Translational control contributes significantly to the overall regulation of gene expression. Indeed, regulation at the level of translation provides quicker adaptive and usually reversible responses to environmental cues than transcriptional regulation as it acts on pre-existing mRNAs by simply turning their expression on or off (Sonenberg and Hinnebusch, 2009). As such, translational control critically contributes to the maintenance of overall cellular homeostasis, suggesting that its dysregulation has detrimental effects on cell proliferation and, more broadly, on health (Scheper et al., 2007). In a rapidly growing number of cases, mutations and/or impaired expression of many translational components have been shown to either directly lead to or significantly contribute to various pathological conditions, including cancer (reviewed in de la Parra et al., 2018b; Robichaud and Sonenberg, 2017; Spilka et al., 2013; Sriram et al., 2018).

Translation can be divided into four stages with the initiation stage playing the most critical role with respect to translational control. It begins with the formation of the 43 S pre-initiation complex (PIC) composed of the 40 S ribosomal subunit, the eIF2*GTP*Met-tRNA_i^Met ternary complex (eIF2-TC), and eIFs 1, 1 A, 3, and 5 (reviewed in Valásek, 2012). The next step is the mRNA recruitment to form the 48 S PIC, which is facilitated by the eIF4F cap-binding complex in concert with eIF3. Once the mRNA is loaded, the 48 S PIC starts scanning the mRNA’s 5’ untranslated region (UTR) until the start codon has been recognized by base-pairing with the Met-tRNA_i^Met anticodon. This drives conformational changes in the 48 S PIC, co-operatively mediated by eIF1, eIF1A, eIF2, eIF3 and eIF5, resulting in the closure of the 40 S mRNA binding channel and subsequent ejection of the eIF2*GDP*eIF5 subcomplex to allow subunit joining to commence (reviewed in Hinnebusch, 2017).

Cellular stresses primarily inhibit cap-dependent translation initiation via the eIF4F and eIF2-TC complexes. The former is under the control of two major stress-signaling hubs – the anabolic kinase – the mechanistic target of rapamycin complex 1 (mTORC1) and the mitogen-activated protein kinases (MAPKs). They promote growth under nutrient-rich and stress-free conditions and deactivate in response to limited supply of amino acids, oxygen, ATP, growth factors or nutrients and play a role in the unfolded protein response (Bhat et al., 2015; Lindqvist et al., 2018; Roux and Topisirovic, 2018). These pathways are hyperactive in the vast majority of cancers, mainly due to activating mutations of RAS, BRAF and PI3K or loss-of-function mutations of the tumor suppressor PTEN, which are among the most common genetic perturbations in cancer (Kandoth et al., 2013). The latter – eIF2-TC – is a major target of the pro-survival, adaptive pathway called integrated stress response (ISR) encompassing four members of the eIF2α kinase family (PERK, PKR, GCN2, and HRI) that respond to a multitude of stimuli including nutrient limitation and endoplasmic reticulum stress by rapidly shutting down general translation (Pakos-Zebrucka et al., 2016).

Mitogen-activated protein kinase (MAPK) cascades have been shown to play a key role in the transduction of extracellular signals into cellular responses. Three families of MAPK signaling pathways have been well characterized in mammalian cells: extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase/stress-activated protein kinase (JNK/SAPK), and p38 kinase (Cargnello and Roux, 2011; Zhang and Liu, 2002). The activation of MAPKs is a multistep process. In case of the best described Raf-MEK-ERK pathway, the essential linkers of epidermal growth factor receptors and other receptor tyrosine kinases with the MAPK cascade include adaptor protein Grb2, a Son of Sevenless (SOS) family of guanine nucleotide exchange factors, and a small GTP binding protein Ras (Figure 1A). The MAPK cascade begins with MAPKKK (represented by c-Raf) which phosphorylates and activates MEK1 and MEK2 (MAPKKs). MEKs eventually phosphorylate, and thereby activate p44 MAPK and p42 MAPK, also known as ERK1 and ERK2, respectively (Lavoie et al., 2020; Stokoe et al., 1994). The activated ERKs then translocate to the nucleus where they phosphorylate and transactivate various transcription factors such as Elk-1, c-Jun, and c-Fos, thereby altering gene expression to promote growth, differentiation, or mitosis. The ERK/MAPK pathway also directly regulates translational machinery, for example by stimulating the activity of both ribosomal S6 protein kinases (RSKs; RSK1-4 in humans; Carriere et al., 2008) and MAPK-interacting kinases (MNKs; MNK1 and 2 in humans; Buxade et al., 2008; Fukunaga and Hunter, 1997; Waskiewicz et al., 1997). MNKs interact with eIF4G and phosphorylate eIF4E at Ser209, a site that increases the oncogenic potential of eIF4E (Topisirovic et al., 2004) and facilitates translation of specific mRNAs related to migration, metastasis and inflammation (Furic et al., 2010; Pyronnet et al., 1999; Robichaud et al., 2015). Following the stimulation of the ERK pathway, activated RSKs phosphorylate rpS6, eIF4B, eEF2K, and PDCD4, which are all important translational regulators (reviewed in Bhat et al., 2015; Robichaud and Sonenberg, 2017). RSKs have also been implicated in the regulation of other signaling pathways involved in growth control, stress-response and cancer including the regulation of mTORC1 (reviewed in Roux and Topisirovic, 2018). For example, RSKs have been shown phosphorylate and inhibit tuberous sclerosis complex 2 (TSC2) that acts as the negative regulator of mTORC1 signaling (Roux et al., 2004, Figure 1A).

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It has been shown that alterations in genomic sequences, protein levels, stoichiometry, and/or activity of eIFs and other components of the translational apparatus and relevant signaling cascades, as well as in cis-regulatory elements of mRNAs, can circumvent the stress-induced translational shutdown that these pathways impose by allowing selective recruitment of mRNAs, rewiring translational control of mRNA cis-regulatory elements, and/or enabling alternative start codon selection (Kovalski et al., 2022; Robichaud et al., 2019; Sriram et al., 2018). Naturally, this leads to various pathologies. Overexpression of eIF4F complex components, for example, has been observed in various cancer entities (Bhat et al., 2015; De Benedetti and Harris, 1999; Lazaris-Karatzas et al., 1990; Ruggero, 2013; Shuda et al., 2000; Truitt and Ruggero, 2016). In addition to eIF4F components, dysregulation of the multi-subunit eIF3 complex is also involved in oncogenesis. Ten of the 12 eIF3 subunits have been linked to human cancers, many due to overexpression (a, b, c, d, g, h, m or i), some due to underexpression (e and f) (reviewed in de la Parra et al., 2018b; Gomes-Duarte et al., 2018; Hershey, 2015; Valášek et al., 2017; Zhang et al., 2007). In this regard, it is worth stressing that some eIF3 subunits have been shown to directly interact with specific mRNAs (Lee et al., 2015), eIF3d together with DAP5 was shown to facilitate alternate form of cap-dependent mRNA translation (de la Parra et al., 2018a), and eIF3k was recently proposed to serve as a regulator of the balanced ribosome content (Duan et al., 2023). Despite all these observations, the exact mechanism(s) by which eIF3 affects various pathologies is virtually unknown. Moreover, previous studies that investigated the effect of eIF3 in specific diseases focus on altered levels of only one of the eIF3 subunits, while neglecting the need to consider the context of altered expression of the entire eIF3 complex.

Here, we draw attention to this unfortunate trend. eIF3 is the largest and most complex eIF of all eIFs, it is modular in nature (Figure 1B) (reviewed in Valášek et al., 2017; Wolf et al., 2020), and plays multiple roles not only in translation initiation, but also in termination, ribosomal recycling and stop codon readthrough (Beznosková et al., 2013; Beznosková et al., 2015; Pisarev et al., 2007; Poncová et al., 2019). Each of these roles may be regulated differently by individual subunits and/or their combinations, making it practically impossible to study them in isolation. Eight eIF3 subunits (a, c, e, f, h, k, l, and m) form a structural scaffold called the PCI (Proteasome, COP9, eIF3) / MPN (Mpr1-Pad1 N-terminal) octamer, whereas the remaining four non-octameric subunits (b, d, g, and i) are likely to be more flexible. The essential subunits b, g, and i form a separate module that is connected to the octamer via the C-terminal domain (CTD) of eIF3a; together they are called YLC for Yeast Like Core (Wagner et al., 2016). The eIF3d subunit is located on the eIF3 periphery and is attached to the octamer primarily via eIF3e, but partly also via eIF3a and c (Bochler et al., 2020; Brito Querido et al., 2020). On the 40 S subunit, the octamer is positioned close to the mRNA exit site, whereas the mobile YLC shuffles between the mRNA entry site and the subunit interface during the AUG selection process (des Georges et al., 2015; Llácer et al., 2021; Simonetti et al., 2020).

We recently reported a comprehensive in vivo analysis of the modular dynamics of the human eIF3 complex (Wagner et al., 2020; Wagner et al., 2014; Wagner et al., 2016). Using a systematic individual downregulation strategy, we showed that the expression of all 12 eIF3 subunits is interconnected such that perturbance of the expression of one subunit results in the down-regulation of entire modules leading to the formation of partial eIF3 subcomplexes with limited functionality (Herrmannová et al., 2020). eIF3d is the only exception in this respect, as its downregulation does not influence expression of any other eIF3 subunit. Here, we took advantage of this knowledge to examine the specific effect of selected eIF3 subcomplexes on the translation efficiency, transcriptome-wide, in HeLa cells.

In this study, we interrogated the effects of altering composition of eIF3 by depleting three eIF3 subunits in HeLa cells, followed by RNA-Seq and Ribo-Seq to elucidate their role in translational regulation of specific mRNAs by differential expression analysis. We focused on differential translation efficiency genes (DTEGs) and identified several distinct groups of DTEGs associated with individual eIF3 knock-downs.

Interestingly, while DTEGs identified in eIF3d^KD and eIF3e^KD largely overlapped, considerably more DTEGs were identified in eIF3d^KD then in eIF3e^KD, correlating well with the severity of the growth phenotype of both knock-downs, with the former being more detrimental despite not affecting eIF3 integrity (Wagner et al., 2016).

Why it is so is currently unknown. Perhaps the simplest explanation is that the higher number of DTEGs identified in the same pathways in eIF3d^KD vs. eIF3e^KD originated in different eIF3d protein levels between the two knock-downs. Despite being markedly co-downregulated in eIF3e^KD, eIF3d levels were more dramatically reduced in eIF3d^KD compared to eIF3e^KD, as expected. Furthermore, eIF3d is a peripheral subunit that binds to the head of the 40 S subunit (mainly via the small ribosomal protein Rps16 Herrmannová et al., 2020) and is connected to the eIF3 octamer not only via eIF3e but also via the eIF3a and c subunits (Bochler et al., 2020). Therefore, we hypothesize that eIF3d may remain partially operational even in the absence of eIF3e, and therefore the more severe phenotype in eIF3d^KD is likely to stem from lower levels of the eIF3d protein compared to eIF3e^KD. Following this logic, phenotypes that are common to eIF3d^KD and eIF3e^KD are likely a consequence of reduction in eIF3d protein levels. Another way to explain this ostensible paradox is that eIF3d could have functions outside of the eIF3 complex. The fact that in the absence of eIF3e – its interaction partner, eIF3d gets rapidly degraded, however, speaks against this option. It is also possible that eIF3d, as a head-interacting subunit close to an important head-situated ribosomal protein RACK1, which interacts with eIF3c and serves as a landing pad for regulatory proteins (Kouba et al., 2012; Nilsson et al., 2004), could be a target of signaling pathways. This may render this eIF3 subunit critically important for translation of specific mRNAs. In support, eIF3d (in the context of entire eIF3) was shown to be regulated by stress-triggered phosphorylation (Lamper et al., 2020) and, together with DAP5, it was shown to promote translation by an alternate cap-dependent (eIF4F-independent) mechanism (de la Parra et al., 2018a; Lee et al., 2016).

In any case, perhaps the most intriguing finding is that eIF3d^KD and, to a large extent, also eIF3e^KD result in translational repression of a marked number of the MAPK pathways constituents, significantly reducing their protein levels, despite the apparent compensatory mechanisms that increase their mRNA levels. Somewhat unexpectedly, despite the downregulation of a number of its components, eIF3d^KD and eIF3e^KD but also eIF3h^KD appear to activate MAPK/ERK pathway as evidenced by the elevated ERK1/2 and c-Jun phosphorylation. Notably, the eIF3d influence on the MAPK/ERK pathway was previously demonstrated in HTR-8/SVneo cells, where overexpression of eIF3d led to hypo-phosphorylation of MEK1 and ERK1/2, while downregulation had the opposite effect; however, no significant effect on the protein levels of its components was detected in this study (Li et al., 2021). Importantly, since the increased ERK1/2 phosphorylation was observed not only in eIF3d^KD and eIF3e^KD but also in eIF3h^KD, where expression of MAPK pathways proteins is unaffected, we conclude that activation of the MAPK/ERK pathway is a general consequence of the compromised eIF3 function.

Expression of the transcription factor c-Jun is partially controlled by the MAPK/ERK pathway and, consistently, we found that it is strongly upregulated in all three knock-downs. Moreover, we observed that all three knockdowns activated c-Jun as evidenced by its increased nuclear translocation. Although JUN mRNA was previously shown to interact directly with eIF3 through a stable stem loop occurring in its 5’ UTR, which positively stimulated its translation (Lee et al., 2015), in this study we observed upregulation of the JUN mRNA abundance, whereby the depletion of eIF3d, e, or h had no conspicuous effect on its translational efficiency. This can be explained by a recent report showing that eIF3d binds to JUN mRNA with increased affinity during chronic ER stress, suggesting that eIF3d-dependent translational regulation may be triggered by stress (Mukhopadhyay et al., 2023). Because c-Jun is a component of the AP1 transcription factor complex, wherein it either homodimerizes or heterodimerizes with other AP1 proteins, we examined the effects of eIF3 subunit depletion on mRNA, FP and TE trends of other AP1 family members. Similarly to c-Jun, only FOSL1 and ATF3 showed increased mRNA levels and FP counts with no net changes in TE, whereas other members of the JUN (JUNB and JUND) and FOS (FOS, FOSB, FOSL2) families remained unaltered. In addition, the ATF family members ATF2 and ATF4 were even downregulated (Supplementary file 1). Notwithstanding that our data suggest that impairment of eIF3 function may be compensated by the activation of the MAPK/ERK pathway, further research is required to unravel the full spectrum of changes associated with the MAPK pathways in eIF3-impaired cells. At the current state of our knowledge, we can only speculate that when translation is compromised, cells attempt to counteract it in two ways: (1) they produce more ribosomes to increase translational rates, and (2) activate MAPK signaling to send pro-growth signals that may ultimately further increase ribosome biogenesis, as further discusses below.

Recently, Lin et al. carried out Ribo-seq analysis in MCF-10A cells downregulated for eIF3e (Lin et al., 2020). They identified 240 downregulated and 220 upregulated eIF3e^KD-specific DTEGs that overlap very poorly with our DTEGs (data not shown). In addition, they identified a set of 2683 eIF3e-dependent mRNAs encoding many proteins associated with mitochondrial and membrane functions that showed increased ribosome density between codons 25 and 75 in eIF3e^KD. Those mRNAs were therefore proposed to require eIF3e to recruit chaperons to promote their early elongation. While this set of genes displayed a significant overlap with our DTEGs (see below), we did not observe a similar phenomenon (a specifically increased ribosome density early on) in our datasets. In metagene analysis, we did observe some accumulation of footprints at the beginning of the CDS, but this was present in all samples, including NT, whereby peaks were located around codon 20 downstream of the translation initiation sites (Figure 7—figure supplement 1A).

Nonetheless, still considering that the ‘accumulation effect’ observed by Lin et al., most probably resulting from ribosome stalling and/or slower elongation rates, could artificially increase FPs and TE without actually increasing synthesis of the corresponding proteins in our datasets, we trimmed the first 225 nt (75 codons) from all mRNA’s CDSs, and repeated the differential expression analysis in the same way. When compared, the two DE analyses showed approximately 80–90% identical DTEGs and high correlation (Spearman correlation coefficient of eIF3d^KD: 0.924, eIF3e^KD: 0.925, eIF3h^KD: 0.834), suggesting that our DE analysis is not biased by ribosome accumulation in the first 75 codons and reflects true changes in translation efficiency.

Despite this ‘accumulation effect’ discrepancy, comparing our DTEGs with the Lin et al. eIF3e-dependent data, we found a very significant overlap of 446 genes between the eIF3e-dependent mRNAs and our eIF3d^KD ‘all upregulated’ dataset (p<1 × 10^–53), while the overlap with eIF3e^KD ‘all upregulated’ – 251 genes – was surprisingly smaller (p<1 × 10^–24). Moreover, our KEGG enrichment analysis of eIF3e-dependent mRNAs from Lin et al. revealed the same top 2 KEGG pathways (data not shown) as in case of eIF3d^KD and eIF3e^KD ‘all upregulated’ groups; that is Lysosome and Protein processing in ER (Figure 3A and Figure 3—figure supplement 1A). Considering that eIF3d was co-depleted with eIF3e in the eIF3e^KD of Lin et al., it is conceivable that not eIF3e but the eIF3d subunit (or cooperatively both subunits together) ensures loading of quality control factors to promote elongation of transmembrane proteins. In any case, resolving this issue will require further research.

Based on our analysis, eIF3e seems to be primarily involved in controlling the balanced production of mature ribosomes, as its loss resulted in significant number of ‘unique upregulated’ DTEGs with strong enrichment for the KEGG Ribosome and GO Translation and Ribosome biogenesis terms (Figure 3C and Figure 3—figure supplement 1B). Focusing directly on RPs, nearly twice as many RPs with significantly altered TEs were identified in eIF3e^KD compared to eIF3d^KD (32 vs 16) and a similar pattern was also evident at the FP level (91 vs 64). However, despite this obvious difference, RPs were increased similarly in both eIF3d^KD and eIF3e^KD (Figure 3E, F and H). This discrepancy between our Ribo-Seq and Western blot results in eIF3e^KD versus eIF3d^KD (Figure 3D vs. E) might be explained by the enhanced stabilization of RPs by the previously described compensatory dosage mechanism at the protein level (Ishikawa, 2021). In particular, to preserve the stoichiometry of the ribosome when numerous RPs are translationally upregulated, the rest of the RPs would be protected from degradation. This speculation can be supported by the impact of all knock-downs on translation efficiency of different E3 ubiquitin ligases, as described for eIF3h^KD (Supplementary file 2B). Similar effect of increased RPs production was recently reported also for eIF3k^KD (Duan et al., 2023). Of note, eIF3e^KD also decreases protein levels of eIF3k and eIF3l and thus this RP-specific effect could be at least partially attributed to the loss of eIF3k. However, increased RP production was also observed in eIF3d^KD, where eIF3k is normally present, but was not observed in eIF3h^KD, where eIF3k and eIF3l are also co-depleted. Thus, although it now seems well-established that the eIF3 complex exerts control over RP production, the exact contribution of its individual subunits to this regulation awaits further analysis.

An important hint could be that all cytoplasmic RPs with increased TE in eIF3e^KD (and most of mitochondrial RPs) were upregulated solely at the translational level (their mRNA levels did not change), which may well correspond to the fact that RP mRNAs belong to the TOP mRNAs family. These mRNAs contain a polypyrimidine tract in their 5’ UTR and were suggested to be regulated solely at the translational level in response to stress or unfavorable growth conditions through mTORC1 or PI3-kinase pathways (Cockman et al., 2020; Philippe et al., 2020; Stolovich et al., 2002). Besides RPs, this family also includes some translational initiation and elongation factors (for the list of TOP mRNAs see Supplementary file 1). Accordingly, we found that TOP transcripts were significantly translationally activated in eIF3d^KD and eIF3e^KD (Figure 4A–C), suggesting a role of eIF3e and eIF3d in repressing translation of TOP mRNAs under normal cellular conditions. Interestingly, since mTOR-activated translation of non-TOP transcripts was predominantly translationally offset (buffered) in eIF3d^KD and eIF3e^KD cells (Figure 4—figure supplement 1A–C), it appears that both effects are independent of each other; that is, mTOR does not generally operate via eIF3 and eIF3 does not require mTOR activation to fine tune RP production. It is also noteworthy that upregulation of cytosolic ribosomal proteins has previously been reported in association with the eIF3m downregulation – albeit only at transcriptional level (Smekalova et al., 2020), or with downregulation of eEF2 (Gerashchenko et al., 2020).

In addition, as we were unable to find any specific sequence motif in the 5’ UTRs of the mRNAs whose TE was affected in eIF3 subunit-depleted cells, we cross-referenced our eIF3d^KD ‘all’ and eIF3e^KD ‘all’ groups with transcripts that physically interact with eIF3 as reported by Lee et al., 2015. We found that out of the 402 transcripts with a putative eIF3 binding sites in their 5’ UTRs, 92 and 65 had significantly altered TE in the eIF3d^KD and eIF3e^KD, respectively (51 were common for both eIF3d^KD and eIF3e^KD; Figure 7—figure supplement 1B). This notable overlap involving for example the MAPK1 gene further underscores the robustness of our screening approach.

Among the few downregulated DTEGs in eIF3h^KD, we found ATF4 and MDM2 mRNAs, whose translation is tightly regulated by the presence of uORFs in their 5’ UTRs in response to specific stress conditions (Akulich et al., 2019; Dey et al., 2010; Jin et al., 2003; Smirnova et al., 2024; Vattem and Wek, 2004). Here, we showed that these mRNAs were translationally downregulated in our knock-downs under non-stress conditions and, accordingly, their most 5’ short uORFs (or St-st in the case of ATF4) accumulated FPs, most probably slowing down the flow of ribosomes downstream. The fact that transcripts of these genes contain uORFs in their 5’ UTRs also correlates well with our finding that 5’ UTRs of ‘downregulated translation only’ DTEGs have a strong tendency to contain AUG-initiated uORFs (Figure 7D, E). Noteworthy, the impaired ATF4 induction in our eIF3 knock-downs under ER stress (Figure 6D), was reported before also for knock-downs of eIF3a, eIF3c, eIF3d, eIF3g, but not eIF3l (Guan et al., 2017). Moreover, eIF3h and specifically modified eIF3a have been directly shown to modulate resumption of scanning from ATF4’s uORF1 for efficient reinitiation downstream; that is one of the key steps in the ATF4 translational control (Hronová et al., 2017; Shu et al., 2022). Therefore, we propose that the intact eIF3 complex is required for stimulation of reinitiation on uORF-containing mRNAs, such as ATF4 and MDM2 to ensure their efficient translation on demand. Interestingly, the impact on translation of MDM2 mRNA could indicate that eIF3 is also linked to the key tumor suppressor p53 (Marine and Lozano, 2010).

Among a few upregulated DTEGs in eIF3h^KD, we noticed PRRC2A, which was significantly upregulated in all three knock-downs and was recently identified by the Teleman’s group as a protein interacting with the translational machinery, including eIF3. Interestingly, they found that PRRC2 proteins affected translation initiation by promoting leaky scanning (Bohlen et al., 2023) and that the triple knock-down of all PRRC2 proteins (PRRC2A, PRRC2B, PRRC2C) specifically decreased translation efficiency of mRNAs harboring uORFs in their 5’ UTRs, including ATF4 and RAF1 studied also here, similar to our eIF3 knock-downs. They concluded that uORFs are a key element determining PRRC2-dependence. Thus, we speculate that by increased expression of PRRC2A (and also PRRC2B and PRRC2C in eIF3d^KD and eIF3e^KD), cells attempt to compensate for ineffective translation of mRNAs with short uORFs, which often encode regulatory proteins. Intriguingly, all PRRC2 proteins themselves have a highly translated overlapping-uORF in their 5' UTR, suggesting that they might be a subject to an autoregulatory mechanism.

Although our results presented here and summarized in Figure 7F are limited to HeLa cells and future work is needed to confirm their universal validity in other cells and tissues, a growing body of evidence suggests that eIF3 plays a critical role in translational regulation of specific mRNAs encoding regulatory proteins, very often utilizing uORFs and/or specific secondary structures in their 5’ UTRs. Here, we demonstrate that alterations in the eIF3 subunit stoichiometry and/or eIF3 subcomplexes have distinct effects on the translatome; for example, they affect factors that play a prominent (either positive or negative) role in cancer biology (e.g. MDM2 and cJUN), but the resulting impact is unclear so far. Considering the complex interactions between these factors as well as the complexity of the eIF3 complex per se, future studies are required to delineate the specific oncogenic and tumor suppressive pathways that play a predominant role in mediating the effects of perturbations in the eIF3 complex in the context of neoplasia. Taking into account that malignant cells exhibit augmented activity of many components of the translation machinery and are inherently believed to become ‘addicted’ to elevated protein synthesis (Ruggero, 2013; Silvera et al., 2010), this type of research is important because it is increasingly apparent that targeting general components of the canonical translational machinery, such as eIF3, which acts upstream of effector proteins directly involved in tumorigenesis, may hold promise for overcoming the major obstacle associated with intratumor heterogeneity.

The datasets generated during this study were deposited in the GEO database with the following accession number: GSE216967. All custom programs source code are published at GitHub, copy archived at Jelinek, 2024.

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Author details

1. Anna Herrmannová

   Laboratory of Regulation of Gene Expression, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Jan Jelínek

   For correspondence

   herrmannova@seznam.cz

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3500-3212

2. Jan Jelínek

   Laboratory of Bioinformatics, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – original draft

   Contributed equally with

   Anna Herrmannová

   Competing interests

   No competing interests declared

3. Klára Pospíšilová

   Laboratory of Regulation of Gene Expression, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

4. Farkas Kerényi

   Laboratory of Regulation of Gene Expression, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Tomáš Vomastek

   Laboratory of Cell Signaling, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Kathleen Watt

   Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6642-9756

7. Jan Brábek

   Lady Davis Institute, Laboratory of Cancer Cell Invasion, Faculty of Science, Charles University, Prague, Czech Republic

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

8. Mahabub Pasha Mohammad

   Laboratory of Regulation of Gene Expression, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

9. Susan Wagner

   Laboratory of Regulation of Gene Expression, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

10. Ivan Topisirovic

    Lady Davis Institute, Gerald Bronfman Department of Oncology, Department of Biochemistry, Division of Experimental Medicine, McGill University, Montréal, Canada

    Contribution

    Formal analysis, Validation

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-5510-9762

11. Leoš Shivaya Valášek

    Laboratory of Regulation of Gene Expression, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    valasekl@biomed.cas.cz

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8123-8667

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