4": "The authors have addressed all of our concerns and the revised manuscript is much improved. The addition of the proteomic data really strengthens this study. Very interesting paper.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter