Inhibiting translation elongation by reducing eIF5A activity induces feedback inhibition of initiation, limiting tumour cell proliferation

Domain: biology
Published: 2025-12-13
Editorial outcome: major_revision

Abstract
Cancer development is associated with dysregulation of the translatome, and targeting canonical eukaryotic initiation and elongation factors can offer treatment avenues for various neoplasms. Emerging evidence indicates that dysregulated mRNA elongation, involving alterations in eEF2 activity and eIF5A expression, also contributes to tumour cell growth. In this study, we investigate whether targeting eIF5A with the inhibitor GC7 is a viable strategy to curtail aberrant cell growth. Our findings demonstrate that inhibiting elongation by reducing eIF5A activity induces feedback inhibition of initiation through eIF2α phosphorylation, decreasing ternary complex formation and shutting down bulk protein synthesis. Employing dynamic SILAC, we identify proteins impacted by reduced eIF5A activity, and show their decreased translation results from feedback inhibition to initiation or other processes downstream of eIF5A. Decreased eIF5A activity impairs mitochondrial function, which activates signalling through HRI to eIF2α phosphorylation, reducing cancer cell proliferation. These effects are reversed by treatment with the integrated stress response inhibitor, implying that the impact of GC7 on cancer cell proliferation is mediated via translation initiation rather than elongation inhibition. These data suggest that eIF5A inhibition could be used to target cancer cells that depend on mitochondrial function for their proliferation and survival.

Introduction
Neoplasia is associated with a dysregulated translatome, and it is recognised that cancerous cells rewire protein synthesis to adapt to the adverse conditions within the tumour microenvironment1. Novel therapeutic interventions under development that target protein synthesis for the most part focus on inhibition of selected eukaryotic initiation factors e.g. eIF4A12. However, the data show that the elongation stage of protein synthesis also represents a major regulatory node2. For example, in colon cancers associated with adenomatous polyposis coli (APC) deletion, increases in protein synthesis occur through activation of translation elongation and not initiation, mediated by increased eEF2 activity3. Elevated expression of the elongation factor eIF5A is also associated with the development of several types of cancer4,5, including lung metastasis6 and eIF5A is a prognostic marker for poor lung adenocarcinoma patient outcome, however a lack of understanding of its role in mRNA translation restricts therapeutic targeting strategies7–10.

The two isoforms of eIF5A (1 and 2) are the only proteins known to undergo the post-translational modification hypusination. This process is orchestrated by DHPS and DOHH, which add the epsilon aminobutyl moiety of spermidine to lysine 51 of eIF5A and perform the final hydroxylation of the molecule, respectively11–14. Upon activation, eIF5A binds to the E-site of the ribosome, projecting the hypusine-containing domain towards the P-site. This positioning allosterically facilitates peptide bonds formation15. The degree of specific peptide requirement for eIF5A function for translation elongation appears to be related to the system studied4,16,17. For example, data from yeast and in vitro assays, strongly suggest that di- and tri-peptide sequences that are enriched for prolines have a high requirement for eIF5A16,18,19. In mammalian cells it is less clear. There are examples of eIF5A impacting on the synthesis of selected proteins e.g. c-Myc, however, global quantitative proteomics approaches have not led to the identification of specific peptide motifs, suggesting its function is more nuanced in higher eukaryotes20–24.

Studies show that inactivation eIF5A or a reduction in its protein levels using siRNA-based approaches or knockout strategies results in mitochondrial dysfunction in fission yeast, MEFs, macrophages and mice4,16,17,22. This mitochondrial impact could be related to the toxicity observed in pre-clinical studies of the eIF5A inhibitor GC7 which restricted its clinical use24. However, providing such toxicities are understood and managed, selective targeting of mitochondrial complexes can provide an effective strategy to treat cancers that are highly dependent on oxidative phosphorylation (OXPHOS) for their survival25. For example, chemotherapy-resistant AMLs show an OXPHOS dependency that can be directly targeted via inhibition of mitochondrial function26.

To determine the utility of targeting eIF5A as an anti-cancer therapeutic we analysed the temporal changes in mRNA translation following treatment with GC7. We identified that a decrease in eIF5A activity resulted in a sequential inhibition of translation elongation and then translation initiation. We used Dynamic SILAC to understand the likely protein targets downstream of eIF5A, and our data show that despite the identification of ribosomal, translation, and mitochondrial proteins, as previously described22, there was no evidence for eIF5A-dependent peptide motifs. We hypothesised that the observed effects likely stemmed from broader disruptions in translation or other processes affected by eIF5A inhibition and used ISRIB, an integrated stress response (ISR) inhibitor27, to identify targets particularly sensitive to eIF2α phosphorylation. We identify the mechanistic link between eIF5A inhibition and eIF2α phosphorylation and show that inhibition of eIF5A activity triggers mitochondrial dysfunction, activating mitochondrial proteases OMA1 and DELE128, which in turn activate the eIF2α kinase, HRI, leading to inhibition of translation initiation via phosphorylation of eIF2α. We examined the impact of GC7 in a panel of control and tumour-derived cell lines and identify a link between functionality of mitochondria and sensitivity to GC7. Moreover, we found that mitochondrial stress sensed through OMA1-DELE1-HRI inhibits cancer cell proliferation thorough a P-eIF2α-dependent mechanism.

Taken together, these data suggest that cancer cells with minimal reserve mitochondria capacity, or tumours which are dependent on OXPHOS for survival and growth, are more sensitive to eIF5A inhibition and that devising therapeutic interventions that target this protein could provide a viable therapeutic option.

Results
Inhibition of hypusination leads to a sequential inhibition of translation elongation and initiation

Previous studies on eIF5A have predominantly examined downstream changes after prolonged periods of eIF5A inhibition e.g., 24h. To comprehensively explore mRNA translation dynamics upon eIF5A inhibition, we assessed the impact of the eIF5A hypusination inhibitor, GC7, over a time course, from 30 min to 24h. As high levels of eIF5A are a prognostic marker for poor lung adenocarcinoma patient outcome, we chose to study the effect of GC7 in A549 cells and observed a significant reduction in hypusination within 3h (Fig. 1a, b). We used an antibody specific to eIF5A1 as it is the predominant isoform expressed in A549 cells (Supplementary Fig. 1a), and observed a small reduction in expression after 24h (Fig. 1c), in line with previous observations that hypusination stabilizes eIF5A13. The decrease in hypusination at 3h correlated with a significant increase in p-eEF2, a recognized marker of translation elongation inhibition (Fig. 1d). To evaluate translation initiation, levels of p-eIF2α and p-4EBP1 were assessed. P-eIF2α levels significantly increased from 6h (Fig. 1a, e) and correlated with a significant increase in ATF4 expression (Fig. 1a, f). Conversely, phosphorylation of 4EBP1 was not significantly diminished until 18h, indicating that the inhibition of mTOR signalling did not occur during the early stages of eIF5A inhibition (Supplementary Fig. 1b, c). Taken together, these data suggest that following inactivation of eIF5A there is a correlation between the inhibition of elongation followed by the inhibition of initiation.

To confirm that the induction of ATF4 expression was dependent on p-eIF2α observed from 6h, we utilised mouse embryonic fibroblasts (MEFs) harbouring an alanine substitution at position 51 (S51A) to prevent phosphorylation of eIF2α. We treated S51A and WT MEFs with GC7 for 24h which decreased hypusine and increased p-eEF2 levels (Supplementary Fig. 1f–i). Although WT MEFs significantly increased ATF4 expression after treatment with GC7, this response was absent in S51A MEFs, suggesting the induction of ATF4 was dependent on p-eIF2α (Supplementary Fig. 1f–k).

To understand the effects of eIF5A inhibition on protein synthesis, a puromycin incorporation assay was used (Fig. 1g, h). Western blot analysis revealed an increase of puromycin incorporation during the early stages of GC7 treatments (0.5h). As puromycin incorporates into the A-sites of ribosomes, this increase may reflect the accumulation of slow-moving ribosomes on mRNAs, in line with the observed increase in p-eEF2, rather than increased translation rates. However, there was a decrease in puromycin incorporation from 18h (Fig. 1g, h) which is likely associated with the inhibition of translation initiation. Polysome profiling also showed a small increase of heavy polysomes after 30 min of GC7 treatment, indicative of either ribosome slowing or stalling on the mRNA (Fig. 1i). Conversely, an increase in free 80S ribosomes was not observed until 6h, correlating with p-eIF2α and ATF4 levels, and suggesting that the inhibition of elongation occurs before the inhibition of initiation (Fig. 1i). To validate the presence of slow-moving ribosomes in the early stages of eIF5A inhibition, a harringtonine pulse-chase ribosome run-off assay was conducted at 3h of GC7 treatment. In this assay, cells are treated with harringtonine to prevent 80S ribosome loading and ribosome elongation rates can be inferred from the subsequent loss of polysomes. Consistent with the data above, eIF5A inhibition resulted in a significant increase in ribosomes remaining on the mRNA after harringtonine treatment compared to untreated cells, suggesting that treatment with GC7 slowed elongation rates and ribosome run-off (Fig. 1j and Fig. S2a, b).

To determine if the effects of GC7 were specific to the inhibition of hypusination, we first used siRNAs to deplete eIF5A1 and 2. Prolonged eIF5A1/2 depletion recapitulated the effects of GC7 by increasing p-eIF2α and ATF4 expression, as well as decreasing overall rates of protein synthesis (Supplementary Fig. 3a–f). Furthermore, depletion of the enzymes responsible for hypusination (DHPS and DOHH) also signals to increase p-eIF2α, suggesting that the effects observed with GC7 are due to the specific impact on hypusination (Supplementary Fig. 3g, h).

Finally, to evaluate whether eIF5A inhibition leads to ribosome stalling and activates the ribosome quality control system (RQC), we employed our established ribosome stalling assay29 in HeLa cells. As HeLa cells displayed a dose-dependent inhibition of cell proliferation up to 40 µM GC7 (Supplementary Fig. 4a), we reasoned that this higher concentration would induce the greatest number of stalled ribosomes and therefore used this dose for the stalling assays. Consistent with the response in A549 cells, HeLa cells exhibited a similar reduction of hypusination and increase in ATF4 expression following GC7 treatment (Supplementary Fig. 4b–e). Whereas anisomycin robustly induced ribosome stalling (Supplementary Fig. 4f), GC7 treatment resulted in only a marginal increase in disomes due to ribosome collisions (Supplementary Fig. 4g). Furthermore, no phosphorylation of p38 MAPK (Supplementary Fig. 4e), a marker of ZAK activation following ribosome collisions, was detected at earlier time points. Overall, these data suggest that eIF5A inhibition results in a small increase in ribosome collisions, which is insufficient to activate ZAK.

Taken together, these data suggest that inhibition of eIF5A activity slows ribosome elongation and translation initiation is subsequently inhibited, possibly in an eIF2α-dependent manner (Fig. 1k).

eIF5A inhibition results in ribosomal and mitochondrial protein downregulation without evidence for eIF5A-dependent Di- or tripeptide sequences

To explore the impact of eIF5A inhibition on translation, dynamic SILAC was used to quantify the newly synthesized proteome at 3, 6 and 24h. This assay requires cells to be pulsed with heavy labelled SILAC media for the duration of GC7 treatment, to determine the change in heavy labelled peptides and hence infer changes to the total proteome. A PCA comparison shows the consistency of biological replicates and suggests most variation is associated with PC1, which separates the time points of treatment (Supplementary Fig. 5a). There was a significant change in the proportion of newly synthesized proteins after 6h of GC7 (Fig. 2a), by which time 182/1978 quantified proteins showed a significant decrease in the proportion produced (Fig. 2b). At 24h, 1950/2121 proteins showed a significant decrease in the proportion of newly synthesized proteins, indicating that translation was globally inhibited, in agreement with the phosphorylation of eIF2α, and eEF2 (Fig. 1a, d, e). GO term over-representation analysis was used to identify functional groups enriched in the proteins significantly impacted by 6h GC7 treatment. This indicated that cytosolic ribosomal proteins and mitochondrial matrix proteins were the most significantly impacted by GC7 treatment (Fig. 2c). Since mitochondrial matrix proteins were particularly affected, we separated mitochondrial proteins by their sub-mitochondrial localisation and observed that matrix and inner-membrane proteins were significantly more impacted by GC7 than inner-membrane space and outer membrane proteins at 6h and 24h (p < 0.05; Two-sample Kolmogorov-Smirnov test; Fig. 2d).

Previously, eIF5A has been implicated in the efficient translation of specific dipeptides and tripeptides, especially those containing proline16,18. However, we did not find an enrichment or any relationship between polyprolines and the effect of eIF5A inhibition18 (Supplementary Fig. 5b, c). We therefore performed unsupervised statistical learning using ridge regression to identify the amino acid compositions that were predictive of a significant impact of eIF5A inhibition at 6h. We used the frequency of each amino acid and each possible dipeptide and the presence of each possible tripeptide as features and an 80:20 split between training and hold-out test data. We achieved an area under the curve for the Receiver Operator Curve of 0.64 against the test data, indicating that the model was able to predict the impact of eIF5A inhibition with better than random accuracy (Supplementary Fig. 5d). The positively charged amino acids lysine and arginine were amongst the best predictors (Supplementary Fig. 5e). However, when the proteins were divided into functional groups based on the enriched GO terms (Fig. 2c), there was no relationship between the frequency of lysine and arginine and the impact of eIF5A inhibition (Supplementary Fig. 5f, g). Furthermore, we did not observe the relationship previously implicated between amino acid composition and the impact of eIF5A inhibition or knockdown on tripeptides and amino acids at any time point (Supplementary Fig. 5h). These data strongly suggest our regression merely identified weak associations between amino acid composition and functional groups of affected proteins. In summary, while the impact of eIF5A inhibition on the newly synthesized proteome is clear, there is no strong evidence that this is due to a dependence of specific amino acid motifs on eIF5A for efficient translation.

Uncoupling translation initiation inhibition from eIF5A inhibition revealed eIF2α-dependent mRNAs

The integrated stress response (ISR) is a conserved signalling pathway that allows cellular adaptation following exposure to a diverse range of stress stimuli. The ISR centres on the phosphorylation of eIF2α, which inhibits general protein synthesis while enabling the selective translation of mRNAs encoding stress-response proteins to restore homeostasis30. Four eIF2 kinases (HRI, PKR, PERK and GCN2) are activated in response to a diverse range of stress stimuli including oxidative stress, viral infection, unfolded proteins, amino acid deprivation and ribosome collisions31. Given that we show there is feedback inhibition from a reduction in eIF5A activity to p-eIF2α (Fig. 1), we reasoned that the absence of sequence specificity among the targets identified could reflect that the dual impact of initiation and elongation inhibition was confounding our data. Therefore, we used the integrated stress response inhibitor (ISRIB), which antagonizes the impact of ISR activation32, in combination with GC7 treatment to uncouple these events (Fig. 3). As the inhibition of translation initiation occurred within 6h of GC7 treatment (Fig. 1e), A549 cells were pre-treated with ISRIB for 2h to ensure complete alleviation of ISR signalling prior to the addition of GC7 (Fig. 3a). Since mitochondrial protein expression has been shown previously to be impacted by eIF5A inhibition22, we tested a range of mitochondrial proteins that were shown to be either downregulated (NDUFS1, TIMM44 and SDHA, MRPS15) or absent (SDHB, MRPL37 and TOMM20) from our SILAC dataset (Supplementary Data 1). In addition, we tested two cytosolic ribosomal proteins (RPS6 and RPS25) that were also downregulated after 24h GC7 treatment. We observed a reduction in hypusination and the protein levels of all downregulated targets from our SILAC data at 24h (Fig. 3a and Figure S6a). Ribosomal protein levels and TOMM20, which exhibited a more modest reduction in the SILAC experiment, also showed a small decrease at 24h (Fig. 3a). We hypothesized that the addition of ISRIB would alleviate the effects of eIF2α phosphorylation and subsequent inhibition of translation initiation. We identified 7 proteins (eIF5A, NDUFS1, TIMM44, SDHA, SDHB, MRPL37, and MRPS15) downregulated after GC7 treatment that failed to recover with ISRIB, indicating the reduction in expression occurs via an eIF2B independent mechanism (Fig. 3a–h). Conversely, downregulation of RPS6, RPS25, and TOMM20 was significantly restored in the presence of ISRIB, suggesting these proteins are eIF2α-dependent and their downregulation is consistent with global translation inhibition (Fig. 3i–k). In addition, treatment with a different inhibitor of hypusination, ciclopirox (CPX), also increases p-eIF2α and ATF4 expression, decreases expression of mitochondrial proteins, and inhibits protein synthesis (Supplementary Fig. 6b–k). These findings imply that eIF5A inhibition results in the downregulation of both eIF2α-dependent and eIF2α-independent targets, providing an explanation for why bulk proteome analysis following eIF5A inhibition may not reveal specific eIF5A sequences in the affected proteins.

The reduction in translation initiation following eIF5A inhibition is driven by PERK- and HRI-mediated eIF2α phosphorylation

Following the delineation of eIF5A-specific and non-specific effects on protein synthesis, we aimed to elucidate the mechanism for feedback inhibition from elongation to initiation. As our previous data suggested the inhibition of translation was most likely dependent on the phosphorylation of eIF2α (Fig. 1), all four eIF2 kinases (HRI, PKR, PERK and GCN2) were depleted using siRNAs. We then assessed p-eIF2α levels and the subsequent induction of ATF4 expression following GC7 treatment. These data show that knockdown of HRI using two different siRNAs (Fig. 4a, b) reduced the induction of p-eIF2α by GC7, although only one siRNA showed a significant reduction (Fig. 4c). Moreover, HRI depletion abolished GC7-induced ATF4 expression (Fig. 4d). In addition, knockdown of PERK (Fig. 4e, f) also reduced the induction of p-eIF2α by GC7 to untreated levels (Fig. 4g) and significantly reduced the induction of ATF4 (Fig. 4h). Conversely, knockdown of either PKR (Supplementary Fig. 7a–g) or GCN2 (Supplementary Fig. 7e–h) failed to reduce GC7-induced p-eIF2α and ATF4 expression. Taken together, these data suggest that both HRI and PERK are activated in response to GC7 treatment and elongation inhibition, and may be responsible for mediating the inhibition of translation initiation.

Mitochondrial dysfunction results in activation of OMA1 and DELE1 proteases that activate HRI and induce ATF4 expression

While inhibition of elongation and initiation activate the unfolded protein response (UPR), leading to PERK activation, eIF2α phosphorylation, and ATF4 expression30, the mechanism of HRI activation in our system remains unclear and was investigated further. Previous studies have linked eIF5A inhibition to mitochondrial dysfunction22,33. Therefore, mitochondrial morphology was examined using immunofluorescence confocal microscopy targeting TIMM44, which revealed that after 24h of GC7 treatment that the majority of cells exhibited large, fragmented mitochondria (Fig. 5a, b). GC7 treatment did not result in the generation of ROS, whereas the redox cycling agent menadione stimulated extensive ROS production (Fig. 5c). These data suggest that GC7 causes mitochondrial dysfunction that is independent of ROS accumulation.

A previous study has shown that ROS-independent mitochondrial dysfunction is associated with the activation of the mitochondrial proteases OMA1 and DELE128. Upon activation, OMA1 cleaves DELE1, which translocates to the cytoplasm and activates HRI28. To investigate whether this signalling cascade is activated by eIF5A inhibition, we utilized siRNAs to deplete both OMA1 and DELE1, confirming their downregulation through qPCR (Supplementary Fig. 8a, b). OMA1 protein depletion was confirmed by western blotting (Supplementary Fig. 8c), however, we were not able to determine DELE-1 protein depletion due the lack of specific commercially available DELE-1 antibodies28. However, as DELE-1 protein has been suggested to be degraded within 30 min29,34, we reasoned analysis of mRNA levels should be sufficient. Subsequently, cells were treated with GC7 for 24h, and ATF4 induction was assessed via western blot. Following eIF5A inhibition, ATF4 protein levels increased approximately 5-fold in cells treated with a control siRNA and GC7 (Fig. 5d–i). However, ATF4 induction was significantly reduced upon OMA1 and DELE1 knockdown (Fig. 5d–i). Collectively, these findings support the notion that OMA1 and DELE1 are activated in response to mitochondrial dysfunction, contributing to HRI activation and ATF4 expression following GC7 treatment (Fig. 5j).

EIF5A inhibition induces cell cycle arrest through mitochondrial dysfunction and eIF2α phosphorylation

Our findings suggest that cancer cells with high OXPHOS levels or a reliance on mitochondria for energy production would exhibit increased sensitivity to eIF5A inhibition. To test this hypothesis, we examined the response of A549 derived from lung adenocarcinoma, which is GC7-sensitive, to a cell line derived from healthy tissue, MCF10A. The xCELLigence RTCA platform quantifies cell proliferation in real time, which is expressed in arbitrary units termed cell index. We compared proliferation of both A549 and MCF10A cells following treatment with GC7 and observed a reduction of cell index in A549 cells, whereas MCF10A were less sensitive (Fig. 6a–c). The reduction of cell index in A549 at 48h was not a result of cell death (Supplementary Fig. 9a), but rather a decrease of cell proliferation, as indicated by an accumulation of cells in the G1 phase of the cell cycle over a 72h period (Supplementary Fig. 9b). Conversely, the effect of GC7 on MCF10A was more modest. Although there was no impact on cell cycle distribution (Fig. 9c), there was a small decrease in the rate of proliferation (Fig. 6b), in line with the observed inhibition of protein synthesis (Supplementary Fig. 9d–l).

Cells are reliant on mitochondria for ATP production when they are cultured in galactose-containing media which can highlight mitochondrial liabilities35. Given that GC7 inhibits mitochondrial protein expression of A549 cells (Fig. 2), we compared ATP production in A549 and MCF10A cells grown in either glucose or galactose and treated with GC7. Rotenone was used as a positive control for OXPHOS inhibition. Importantly, ATP production is not impaired by GC7 in A549 cells grown in galactose at early time points, strongly suggesting GC7 is not a direct ETC inhibitor (Supplementary Fig. 10a). However, ATP production is impaired in galactose from 6h after GC7 treatment, but not in cells grown in glucose media, suggesting mitochondrial dysfunction following GC7 treatment (Supplementary Fig. 10a) and correlating with previously observed changes in mitochondrial protein expression (Fig. 2). Conversely, MCF10A showed a small reduction in ATP production from 18h treatment in both glucose and galactose containing media (Supplementary Fig. 10b). To determine the mitochondrial function of A549 and MCF10A cells we measured their oxygen consumption rates (OCRs). Although basal OCR was higher in A549 cells, MCF10A cells had a large reserve capacity of mitochondrial respiration, whereas A549 cells had no reserve capacity, suggesting that A549 cells may be very dependent on mitochondria for energy production as their mitochondria are already working at maximum capacity (Fig. 6d). To understand the impact GC7 had on the mitochondrial function we measured OCR following 6h and 24h GC7 treatment. GC7 treatment reduced the basal OCR of A549 cells within 6h, correlating with the reduction of mitochondrial proteins in our SILAC analysis (Fig. 6e and Figure S10c). Conversely, MCF10A cells were minimally affected (Fig. 6e and Figure S10d), suggesting they rely less on their mitochondria in the steady state. As there was no decrease in ATP production of A549 cells treated with GC7 in glucose media (Supplementary Fig. 10a), but mitochondrial function is inhibited (Fig. 6e), we assessed their ability to utilise glycolysis by determining the extracellular acidification rate (ECAR). A549 cells switched to glycolysis within 6h of GC7 treatment, whereas MCF10A did not enhance glycolysis until 24h, perhaps due to their reduced basal respiration requirements (Supplementary Fig. 10e–g). These data further support the hypothesis that cancer cell lines that are dependent on their mitochondria in the steady state, would be more sensitive to GC7. Our data suggest that cell lines with a dependence on mitochondria, such as MpM36, or those with minimal reserve capacity, such as HeLa37, were sensitive to GC7 (Supplementary Figs. 4a, 11a–d). Conversely, cell lines with larger reserve mitochondrial capacities, such as MCF738 and HT-2939, were insensitive to GC7 (Supplementary Fig. 4a). Additionally, tissue microarrays containing samples derived from lung adenocarcinoma showed a correlation between eIF5A1 and Ki67 positive cells (Supplementary Fig. 11e, f and Supplementary Data 2) and SDHB (Supplementary Fig. 12 and Supplementary Data 3), suggesting a potential sensitivity in this context.

Our data suggest that A549 cells are reliant on their mitochondria for energy production and that GC7-induced mitochondrial dysregulation may limit cancer cell growth. To investigate if the inhibition of cell proliferation in A549 cells was eIF2-dependent32,40–42, cells were co-treated with GC7 and ISRIB. The presence of ISRIB significantly reversed the inhibition of cell proliferation (Fig. 6f) and alleviated the accumulation of cells in the G1 phase of the cell cycle (Fig. 6g), suggesting the impact of GC7 on cell proliferation results from feedback inhibition of initiation, rather than a direct effect on elongation. To confirm the relationship between GC7-dependent phosphorylation of eIF2α and the inhibition of cell proliferation, we again utilised S51A MEF cells. In support of our observations with ISRIB, GC7-induced inhibition of proliferation was significantly reduced in the S51A cell line (Fig. 6h), again suggesting that the effect on cell proliferation is driven by the phosphorylation of eIF2α. To confirm the role of the eIF2α kinases in this mechanism, siRNAs for all four eIF2α kinases were again utilised. Consistent with previous data (Fig. 4), silencing either HRI or PERK rescued GC7-induced inhibition of proliferation (Fig. 6i, j), whereas silencing PKR and GCN2 had no effect on cell proliferation (Supplementary Fig. 10h–i). Finally, to confirm that mitochondrial dysfunction and the activation of HRI via the OMA1 and DELE1 cascade was responsible for the observed effects on cell proliferation, we used siRNAs specific to OMA1 and DELE1. As expected, depletion of both OMA1 and DELE1 reversed GC7-induced inhibition of proliferation (Fig. 6k, l).

These data collectively suggest that following GC7 treatment, activation of OMA1 and DELE1 in response to mitochondrial dysfunction in turn activates HRI to inhibit cancer cell proliferation.

Methods
Ethics
The study design and conduct complied with all relevant regulations regarding the use of human study participants and received ethics committee approval. For tissue microarrays, human resected primary Lung adenocarcinoma was used under ethical approval for the use of surplus tissue in research (REC ref. 16:/WS/0207) and managed by the NHS GGC Biorepository and Pathology Tissue Resource. Tissues were used without specific consent according to the provisions of the Human Tissue Act 2004; in summary, use was part of an ethically approved project, the research team were blinded to patient identity, and no post mortem tissues were involved.

Cell culture
A549 (CCL-185), HeLa (CCL-2), MCF7 (HTB-22), MCF10A (CRL-10317) and HT-29 (HTB-38) were obtained from ATCC. WT and S51A MEFs were a gift from David Ron. A549, HeLa, MCF7, HT-29 and MEFs were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Gibco, 41966-029), supplemented with 10% foetal bovine serum (FBS) (Sigma, F9665). Mesothelioma-derived primary cells (7T, 8T, 9T and 13T) were derived in previous studies36,56 and cultured as outlined in Grosso et al.36, with growth medium consisting of Roswell Park Memorial Institute Medium (RPMI)-1640 (Gibco, 21875-034), supplemented with L-glutamine (2mM) (Fisher, 25030-081), 100U/ml penicillin and 100μg/ml streptomycin (Thermo Fisher, 15140122), 20ng/ml EGF (Peprotech, AF-100-15), 1μg/ml hydrocortisone (Sigma-Aldrich, H0888), and 10% FBS (Sigma, F9665). MCF10A cells were cultured in DMEM/F12 (Life Technologies, 11330) supplemented with 5% horse serum (Labtech, DH-291/500), 20ng/ml EGF (Peprotech, AF-100-15), 10μg/ml insulin (Sigma-Aldrich, I9278), 500ng/ml hydrocortisone (Sigma-Aldrich, H0888) and 10ng/ml cholera toxin (Sigma, C8052). Galactose containing media consisted of glucose-free DMEM (Life Technologies, Gibco) supplemented with 10mM galactose, 2 mM L-glutamine, 1mM sodium pyruvate and 10% FBS. All cell lines were incubated at 37°C and 5% CO2. Details on the compounds used in this study are present in Supplementary Table 1.

Cell growth analysis—crystal violet and xCELLigence proliferation assay
Cell proliferation was assessed through crystal violet staining or real-time monitoring using xCELLigence. For Crystal violet staining, cells were seeded in 6-well plates at a density of 50,000 cells per well. Subsequently, cells were treated with GC7 as described, fixed in 4% paraformaldehyde (PFA) (Thermo Fisher, 043368.9M) for 10 min at 25°C, stained with 0.05% crystal violet, rinsed with water, and air-dried at 25°C. The absorbance at 540nm was quantified using a PowerWave XS2 plate reader (BioTek). For real-time monitoring of cell proliferation, we used the Agilent xCELLigence real-time cell analysis (RTCA) DP platform. This instrument utilises culture plates with embedded microelectrode sensors that allow label-free quantification of cell proliferation, morphology, and adhesion to the plate. Initially, 5,000 cells are seeded in each well, and then the next day, the cell media is replaced, followed by treatment with the respective drug or siRNA transfection mix. Throughout a period of up to 7 days, the machine continuously records changes in impedance measurements, which are provided as an arbitrary value termed cell index, that can be inferred as cell proliferation. For visualization and data analysis, we utilized the RTCA software developed by ACEA Biosciences.

Immunoblotting
After the experimental treatments, cells were subjected to PBS washes and then lysed in Radioimmunoprecipitation assay (RIPA) buffer (50mM Tris-HCl pH 7.4, 150mM NaCl, 1% Triton X-100, 0.1% SDS and 0.5% sodium deoxycholate) supplemented with cOmplete™ EDTA-free protease inhibitor cocktail (Merck, 11697498001), PhosSTOP™ (Merck, 4906845001), and Benzonase® Nuclease (Millipore, 70746) at a specified concentration of 0.2 U/ml. Protein concentration quantification was accomplished using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific) following the manufacturer’s guidelines. Subsequently, protein lysates were resolved on NuPAGE™ 4–12% gels (Thermo Fisher, NP0321BOX). Electrophoresis was carried out in 1X MOPS running buffer at 150V for approximately 1.5h. Proteins were transferred to an Immobilon®-FL PVDF Membrane (Millipore, IPFL00010) and membranes were blocked using 5% milk in Tris-buffered saline, 0.1% Tween-20 (TBST). Membranes were incubated with primary antibodies overnight at 4°C and incubations with secondary antibodies were carried out for 1-h at room temperature. Membranes were imaged using the Odyssey infrared system (LI-COR Biosciences). Comprehensive details about the antibodies utilized can be seen in Supplementary Table S2. Densitometry analysis was carried out using Image Studio Lite. All signal for phosphorylated proteins were normalised to the total abundance of the corresponding protein. All non-phosphorylated proteins were normalised relative to Actin.

Immunofluorescence and confocal microscopy
A549 cells were cultured on a 1.5mm glass coverslip within a six-well culture plate at a density of 50,000 cells per well. Following the specified durations of GC7 treatments, cells underwent PBS washing and fixation in 4% formaldehyde (Sigma-Aldrich, F8775) for 10 min at room temperature (RT). Subsequently, permeabilization was conducted using PBS-0.2% Triton X-100 for 30 min at RT. Following a blocking step in 3% bovine serum albumin (BSA) (Sigma-Aldrich, A-7906) in PBS supplemented with 0.1% Triton X-100 (PBST), cells were subjected to overnight incubation with anti-mitochondrial protein TIMM44 antibody at 4°C. After washing in PBST, cells were further incubated with Goat anti-Rabbit IgG (H+L) Highly Cross-Absorbed secondary antibody Alexa Fluor™ 488 (Invitrogen, A-11034) for 1h at 25°C. Nuclei were stained with Hoechst dye for 5 min at 25°C (Invitrogen, 33258) followed by additional washes with PBST. Finally, cover slips were mounted on slides using ProLong Diamond Antifade Mounting Media (Invitrogen, P36970). Confocal images were acquired using an inverted Zeiss LSM880 confocal with Airyscan using the Plan-Apochromat 63x/1.4 Oil DIC. Image acquisition was performed using Zen Black software (Zeiss). Mitochondrial morphology (elongated or fragmented) was assessed in 80 cells per condition per biological repeat. Image analysis was carried out using Fiji software.

Flow cytometry
All experiments were conducted using BD LSR Fortessa or BD FACS Canto II flow cytometers. A total of 10,000 events were acquired for each condition and data was analyzed using FlowJo data analysis software (Version 10.10). For quantification of cell death, cells were resuspended in Annexin binding buffer (BD Bioscience) and incubated with Annexin V-FITC (Invitrogen, BMS500FI-300) and Draq7 (Abcam, ab109202) for 20 min at room temperature. For cell cycle analysis, cells were trypsinised, pelleted and fixed in ice cold 70% ethanol in PBS overnight at 4°C. Fixed cells were incubated with either FxCycle Violet DNA stain (4,6-diamidino- 2-phenylindole dihydrochloride) (Thermo Fisher Scientific) in 0.1% BSA/PBS overnight at 4°C, or 20 μg/ml propidium iodide (PI) in 1% Triton X-100/PBS and 0.1mg/ml RNase A overnight at room temperature. Cell cycle state was determined using Dean-Jett-Fox modelling57.

TMA staining and quantification
Tissue microarrays (1mm cores) were constructed from formalin fixed paraffin embedded (FFPE) human resected primary Lung adenocarcinoma from the LATTICeA cohort. 4µm sections were mounted onto TOMO hydrophilic adhesive microscope slides (Matsunami).

Automated multiplex immunofluorescent staining was performed on the Ventana Discovery Ultra platform (Roche Tissue Diagnostics, RUO Discovery Universal V2 1.00.0019) using a fluorophore tyramide-based signal amplification system (Akoya Biosciences). Slides were baked on board at 60°C, followed by a dewax step for 24 min at 69°C, and heat induced epitope retrieval was applied for 32 min at 95°C using a Ph9 solution (CC1, Roche Tissue Diagnostics, 950–500). Discovery Inhibitor (Roche Tissue Diagnostics, 760-4840) was applied for 12 min to block any previous endogenous peroxidase. The primary-secondary-antibody pairs used in the multiplex panel alongside their assigned opal fluorophore were applied in a sequential manner, respectively, are as follows: pan-CK AE1-3 (Leica Biosciences, NCL-L-AE1-3, 1:250, 28 min), OmniMap-anti Ms HRP (Roche Tissue Diagnostics, 760–4310, 12 min), Opal 620 (Akoya Biosciences, OP001004, 1:100, 8 min); Hypusine (Sigma-Aldrich, ABS1064-I, 1:100, 1h), Opal 520 (Akoya Biosciences, OP001001, 1:400, 8 min); Ki67 (30-9) (Roche Tissue Diagnostics, 790-4286, RTU, 20 min), OmniMap-anti Rb HRP (Roche Tissue Diagnostics, 760–4311, 12 min), Opal 570 (Akoya Biosciences, OP001003, 1:200, 8 min); eIF5A (Abcam, ab137561, 1:500, 32 min), OmniMap-anti Rb HRP (Roche Tissue Diagnostics, 760–4311, 12 min), Opal 690 (Akoya Biosciences, OP001006, 1:300, 8 min); SDHB (EPR10880), (Abcam, ab175225, 1:2000, 32 min), OmniMap-anti Rb HRP (Roche Tissue Diagnostics, 760–4311, 12 min), Opal 650 (Akoya, OP001005, 1:300, 8 min). Following each detection with opal throughout the multiplex sequence, a de-crosslinking step was included to denature any previous antibody binding using a heat-and-pH mediated approach at 100°C using a pH6 solution for 24 min (CC2, Roche Tissue Diagnostics, 950–123). Whole slide images were scanned on the Pheno Imager multispectral slide scanner (Akoya Biosciences, version V1.0.13) using a 10× objective. Regions of interest were selected using Phenochart (Akoya, Biosciences) (v1.1.0) and individual core regions of interest were acquired at 20× for further analysis. Core images were spectrally unmixed using Inform (Akoya Biosciences) (version 2.6.0). Visiopharm was used for all image analysis. For tissue segmentation, a bespoke deep learning algorithm (version 2023.01.3.14018) was trained on annotated images verified by a pathologist using the deep learning module U-Net and DAPI, Cytokeratin, and autofluorescence features to segment tumour, stroma, and necrosis regions of interest (ROIs). For cell detection, an additional deep learning algorithm (version 2022.12.0.12865) was trained using Dapi and Ki67 inputs to generate background, boundary, and nuclear features to detect nuclei and to generate Ki67+ and Ki67- nuclear classes for output. Post processing steps were included to remove background and separator labels, and to turn the Ki67+ and Ki67- labels to a single nuclear label. Cytoplasmic labels were generated by dilating nuclear labels by 20 and 10 pixels for tumour and stromal cells, respectively. Feature heatmaps were created to separate nuclear and cytoplasm objects where cells were merged. Output variables were generated for ROI area, mean pixel intensities for each marker, and XY coordinates for each cell and were exported for each core image for subsequent statistical analysis. Measures obtained from Visiopharm were analysed using R (version 4.2.2) with tidyverse packages (version 2.0.0). Stained tissue sections have been preserved.

Puromycin incorporation assay
Cells were treated with 10 µg/ml puromycin for 10 min before being harvested and lysed in RIPA buffer. Subsequently, the samples underwent immunoblotting, as detailed previously. Loading controls were carried out using Coomassie blue gel staining or Ponceau S membrane staining. For Coomassie blue, gels were stained with InstantBlue® Coomassie Protein Stain (Abcam, ab119211) for 15 min. For Ponceau S, membranes were incubated with Ponceau S for 5 min at room temperature and washed 3 times before imaging. Analysis was performed using Image Studio Software v. 5.2.

Polysome profiling
For polysome profiling, an initial seeding of 1×106 cells was carried out in a 15cm plate. Subsequently, cells were allowed to adhere and grow for the appropriate duration. Following this, the media was changed, and the cells were treated with GC7 for the designated period. Prior to harvesting, the cells underwent a wash with ice-cold PBS supplemented with cycloheximide at 100µg/ml. The cells were then lysed in buffer containing 100mM NaCl, 5mM MgCl2, 15mM Tris-HCl pH 7.4, 0.2M sucrose, 0.5% IGEPAL, 100 U RNasin, 1mM DTT, and 0.1mg/ml cycloheximide. Protein content was quantified using the BCA protein quantification kit (Thermo Fisher, J63283). Approximately 2–2.5mg of protein per sample was loaded onto sucrose gradients (10–50%) and subjected to centrifugation at 247,767×g for 2h at 4°C. The gradient was displaced from the bottom of the tube using a density gradient fractionation system with continuous monitoring of the absorbance at 254nm (Teledyne ISCO).

Ribosome run-off assay
Cells were seeded and treated as for standard polysome profiling assays. However, after treatment with GC7 for 3h, both control and GC7-treated cells were additionally exposed to 2 µg/ml harringtonine (Supplementary Table 1), to inhibit ribosome loading58, for either 0, 3, 4, or 5 min. Cells were treated with 100 µg/ml cycloheximide for 3 min and lysed in polysome profiling lysis buffer. Samples were subjected to polysome profiling analysis and polysome density was quantified as previously described59 by calculating the area under the curve. Ribosome run-off was determined by comparing polysome density from untreated and GC7 treated cells following treatment with harringtonine for 5 min.

Ribosome Stalling Assay
Stalling assay was carried out as described in ref. 60. After exposure to 0.2 µM anisomycin for 15 min and 10 µM GC7 for 3h, respectively, cells were collected in lysis buffer containing 20mM HEPES pH 7.5, 100mM NaCl, 5mM MgCl2, 100μg/ml digitonin, 100μg/ml cycloheximide, cOmplete mini EDTA-free protease inhibitor cocktail (Merck, 11697498001), and 200 U RNase inhibitor (NEB, M0307L). The lysates were incubated at 4°C for 5 min and subsequently centrifuged at 17,000×g for 5 min at 4°C. Then, 1mM calcium chloride was introduced, and the samples underwent digestion with 500 U micrococcal nuclease (NEB, M0247S) at 22°C for 30 min. Following this, 2mM EGTA was added to terminate the reaction. Approximately 2–2.5mg of protein per sample were applied to a 10–50% sucrose gradient, which underwent centrifugation at 247,767×g for 2h at 4°C. Finally, the gradients were processed through an ISCO density gradient fractionation system, with the absorbance continuously measured at 254nm.

qPCR
To validate the efficacy of OMA1 and DELE1 knockdown following siRNA transfections, quantitative PCR (qPCR) analysis was employed. Specific primers for OMA1 and DELE1 mRNA were designed (Supplementary Table 3). After siRNA transfections, cells were harvested in Trizol Reagent (Thermo Fisher, 15596026), and RNA extraction was performed following the manufacturer’s protocol. RNA concentration was determined using a Nanodrop 2000 (Thermo Scientific), and 1µg of RNA per sample was utilized for cDNA synthesis with Superscript™ III Reverse Transcriptase (Thermo Fisher, 18080093), following the manufacturer’s instructions. Subsequent qPCR was conducted using SensiFAST SYBR Lo-ROX Kit (Meridian Bioscience, BIO-94020) on the QuantStudio™ 6 Flex (Thermo Scientific).

siRNA transfections
For siRNA transfection, ON-TARGETplus™ siRNAs from Horizon (Supplementary Table 4) were used, along with Lipofectamine™ RNAiMAX transfection reagent (Thermo Fisher Scientific, 13778075), following the manufacturer’s protocol. Cells were initially seeded at a density of 50,000 cells per well in a 6-well plate or at 5000 cells/well in a 16-well xCELLigence plate. The following day, cells were supplemented with the appropriate transfection mix and further incubated for 18h. Subsequently, the media was changed, and the cells were allowed to grow for an additional 24–72h, depending on the specific experiment. Following this, the cells were treated with GC7 and either subjected to growth monitoring in an xCELLigence proliferation assay or harvested for western blot or qPCR analysis, as described previously.

ROS detection assay
For ROS detection we used ROS-Glo™ H2O2 Assay (Promega, G8820), according to manufacturer’s protocol. Menadione (Supplementary Table 1) was used as a positive control.

ATP assays
CellTiter-Glo® 2.0 Cell Viability Assay (Promega, G9241) was used to quantify ATP levels according to manufacturer’s protocol. Rotenone was used as a positive control.

Extracellular flux analyses
Real-time oxygen consumption rates (OCR) were measured using a Seahorse XF96 extracellular flux analyser (Agilent). A549 (2×104) and MCF10A (3 ×104) cells were seeded in glucose containing media in Seahorse Biosciences XF96 plates. Cells were washed in unbuffered serum-free DMEM Seahorse Assay Medium and incubated in a non-CO2 37°C incubator for 1h before placing in the Seahorse XFe96 Analyzer. Oligomycin (2µM), FCCP (500nM), rotenone/antimycin A (2µM) were added sequentially. Mean OCR values were obtained from a minimum of four technical replicates and normalised to total protein content.

Dynamic stable isotope labelling by amino acids in cell culture (SILAC) mass spectrometry
Dynamic SILAC was conducted using the SILAC Protein Quantitation Kit (Trypsin) that contained media supplemented with either light or heavy labelled lysine and arginine. A total of 1×106 cells were seeded in a 15cm plate and allowed to attach and grow for 48h under standard growth conditions, as detailed earlier. Subsequently, the media was replaced with either normal media in the control cells or media enriched with heavy labelled 13C6 15N2 L-lysine-2HCl and 13C6 15N4 L-arginine-HCl for the same duration as the GC7 treatments. Subsequently cells were harvested in 25mM AMBIC containing 0.2% RapiGest detergent (Waters). 50μg of total protein of each sample was diluted to 130μl total volume using 25mM AMBIC containing 0.1% RapiGest (w/v) (Waters). The cysteines were reduced by adding DTT to a final concentration of 4mM and then alkylated by adding iodoacetamide to a final concentration of 14mM. Proteins were digested by adding 1μg trypsin (Promega, UK) for 18h at 37°C. Peptides were acidified with the addition of trifluoroacetic acid to a final concentration of 0.5% (v/v). The acidified tryptic digest was then centrifuged at 13,000×g for 15 min and the supernatant subjected to LC-MS/MS analysis.

LC-MS/MS was performed on an Ultimate U3000 HPLC (Thermo Fisher Scientific, San Jose, USA) hyphenated to an Orbitrap Eclipse mass spectrometer (Thermo Fisher Scientific, San Jose, USA). Peptides were trapped on a C18 Acclaim PepMap 100 (5µm, 300µm×5mm) trap column (Thermo Fisher Scientific, San Jose, USA) and eluted onto a 50cm Easy Spray column (Thermo Scientific Dionex, San Jose, USA) using 150-min gradient of acetonitrile (5–35%). For data dependent acquisition, a dual FAIMS compensation voltage experiment was performed (-45v, -65v), with a cycling time of 1.5s between the two experiments. MS1 scans were acquire data resolution of 120,000 (AGC target of 4e5 ions with a maximum injection time of 50ms), followed by MS2 scans acquire data resolution of 15,000 (AGC target of 5e4 ions with a maximum injection time of 25ms) using a collision-induced dissociation energy of 30 and an isolation window of 0.7. Dynamic exclusion of fragmented m/z values was set to 60s. Raw data were imported and processed in Proteome Discoverer v2.5 (Thermo Fisher Scientific). The raw files were submitted to a database search using Proteome Discoverer with Sequest HT against the UniProt reference proteome for Homo sapiens using SILAC quantification method for both heavy lysines and arginines. The processing step consisted of a double iterative search using INFERIS Rescoring algorithm on a first pass with methionine oxidation set as variable and cysteine carbamidomethylation, heavy arginine and lysine as fixed modifications. For the second pass, all spectra with a confidence filter worse than high were researched with Sequest HT including additional common protein variable modifications [Deamidated (N, Q); gln to pyro-Glu (Q); N-terminal acetylation and Methionine loss). The spectra identification was performed with the following parameters: MS accuracy, 10 ppm.; MS/MS accuracy of 0.02 Da; up to two trypsin missed cleavage sites were allowed. Percolator node was used for false discovery rate estimation and only rank 1 peptide identifications of high confidence (FDR<1%) were accepted. Subsequent data analysis procedures were executed as described below.

Proteomics data analysis
Source code for the proteomics data analysis to perform all the analyses below is available from https://github.com/MRCToxBioinformatics/eIF5A_hypusination_inhibition_silac v0.1. Peptide-level quantifications from Proteome Discoverer were processed to obtain protein-level newly synthesized protein proportions. Peptides matching contaminant proteins or multiple master proteins were excluded. The heavy and light peptide intensities were used to calculate the proportion of nascent peptide as heavy/(light+heavy). Peptide-level proportions were then aggregated to protein level by taking the median proportion. Proteins with only one peptide were excluded.

Differential abundance of newly synthesized proteins was performed using DEqMS. Since DEqMS expects a Gaussian-distributed abundance, we converted the proportions back to a ratio of heavy and light intensities, where the ratio is calculated as proportion/(1-proportion). Ratios were log2-transformed to make them Gaussian distributed. The DEqMS model was ratio~condition+replicate, where the replicate was a blocking factor, and the condition was GC7 or control. DEqMS models the relationship between ‘peptide count’ and variance to share information between proteins. We followed the developer’s advice and used the minimum number of peptides per protein across the replicates with a quantified ratio as the peptide count. Only proteins with at least two quantified ratios in both conditions for a given time point could be tested. To identify proteins with a significant change in the newly synthesized protein ratio, we used the adjusted p-value calculated by DEqMS and a 5% False Discovery Rate (FDR) threshold61.

The identification of over-represented Gene Ontology (GO) terms was performed using goseq, which enables adjustment for bias towards proteins for which there was more statistical power to detect a significant difference. The peptide count used with DEqMS, as described above, was provided as the bias factor. P-values were adjusted for multiple testing using the False Discovery Rate procedure61. GO terms over-represented with an adjusted p-value less than 0.01 (1% FDR) were taken as significant. GO terms with fewer than 10 proteins in the foreground were excluded. Following this, redundant GO terms were removed, and the magnitude of the over-representation was estimated using the functions remove_redundant_go and estimate_go_overrep from camprotR v0.0.0.9000. The complete human Mitocarta 3.0 database table was downloaded on 26 July 2023. This denoted the sub-mitochondrial localisation of each protein. Proteins with multiple localisations were excluded from the visualisations. We used the uniprot_map function from uniprotREST to obtain the amino acid sequence for each protein and calculated the frequency of each amino acid and di/tri-peptide. We used binomial ridge regression to determine the amino acid composition features which were predictive of a significant loss of newly synthesized protein at 6h. This was performed using the cv.glmnet function from the glmnet package.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this work, Sfakianos and colleagues studied the effects and molecular consequences of EIF5A inhibition in tumor cells. Through a comprehensive approach based on several technologies, they propose that inhibition of EIF5A hypusination causes a bi-phasic translational response characterized by an early inhibition of protein elongation, which feeds back on a later eIF2alpha-mediated inhibition of translational initiation, and consequent decrease of global protein synthesis. This mechanism would impair mitochondrial function and induce apoptosis in tumor cells characterized by high OXPHOS, such as A549 cells. The proposed model of an elongation-to-initiation feedback mechanism provides novel information to the EIF5A/translation field. However, there are major limitations in this study that dampen the enthusiasm for the whole story: 1) Approach: the work is almost entirely based on the use of the DHPS inhibitor GC7 to inhibit EIF5A function, mostly using one cell line (A549). It is generally believed in the field, and documented in some reports (e.g. Oliverio et al. Amino Acids 2014 DOI 10.1007/s00726-014-1821-0; Landau et al., JBC 2010 https://doi.org/10.1074/jbc.M110.106419 ), that GC7 has effects unrelated to DHPS inhibition and may directly target mitochondrial proteins. Even if lower doses and specific cell lines have been used in this work, authors have not provided evidence of the specificity of GC7 under their conditions. In Figure S3 it is shown that the knockdown of both EIF5A1 and 2 induces markers of ISR and decrease of translation rate. However, the authors have knocked down EIF5As, but not DHPS, the actual target of GC7. To demonstrate the time-dependent effect of decreased/abrogated EIF5A hypusination, the authors should use inducible DHPS knockdown and/or cells stably expressing a mutant EIF5A lacking the hypusinated lysine (i.e. EIF5AK50R) in all the experiments shown in the manuscript. As the paper stands now, only the consequences of GC7 treatment are carefully documented, but this is not a trustable reflection of decreased EIF5A hypusination and function. 2) Previous literature/novelty: Previous publications not mentioned by the authors have addressed issues overlapping with this paper, sometimes providing evidence in conflict with the present model. a) The group of Chaim Kahana described the effect of EIF5A inhibition on translational initiation (Landau et al., JBC 2010 https://doi.org/10.1074/jbc.M110.106419). Using mouse fibroblasts, they observed that treatment with GC7 inhibited translational initiation and induced eIF2alpha phosphorylation, although with kinetics that were different from this submitted paper. Indeed, they saw an early (from 3h) increase of phosphorylated eIF2alpha (Fig 6B), in contrast with the present manuscript, where the authors claim a later (24h) activation of this protein. Of note, upon interference of EIF5A, Kahana and collaborators failed to reproduce the GC7-mediated decrease of initiation (Fig 4), whereas they noticed decreased elongation. This discrepancy was attributed to non-specific effect of GC7. Authors (who have also used mouse fibroblasts, Fig 1E) need to address these conflicting data. In another paper (Sun et al., J Cell Physiol. 2010 Jun;223(3):798-809.doi: 10.1002/jcp.22100) it was shown that deletion of EIF5A prevents apoptosis, while its overexpression induces cell death, causing activation of the intrinsic mitochondrial pathway. Therefore, the observations are in stark contrast with the submitted report, where EIF5A inhibition with GC7 induces apoptosis. Of importance, Sun et al. showed that the cytotoxic effect of EIF5A was not due to hypusination since overexpression of hypusination-defective EIF5A mutants still induced apoptosis (Fig 2 and 3). This conflicting data should also be carefully addressed and taken into account when planning the experiments. 3) Clinical relevance: Another major limitation of this study is the lack of in vivo evidence. A central claim of this paper is that hypusinated EIF5A inhibition may represent a plausible novel strategy to target tumors dependent on mitochondrial function. However, only in vitro studies are shown (Fig 6). It would be important to provide solid evidence of the pathophysiological importance of these findings using relevant in vivo models of tumors harboring inducible DHPS and/or EIF5AK50R mutants to recapitulate the in vitro observations. Additional Points 1- The representative Figure 1A looks different from the quantifications shown on the side (B-F). 2- The decrease of hypusination (1A-B) after 0.5-3-6 hrs is quite modest (20-25%). It is difficult to concur that it might be biologically relevant. 3- The dowregulation of total EIF5A after longer GC7 exposure (1A-C) is also concerning and not well explained. 4- The claimed temporal changes of p-eEF2 and p-eIF2alpha and ATF4 are not very convincing: judging from figures 1A, E, F, variable and subtle differences of questionable relevance seem to occur in the earlier time points. 5- Data in Fig S1 are also not convincing. The early increase of puromycin incorporation in Fig 1G does not look very strong. 6- Figure S3: only one band is shown for EIF5A1 and 2. These proteins migrate differently and typically form a doublet. Do A549 cells express both isoforms? In this case, both bands should be detected. 7- Figure S4A why 40 uM of GC7 was used in HeLa cells and 10 uM in A549? Did the authors perform dose-response studies? 8- Figure 6A-B the effect of GC7 on MCF10A is defined as non-significant but the proliferation rate shown in Figure 6B seems reduced by GC7. Perhaps the number of replicates should be increased.", "reviewer_2": "In this article, Sfakianos and colleagues provide evidence suggesting that the pharmacological inhibition of eIF5A hypusination triggers eIF2alpha phosphorylation and suppresses global protein synthesis. The authors then took advantage of dynamic SILAC to identify the alterations in the proteome enticed by suppression of eIF5A activity., and on a selected subset of targets distinguished those that were mediated by feedback induction of ISR. The authors also provided some evidence that the mechanisms of induction of eIF2alpha phosphorylation upon eIF5A inhibition may be the consequence of activation of the UPR/PERK axis and impaired mitochondrial function which appears to be mediated by OMA1 and DELE1 proteases thus leading to activation of HRI. Finally, the authors show that the pharmacological inhibition of eIF5A hypusination leads to cancer cell death which appears to at least in part be mediated by ISR induction. Overall, it was thought that this report is of broad interest to mRNA translation and cancer research communities since it puts forward a previously unappreciated model of reprogramming of protein synthesis and cell death induction triggered by the inhibitor of eIF5A hypusination. It was thought that the most of applied methodology was appropriate. In general, provided data were thought to adequately support author’s conclusions. Nonetheless, several concerns were raised as outlined below whereby it was thought that addressing them may further solidify this study. Major comments: -Throughout the manuscript, it remained rather unclear how densitometry was performed (e.g., what was used to normalize the data). Considering that the article heavily relies on this analysis, this information should be provided in each figure legend. -Line 455: “Western blot analysis of puromycin incorporation revealed an augmented incorporation in the early stages of GC7 treatments, which is likely indicative of an accumulation of slow-moving ribosomes on the mRNAs and a reduction in elongation”. I am a bit confused by this statement, as it is somewhat unclear to me how slowed elongation may result in the induction of puromycin incorporation. Can the authors elaborate on this? These effects appear to parallel the induction of ISR and thus the alternative explanation may be that the shut-down of global protein synthesis may at least in part occur via induction of ISR, while the effects of modest inhibition of eIF5A hypusination (early time points) on global mRNA translation may be marginal. Although authors speculate that relatively late downregulation of global protein synthesis upon GC7 treatment may be due to inhibition of initiation, it appears warranted to test the effects of GC7 treatment on global protein synthesis in S51A eIF2alpha KI MEFs. Finally, the technical quality of the figure 1G is not the greatest and should likely be improved. -Polysome profiles presented in figure 1I may not of be of sufficient resolution to substantiate the authors claim that 80S peak remains unchanged upon GC7 treatment. Namely, in the provided polysome tracings 80S peak appears to be unidentifiable. -Quantification in S3H does not seem to match blot in S3G. -The relationship between tested proteins in Figure 3 and proteome analysis should be specified. Of note, it was hard to find out actual proteins that were affected in SILAC study as well as raw data associated with it. Were OMA1 and DELE1 levels affected by GC7? Also, the ½ lives of ribosomal proteins are notoriously long and thus the authors may want to check what they may be for rpS6 and rpS25 as this may mask the effects on de novo synthesis of these proteins. - A549 and MCF10A cells are highly genetically heterogenous. Thus, it was thought that it would be warranted to include isogenic non-transformed and transformed cell lines in order to determine whether the effects of GC7 are specific to cancer cells and/or tone down associated claim. -It appears somewhat ambiguous to define A549 cells as those having “high OXPHOS” levels. What was the criteria for this? When A549 cells were cultured in galactose containing media, the ATP production was decreased (Figures 6F and G) which suggests that their mitochondria are actually dysfunctional, and unlikely to exhibit “high OXPHOS” levels. Moreover, when A549 cells were forced to rely on mitochondrial metabolism by culturing them on galactose without glucose exhibited enhanced sensitivity to GC7 (Figure 6H). Considering this, it was thought that the authors may want to reconsider whether the sensitivity to GC7 is linked to “high OXPHOS levels” or perhaps on mitochondrial dysfunction which appears to be more likely from the aforementioned experiments. Finally, it appears warranted to include some respirometry studies and determine bioenergetic profiles of investigated cell lines as well as the effects of GC7 on bioenergetics +/- ISRIB. Minor issues: -More comprehensive introduction of ISR may be warranted. -The article needs some polishing when it comes to consistency and editing. In addition, the figure legends were found to be somewhat incomplete whereby some important details (e.g., representation of the error bars i.e., SD or SE), number of replicates in some panels are missing, etc.) appear to be missing. -Alternative genetic approaches (e.g., depletion of DHPS) to parallel GC7 studies may be more appropriate than depleting eI5A1/2. -Although the immunogen used to generate eIF5A Ab appears to be proprietary, the authors should specify whether this antibody recognizes both eIF5A1 and 2. Alternatively, RT-qPCR should be used to validate the level of KD of each isoform by RNAi. -Some explanation should be provided why the cells were pretreated with ISRIB, and why ISRIB was not applied after GC7 if the inhibition of eIF5A hypusination precedes ISR. -The role of mTOR in the observed phenomena remains unclear. This appears to be pertinent as GC7 appears to affect mTOR targets implicated in elongation (e.g., eEF2K, albeit this may be AMPK too) and initiation (e.g., 4E-BPs). Albeit it is understandable that this may be out of the scope of this study, this should be reflected in discussion section. Out of curiosity: Are the levels of DOHH sensitive to mTOR inhibition and/or are they known to be regulated by ATF4? I hope that the authors will find these comments helpful and of sufficient pathos. Sincerely I/Topisirovic", "reviewer_3": "eIF5A-dependent feedback inhibition from mRNA translation elongation to initiation induces apoptosis in tumor cells by Sfakianos and colleagues this manuscript reports investigations into the action of a well-characterized eIF5A-directed inhibitor called GC7 on protein synthesis, mitochondrial function and cell viability in cancer-derived cell lines, particularly A549 cells, a lung adenocarcinoma cell model. eIF5A and its poorly expressed isoform eIF5A2 are the only proteins post-translationally modified with hypusine, a polyamine derived from spermidine. GC7 inhibits DHPS, the enzyme that performs the first step of hypusination, leading to poorly functioning eIF5A. Here GC7 addition impacted both hypusination and levels of eIF5A. eIF5A binds vacant ribosome E sites and stimulates peptide bond formation between A and P-site bound aa-tRNAs, thus promoting elongation, including formation of the first peptide bond. eIF5A can also enhance translation termination and has been shown by structural studies to be associated with stalled ribosomes/disomes and RQC intermediate complexes. Although impact on elongation is made clear, in this study there are no assays to specifically examine any potential impact of GC7 on termination. The study provides evidence that GC7 treatment leads to activation of other translational control pathways that act to diminish global protein synthesis. This makes mechanistic sense as other studies are consistent with feedback control systems operating to curtail protein synthesis in various ways when one later step is limiting. In recent years GCN2 has been a focus for studies showing ribosome stalling activates eIF2 phosphorylation. In contrast in A549 cells it appears GCN2 is not involved following GC7 treatment and instead attention is focused on mitochondria and the recently described OMA1/DELE1 mediated activation of HRI. By use of an ISR inhibitor that acts downstream of kinase activation (ISRIB) it is shown that preventing the feedback loop to dampen initiation can rescue translation of some of the proteins impacted by GC7 (FIG 3). Although it appears from Fig. 3A that only those proteins modestly sensitive to GC7 are rescuable. The way the quantification is shown does not make this easy to verify. Other proteins tested here that appear more severely impacted by GC7 are not rescued by ISRIB at 24 hrs are mitochondrially targeted proteins. When comparing A549 and MCF10A cells the major difference observed was not in translation signalling but in ATP levels, again providing a mitochondrial focus to GC7 sensitivity. Overall the work identifies and characterizes a feedback loop between the elongation and initiation phases of protein synthesis and shows that this relies in large part on signalling from mitochondria to HRI and eIF2 phosphorylation. The western blots make it clear that other signals are also being relayed from eIF5A, eg via 4E-BP1 and eEF2 that are not further explored here. The work utilizes a wide range of molecular techniques and appears generally carefully performed and interpreted. The main conclusion is that cells relying more heavily on mitochondria including the cancer lines studied here are more susceptible to GC7 than other cells and that it may therefore have some therapeutic value that would require further study to assess. However what the key features of GC7-resistant cells verses sensitive cells is not well explored. However I have quite a few questions and suggestions regarding the work and its presentation that are outlined below. Points. Major 1. The text states that MCF10A were identified as GC7 resistant cells but does not indicate what screen was done. 2. The methods make it clear different media were used to culture A549 and MCF10A cells. Could this change contribute to the observed differential GC7 sensitivity? 3. Western blot quantification. Throughout the MS almost every western it appears that ‘lane 1’ has been normalized to ‘1’ for all signal quantification. The impact of this is that in the control samples there is no variation around the mean, while there is for all other samples. Thus for the statistics the samples all have unequal variance with the control. Instead of selecting 1 lane to normalize to it is a better practice to normalize all lanes to the mean signal across all the blot lanes. In that way all samples will have meaningful error bars and if there is no variation all lanes will be approximately 1. This permits the stats to be done in a valid way. Implementing this change will impact almost every main figure and many of the supplementary figures. 4. P12 ribosome run off assay. From the data presented it is not obvious how differences in polysome density were calculated. It is clear from Supp Fig 2 that there is a large increase in free 80S upon HH treatment in both control and GC7 cells, but as the 80S peak goes dramatically off scale it becomes difficult to accurately quantify P/M ratios and rule out differential loadings. The methods description does not help the reader here and the presented polysome trace data do not appear different ±GC7. In panel C should the timescale be in minutes? How the numbers in Fig 1J are derived is also unclear. 5. Line 497. The ‘dynamic’ SILAC assay could be introduced better as many will not be as familiar with this type of assay. I think using the term ‘nascent’ to describe heavy labelled proteins that may have been made at anything up to 24hours before the assay point is misleading. In protein synthesis, ‘nascent proteins’ are typically defined as those that are undergoing synthesis and so are within/exiting the ribosome exit tunnel, not those made hours before. Please consider a different term. What cells were used for this assay? How many replicates are used, the Figure 2A appears to show 2 reps only for GC7 treated cells and 3 for the control, is this correct? If only 2 this will affect the statistical power. Have PCA or other whole dataset comparisons been done to visualise the data and analyze its consistency as a whole? 6. The analysis presented in Supplementary Figure 5D is not at all clear. What is being shown here? What do 0, 1, 2,3 denote on the X axis of each graph? What does AAs: 1-3 mean? 7. What is the source of the ribosome pausing data in Figure s5G? 8. While the SILAC did not find any evidence of poly-proline involvement the authors should acknowledge that they have not sampled the entire proteome. Many poly-proline proteins are poorly expressed, others are secreted, so may not have been captured here. Hence the assay may not have been sensitive enough to capture many poly-proline substrates. Was any western blotting done of well-known poly-proline proteins not covered by the MS analyses? 9. Figure 3 The impact of ISRIB is quite modest, at best ~1.5 fold. It would be good to acknowledge the other impacts seen eg on 4E-BP phosphorylation suggest involvement of mTOR and eg eEF2K indicate wider impacts. 10. Figure 4/Supp Fig 7. siRNA experiments. what cell type used here? 11. The HRI activation model proposed invokes processing and activation of DELE1 by OMA1. Has a change in isoform L-DELE1 to S-DELE1 been observed in MS data with GC7 or via western blotting? This would further support the proposed model. 12. Do GC7 resistant MCF10A cells exhibit the mitochondrial morphology changes seen for A549 cells following GC7 treatment (ie as shown in Figure 5A/B for A549s) minor 13. Supplementary Fig 1B the graph X axis is out of order 18 hours before 6? Why? 14. Supplementary Fig 1E/H. Was it known previously that p-eEF2 levels are very low in S51A MEFs? 15. Any thoughts as to why eEF2-P signalling is highly responsive to GC7 here? 16. P20 the reason for the switch to HeLa cells is not made clear. 17. Line 533. There are now 6 eIF2 kinases known including MARK2 and FAM69C not studied here. 18. Line 534 eIF2AK2 is wrong here. 19. Supplementary Fig 11. Adding panel numbers and labeling across this figure would help. The stained image text and graph labels are all too small. 20. Line 709 there is no reference 51. 21. Line 713. In the referenced study the polyproline stretch in TIM50 is implicated in eIF5A sensitivity, however while this is found in yeast TIM50 it is not conserved in human TIMM50, so the mechanisms are likely distinct. This could be clarified in the text.", "reviewer_4": "The manuscript entitled “eIF5A-dependent feedback inhibition from mRNA translation elongation to initiation induces apoptosis in tumour cells” by Sfakianos et al. is focused on the activity of the translation factor eIF5A and proposes a new pathway of translation regulation. The authors investigate the use of a well known hypusination inhibitor, GC7, in several cancer cell lines to better understand the molecular mechanism through which this molecule inhibits translation. Results clearly show that inhibition of eIF5 activity by GC7, but also by knock down with siRNA, has effects not only on translation elongation but also on translation initiation and that the link between eIF5A and translation initiation involves mitochondrial dysfunction, activation of mitochondrial proteases which in turn induce eIF2 phosphorylation by HRI and PERK kinases. The authors claim that this pathway induces apoptosis in some cancer cell types which appear to be eIF5A sensitive and, therefore, suggest that GC7 could represent a potential pharmacological approach to target cancers which are particularly dependent on mitochondrial function for their proliferation. The manuscript is interesting and presents novel findings shedding light on the mechanism of action of an important translation regulator such as eIF5A. It is well written and well documented in relation to the literature. In my opinion it deserves publication in this journal with the following revisions: Line 57: Acronyms (APC) must be specified. Line 69: The statement “This positioning allosterically facilitates peptide bond formation” needs a reference. Line 437- In Fig. S1 graph B, indicating the level of p-4EBP1/4EBP1, the point at 6 h has been misplaced. Line 455- Puromycin incorporation assay. Although I remained convinced of the result, the technical quality of this western blot image is not so high. Why only low MW proteins are visible? Which is the amount of cell lysates loaded on gels? Is there a reason for such a short time for Puromycin incorporation (5 min) and for the concentration of Puromycin (2 μM would be a better choice)? Can the authors comments on this? Line 474 – eIF5A depletion by siRNA It should be specified if the antibody used in the western blot is recognizing both isoforms of eIF5A. Antibodies specific for the two proteins are available and often the name eIF5A has been used to refer to eIF5A1. Line 485 – Ribosome stalling assay The authors should explain why the GC7 concentration has been raised to 40 μM instead of 10 μM used in all other experiments. Line 494 – The authors state that “Inhibition of eIF5A activity result in reduced translation initiation through an eIF2α-dependent feedback mechanism”. I believe that it is premature to state that. Data obtained so far do not really suggest that the mechanism is eIF2α-dependent. Line 551 – The decrease in total eIF5A in the presence of GC7 is not really consistent throughout the different experiments. Here (Fig.3A) it appears to be reduced already at 3h while in Fig 1A it is not and in Fig. 4A even at 24h it is not reduced. Do authors have an explanation for this? Line 589 – OMA1 and DELE1 depletion with siRNA: the knockdown of the two mRNA has been shown by RT-PCR but a western blot showing the decrease of the two proteins should be presented as well. Line 603 – The graphs in Fig 6A and 6B should be homogeneous: they should have the same Y axes and X axes should start at 0. Line 711 – The authors interpret their results by proposing that “eIF5A inhibition could induce the down-regulation of proteins within the TIM23 complex”. This statement should be easily verified in the proteomic experiment. Is there any evidence of a down-regulation of any of the proteins belonging to the TIM23 complex? If not do authors have any explanation for this The results of the SILAC proteomic experiments (the list of the proteins down-regulated in the presence of GC7) should be made available."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "While the authors have addressed some of the initial points raised during the first round of review, the two core concerns remain inadequately resolved. As a result, the strength of the central conclusions continues to be questionable. My detailed comments are as follows: 1. Specificity of GC7 and demonstration of DHPS-dependence The central claim of a time-dependent translational feedback mechanism mediated by EIF5A hypusination remains insufficiently supported. I had previously suggested a time-course analysis using inducible (e.g., doxycycline-inducible) DHPS knockdown to convincingly demonstrate that the effects observed with GC7 are indeed attributable to DHPS inhibition. Instead, the authors have employed a constitutive shRNA approach targeting both DHPS and DOHH, observing effects on initiation but not elongation. This is not equivalent to the requested approach and fails to capture the temporal dynamics shown with GC7. I strongly recommend repeating the experiments using an inducible DHPS knockdown system and collecting time-point data that parallel the early vs. late effects seen with GC7 treatment. Additionally, I had proposed the use of EIF5A K50R mutants to test the role of hypusination directly. The authors claim that these mutants are unstable and cite Khang et al. (JBC 1994, Ref #34 in the manuscript and Ref #1 in the rebuttal letter) to support this. However, that study provides no data regarding K50 hypusination or EIF5A protein stability. In contrast, Sun et al. (J. Cell. Physiol. 2010; 223(3):798-809) demonstrate that WT and K50R EIF5A1 are expressed at similar steady-state levels (see their Figure 2). Therefore, the authors are still encouraged to test this mutant construct as it remains a valid and mechanistically informative tool. 2. In vivo relevance No in vivo validation has been provided. As noted previously, animal models would be essential to assess both the pathophysiological significance and therapeutic relevance of the proposed mechanism. Without in vivo experimentation, the translational claims remain speculative. 3. EIF5A1 vs EIF5A2 The issue of isoform specificity has not been satisfactorily addressed. The authors assert that EIF5A1 is the predominant isoform in A549 cells and cite Elvira-Blázquez et al. (EMBO J 2024) in support. However, this reference does not provide isoform-specific expression data in A549. Contrary to the authors’ claim, other studies show that EIF5A2 is detectably expressed in A549 cells. For example, Cell Death Dis. (2022) 13:683 reports EIF5A2 protein expression in this cell line. Furthermore, EIF5A2 levels are upregulated following EIF5A1 knockdown, as shown in Cell Communication and Signaling (2023) 21:54 (see Figure 2). This suggests potential compensatory or redundant functions that have not been adequately explored. Moreover, in Figure S3A, the EIF5A1/2 blot shows only a single band. Given the distinct molecular weights of the two isoforms, a doublet should be observed. The lack of isoform discrimination raises concerns about antibody specificity or gel resolution, and should be clarified. In summary, the manuscript continues to suffer from key conceptual and experimental gaps: - The reliance on GC7 without sufficient genetic confirmation of DHPS involvement; - The lack of in vivo data undermining translational claims; - The unresolved EIF5A1/EIF5A2 distinction, which raises questions about isoform-specific functions and compensation. Given these outstanding issues, I do not consider the manuscript suitable for publication in Nature Communications in its current form.", "reviewer_2": "I thought that the authors addressed my comments in a satisfactory manner and I have no further concerns. Sincerely I/Topisirovic", "reviewer_3": "The authors have made significant changes to the manuscript that address all my prior concerns.", "reviewer_4": "The revised manuscript entitled “eIF5A-dependent feedback inhibition from mRNA translation elongation to initiation limits tumour cell proliferation” by Sfakianos et al. focuses on the activity of the translation factor eIF5A and proposes a new pathway of translation regulation. The use of the well-known hypusination inhibitor, GC7, in cancer cell lines, helps to shed light on the molecular mechanisms, showing that inhibition of eIF5 activity has effects not only on translation elongation but, with a feedback mechanism, also on translation initiation and that eIF5A effects do not depend on specific amino acids motifs. By using GC7 in combination with ISRIB the authors manage to uncouple the two processes and to identify the targets of the two pathways. The authors present data demonstrating that the link between eIF5A and translation initiation involves mitochondrial dysfunction, activation of mitochondrial proteases which in turn induce eIF2 phosphorylation by HRI and PERK kinases. In conclusion results suggest that GC7 could represent a potential pharmacological approach to target cancers which are particularly dependent on mitochondrial function for their proliferation. The manuscript is very interesting and presents novel findings shedding light on the mechanism of action of an important translation regulator such as eIF5A and on its potential use as pharmacological target. The methods are detailed and have been implemented according to the revision In my opinion the authors have replied to the revisions required and have offered new evidence to support their conclusion, therefore, the manuscript can be accepted for publication in this journal."}, "author_response": "As the reviewer acknowledged, the concentrations used in studies which suggest that GC7 has possible DHPS-independent effects were in considerable excess over those used herein and in addition to much longer time points, increasing the possibility of assessing secondary and not primary events. To limit the potential of indirect effects we used a concentration 10-fold and 20-fold lower than Landau et al and Oliverio et al, respectively, and our mechanistic investigations were carried out over 30 minutes – 24 h rather than 72 hr treatment (eg as in Landau and Oliverio). Utilising eIF5A mutants is problematic; hypusination stabilises eIF5A 1, and mutant eIF5A-K50 proteins are generally unstable, which would make these experiments challenging. It is also likely that this approach would inhibit on translation prior to the addition of GC7 making it hard to draw conclusions. We have instead added the following data to provide more evidence for the specific effect of GC7 on eIF5A activity: 1. We have used siRNA depletion of both enzymes involved in hypusination (DHPS and DOHH) and observed that DHPS/DOHH depletion resulted in the induction of p-eIF2α (Fig S3G+H), correlating with observed effects of eIF5A1/2 knockdown (Fig S3A-D) and treatment with GC7 (Fig 1). 2. We used a different inhibitor of the hypusination pathway, ciclopirox (inhibitor of DOHH), and observed the induction of p-eIF2α/ATF4 (Fig S6B-D) and inhibition of protein synthesis (Fig S6J+K), correlating with GC7 treatment. 3. Ciclopirox also reduced mitochondrial protein expression (Fig S6E-I), again correlating with our observations with GC7. 4. We determine the mitochondrial function of both A549 and MCF10A cells. These data show that A549 have increased basal respiration rates and no reserve capacity (Fig 6D), suggesting that A549 cells may be more dependent on their mitochondria for energy production and providing a further link to why they are sensitive to GC7. In support of this observation, basal respiration of A549 is reduced within 6 h GC7 treatment, in line with our proteomic analysis (Fig 2), whereas MCF10A were unaffected. These data provide additional mechanistic understanding of how GC7 is impacting A549 mitochondria function. While we accept that these additional data are correlative, they strongly suggest that the effects observed of GC7 on A549 and mitochondria are specific to the inhibition of hypusination. ... [Author response continues for all reviewers]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "I appreciate the effort made in revising the manuscript. However, the key concerns remain unresolved, particularly regarding the specificity of the experimental system and the robustness of the mechanistic conclusions. The central claim — that inhibition of elongation through reduced eIF5A activity leads to feedback inhibition of translation initiation — is conceptually interesting. However, this model is based entirely on experiments using GC7, a compound with known and emerging off-target effects. In particular, data presented at the 2024 International Conference on the Biological Roles of Polyamines (Kobe, Japan) reported that GC7 can inhibit mitochondrial complex I, which is known to induce energetic stress, activate the integrated stress response (ISR) via eIF2α phosphorylation, and lead to global inhibition of translation initiation. This mechanism closely mimics the effects described in the manuscript, raising the possibility that the observed phenotype is not mediated by inhibition of eIF5A hypusination, but rather by indirect activation of ISR due to mitochondrial dysfunction. These concerns are reinforced by observations from our group, which has also noted off-target effects of GC7 in unrelated experimental contexts, consistent with the possibility that the observed translation phenotypes may not be solely due to inhibition of eIF5A hypusination. This issue is particularly critical because the temporal sequence proposed by the authors — early inhibition of elongation (3 h), followed by delayed inhibition of initiation — is demonstrated exclusively using GC7. Since this biphasic response is a central conceptual point of the manuscript, the lack of orthogonal validation substantially limits the strength and specificity of the conclusions. While the authors argue that genetic approaches may not fully recapitulate the timing of pharmacological inhibition, this does not justify their exclusion. At least two complementary strategies are both feasible and necessary: An inducible DHPS knockdown or knockout system, enabling controlled, time-resolved hypusination inhibition while avoiding off-target drug effects. A DHPS knockout cell line rescued with a non-hypusinable eIF5A mutant (e.g., K50A or K50R). Although the authors mention potential instability, this is not demonstrated experimentally. Titration of the expression vector could mitigate this issue. The immunoblot shown at 72 h is not informative in this regard and is not directly comparable to earlier time points (e.g., 48 h). The possibility that protein stability and response dynamics (e.g., DHPS, eIF5A) vary across cell types is another relevant factor that is diregarded by the authors. In summary, while the study raises interesting hypotheses about translation regulation, the exclusive reliance on GC7, without genetic validation, prevents a clear attribution of the observed effects to eIF5A hypusination. Additional experiments are required to disentangle direct effects from GC7-specific off-target stress responses.", "reviewer_5": "I have carefully reviewed the revised manuscript by Sfakianos & Raven et al., along with the reviewer-1 comments and the author rebuttals. The central concern raised by Reviewer-1 relates to potential off-target effects of the GC7 compound. There is indeed some literature describing eIF5A-independent cellular effects of GC7 (e.g. on autophagy - Oliverio et al., 2014), and as with most small-molecule inhibitors, some degree of off-target activity is not unexpected (e.g. for instance, even some of our most specific kinase inhibitors used in clinic hit dozens of other targets). The critical question, however, is whether the molecular and cellular effects of GC7 reported here can be confidently attributed to inhibition of eIF5A. To address this point, the authors present several independent lines of evidence: 1- RNAi-mediated depletion of eIF5A reproduces the effects of GC7. 2- A distinct small-molecule inhibitor of the pathway (CPX), which targets DOHH rather than DHPS (target of GC7), also recapitulates the GC7 effects. 3- RNAi-mediated depletion of DOHH and DHPS similarly phenocopies GC7 treatment. 4- The observed effects can be rescued by ISRIB, demonstrating that the downstream pathway identified here is essential for the cellular phenotype. 5- A rescue is also achieved by mutating eIF2α on S51, the inhibitory phosphorylation site mediating the initiation block downstream of eIF5A. It would be highly improbable for the experiments 1, 2, & 3 to yield concordant results solely due to off-target effects. As a result, these findings strongly support the conclusion that the cellular effects of GC7 are mediated via inhibition of eIF5A function. Furthermore, experiments 4 and 5 provide compelling evidence of functional epistasis between the the translation-initiation regulation described here and the phenotypic consequences of GC7 treatment (as opposed to other potential off-targets effects of this compound), further strengthening the conclusions of the study. Taken together, the presented evidence in my view is very convincing. While I fully appreciate the initial concerns of the reviewer-1 regarding GC7 specificity, the breadth of additional supporting data, using multiple different means of targeting the same pathway, substantially mitigates this issue. I therefore recommend that the manuscript is accepted for publication. Importantly, I do not believe the inducible knockdown experiment that the reviewer is suggesting in their latest comments will add anything further, as RNAi still takes a while to significantly deplete a protein target (inducible or not), so the result of this experiment will be basically the same as what is already shown in terms of RNAi-mediated eIF5A depletion. My only minor suggestion to the authors is that in the Discussion section they may want to acknowledge the potential for GC7 having off-target effects, but also clearly articulate why their experimental results argue strongly against this being a concern. Summarising the key supporting lines of evidence mentioned above would pre-empt similar concerns from future readers and would further strengthen the manuscript in my opinion."}, "author_response": "The data highlighted by the reviewer is of course interesting, but it is non-peer reviewed and unpublished data (i.e., unclear what model system/techniques/concentration of GC7 was used), and we are therefore unable to access it to take it into consideration. However, our data utilising the glu/gal model suggests GC7 is not a direct ETC inhibitor as ATP does not decrease until >6 hr treatment in galactose (supplementary figure 10a), whereas direct ETC inhibitors would typically reduce ATP production in galactose within 1 hr. In addition, ATP is reduced in MCF10A to the same extent in glucose and galactose (supplementary figure 10a), again suggesting GC7 is not a direct ETC inhibitor. We have amended the text to make clear that the data suggests GC7, at least at the concentrations used within our study, is not a direct ETC inhibitor. ... [Author response continues]"}]

Decision letter