1. Biochemistry and Chemical Biology
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Genome-wide effects of the antimicrobial peptide apidaecin on translation termination in bacteria

  1. Kyle Mangano
  2. Tanja Florin
  3. Xinhao Shao
  4. Dorota Klepacki
  5. Irina Chelysheva
  6. Zoya Ignatova
  7. Yu Gao
  8. Alexander S Mankin  Is a corresponding author
  9. Nora Vázquez-Laslop  Is a corresponding author
  1. Center for Biomolecular Sciences, University of Illinois at Chicago, United States
  2. Department of Pharmaceutical Sciences, University of Illinois at Chicago, United States
  3. Institute of Biochemistry and Molecular Biology, University of Hamburg, Germany
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Cite this article as: eLife 2020;9:e62655 doi: 10.7554/eLife.62655

Abstract

Biochemical studies suggested that the antimicrobial peptide apidaecin (Api) inhibits protein synthesis by binding in the nascent peptide exit tunnel and trapping the release factor associated with a terminating ribosome. The mode of Api action in bacterial cells had remained unknown. Here genome-wide analysis reveals that in bacteria, Api arrests translating ribosomes at stop codons and causes pronounced queuing of the trailing ribosomes. By sequestering the available release factors, Api promotes pervasive stop codon bypass, leading to the expression of proteins with C-terminal extensions. Api-mediated translation arrest leads to the futile activation of the ribosome rescue systems. Understanding the unique mechanism of Api action in living cells may facilitate the development of new medicines and research tools for genome exploration.

Introduction

The ribosome translates mRNA into protein and represents one of the main antibiotic targets in the bacterial cell. Various steps of protein synthesis are inhibited by natural and synthetic antibacterials (Lin et al., 2018; Polikanov et al., 2018; Wilson, 2009). A number of antibiotics impede the initiation of translation by preventing mRNA binding or departure of the ribosome from the start codon (Lin et al., 2018; Polikanov et al., 2018; Wilson, 2009). Numerous inhibitors affect translation elongation by interfering with mRNA decoding, peptide bond formation, translocation, or passage of the nascent protein through the exit tunnel (Lin et al., 2018; Polikanov et al., 2018; Vázquez-Laslop and Mankin, 2018; Wilson, 2009). However, no antibiotics were known to specifically target the termination step of protein synthesis. It was only recently that apidaecin, an antimicrobial peptide from honeybees (Casteels et al., 1989), was described as the first antibiotic specifically targeting translation termination (Florin et al., 2017).

Apidaecin is an 18-amino acid proline-rich antimicrobial peptide (PrAMP). Various PrAMPs are produced by insects and mammals to protect the host from bacterial infections (Graf and Wilson, 2019; Scocchi et al., 2011). The antimicrobial activity of PrAMPs is based on their ability to bind to the bacterial ribosome and inhibit protein synthesis (Castle et al., 1999; Krizsan et al., 2014; Mardirossian et al., 2014). PrAMPs bind in the vacant nascent peptide exit tunnel of the large ribosomal subunit (Gagnon et al., 2016; Roy et al., 2015; Seefeldt et al., 2016; Seefeldt et al., 2015). Most of the PrAMPs described to date invade the peptidyl transferase center (PTC) and by hindering the binding of the first elongator aminoacyl-tRNA, arrest the ribosome at the start codon (Gagnon et al., 2016; Seefeldt et al., 2016).

The action of apidaecin is principally different. Even though this PrAMP also binds in the exit tunnel and closely approaches the PTC, it does not directly obstruct the catalytic site (Florin et al., 2017; Krizsan et al., 2015). Biochemical analyses carried out with the synthetic peptide Api137 (Berthold et al., 2013; referred to, throughout, as Api) showed that Api traps deacyl-tRNA in the P site and class one release factors (RF1 or RF2) in the A site. The model that emerged from these in vitro studies (Florin et al., 2017) postulated that Api can bind only after the ribosome has reached the stop codon, associated with RF1/RF2, and released the fully-synthesized protein, because only then the exit tunnel becomes available for Api binding. This view implied that Api specifically targets the post-release ribosome. Strikingly, however, addition of Api to a cell-free translation system could also lead to the accumulation of unhydrolyzed peptidyl-tRNA, indicating that Api is also able to interfere with peptidyl-tRNA hydrolysis at stop codons (Florin et al., 2017). To explain this observation, it was proposed that Api-mediated trapping of RF1/RF2 on the post-release ribosomes would prevent the release of completed proteins at the remaining Api-free ribosomes (Florin et al., 2017).

The proposed mechanism of Api action has been inferred primarily based on in vitro biochemical and structural studies involving a limited number of artificial substrates and reporters. Furthermore, there is a seeming discrepancy regarding the in vitro and in vivo modes of Api action, since the translation of a GFP reporter protein was significantly inhibited by Api in bacterial cells, whereas expression of the same reporter in a cell-free system was only marginally affected even at high Api concentrations (Krizsan et al., 2015; Krizsan et al., 2014). Therefore, the consequences of Api action upon translating ribosomes in the living cell have remained unknown.

To investigate the genome-wide impact of Api on translation in bacterial cells we used ribosome profiling (Ribo-seq). Our analysis revealed that Api globally inhibits translation termination. The general arrest of translation at the end of the ORFs leads to a pronounced queuing of elongating ribosomes behind those occupying stop codons. Strikingly, the treatment of E. coli with Api results in a dramatic stop codon readthrough that generates proteins with C-terminal extensions. Our data also show that, trying to mitigate the pervasive Api-induced readthrough, cells engage the ribosome rescue systems, which nevertheless remain ineffective because of the Api action.

Results

Api redistributes ribosomes within mRNAs

Prior to carrying out the Ribo-seq experiment, we first optimized the conditions of Api treatment to achieve a significant antibiotic effect while minimizing undesirable secondary events. Incubation of E. coli (BL21) cultures for 1 min with increasing concentrations of Api resulted in severe inhibition of protein synthesis (Figure 1—figure supplement 1A). Exposure to 1.25 µM of Api, which corresponds to the minimal inhibitory concentration (MIC) that prevents cell growth, reduced translation by as much as 75%. Treatment for 2 min with 4× MIC diminished protein synthesis to nearly 10% of the control and extending the treatment to 10 min achieved ~94% inhibition (Figure 1—figure supplement 1B). These results confirmed the previous assertion that Api acts as a general inhibitor of bacterial translation (Castle et al., 1999; Krizsan et al., 2014) and guided the selection of the treatment conditions for the subsequent experiments.

We then proceeded to the Ribo-seq experiments to obtain an unbiased view of how treatment with Api alters the global distribution of translating ribosomes in cellular mRNAs (Ingolia et al., 2009; Oh et al., 2011). Exponentially-growing E. coli cells were incubated for 5 min with 4× MIC of Api, conditions that led to nearly complete inhibition of translation (Figure 1—figure supplement 1B), and ribosome-protected mRNA fragments (ribosome footprints) were isolated, sequenced, and mapped to the E. coli genome (Becker et al., 2013; McGlincy and Ingolia, 2017). Exposure to Api caused a dramatic redistribution of ribosomes along mRNAs. The general pattern observed within individual genes revealed a buildup of ribosome density toward the ends of the ORFs, often spilling into the 3’-intergenic regions (Figure 1A, Source data 1). In operons with narrowly spaced genes, the ribosome density was primarily observed at the junctions spanning 3’-terminal codons of the upstream ORF and 5’-proximal codons of the downstream ORF (Figure 1B). Overall, these effects are drastically different from those observed previously with the antibiotic retapamulin, a specific inhibitor of translation initiation, which generates precise and sharp peaks representing ribosomes occupying start codons (Meydan et al., 2019). To mitigate the uncertainty of assigning reads at the junctions of adjacent or overlapping genes in the operons, we limited our subsequent analysis to actively translated genes that are separated by at least 50 nt from the nearest gene (see Materials and methods). The metagene analysis of ribosome coverage near the stop codons of the well-separated genes (Figure 2A) revealed several major effects of Api action that will be discussed in the following sections.

Figure 1 with 1 supplement see all
Api redistributes ribosomes toward the ends of mRNA ORFs.

(A) Comparison of ribosome footprint density in the tktA and dptA ORFs in untreated E. coli cells with that in cells treated with either Api or the translation initiation inhibitor retapamulin (Ret). The position of the ribosome footprint was assigned to the first nucleotide of the codon positioned in the P site, presumed to be at a distance of 16 nt from the 3’ end of the read (Woolstenhulme et al., 2015). (B) Ribosome footprint density within genes of the his operon in cells treated with no antibiotic, Api or Ret. The Ret Ribo-seq data are from Meydan et al., 2019.

Figure 2 with 3 supplements see all
Api treatment leads to the accumulation of translating ribosomes near stop codons and depletion of free RFs in the cell.

(A) Metagene plot of the average normalized ribosome occupancy near the annotated stop codons of genes in cells treated (orange) or not (gray) with Api. The solid line indicates the mean value and the shadow plot reflects the standard error between two independent biological replicates. The metagene plot is based on 836 actively translated genes separated from neighboring genes by ≥50 nt. The cartoon illustrates ribosomes stalled at the stop codon in the post- or pre-release state, ribosomes queued upstream from those stalled at termination sites, and ribosomes present in mRNA regions downstream from the main ORF stop codon (readthrough). (B) Analysis of ribosome-associated and free RF2 in untreated cells and cells treated with Api. Ribosomes from lysates of untreated or Api-treated cells were pelleted through a sucrose cushion. The presence of RF2 in the ribosome pellet or in the post-ribosomal supernatant was tested by western blotting using the anti-RF2 serum. The purified E. coli His6-tagged RF2 was used as a control (lane marked ‘RF2’). An unknown protein (indicated with an asterisk) cross-reacting with the anti-RF2 serum served as the loading control. The cartoon illustrates how Api depletes free RFs by sequestering them on the ribosome.

Api acts as a global inhibitor of translation termination

A prominent outcome of Api treatment is a significant (~tenfold) increase of the average normalized ribosome density at stop codons (Figure 2A). Approximately 11% of all the ribosome footprints in Api-exposed cells map to stop codons. This result reveals Api as a potent global inhibitor of translation termination in bacterial cells and expands the previous findings of the in vitro experiments which showed that Api could arrest ribosomes at the stop codon of several model genes during cell-free translation (Florin et al., 2017).

Given the disparity in ribosome occupancy at and around termination sites in individual genes (Figure 1A), we computed stop codon (SC) scores to systematically assess the extent of Api-induced ribosome stalling in these regions. The SC score reports the ribosome density within the last three codons of the genes (i.e., the last two sense codons and the stop codon of the ORF) relative to the average density across the entire gene (Figure 3A, Figure 3—figure supplement 1). In the Api-treated cells, the SC scores of 97% of genes shifted to higher values compared to the untreated control. Although SC scores varied significantly between the genes, more than half (56%) of the genes in the Api sample exhibited values above 8 (log2 = 3), whereas only 1% of the genes in the control cells showed such high scores, reflecting the dramatic increase in stop codon occupancy caused by Api (Figure 3A).

Figure 3 with 3 supplements see all
Api arrests ribosomes at translation termination sites.

(A) Top: Stop codon (SC) score was calculated for actively translated genes separated by ≥50 nt as the ratio of ribosome density near the stop codon (‘SC’, orange rectangle) to the ribosome density within the entire ORF (light blue rectangle) (see Materials and methods for details). Bottom: Histogram of the SC scores of genes from cells treated (orange) or not (gray) with Api. (B) pLogo analyses of amino acid sequence bias around the termination regions of the top 50% SC-scoring versus the bottom 50% SC-scoring genes from Api-treated cells (top) or control cells (bottom).

We considered the possibility that the SC score variation between the genes could be due to a differential action of Api toward RF1 and RF2. Indeed, the Api sample SC scores at the RF2-specific UGA codons were somewhat lower than those at the RF1-specific UAG codons, but the difference was fairly modest and generally followed the trend observed in the untreated control (Figure 3—figure supplement 2). This result shows that Api efficiently acts upon both class 1 RFs, thereby contradicting a recent suggestion that Api preferentially traps RF1 over RF2 (Kuru et al., 2020). In search for other features that could be associated with a more pronounced Api-induced ribosome stalling at the ends of specific genes, we analyzed the C-terminal sequences of the proteins encoded in ORFs with high SC-scores (Figure 3B). The pLogo analysis showed statistically significant prevalence of glycine residues at the C-termini of such proteins, a tendency not observed in high SC-scoring ORFs in the untreated cells. This result indicates that Api does not simply exacerbate inherent difference in translation termination efficiency between the genes, but rather acts in a context-specific manner.

No remarkable sequence context features were detected at the regions downstream of the stop codons of the high-SC score genes (Figure 3—figure supplement 3A). Similarly, none of the other examined properties, including mRNA abundance, its propensity to form stable secondary structures, or the number of ribosomes per mRNA molecule show any strong correlation with the SC scores of the genes in the Api-treated cells (Figure 3—figure supplement 3B).

Taken together, our data revealed that the primary mode of Api action in the cell is inhibition of translation termination and that this effect is influenced by sequence context.

Api-induced translation arrest at stop codons results in queuing of the elongating ribosomes

At least three distinct waves of ribosome density are observed in the metagene profile upstream from the stop codons in the Api sample (Figure 2A). The crests of the waves are spaced by ~27 nt, a distance corresponding to the peak of the footprint length distribution (Figure 2—figure supplement 1), suggesting that this upstream density likely represents elongating ribosomes queued behind those arrested at stop codons. Even though ribosome collisions have been observed in bacteria and eukaryotes exposed to translation elongation inhibitors (Kearse et al., 2019; Mohammad et al., 2019), the extent of ribosome queuing in the Api-treated cells is especially pronounced. Because the queued ribosomes occupying inner codons of the ORFs carry nascent polypeptides they should be refractory to Api binding (Florin et al., 2017). Yet, despite being Api-free, they are unable to complete the synthesis of the encoded protein and participate in new rounds of translation.

Api treatment leads to pervasive stop codon readthrough

The metagene profile of the Api sample shows a dramatic increase in the number of ribosome footprints mapping to the regions downstream from the stop codons of the well-separated genes (Figure 2A). Similar to the SC score, we created the readthrough (RT) score to quantify the ribosome density within the first 40 nt of the 3’UTR relative to the ORF-associated density. In the control sample, the RT score of most genes is very low because few ribosomes are found downstream of stop codons. In contrast, in the Api sample, 90% of the genes have an RT score greater than 1, indicating higher relative density in the 3’UTR than in the ORF itself due to pervasive and efficient stop codon bypass (Figure 4A, Figure 4—figure supplement 1). We found no strong correlation between the stop codon identity and the RT score, indicating that all the stop codons are bypassed with comparable efficiency in the Api-treated cells, in contrast to the control cells, where UAA genes have significantly lower RT scores (Figure 4A, Figure 4—figure supplement 2).

Figure 4 with 6 supplements see all
Api leads to stop codon bypass and generation of proteins with unintended C-terminal extensions.

(A) Top: Readthrough (RT) score was calculated for actively translated genes separated by ≥50 nt as the ratio of ribosome density within the 40 nt downstream from the stop codon (‘RT’, orange rectangle) to the ribosome density within the associated ORF (light blue rectangle). Bottom: Histogram of the RT scores of genes from cells treated (orange) or not (gray) with Api. (B) The number of proteins with C-terminal extensions, peptides belonging to the C-terminal extensions of proteins, and peptide-spectrum matches (PSMs) identified by mass spectrometry in cells treated with 0.5× MIC (light orange bars) or 4× MIC (orange bars) of Api. With the same data-filtering criteria, no such products were detected in the control cells. (C) The number of peptides identified using relaxed criteria (see Figure 4—figure supplement 3B) in cells treated with 0.5× MIC of Api belonging to C-terminal extensions of proteins generated via bypassing the stop codon of the main ORF by amino acid misincorporation (0-frame), or as a result of −1 or +1 ribosomal frameshifting (fs).

The ribosome footprints in the 3’UTRs could represent post-termination ribosomes that have released the completed protein but somehow remained associated with mRNA and migrated past the stop codon. Alternatively, the 3’UTR footprints may correspond to ribosomes that failed to release the completed protein at the stop codon, bypassed it, and continued translation into the downstream segment of mRNA. Such a scenario could be caused by the depletion of available RF1/RF2 trapped by Api on terminating ribosomes. To test the feasibility of the latter model, we examined to which extent Api treatment affects the abundance of free RFs, more specifically of RF2, which by far outnumbers RF1 in the E. coli cytoplasm (Schmidt et al., 2016). Strikingly, unlike those of the control sample, the ribosome-depleted lysates of Api-treated cells essentially lacked free RF2 (Figure 2B). In addition, we reasoned that if 3’UTR-associated ribosomes are engaged in translation they should preferentially stall at the downstream stop codons. Indeed, metagene analysis shows a peak of ribosome footprints at the first in-frame downstream stop codon (Figure 4—figure supplement 3A), indicating that ribosomes that bypassed the termination signal of the main ORF are actively translating downstream mRNA sequences.

We used shotgun proteomics to test whether exposure of cells to Api leads to productive translation of the non-coding mRNA segments and generation of proteins with C-terminal extensions. To allow for the accumulation of proteins synthesized during Api treatment, we exposed cells for 1 hr to a sublethal concentration of Api (0.5× MIC) permitting protein synthesis to continue, even if at a reduced level. In addition, we also incubated cells for 1 hr with a higher Api concentration (4× MIC). We then analyzed the proteins synthesized in treated and control cells, using custom algorithms to search for tryptic peptides encoded in the 150 nt-long segments downstream from the stop codons of the annotated ORFs (see Materials and methods). Even when applying strict filtering criteria (Gessulat et al., 2019), we were able to identify a significant number of peptides encoded in the genomic regions downstream from the ORF stop codons in both of the Api samples (Figure 4B and Source data 3). The same analysis found no such peptides in the control sample. The number of identified peptides encoded in the 3’UTRs increased further upon applying a less strict filter (Figure 4—figure supplement 3B). These results argued that genome regions downstream of the stop codons of the annotated ORFs are actively translated in the cells exposed to Api. Approximately half (49%) of the identified ‘extension’ peptides were encoded in frame with the upstream ORF (Figure 4C) and thus, were likely produced by the ribosomes that misread the main ORF stop codon as a sense codon and continued translating the downstream mRNA sequence. Consistently, in the three identified tryptic peptides encoded in mRNA sequences spanning the ORF termination signal, the stop codon was mistranslated by a near-cognate aminoacyl-tRNA (Figure 4—figure supplement 4). The other detected downstream peptides were encoded in the (−1) or (+1) frames relative to the main ORF (Figure 4C and Source data 3). These peptides also likely belong to C-terminal extensions of the main-ORF proteins given that stop codons can be bypassed via frameshifting of the ribosomes stalled at termination sites in the pre-release state, as was observed in the experiments of Gross and coworkers (Baggett et al., 2017). The sample size of the ‘extension’ peptides was insufficient to systematically identify the mRNA or nascent protein features conducing to stop codon bypass via frameshifting. However, the presence of a slippery sequence preceding the stop codon (e.g. in arcB; Figure 4—figure supplement 5A) or the possibility of repairing of peptidyl-tRNA in a different frame (e.g. in rplE; Figure 4—figure supplement 5B) could be among the factors contributing to the synthesis of identified proteins’ C-terminal extensions.

Taken together, our results show that stop codon bypass, active translation of 3’ UTRs, and production of proteins with C-terminal extensions constitute major consequences of Api action.

Puromycin alters the Api stalled ribosome pattern

The results of our analysis suggest that the complex metagene profile observed in the vicinity of stop codons in the Api-treated cells is accounted for by distinct populations of ribosomes: the post-release ribosomes in complex with RF1/RF2 arrested by Api at the stop codons of the main ORF and the ribosomes carrying peptidyl-tRNA (including the queued ribosomes, some of the ribosomes in 3’ UTRs, and a fraction of ribosomes stalled at the stop codons in pre-release state). We hypothesized that this complex profile could be at least partially simplified by clearing from mRNA the ribosomes carrying peptidyl-tRNA. For that, we used puromycin (Pmn), an antibiotic that releases the nascent protein from ribosomes whose PTC A site is accessible (Traut and Monro, 1964). Indeed, metagene analysis showed that Pmn treatment of lysate from Api-treated cells significantly reduced the relative amounts of queued ribosomes and 3’UTR ribosomes and, consequently, increased the relative occupancy of stop codons (Figure 2—figure supplement 2). Importantly, this does not imply that Pmn causes more ribosomes to associate with stop codons but that a higher fraction of the remaining mRNA-bound ribosomes are positioned at termination sites. However, even upon Pmn treatment, some ribosomes remained within the ORFs, suggesting that either some of the translation complexes are refractory to Pmn action or that Pmn treatment requires further optimization. The remaining footprints still found in 3’UTRs after Pmn treatment may represent the ribosomes arrested at the downstream stop codons: Api-trapped RFs would block Pmn binding.

Api treatment distorts the cell proteome and activates cellular ribosome rescue systems

Exposure of cells to Api leads to significant deregulation of the proteome. One of the most prominent effects is an increase in the relative content of ribosomal proteins and other factors related to the function and assembly of ribosomes (Figure 5-figure supplement 1A and Source data 4). Among the top 50 proteins whose abundance is increased in the cells exposed to 0.5× MIC of Api, 19 are related to translation (Figure 5—figure supplement 1B). This shift may reflect an attempt to compensate for the reduced protein synthesis capacity caused by the Api inhibitory action (Source data 4). Curiously, 13 other proteins among the top 50 are either associated with the inner or outer membrane or reside in the periplasm (Figure 5—figure supplement 1B), suggesting a still unknown link between Api action and protein secretion.

Analysis of the Ribo-seq data showed that the highest peak of ribosome occupancy in the Api-treated cells corresponds to the stop codon of the degron-encoding sequence in tmRNA (Figure 5A,B). tmRNA is a component of a conserved bacterial system that rescues stalled mRNA-associated ribosomes with an empty A site (Buskirk and Green, 2017; Moore and Sauer, 2005). Following tmRNA binding to the A-site, the ribosome switches templates, synthesizes the tmRNA-encoded degron sequence, and terminates translation, using class 1 RFs, at the UAA stop codon (Buskirk and Green, 2017; Moore and Sauer, 2005). tmRNA is known to operate on ribosomes that reach the 3’ end of non-stop mRNAs or, potentially, those stalled due to scarcity of A-site ligand (Ivanova et al., 2004; Janssen et al., 2013; Li et al., 2007; Subramaniam et al., 2014). Both types of tmRNA substrates are generated in the cells treated with Api. First, the Api-stimulated stop codon bypass enables the translating ribosomes to approach the 3’-termini of RNA transcripts (Figure 5C,D), where they could be recognized as requiring rescue. In addition, ribosomes stalled at stop codons in a pre-release state, and possibly the queued ribosomes as well, whose A site is vacant, could serve as tmRNA substrates.

Figure 5 with 1 supplement see all
Alteration of the proteome and activation of the ribosome rescue systems in response to Api.

(A) The density of ribosome footprints throughout the E. coli genome in cells treated or not with Api. (B) The density of ribosomal footprints at the termination region of the tmRNA ORF encoding the degron tag. The position of the main stop codon and two in-frame downstream stop codons of the tmRNA are indicated with red triangles. (C) Ribosome footprints near the 3’-ends of the rpmF transcript in Api-treated cells. The RNA-seq coverage is shown below. (D) The Metagene plot of summed normalized reads toward the 3’ termini of 1006 mRNAs (Dar and Sorek, 2018; see Materials and methods). (E) The density of ribosome footprints in the arfA gene in Api-treated or control cells. The gray triangle marks the position of the RNase III cleavage site involved in the tmRNA- and ArfA-dependent regulation of arfA expression.

The sharp increase of ribosome footprints at the tmRNA stop codons in the Api samples (Figure 5B) argues in favor of the massive recruitment of tmRNAs to the stalled ribosomes. However, due to the Api-mediated depletion of the available RFs (Figure 2B), the degron-tagged protein cannot be released, thereby preventing proper tmRNA function and recycling. Consistently, the cell attempts to activate the two other E. coli backup rescue factors, ArfA and ArfB (reviewed in Buskirk and Green, 2017). For the arfA gene in the Api-treated cells, we observed a nearly tenfold increase in the abundance of its transcript and a more than sevenfold higher number of ribosomes associated with its mRNA (Figure 5D and Source datas 5 and 6). The relative amount of arfB transcripts increases by ~40% and there are ~4 times more ribosomes per arfB transcript (Source datas 5 and 6). However, because ribosome rescue by tmRNA or arfA requires the availability of RFs and because ArfB, which acts as a termination factor itself, becomes trapped in the ribosome by Api (Chan et al., 2020), it is unlikely that either of the rescue systems can mitigate the consequences of Api action. Consistently, deletion of tmRNA, arfA, or arfB genes does not increase cell sensitivity to Api (Source data 7).

Api increases start codon occupancy at some genes

While the predominant effect of Api on cellular translation is the build-up of ribosome density in the vicinity of stop codons (Figure 2A), a metagene analysis of the 5’ ends of genes showed a moderate increase (~twofold) of the number of ribosome footprints at the start codons of the ORFs in the Api sample compared to the control (Figure 2—figure supplement 3A). Among the genes with the strongest start codon effects, we selected zntA, srmB, cysS, tyrS, and yhbL to test in vitro whether Api directly delays the ribosome departure from the initiator codon. While translation arrest at stop codons is readily detected by toeprinting analysis at 50 µM of Api (Florin et al., 2017), no start codon effects were observed at this concentration of the inhibitor in any of the tested ORFs. However, increasing Api concentration to 2 mM led to the appearance of a start codon toeprint bands in the zntA and tyrS templates (Figure 2—figure supplement 3B–C); the start codon effects were much weaker or completely absent with the other tested genes. The moderate effect of Api on translation initiation is reminiscent of the main mode of action of other PrAMPs (Gagnon et al., 2016; Roy et al., 2015; Seefeldt et al., 2016; Seefeldt et al., 2015) and could be explained by its ability to bind with a reduced affinity to the ribosome even in the absence of RF1/RF2 (Florin et al., 2017; Kolano et al., 2020; Krizsan et al., 2014). However, it remains to be elucidated whether the mechanism of the weak inhibition of translation initiation by Api is comparable to that of other PrAMPs.

Discussion

Ribo-seq and proteomics studies revealed a complex pattern of events triggered by the translation termination inhibitor Api in bacteria.

One of the main effects of Api in the cell is the arrest of translation at the stop codons of the ORFs. A larger fraction of the ribosomes associated with stop codons is likely arrested in a post-release state, with Api bound in the exit tunnel, trapped RF1 or RF2 in the A site, and deacylated tRNA in the P site. Most of the available RFs are sequestered in these complexes (Figure 2B), which explains why overexpression of class 1 RFs reduced the sensitivity of E. coli to Api (Matsumoto et al., 2017). In normal conditions, however, because the number of ribosomes in the cell exceeds the copy number of RF1 and RF2 (Schmidt et al., 2016), some ribosomes likely become stalled at stop codons in a pre-release state, with a vacant A site and the completed protein still esterifying P-site tRNA. It is difficult to determine experimentally what fraction of the stop codon associated footprints belongs to the pre-release ribosomes. However, because the number of the total stop codon footprints (~11% of all mapped footprints) roughly matches the ratio between ribosomes and RFs in the cell (Schmidt et al., 2016), it is likely that most of the ribosome density detected at stop codons is accounted for by Api-stalled post-release ribosomes. A fraction of the stop codon peak could be also possibly attributed to the post-release Api-free ribosomes that have not yet been recycled (Schuller et al., 2017).

The extent of Api-induced ribosome arrest at stop codons differs in magnitude between genes. At least some of the gene- or nascent protein-specific effects of the Api action could be linked to the kinetics of extrusion of the nascent chain from the exit tunnel, which is a pre-requisite for Api binding. The nascent chains that would linger for a longer time after their separation from the P-site tRNA would allow more time for RF1/RF2 dissociation before Api reaching its binding site in the exit tunnel. Consistently, we note a modest trend for arrest at the RF2-specific UGA codons to be somewhat less pronounced than that at the UAA or UAG stop codons (Figure 3—figure supplement 2) possibly because the residence time of RF2 on the post-release ribosome could be shorter than that of RF1 (Adio et al., 2018), which would provide Api with a lesser chance to trap the ribosome terminating at the UGA codons. We should note, however, that the available data are insufficient to distinguish whether the observed context effects operate upon the post- or pre-release fractions of the stop codon-associated ribosomes.

Api-induced ribosome stalling at stop codons causes severe ribosome queuing. The impressive ability of Api to cause queuing likely stems from its unique mechanism of action. In contrast to elongation inhibitors, which simultaneously arrest ribosomes at many codons within the gene, Api preferentially arrests translation at a single site, the stop codon; the trailing ribosomes on the same mRNA continue translation until they run into the traffic jam caused by the termination site roadblock. The pronounced ribosome queuing in the Api-treated cells illustrates a distinctive feature of a translation termination inhibitor as an antimicrobial: Api needs to bind to only a few terminating ribosomes to preclude a much larger number of inhibitor-free ribosomes from participating in translation. Termination arrest and ribosome queuing, however, did not increase the number of heavy polysomes in the cell, likely because in fast-growing cells ~90% of the ribosomes are already associated with mRNAs (Bartholomäus et al., 2016; Bremer and Dennis, 1996) and thus, blocking termination would not perceptibly increase ribosome loading per mRNA.

Our Ribo-seq experiments revealed a striking and pervasive Api-induced genome-wide stop codon readthrough resulting in an increased abundance of ribosome footprints downstream of ~99% of the analyzed genes. The appearance of peptides encoded in the downstream mRNA sequences, revealed by shotgun proteomics, and the increased ribosome occupancy of the downstream stop codons strongly argue that in the Api-treated cells a sizable fraction of ribosomes translate past the end of the ORFs and synthesize proteins with C-terminal extensions. Stop codon bypass is likely a consequence of the Api-mediated depletion of available RFs (Figure 2B) that increases the residence time of pre-release state ribosomes at the stop codons of the ORFs, leading ultimately to readthrough.

Some level of stop codon readthrough can be also induced by antibiotics that render the ribosome error-prone, such as aminoglycosides, odilorhabdins, or negamycin (Olivier et al., 2014; Pantel et al., 2018; Polikanov et al., 2014; Thompson et al., 2004; Wangen and Green, 2020). However, Api’s ability to stimulate bypass appears to be more robust, leading to the appearance of ribosome footprints downstream of not only the first but also of the subsequent stop codons (Figure 4—figure supplement 3A). Because Api does not inhibit translation elongation (in contrast to the aforementioned antibiotics; Cabañas et al., 1978; Polikanov et al., 2014; Wang et al., 2012), more ribosomes can reach the ends of the ORFs in the Api-treated cells, thereby triggering a higher rate of stop codon readthrough. Additionally, unlike miscoding antibiotics, Api does not reduce the general accuracy of translation and thus, could be exploited for medical applications where a premature stop codon readthrough is desirable (Huang et al., 2019; Keeling et al., 2014). Api-mediated stop codon suppression can be also instrumental in synthetic biology, for example, for enhancing the incorporation of non-canonical amino acids (Florin et al., 2017; Kuru et al., 2020).

Api-induced stop codon bypass is achieved by either misincorporation of a near-cognate amino acid at the stop codon or via (−1) or (+1) frameshifting leading to the synthesis of proteins with C-terminal extensions. Although under our Api treatment conditions, we did not detect any significant accumulation of protein aggregates (Figure 4—figure supplement 6), it remains possible that accumulation of proteins with C-terminal appendages could contribute to the antimicrobial activity of Api since these aberrant extensions may negatively affect the proteins’ catalytic capacity, ligand binding, or interactions with other polypeptides.

By allowing translation to proceed to the 3’ ends of mRNA transcripts (Figure 5C,D) and by prompting some of the translating ribosomes to stall at the stop codons in a pre-release state, Api triggers the activation of the ribosome rescue systems. However, in the cells exposed to Api, the rescue systems are likely worthless. tmRNA-mediated rescue of stalled ribosomes relies on canonical translation termination at the UAA codon of the degron ORF in tmRNA (reviewed in Buskirk and Green, 2017). Yet, the lack of available RF1/RF2 in the Api-treated cells prevents the release of the degron-tagged protein, therefore sequestering tmRNA. The depletion of the available tmRNA pool leads to activation of the backup rescue systems, which nevertheless, should be also ineffectual in the Api-treated cells. ArfA facilitates translation termination at non-stop mRNAs by interacting with the empty A site of the small ribosomal subunit and promoting binding of RF2, which can then release the stalled protein. Once again, the lack of available RF2 in the Api-treated cells should prevent ArfA from carrying out its rescue mission. This idea is supported by the appearance of an exceedingly high ribosome density peak at the end of the non-stop arfA mRNA (Figure 5E), where the stalled ribosomes are expected to be released by the action of the ArfA protein itself (Garza-Sánchez et al., 2011). Despite acting independently from the primary RFs, the alternative RF ArfB, should be equally inconsequential because Api can also trap ArfB on the ribosome (Chan et al., 2020) and deplete its available pool. Thus, it appears that none of the known ways that the bacterial cell normally exploits for salvaging stalled ribosomes would be of much use when the translation is inhibited by Api. In agreement with this notion, the inactivation of any of the rescue systems does not sensitize E. coli to the Api action (Source data 7).

One of the unexpected aspects of Api action revealed by the in vivo studies is the increased ribosome occupancy of start codons of some genes. These effects could be related to the reported low affinity of Api for the ribosome with the vacant nascent peptide exit tunnel (Florin et al., 2017; Kolano et al., 2020; Krizsan et al., 2014). Once associated with the ribosome at the start codon, Api may block the first peptide bond formation by displacing the fMet moiety of the initiator tRNA in the P site or may interfere with the first act of translocation. Because the initiation effects could be observed in vitro only at very high concentrations of Api (Figure 2—figure supplement 3B–C), the Api-induced ribosome stalling at start codons in the cell could involve additional factors absent in the cell-free system or result from the accumulation of aberrant ribosomes.

On the basis of our findings, we can propose a model of Api action in the bacterial cell involving multiple, not necessarily sequential effects (numbered 1 to 5 in Figure 6). The immediate effect of Api is arresting the ribosomes in the post-release state at stop codons and sequestering the available RF1/RF2 (1). Because ribosomes outnumber RFs, the depletion of available RFs leads to stalling of a fraction of the translating ribosomes at the stop codons in the pre-release state, with peptidyl-tRNA bound in the P-site and an empty A site (2). The trailing ribosomes that carry incomplete polypeptides form a stalled queue behind those trapped at the stop codons (3a,3b). A prolonged arrest of pre-release ribosomes at termination leads to the eventual bypass of stop codons via misincorporation of a near-cognate tRNA or frameshifting resulting in the synthesis of proteins with C-terminal extensions (4). Some of the ribosomes that bypassed the main ORF stop codon may ultimately reach the mRNA 3’ end. Accumulation of ribosomes with vacant A-site (stalled at the 3’ ends of the RNA transcripts or at the stop codons) triggers the massive but futile engagement of the ribosome rescue systems (5). The combination of these effects causes rapid and efficient cessation of translation.

A model of Api action in the bacterial cell.

Understanding the mode of Api action in the bacterial cell helps to rationalize the seeming discrepancy between its marginal effect on reporter expression in a cell-free system and the strong inhibition of translation in vivo (Castle et al., 1999; Krizsan et al., 2015; Krizsan et al., 2014). Exposure of cells to Api, leads to translation arrest at stop codons of multiple ORFs, ribosome queuing and depletion of available RFs resulting in a rapid halt of protein synthesis. By contrast, in cell-free systems, where only one type of mRNA is present, many mRNA molecules will be translated at least once before the Api-induced depletion of the RF pool, thereby resulting in poor inhibition of the reporter expression.

The use of Ribo-seq in combination with the translation initiation inhibitor retapamulin was instrumental in identifying translation start sites in bacteria, revealing a universe of cryptic protein-coding sequences (Meydan et al., 2019; Weaver et al., 2019). Ribo-seq, enhanced by the use of a translation termination inhibitor, could bolster our ability to detect the translated sequences in the bacterial genomes. Because the effects of Api are multifaceted and lead not only to translation arrest at the stop codons but also to ribosome queuing and stop codon readthrough (Figure 2A), its immediate application for mapping translation termination sites could be challenging. Nevertheless, with additional tricks, for example, properly optimized Pmn treatment to remove queued and elongating ribosomes (Figure 2—figure supplement 2), Api could be repurposed for genome-wide analysis of stop codons of protein-coding regions. The use of Ribo-seq in combination with inhibitors of translation initiation and termination could provide new insights into the principles of genetic encoding and regulation of gene expression.

Materials and methods

Key resources table
Reagent type (species)
or resource
DesignationSource or referenceIdentifiersAdditional
information
Strain, strain background (Escherichia coli)BL21 (ΔtolC)Meydan et al., 2019
Strain, strain background (Escherichia coli)BW25113 (ΔarfA)Baba et al., 2006
Strain, strain background (Escherichia coli)BW25113 (ΔarfB)Baba et al., 2006
Strain, strain background (Escherichia coli)BW25113 (ΔsmpB)Baba et al., 2006
Strain, strain background (Escherichia coli)BW25113Baba et al., 2006
AntibodyAnti-RF2 (rabbit serum polyclonal)Cruz-Vera laboratory(1:500)
Sequence-based reagentzntA_FIntegrated DNA TechnologiesPCR primersTAATACGACTCACTATAGGGAAACTTAACCGGAGGATGCCATGTCG
Sequence-based reagentzntA_RIntegrated DNA TechnologiesPCR primersGGTTATAATGAATTTTGCTTATTAACTTTGAACGCAGCAAATTGAGGGGC
Sequence-based reagenttyrS_FIntegrated DNA TechnologiesPCR primersTAATACGACTCACTATAGGGTTATATACATGGAGATTTTGATGGCAAGC
Sequence-based reagenttyrS_RIntegrated DNA TechnologiesPCR primersGGTTATAATGAATTTTGCTTATTAA
CGACTGGGCTACCAGCCCCCG
Sequence-based reagentLinker oligonucleotide: NI-810-NI817McGlincy and Ingolia, 2017/5Phos/NNNNNATCGTAGA
TCGGAAGAGCACACGTCTGAA/3ddC/
Sequence-based reagentRT primerIntegrated DNA Technologies/5Phos/RNAGATCGGAAGAGCGTCGTGTAGGGAAAGAG/iSp18/GTGACTGGAGTTCAGACGTGTGCTC
Peptide, recombinant proteinApi137NovoPro Biosciences Inc
Commercial assay or kitOligo Clean and Concentrator kitZymo ResearchD4060
Commercial assay or kitMEGAclearThermo FisherAM1908
Commercial assay or kitMicrobEXPRESSThermo FisherAM1905
Commercial assay or kitHigh pH reverse phase peptide fractionationThermo Fisher84868
Chemical compound, drugGMPPNPSigmaG0635
Chemical compound, drugL-[35S]-methioninePerkin Elmer
Peptide, recombinant proteinMNase (S7 Microccocal Nuclease)Roche10107921001
Peptide, recombinant proteinDNase I, RNase freeRoche04716728001
Peptide, recombinant proteinSUPERase*InThermo FisherAM2696
Peptide, recombinant proteinT4 PNKNew England BiolabsM0201L
Peptide, recombinant proteinMth RNA LigaseNew England BiolabsE2610S
Peptide, recombinant protein5’ DeadenylaseNew England BiolabsM0331S
Peptide, recombinant proteinRecJfNew England BiolabsM0264S
Peptide, recombinant proteinSuperscript IIIInvitrogen18080–093
Peptide, recombinant proteinCircLigase ssDNA LigaseLucigenCL4115K
Peptide, recombinant proteinPhusion high-fidelity polymeraseNew England BiolabsM0530S
Peptide, recombinant proteinMS grade modified TrypsinPromegaV5113
Software, algorithmScripts for RNA-seq and Ribo-seq metagene analysishttps://github.com/adamhockenberry/ribo-t-sequencing
Software, algorithmScripts for Ribo-seq processinghttps://github.com/mmaiensc/RiboSeq
Software, algorithmBedtoolshttps://github.com/arq5x/bedtools2
Software, algorithmpLogohttps://plogo.uconn.edu/
Software, algorithmBowtie2https://github.com/BenLangmead/bowtie2v2.2.9
Software, algorithmcutadapthttps://github.com/marcelm/cutadapt/
Software, algorithmMass-spectrometry raw data conversionChambers et al., 2012
Software, algorithmPeptide database searchingXu et al., 2015
Software, algorithmPeptide database searchingKong et al., 2017
Software, algorithmCustom databases used for peptide searchinghttp://git.pepchem.org/gaolab/api_ribo_ext
Software, algorithmData analysis scripts in Pythonhttp://git.pepchem.org/gaolab/api_ribo_ext

Reagents

Api137 (Berthold et al., 2013), which we refer to simply as Api, was synthesized by NovoPro Biosciences Inc DNA oligonucleotides were synthesized by Integrated DNA Technologies.

Protein synthesis inhibition assay

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The inhibition of cellular protein synthesis by Api was analyzed by metabolic labeling as described previously (Meydan et al., 2019) with the following modifications. E. coli BL21 ∆tolC cells were grown overnight in MOPS medium (Teknova) lacking methionine (MOPSΔMet). Cells were diluted 1:200 into fresh MOPS-Met and grown at 37°C until the culture reached a density of A660 of 0.2. Aliquots of the exponentially growing cells were added to tubes containing appropriately diluted Api in MOPSΔMet medium to obtain 0.75, 1.5, 3.1, 6.25, 12.5, 25, and 50 µM as final Api concentrations in the total volume of 100 µL. After 2 min incubation, 28 µL were transferred to another tube containing 2 µL MOPSΔMet medium supplemented with 0.3 µCi of L-[35S]-methionine (specific activity 1,175 Ci/mmol; MP Biomedicals). Following 90 s incubation, the content was transferred onto Whatman 3 MM paper discs pre-wetted with 5% TCA and the procedure was continued as described previously (Meydan et al., 2019).

The time course of protein synthesis inhibition was performed as described above, except that Api was directly added to a final concentration of 6.25 µM to the exponentially growing culture in MOPSΔMet medium and aliquots were taken at 0, 2, 5, and 10 min.

Cell growth and cell lysates preparation for Ribo-seq experiments

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The Ribo-seq experiments were performed as described previously (Meydan et al., 2020; Meydan et al., 2019) using cells grown in MOPS medium. Briefly, the overnight cultures of E. coli BL21 ∆tolC cells were diluted 1:200 in 150 mL of medium and grown at 37°C in flasks until reaching density of A600 ~0.4. For Api-samples, 4.12 mg of dry Api powder was added directly to the cultures to a final concentration of 4× MIC and incubation with shaking continued for 5 min. Untreated (control) and Api-treated cells were harvested by rapid filtration, flash frozen in liquid nitrogen and cryo-lysed in 650 µL lysis buffer (20 mM Tris pH 8.0, 10 mM MgCl2, 100 mM NH4CL, 5 mM CaCl2, 0.4% Triton X-100, 0.1% NP-40) supplemented with 65 U RNase-free DNase I (Roche) and 208 U Superase•In RNase inhibitor (Invitrogen). The pulverized cells were thawed at 30°C for 2 min and incubated in an ice water bath for 20 min and lysates were clarified by centrifugation at 20,000 × g for 10 min at 4°C. One aliquot of the clarified lysates was preserved frozen for RNA-seq analysis, another aliquot was immediately treated with MNase (see below); the third aliquot was treated by puromycin (Pmn) as follows. The cell lysates were supplemented with 1 mM Pmn (Millipore-Sigma), 10 mM creatine phosphate (Millipore-Sigma) and 40 µg/mL creatine kinase (Roche), and incubated for 10 min at 25°C with shaking at 1400 rpm. Cell lysates, treated or not with Pmn, were subjected to MNase treatment by adding 450 U MNase (Roche) per 25 A260 units of lysate, 3 mM GMPPNP, and incubating for 60 min at 25°C. The MNase reaction was quenched by the addition of EGTA (5 mM final concentration). Following sucrose gradient centrifugation and collection of the 70S ribosome peak (Becker et al., 2013), subsequent isolation of ribosomal footprints and library preparation was performed as described (McGlincy and Ingolia, 2017).

Processing of ribosome footprints for Ribo-seq analysis

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A custom script was used to demultiplex the samples, remove the linker barcode and any nts 3' of the linker barcode, and then remove five nts from the 3' end and 2 nt from the 5' end, which were added as part of the library design (McGlincy and Ingolia, 2017; Meydan et al., 2020; Meydan et al., 2019). Bowtie2 (v2.2.9) within the GALAXY pipeline first aligned the trimmed reads to the non-coding RNA sequences. The remaining unmapped reads were aligned to the reference BL21 genome (GenBank ID CP001509.3; Meydan et al., 2020; Meydan et al., 2019). The 26 to 39 nt-long reads were used in the subsequent analyses. After analyzing the footprints at the start codons, the first position of the P-site codon was assigned 16 nt from the 3’ end of the read, in-line with the 15 nt offset previously proposed (Mohammad et al., 2019).

Metagene analysis

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The metagene analyses at the annotated start and stop regions followed the previously described protocol (Aleksashin et al., 2019). For the inclusion in the analysis, the well-separated ORFs had to satisfy three criteria in all four datasets (two control and two Api-treated biological replicates): (i) The ORF length is at least 300 nt, (ii) at least 5% of the positions within the coding sequence had assigned reads values above zero, and (iii) the average number of rpm per nt in an ORF is ≥0.005. Ribosome footprint density at each nucleotide was normalized to the average coverage of the entire ORF plus 50 nt up- and downstream. The mean of the normalized values was calculated and plotted for the regions around the start and stop codons of the ORF.

The metagene analysis of the first in-frame stop codons of the downstream regions was carried out using 3769 genes with such codons present within the 225 nt downstream of the annotated ORF. Normalized read coverage, calculated for each nucleotide within a 200 nt window centered around the last nucleotide of the downstream stop codons, was determined by dividing ribosome footprint coverage at each position by the maximum coverage value within the window. Normalized coverage was then summed for each position in the windows and plotted.

To analyze the metagene coverage at the 3’ termini of mRNAs, we first converted the 3’ termini coordinates reported in the E. coli BW25113 strain (Dar and Sorek, 2018) to coordinates in the BL21 strain, used in this work. We then followed a similar normalization and summing procedure to that used in the first in-frame stop codon analysis. However, the window of nts for normalization included 100 upstream and 20 downstream of the mRNA 3’ termini.

Calculation of stop codon- and readthrough scores

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The well-separated genes included in the analysis were required to meet two criteria in all four datasets (two control and two Api-treated biological replicates): (i) they were separated from the upstream and downstream adjacent genes by at least 50 nt, and (ii) they have a coverage density of at least 0.1 reads per nt in the protein-coding sequence, excluding the first and last 9 nt of the ORF. The stop codon (SC) region was assigned as nine 3’-terminal nt of the ORF (the last two sense codons and the stop codon); the readthrough (RT) region encompassed 40 nt downstream from the ORF stop codon. The SC and RT scores were calculated by dividing the ribosome density within the SC region or the RT region, respectively, by the total ORF density. The mean SC and RT scores of each gene were used in further analyses.

RNA-seq analysis

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Aliquots of the clarified cell lysates from the samples prepared for the Ribo-seq analysis (see above) were used to extract and analyze total RNA as previously described (Aleksashin et al., 2019). Briefly, total RNA was purified by acidic phenol extraction, short RNAs and rRNA were subtracted using the MEGAclear (Ambion) and MicrobEXPRESS (Ambion) kits, respectively, mRNA was fragmented via alkaline fragmentation and, after size-fractionation and isolating fragment sin the 15–45 nt range, converted to the library for next-generation sequencing.

The RNA-seq analysis was performed as previously described (Aleksashin et al., 2019). Briefly, Cutadapt was used to process and demultiplex the reads. Kallisto (v.0.44; Bray et al., 2016) and Sleuth (v.0.30; Pimentel et al., 2017) were used to align the reads and perform a differential expression analysis, respectively. To align reads to the ssrA transcript, the GALAXY pipeline was used for alignment and quantifying reads per million after filtering out reads aligning to the non-coding RNA.

Analysis of free RF2 abundance

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E. coli BL21ΔtolC cells were grown in 100 mL of MOPS media to a density of A600 ~0.5, split into separated flasks and either treated with 4× MIC of Api for 5 min or left untreated (control). The flasks were cooled in an ice water bath and cell cultures were centrifuged in a rotor JA-25.50 (Beckman) at 4°C and 4000× g for 10 min. The cell pellet was resuspended in 400 µL cold lysis buffer (20 mM Tris pH 8.0, 10 mM MgCl2, 100 mM NH4Cl, 5 mM CaCl2, 0.4% Triton X-100, 0.1% NP-40), 10 U/µL RNAse free DNase I (Roche) and 20 U/µL Superase•In RNase inhibitor (Invitrogen) frozen in liquid nitrogen and cryo-milled. The pulverized cells were thawed at 30°C for 2 min, incubated in an ice water bath for 20 min and lysates were clarified by centrifugation for 10 min at 20,000 × g and 4°C. About 150 µL of the lysate was layered onto a 600 µL sucrose cushion (25% sucrose, 20 mM tris pH 8.0, 10 mM MgCl2, 100 mM NH4Cl) in a TLA100.2 rotor (Beckman) and centrifuged for 1 hr at 100,000 rpm and 4°C. The supernatant was collected, and the ribosome pellet was resuspended in buffer (20 mM Tris pH 8.0, 10 mM MgCl2, 100 mM NH4Cl, 1% SDS). Samples were separated by 4–20% SDS-PAGE gels, then electroblotted onto nitrocellulose membranes followed by Ponceau staining and probing with rabbit anti-RF2 antibody. Antibody binding was visualized using a secondary antibody (goat anti-rabbit IgG labeled with horseradish peroxidase) and imaged chemiluminescence.

Sample preparation

For proteomics analysis

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All protein preparation steps were performed on ice. The cell pellet from each sample was resuspended in 2 mL TNI lysis buffer (50 mM Tris-HCl, pH 7.5, 250 mM NaCl, 0.5% Igepal CA-630, 1 mM EDTA, and one complete ULTRA EDTA-free Protease Inhibitor cocktail tablet), sonicated using a probe sonicator (Branson, CT) at 30% power at 55 kHz repeatedly for 20 s with 10 s interval to cool down the lysate until sufficiently clear lysate was obtained. The lysate was then clarified by centrifuging for 20 min at 13,000 g at 4°C in order to remove the cell debris. The cleared supernatant was transferred to a fresh tube and total protein was precipitated by the addition of CHCl3/MeOH as previously described (Gao and Yates, 2019). Air-dried total protein pellet from each sample was dissolved in 100 mM Tris-HCl pH 8.5, 8 M urea. Protein concentration was measured by BCA protein assay kit (Thermo Fisher). About 100 μg of protein from each sample was digested with trypsin (Promega) at 37°C for 24 hr with an enzyme to protein ratio 1:100 (w/w). After the addition of 96% formic acid to the final concentration of 5%, samples were briefly centrifuged at 13,000 g and peptide-containing supernatant was used for further analysis Peptides were fractionated into eight fractions using High-pH Reversed-Phase Peptide Fractionation Kit (Thermo Fisher). Peptide fractions were dried in SpeedVac (Eppendorf, Germany) and resuspended in 20 μL of 0.1% formic acid.

Mass spectrometry analysis

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Individual fractions of the tryptic peptides were separated by a capillary C18 reverse phase column of the mass-spectrometer-linked Ultimate 3000 HPLC system (Thermo Fisher) using a 90 min gradient (5–85% acetonitrile in water) at 300 nL/min flow. Mass spectrometry data were acquired by an Orbitrap QExactive HF system (Thermo Fisher) with nanospray ESI source using data-dependent acquisition. Raw data were collected and processed by the ProLuCID search engine (Xu et al., 2015) and an in-house pipeline.

Raw files were converted into MSn files using in-house converter or mzml files using MSconvert (Chambers et al., 2012) and then searched by both ProluCID (Xu et al., 2015) and MSFragger (Kong et al., 2017) using standard and extended reference proteomes (see below). The identified peptide sequences were first mapped to the in silico-generated extended proteome, then cross-referenced to the standard reference proteome (Uniprot E. coli BL21 reference proteome downloaded 2/20/2020). The 'extension' peptides were identified by subtracting all the peptides found in standard reference proteome from the peptides found in the extended proteome. All peptides mapped to the 150 nt-long genomic regions downstream of the stop codons of each annotated ORF and those covering both the tail of annotated ORFs and head of the downstream of the stop codons were reported. All scripts processing the data were written in Python 3.7 and are available at http://git.pepchem.org/gaolab/api_ribo_ext.

The standard reference proteome was constructed using the annotated reference genome of E. coli, strain BL21 DE3 (NCBI GenBank: CP001509.3). Extended reference proteome was generated by amending standard reference proteome with the proteome that could be encoded in three frames in the 150 nt-long genomic regions downstream of the stop codons of the 2100 highly expressed annotated genes (>112 sequence counts or 17.2 RPMs, the median of all genes, in the Ribo-seq dataset) in the Api-treated sample. The bypass of the first downstream stop codon was presumed to take place in 0/ (+1)/(−1) frame using the rules described above for the primary stop codon. The subsequent downstream stop codons were presumed to be translated in 0 frame.

To investigate the readthrough pattern of the primary stop codon, another custom proteome database was constructed with the primary stop codon translated into each of the 20 natural amino acids. The mass spectrometry data were then re-processed the same way as described above except using this new extended database.

To elucidate the changes in the proteome among different conditions, all the proteins identified from the above-mentioned search, regardless of the extension status, were quantified using a semi-quantitative label-free quantitation method using normalized spectral abundance factor (NSAF) to represent the relative expression level of each protein (Zybailov et al., 2005). All the low confident proteins, that is, proteins with <5 spectral counts, were removed from the analysis. The rest of the proteins were compared using the NSAF ratio between two conditions and sorted using a gamma distribution cumulative distribution function. The top 50 genes chosen from each comparison were used as input for pathway analysis using String-DB (Szklarczyk et al., 2019). A heat map of all the protein expression levels across all conditions was generated using NSAFs (Figure 5—figure supplement 1A). Genes and conditions were both clustered using a hierarchical k-mean clustering algorithm (Gao et al., 2019). The color represents the relative expression level (NSAFs) of each protein quantified as mentioned above.

Toeprinting assay

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DNA templates for toeprinting containing T7 promoter and an annealing site for the toeprinting primer were generated via PCR by amplifying the corresponding gene sequences from the E. coli BL21 genomic DNA. The following DNA primers were used for preparing the respective gene templates: zntA (TAATACGACTCACTATAGGGAAACTTAACCGGAGGATGCCATGTCG and GGTTATAATGAATTTTGCTTATTAACTTTGAACGCAGCAAATTGAGGGGC), tyrS (TAATACGACTCACTATAGGGTTATATACATGGAGATTTTGATGGCAAGC and GGTTATAATGAATTTTGCTTATTAACGACTGGGCTACCAGCCCCCG), srmB (TAATACGACTCACTATAGGGGCCCCACACAGAGGTAGAACATGACTGTAACG and GGTTATAATGAATTTTGCTTATTAACGGCTTCCAGCAGGCTTTCGTCG), ybhL (TAATACGACTCACTATAGGGTATATCTTCAGGAGATCGTCATGGACAGATTCC and GGTTATAATGAATTTTGCTTATTAACGATTGCAAGCCAGCCCGGGGTTGTACG), cysS (TAATACGACTCACTATAGGGATGTCTAAACGGAATCTTCGATGCTAAAAATCTTC and GGTTATAATGAATTTTGCTTATTAACTGAATAGGCTTAAATTCCTCTTTTTGGCG). The toeprinting assay was performed as previously described (Orelle et al., 2013) using the PURExpress system (New England Biolabs). When needed, Api was added to the final concentration of 50 µM or 2 mM. The final concentration of edeine was 50 µM. Following 10 min of translation, reverse transcription was carried out for 10 min using toeprinting primer NV1 GGTTATAATGAATTTTGCTTATTAAC (Vazquez-Laslop et al., 2008). Reverse transcription products were separated on a 6% sequencing gel. The gel was dried, exposed to a phosphorimager screen, and visualized in a Typhoon phosphorimager (GE Healthcare).

Aggregation assay

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E. coli BL21ΔtolC cells were grown in MOPS media to a density of A600 ~0.4 and then treated for 30 min with 2× MIC of either Api, spectinomycin (60 µg/mL), or streptomycin (1 µg/mL). Control antibiotic treatment conditions were those affording maximum protein aggregation (Ling et al., 2012). Cells were lysed and protein aggregates were isolated as previously described (Tomoyasu et al., 2001). The aggregates were separated with SDS-PAGE and visualized by silver staining.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
    Modulation of chemical composition and other parameters of the cell by growth rate
    1. H Bremer
    2. P Dennis
    (1996)
    In: F. C Neidhardt, J. L Ingraham, E. C. C Lin, K. B Low, B Magasanik, W. S Reznikoff, M Riley, M Schaechter, H. E Umbarger, editors. Escherichia Coli and Salmonella: Cellular and Molecular Biology. Washington: ASM Press. pp. 1553–1569.
  10. 10
    Ribosome pausing, arrest and rescue in Bacteria and eukaryotes
    1. AR Buskirk
    2. R Green
    (2017)
    Philosophical Transactions of the Royal Society B: Biological Sciences 372:20160183.
    https://doi.org/10.1098/rstb.2016.0183
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
    Protein analysis by shotgun proteomics
    1. Y Gao
    2. JR Yates
    (2019)
    In: W. A Tao, Y Zhang, editors. Mass Spectrometry-Based Chemical Proteomics. John Wiley & Sons. pp. 1–38.
    https://doi.org/10.1002/9781118970195
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
    Identification of translation start sites in bacterial genomes
    1. S Meydan
    2. D Klepacki
    3. AS Mankin
    4. N Vázquez-Laslop
    (2020)
    In: V. M Labunskyy, editors. Ribosome Profiling Methods in Molecular Biology. Springer. pp. 281–309.
    https://doi.org/10.1016/B978-0-12-803783-6.00009-2
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67
  68. 68
    The A-Z of bacterial translation inhibitors
    1. DN Wilson
    (2009)
    Critical Reviews in Biochemistry and Molecular Biology 44:393–433.
    https://doi.org/10.3109/10409230903307311
  69. 69
  70. 70
  71. 71

Decision letter

  1. Rachel Green
    Reviewing Editor; Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, United States
  2. Gisela Storz
    Senior Editor; National Institute of Child Health and Human Development, United States
  3. Rachel Green
    Reviewer; Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, United States

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

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Genome-wide effects of the antimicrobial peptide apidaecin on translation termination" for consideration by eLife. Your article has been reviewed by two peer reviewers, including Rachel Green as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.

We now have two reviews on your manuscript, one from myself, and the other from an expert in the field. While we normally would obtain three reviews, the congruence of these reviews was sufficient to return with a response. Overall, both reviewers appreciated the contribution that this work has the potential to make in defining the specificity and impact of apidaecin (Api) on bacterial ribosomes in living cells. Previous biochemical work from the group showed that Api traps deacylated tRNA in the P site, post peptide release, but where RFs remain bound in the A site. This work also showed that there was a broad shut down of termination in the lysates which was interpreted to reflect the sequestration of the RFs by the Api-bound ribosomes. This work uses ribosome profiling and mass spec to demonstrate that ribosomes do indeed accumulate (and stack) at stop codons in cells treated with Api, and that they occasionally read through the stop codon and generate new peptides (presumably those ribosomes that are stalled prior to peptide release). Overall, the work is strong but lacks much in the way of new insight into the role of Api in stopping translation. Both reviewers agreed that there are several points that would need to be addressed in order for this study to make novel contributions to our understanding. Given that addressing these points and the reviews below will require additional experimentation and is expected to take more than two months, the manuscript is being rejected at this time. However, please consider re-submitting to eLife if you are able to address the comments.

1) The analysis of specificity in Figure 3 seemed overstated. First, both reviewers were somewhat concerned that the SC metric included 3' UTR sequence in the analysis, given that readthrough on different mRNAs could be variable, and the location of next stop codon would also be variable, and that these features could differentially impact the SC values – also not sure that it makes any sense to include. A second concern was whether the differences in variance identified in panel B were real or rather were exaggerated by presentation of these data in linear rather than log format. Finally, it is critical that the motifs that appear to arise in the Api-treated cells don't show up in the untreated cells – otherwise it has nothing to do with Api and more to do with natural readthrough propensity transcriptome wide.

2) The collection of ribosomes at stop codons is likely heterogeneous – some are stalled with Api and an RF bound, the others contain peptidyl-tRNA and no RF. Yet these studies fail to separate these two classes and to analyze differences. Preparing ribosome profiling libraries with a salt wash would yield libraries enriched in ribosomes containing peptidyl-tRNAs. Alternatively, overexpression of RF1 might clear a subset of ribosomes and establish that the model is correct (not enough free RF1 to function in termination). The Pm experiment does not make sense. Why do ribosomes in the queue and the 3' UTR clear, but not at the stop codons. Those occupied by RFs might be resistant to Pm but those trapped by a lack of RF should be sensitive. These data make me concerned that the model is not correct.

3) Figuring out whether the overall model is correct should be possible using simple polysome analysis. The prediction is that RFs should be enriched in polysomal fractions when cells are treated with Api. This simple experiment could lend strong support to the models derived from in vitro experiments.

Reviewer #1:

This manuscript using ribosome profiling to characterize the impact of the antimicrobial peptide apidaecin (Api) on bacterial ribosomes in living cells. Previous structural studies have shown that Api binds in the vacant nascent peptide exit tunnel of the ribosome, approaching the PTC, but not directly occupying the catalytic site. Biochemical experiments in vitro showed that Api traps deacylated tRNA in the P site, post peptide release, but where the RFs remain bound in the A site. As such the ribosome is trapped in a post-release state, where the release of the nascent peptide allows binding of Api, thus trapping RF1/2 on the ribosome. However, it was also clear that treatment of lysates with Api led to the accumulation of peptidyl-RNAs, as though the actual release reaction were also inhibited. These ideas led to the model that trapping of RF1/2 on ribosomes would lead to its depletion in the cell and genome-wide build up of ribosomes at stop codons with no way to properly terminate. Here these models are tested.

The data presented are straightforward. The authors present ribosome profiling data with Api, with and without puromycin to independently release nascent peptides, and look at ribosome accumulation throughout the transcriptome. As anticipated, ribosomes accumulate at stop codons, and exhibit queuing behind the lead ribosome with a periodicity of about 27 nts. The authors analyze the stop codon (SC) peak size, observing broad increases for most genes, and then attempted to define some features that correlated with especially increased SC scores (including a Glycine residue at the terminal amino acid of the peptide chain, for example). The authors then characterized stop codon readthrough (RT) using the same data and the same types of analyses. Evidence for RT was corroborated by shotgun mass spec data revealing peptides that represent sequences found in the 3' UTRs. These were disproportionately in frame, though not dramatically so (and there was no statistical analysis to support this). These ideas were consistent with their initial model that depletion of RFs by Api stalled ribosomes would result in broad inhibition of termination, though those ribosomes would not be blocked per se by Api, and if they can escape the stop codon can continue elongating. The final section of the manuscript focuses on the regulation of various bacterial rescue pathways under treatment with Api, but arguing ultimately that none of these pathways can help deal with Api since all of them depend in some way on a termination reaction that utilizes a release factor or related protein (arfB), and this factor would similarly be inhibited by Api.

Overall, the manuscript was clearly written and presented some clear data on the mechanism of action of Api in vivo that is appealingly consistent with the models presented. However, the manuscript did not fully explore features of terminating ribosomes that might have better informed their models. The authors used Pm to show that elongating ribosomes were released from the ORF and to some extent from the 3'UTR (though many are left behind), though ribosomes at stop codons are resistant. These data are consistent with the idea that some ribosomes at the stop codon are bound by RF1/2 and are thus resistant to ribosome dissociation by Pm. The authors also argue however that there are different types of ribosomes stuck at stop codons.… those trapped with Api and those trapped because RF1/2 has been depleted. These should be differently sensitive to salt washing, as one class carries nascent peptide while the other does not. This experiment seems important since the analysis of the SC score will be confounded by these two populations of ribosomes, and so it is not clear whether the Glycine residue contributes to Api action on the stop codon or to preferential sensitivity to RF depletion. Another weakness of the manuscript is in the absence of any orthogonal approaches to define the model that they favor (depletion of RF1/2 on Api bound ribosomes). If RF1/2 are sequestered on ribosomes in the presence of Api, it seems likely that analysis of the distribution of RF1/2 on a polysome profile could provide strong support for such a model that would strengthen the conclusions of the manuscript.

The SC metric was defined by the ribosome density within the last three codons of the gene relative to average ORF density including the 40 nts of the 3 UTR downstream of the stop codon. This calculation was not clear to me – why do the authors include 3' UTR in this analysis.

Reviewer #2:

This manuscript by Mangano et al. describes ribosome profiling experiments that reveal the in vivo activities of apidaecin (Api), an antibiotic peptide in the PrAMP family. They show that treating E. coli cells with Api leads to dramatic enrichment of ribosome density at stop codons as well as the formation of queues of stalled ribosomes upstream of stop codons. Using ribosome profiling and proteomic approaches, they show that ribosomes often read through stop codons or frameshift to continue elongation. These data are largely confirmatory, supporting the model of Florian et al., 2017 that Api binds in the ribosome exit tunnel and traps release factors on the ribosome, titrating them away so that translational termination is globally inhibited.

I have a few concerns about the analyses as described below. In particular, they should test whether effects seen on UGA and other nearby sequence features are specific to Api or are more generally true during translational termination.

Subsection “Api acts as a global inhibitor of translation termination”: The authors state the increase in the stop codon peak shows that Api is "a potent global inhibitor of translation termination." It is possible that the peak at stop codons comes from pre-release ribosomes or post-release ribosomes that have not yet been recycled. In yeast, stop codon peaks arise primarily from post-release ribosomes (meaning recycling is slow, see Schuller and Green, 2017). The authors should state both of these options here and acknowledge that profiling cannot distinguish between them. Then they can argue later based on the readthrough and frameshifting data that these are in fact pre-release ribosomes.

Subsection “Api acts as a global inhibitor of translation termination”: The authors write "the Api treated cells show a much broader distribution compared to those of the untreated control." This is true on an absolute scale, of course, as we see in Figure 3B, but on a log2 scale (Figure 3A) the distributions seem quite similar. Api increases the signal, to be sure, but the relative distribution surrounding the signal remains the same. I argue that the relative distribution is what they really want, since it gives information about how local mRNA or peptide context affects termination.

Figure 3B and subsection “Api acts as a global inhibitor of translation termination”: The Api treated cells have fewer stalled ribosomes at UGA stop codons than UAA or UAG and the authors argue that Api might trap RF2 less efficiently. But this assumes that this effect is specific to Api. It looks to me like in Figure 3B that the same is true of untreated cells (less stalling at UGA than the others), arguing it is not an Api effect, but something intrinsic about UGA and RF2. This would be clearer if Figure 3B had a log2 scale on the y-axis.

Figure 3C and D show sequence features associated with high levels of stalling at stop codons in Api treated cells. What about untreated cells? This analysis would reveal if these features are general to termination or if they are an effect of Api specifically.

Subsection “Api acts as a global inhibitor of translation termination”: The authors cite Oh, 2011 to show that bacterial ribosome footprints are ~27 nt, but this depends on how they are prepared. What is the major footprint size in their data?

Subsection “Api-induced translation arrest at stop codons results in queuing of the elongating Ribosomes”: I don't understand the Pm results. They argue that Pm removes elongating ribosomes in the queue but leaves the stop codon peak intact. Indeed, the data in Figure 2—figure supplement 1A support this. But their model is that the ribosomes at stop codons still have peptidyl-tRNA (i.e. pre-release). So why are they not released by Pm?

Figure 4: Can the authors use the profiling data to identify mRNA features associated with read-through or frameshifting in the treated and untreated samples? Presumably the features are the same in both samples, just amplified by Api?

Figure 5C: is this really showing ribosomes at the end of transcripts? I'm not convinced. They could use publicly available Term-seq data (Sorek, 2016) or operon annotations to determine the 3'-ends. And a metagene plot would be more convincing than one or two anecdotal examples.

Subsection “Api treatment distorts the cell proteome and activates cellular ribosome rescue Systems”: In general start codon peaks vary in intensity, it's hard to say that this is really due to the antibiotic. One of the toeprints looks good, the other weak, but 2 mM is quite high.

Discussion section: But enhanced termination with -3 Arg should give you a lower SC score, which is backwards. -3 Arg and -1 Gly play a role in elongation stalling in the GIRAG sequence in SecM.

https://doi.org/10.7554/eLife.62655.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

We now have two reviews on your manuscript, one from myself, and the other from an expert in the field. While we normally would obtain three reviews, the congruence of these reviews was sufficient to return with a response. Overall, both reviewers appreciated the contribution that this work has the potential to make in defining the specificity and impact of apidaecin (Api) on bacterial ribosomes in living cells. Previous biochemical work from the group showed that Api traps deacylated tRNA in the P site, post peptide release, but where RFs remain bound in the A site. This work also showed that there was a broad shut down of termination in the lysates which was interpreted to reflect the sequestration of the RFs by the Api-bound ribosomes. This work uses ribosome profiling and mass spec to demonstrate that ribosomes do indeed accumulate (and stack) at stop codons in cells treated with Api, and that they occasionally read through the stop codon and generate new peptides (presumably those ribosomes that are stalled prior to peptide release). Overall, the work is strong but lacks much in the way of new insight into the role of Api in stopping translation. Both reviewers agreed that there are several points that would need to be addressed in order for this study to make novel contributions to our understanding. Given that addressing these points and the reviews below will require additional experimentation and is expected to take more than two months, the manuscript is being rejected at this time. However, please consider re-submitting to eLife if you are able to address the comments.

Thank you for carefully examining our work. Following your recommendation, we are resubmitting to eLife the expanded and revised manuscript.

1) The analysis of specificity in Figure 3 seemed overstated. First, both reviewers were somewhat concerned that the SC metric included 3' UTR sequence in the analysis, given that readthrough on different mRNAs could be variable, and the location of next stop codon would also be variable, and that these features could differentially impact the SC values – also not sure that it makes any sense to include. A second concern was whether the differences in variance identified in panel B were real or rather were exaggerated by presentation of these data in linear rather than log format. Finally, it is critical that the motifs that appear to arise in the Api-treated cells don't show up in the untreated cells – otherwise it has nothing to do with Api and more to do with natural readthrough propensity transcriptome wide.

We agree that our original SC metric was confusing and, following the reviewers’ suggestions, we have changed the scoring metrics. Specifically, we have excluded the 3’UTR reads calculating the new SC score by dividing the ribosome density (number of reads) at the last three codons by the total density in the entire ORF – from start to stop. Using the new metrics, the stop codon identity shows a milder effect upon the SC score and, therefore, we have deemphasized this point. Furthermore, to avoid the confusion noted by the reviewers, the data are now presented as a log2 transformed violin plot and are shown not in the main text figure, but in Figure 3—figure supplement 2.

In addition, following the reviewers advise, we now include the pLogo analysis of the C-terminal amino acid sequences of the high-SC scoring genes not only for the Api sample, but also for the untreated control, all based on the new SC scoring metrics. In this case, the statistically significant difference still remains. The results are included in panel B of the revised Figure 3.

2) The collection of ribosomes at stop codons is likely heterogeneous – some are stalled with Api and an RF bound, the others contain peptidyl-tRNA and no RF. Yet these studies fail to separate these two classes and to analyze differences. Preparing ribosome profiling libraries with a salt wash would yield libraries enriched in ribosomes containing peptidyl-tRNAs. Alternatively, overexpression of RF1 might clear a subset of ribosomes and establish that the model is correct (not enough free RF1 to function in termination). The Pm experiment does not make sense. Why do ribosomes in the queue and the 3' UTR clear, but not at the stop codons. Those occupied by RFs might be resistant to Pm but those trapped by a lack of RF should be sensitive. These data make me concerned that the model is not correct.

We fully agree that it would be interesting to know the ratio of pre- and post-hydrolysis ribosomes at stop codons in the Api-treated cells. However, this is a difficult question to address experimentally. Although ribosome profiling with a high-salt wash is an interesting idea, it is unknown whether Api bound ribosomes, where tunnel-lodged Api interacts with the P-site tRNA and the A-site RF1/2 would be susceptible to salt-induced dissociation. Furthermore, because the approaches for computing stop codon occupancy (SC score or metagene) use relative, not absolute values, any changes resulting from salt wash could be difficult to interpret.

The other suggestion – to overexpress RF1 – is also interesting. However, for the aforementioned reasons it is also unlikely to clarify the ratio of the pre- and post release ribosome complexes at stop codons. Furthermore, overexpression of class 1 release factors is known to render cells more resistant to Api (Matsumoto et al., 2017); the MIC difference would make it difficult to find the conditions for an accurate comparison of Api effects upon wt and RF-overexpressing cells.

Being unable to address the ratio of pre-and post-release ribosomes experimentally, we instead calculated the fraction of the stop codon footprints relative to all the mapped footprints and found that ~11% of them map to stop codons in the Api-treated cells (as opposed to ~2% in the control). The resulting fraction of stop codon-associated ribosomes roughly equals the estimated number of cellular RFs (RF2, to be more precise), in exponentially growing cells. Therefore, we believe that most of the ribosomes found at stop codons are associated with RFs and thus, have released the nascent protein.

Accordingly, we have rectified our model by proposing that the majority of stop codon ribosomes are post-release and Api-bound. While this has not changed the implications of our model, it has clarified it and we are thankful for bringing these considerations to our attention. We added a new figure (Figure 6) to illustrate our model.

We believe that out metagene plot of the Pmn-treated Api sample does make perfect sense. We appreciate, however, that some of the confusion could stem from the apparent increase in the magnitude of the ribosome peak at stop codons upon Pmn treatment (former Figure 2—figure supplement 1). Because the drug is expected to remove the ribosomes stalled in a pre-release state, it is natural to expect that the stop codon peak should decrease when the lysates are treated with Pmn. However, our metagene metrics utilizes the relative occupancy, where the scores are computed as a fraction of ribosomes at each nucleotide within the analyzed window. Because Pmn removes a significant fraction of queued and 3’-UTR ribosomes, the fraction of ribosomes at stop codon increases even if some of them are removed by Pmn.To avoid such a confusion, in the revised figure (Figure 2—figure supplement 2) we use a cartoon to illustrate the ribosomes that are removed by Pmn and use the % of normalized RPM for the y axis. We further clarify this point in the text and figure legend.

3) Figuring out whether the overall model is correct should be possible using simple polysome analysis. The prediction is that RFs should be enriched in polysomal fractions when cells are treated with Api. This simple experiment could lend strong support to the models derived from in vitro experiments.

This is a great idea, although we have implemented it in a slightly different format. Instead of analyzing RFs in the polysome fractions, which could be difficult to interpret, we have instead tested the amount of free RF (RF2, to be precise) in the cytoplasm of the Api-treated cells. After lysing the cells and pelleting ribosomes through a sucrose cushion, we tested for the amount of free RF2 in the supernatant. While a robust RF2 band is evident in the supernatant of untreated control cells, free RF2 was essentially undetectable in the Api-treated cells.This result, which is presented in Figure 4—figure supplement 3, strongly confirms our model that Api treatment leads to depletion of free class 1 RFs.

Reviewer #1:

This manuscript using ribosome profiling to characterize the impact of the antimicrobial peptide apidaecin (Api) on bacterial ribosomes in living cells. Previous structural studies have shown that Api binds in the vacant nascent peptide exit tunnel of the ribosome, approaching the PTC, but not directly occupying the catalytic site. Biochemical experiments in vitro showed that Api traps deacylated tRNA in the P site, post peptide release, but where the RFs remain bound in the A site. As such the ribosome is trapped in a post-release state, where the release of the nascent peptide allows binding of Api, thus trapping RF1/2 on the ribosome. However, it was also clear that treatment of lysates with Api led to the accumulation of peptidyl-RNAs, as though the actual release reaction were also inhibited. These ideas led to the model that trapping of RF1/2 on ribosomes would lead to its depletion in the cell and genome-wide build up of ribosomes at stop codons with no way to properly terminate. Here these models are tested.

The data presented are straightforward. The authors present ribosome profiling data with Api, with and without puromycin to independently release nascent peptides, and look at ribosome accumulation throughout the transcriptome. As anticipated, ribosomes accumulate at stop codons, and exhibit queuing behind the lead ribosome with a periodicity of about 27 nts. The authors analyze the stop codon (SC) peak size, observing broad increases for most genes, and then attempted to define some features that correlated with especially increased SC scores (including a Glycine residue at the terminal amino acid of the peptide chain, for example). The authors then characterized stop codon readthrough (RT) using the same data and the same types of analyses. Evidence for RT was corroborated by shotgun mass spec data revealing peptides that represent sequences found in the 3' UTRs. These were disproportionately in frame, though not dramatically so (and there was no statistical analysis to support this). These ideas were consistent with their initial model that depletion of RFs by Api stalled ribosomes would result in broad inhibition of termination, though those ribosomes would not be blocked per se by Api, and if they can escape the stop codon can continue elongating. The final section of the manuscript focuses on the regulation of various bacterial rescue pathways under treatment with Api, but arguing ultimately that none of these pathways can help deal with Api since all of them depend in some way on a termination reaction that utilizes a release factor or related protein (arfB), and this factor would similarly be inhibited by Api.

Overall, the manuscript was clearly written and presented some clear data on the mechanism of action of Api in vivo that is appealingly consistent with the models presented. However, the manuscript did not fully explore features of terminating ribosomes that might have better informed their models. The authors used Pm to show that elongating ribosomes were released from the ORF and to some extent from the 3'UTR (though many are left behind), though ribosomes at stop codons are resistant. These data are consistent with the idea that some ribosomes at the stop codon are bound by RF1/2 and are thus resistant to ribosome dissociation by Pm. The authors also argue however that there are different types of ribosomes stuck at stop codons.… those trapped with Api and those trapped because RF1/2 has been depleted. These should be differently sensitive to salt washing, as one class carries nascent peptide while the other does not. This experiment seems important since the analysis of the SC score will be confounded by these two populations of ribosomes, and so it is not clear whether the Glycine residue contributes to Api action on the stop codon or to preferential sensitivity to RF depletion.

We believe we have addressed most of these concerns in our comments above. As we indicated, we do not have a convincing way to assess the fraction of pre-and post-release ribosomes stalled at stop codons in the Api-treated cells, but our estimates based on the profiling data argue that the majority of them are stalled in the post-release Api-bound state. Nevertheless, we are not sure we have sufficient data to conclude whether C-terminal glycine increases the fraction of pre-or post-release ribosomes and we openly state this in the revised manuscript (subsection “Api treatment leads to pervasive stop codon readthrough”).

Another weakness of the manuscript is in the absence of any orthogonal approaches to define the model that they favor (depletion of RF1/2 on Api bound ribosomes). If RF1/2 are sequestered on ribosomes in the presence of Api, it seems likely that analysis of the distribution of RF1/2 on a polysome profile could provide strong support for such a model that would strengthen the conclusions of the manuscript.

The SC metric was defined by the ribosome density within the last three codons of the gene relative to average ORF density including the 40 nts of the 3 UTR downstream of the stop codon. This calculation was not clear to me – why do the authors include 3' UTR in this analysis.

We think that distribution of RF1/2 on polysome profiles would be hard to interpret.

However, an aforementioned conceptually-similar experiment inspired by this comment (Figure 4—figure supplement 3) convincingly shows sequestration of RFs on the ribosomes in Api-treated cells. In addition, as indicated above, we have changed the SC metric, now excluding the 3’ UTR.

Reviewer #2:

This manuscript by Mangano et al. describes ribosome profiling experiments that reveal the in vivo activities of apidaecin (Api), an antibiotic peptide in the PrAMP family. They show that treating E. coli cells with Api leads to dramatic enrichment of ribosome density at stop codons as well as the formation of queues of stalled ribosomes upstream of stop codons. Using ribosome profiling and proteomic approaches, they show that ribosomes often read through stop codons or frameshift to continue elongation. These data are largely confirmatory, supporting the model of Florian et al., 2017 that Api binds in the ribosome exit tunnel and traps release factors on the ribosome, titrating them away so that translational termination is globally inhibited.

We thank the reviewer for carefully evaluating our manuscript. Nevertheless, we respectfully disagree that our data are largely confirmatory. Firstly, prior to this work, most of what we knew about Api action came from in vitro biochemical experiments or microbiological MIC testing and the proposed models required large leaps of faith. The previous in vitro experiments, which involved artificial substrates, unnatural settings, and non-translating ribosomes were incapable to reveal or even predict the range and magnitude of the in vivo effects. Indeed, neither the pronounced ribosome queuing, nor the massive stop codon readthrough and synthesis of the extended proteins could be revealed in vitro. As an illustration, on the basis of the published biochemical data, we and others thought that Api could be directly used for mapping translation termination sites genome-wide. Little did we know! It is only with our new data that we could think of possible approaches, e.g. Pmn treatment, that could be exploited for achieving such a goal. Similarly, translation all the way to the mRNA 3’ ends and the massive, but futile, engagement of the rescue systems were practically impossible to predict. Thus, we think that our study significantly advances the understanding of how a specific inhibitor of translation termination affects global cellular translation.

I have a few concerns about the analyses as described below. In particular, they should test whether effects seen on UGA and other nearby sequence features are specific to Api or are more generally true during translational termination.

We thank the reviewer for bringing up this point. Indeed, after implementing the new scoring metric, we realized that firstly, the UGA-specific effects are less strong than we originally thought and secondly, they generally match the trend observed in the untreated cells. Therefore, in the revised manuscript we have significantly deemphasized the stalling effect at the UGA codons. On the other hand, the influence of C-terminal glycine appears to be authentic and Api-specific (revised Figure 3B). The comparison with the untreated control, which following the reviewer’s suggestion we have introduced in the revised manuscript, helps to make this point stronger.

Subsection “Api acts as a global inhibitor of translation termination”: The authors state the increase in the stop codon peak shows that Api is "a potent global inhibitor of translation termination." It is possible that the peak at stop codons comes from pre-release ribosomes or post-release ribosomes that have not yet been recycled. In yeast, stop codon peaks arise primarily from post-release ribosomes (meaning recycling is slow, see Schuller and Green, 2017). The authors should state both of these options here and acknowledge that profiling cannot distinguish between them. Then they can argue later based on the readthrough and frameshifting data that these are in fact pre-release ribosomes.

Because the fraction of stop-codon bound ribosomes roughly matches the number of

RFs, we believe that most of the stop codon footprints are accounted for by the post-release, Api- and RF-bound ribosomes. However, we agree that as in the untreated cells, some amount of the stop-codon associated ribosomes are there because of the slow recycling. We mentioned this possibility in the revised manuscript (subsection “Api treatment leads to pervasive stop codon readthrough”) and added the recommended reference.

Subsection “Api acts as a global inhibitor of translation termination”: The authors write "the Api-treated cells show a much broader distribution compared to those of the untreated control." This is true on an absolute scale, of course, as we see in Figure 3B, but on a log2 scale (Figure 3A) the distributions seem quite similar. Api increases the signal, to be sure, but the relative distribution surrounding the signal remains the same. I argue that the relative distribution is what they really want, since it gives information about how local mRNA or peptide context affects termination.

This is a very good point. We got caught up thinking about the SC and RT scores in non-logged scales. After log2 transformation, the SC score distributions are indeed similar and in the revised manuscript we have deemphasized the difference in the range of distribution between the Api and control samples. Importantly, some of the context-specificity trends appear to be Api-specific indicating that Api does not simply amplify the intrinsic difference in the termination rate at stop codons of different genes.

Figure 3B and subsection “Api acts as a global inhibitor of translation termination”: The Api treated cells have fewer stalled ribosomes at UGA stop codons than UAA or UAG and the authors argue that Api might trap RF2 less efficiently. But this assumes that this effect is specific to Api. It looks to me like in Figure 3B that the same is true of untreated cells (less stalling at UGA than the others), arguing it is not an Api effect, but something intrinsic about UGA and RF2. This would be clearer if Figure 3B had a log2 scale on the y-axis.

As suggested by the reviewer, we have converted the scale to log2 and have deemphasized the stop codon-specificity of the observed effects.

Figure 3C and D show sequence features associated with high levels of stalling at stop codons in Api treated cells. What about untreated cells? This analysis would reveal if these features are general to termination or if they are an effect of Api specifically.

We completely agree with the reviewer that comparison with the untreated control is critical and have included it in all the relevant figures, including Figure 3.

Subsection “Api acts as a global inhibitor of translation termination”: The authors cite Oh, 2011 to show that bacterial ribosome footprints are ~27 nt, but this depends on how they are prepared. What is the major footprint size in their data?

The read length distribution in our profiling experiments was fairly broad: it peaked at 27 nt but had a long tail of longer reads. In retrospect, and in view of more recent papers, analyzing longer reads from the di- and trisome peaks could be informative and we are considering carrying such experiment(s) in future. Meanwhile, in the revised manuscript we have included a supplementary figure showing read length distribution (Figure 2—figure supplement 1).

Subsection “Api-induced translation arrest at stop codons results in queuing of the elongating Ribosomes”: I don't understand the Pm results. They argue that Pm removes elongating ribosomes in the queue but leaves the stop codon peak intact. Indeed the data in Figure 2—figure supplement 1A support this. But their model is that the ribosomes at stop codons still have peptidyl-tRNA (i.e. pre-release). So why are they not released by Pm?

We assume full responsibility for not explaining this result in sufficiently clear terms in the original manuscript, but have done our best to fix this problem in the revised paper. Please see our response to a similar comment of the editor (above). In brief, Pmn treatment likely does remove the pre-release fraction of stop codon-associated ribosomes. The confusing increase in the height of the post-Pmn peak at stop codons is due to the fact that it represents normalized, not absolute, reads. Removal of the queued and 3’-UTR ribosomes leads to an increase in the relative amount of the stop codon-bound ribosomes. We have clarified this point by adding a cartoon to the figure (Figure 2—figure supplement 2), using % of normalized reads for the y axis and providing a better explanation in the legend and the manuscript text.

Figure 4: Can the authors use the profiling data to identify mRNA features associated with read-through or frameshifting in the treated and untreated samples? Presumably the features are the same in both samples, just amplified by Api?

This is a good point. However, after re-examining the data and comparing the Api samples with the untreated controls we were unable to identify any strong trends that would be amplified or altered by Api. Therefore, we avoided discussing them in the paper.

Figure 5C: is this really showing ribosomes at the end of transcripts? I'm not convinced. They could use publicly available Term-seq data (Sorek, 2016) or operon annotations to determine the 3'-ends. And a metagene plot would be more convincing than one or two anecdotal examples.

We thank the reviewer for this insightful comment and suggestion of using Term-seq data. The reanalysis of our results using the E. coli Term-seq data showed a clear and robust increase in the occupancy of the mRNA 3’-proximal segments by the translating ribosomes. We included these new results in panel D of the main Figure 5.

Subsection “Api treatment distorts the cell proteome and activates cellular ribosome rescue Systems”: In general start codon peaks vary in intensity, it's hard to say that this is really due to the antibiotic. One of the toeprints looks good, the other weak, but 2 mM is quite high.

The reviewer is correct that start codon peaks vary significantly, both in the control and in Api samples. It is also true that we could reproduce in vitro some of the start codons effects only at a very high concentration of Api (2 mM). This is the reason why we discuss the start codon effects only tentatively. Nevertheless, we felt it was important to bring to the reader’s attention a possibility that due to its binding in the vacant exit tunnel, Api could have other effects on translation rather than only RF trapping.

Discussion section: But enhanced termination with -3 Arg should give you a lower SC score, which is backwards. -3 Arg and -1 Gly play a role in elongation stalling in the GIRAG sequence in SecM.

We agree that the original explanation was confusing. We have now streamlined the discussion of the context-specific effects.

https://doi.org/10.7554/eLife.62655.sa2

Article and author information

Author details

  1. Kyle Mangano

    1. Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Formal analysis, Investigation, Methodology, Writing - original draft
    Contributed equally with
    Tanja Florin
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9862-2374
  2. Tanja Florin

    Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Conceptualization, Investigation, Methodology, Writing - original draft
    Contributed equally with
    Kyle Mangano
    Competing interests
    No competing interests declared
  3. Xinhao Shao

    Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Software, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Dorota Klepacki

    Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Irina Chelysheva

    Institute of Biochemistry and Molecular Biology, University of Hamburg, Hamburg, Germany
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared
  6. Zoya Ignatova

    Institute of Biochemistry and Molecular Biology, University of Hamburg, Hamburg, Germany
    Contribution
    Formal analysis, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9478-8825
  7. Yu Gao

    Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Formal analysis, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3220-4721
  8. Alexander S Mankin

    1. Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - review and editing
    For correspondence
    shura@uic.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3301-827X
  9. Nora Vázquez-Laslop

    1. Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Writing - review and editing
    For correspondence
    nvazquez@uic.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2256-693X

Funding

National Science Foundation (MCB 1951405)

  • Alexander S Mankin
  • Nora Vázquez-Laslop

National Institutes of Health (R35 GM133416)

  • Yu Gao

National Institutes of Health (5T32AT007533-07)

  • Kyle Mangano

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

Acknowledgements

We thank Dr. Mark Maienschein-Cline (University of Illinois at Chicago) for assistance with the NGS analysis and GALAXY pipeline, Dr. Mo Hu (Northwestern University Feinberg School of Medicine) for help with statistics modeling analysis of the proteomics data, Dr. Cruz-Vera (University of Alabama, Huntsville) for providing anti-RF2 antibodies, and members of Mankin/Vàzquez-Laslop and Polikanov labs for discussion. This work was supported by the grant from the National Science Foundation (to NV-L and ASM) MCB 1951405, and the grant from the National Institutes of Health R35 GM133416 (to YG); KM was supported in part by Research Training grant 5T32AT007533-07 in Natural Product Complementary and Integrative Health from the Office of the Director, National Institutes of Health, National Center For Complementary and Integrative Health.

Senior Editor

  1. Gisela Storz, National Institute of Child Health and Human Development, United States

Reviewing Editor

  1. Rachel Green, Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, United States

Reviewer

  1. Rachel Green, Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, United States

Publication history

  1. Received: September 1, 2020
  2. Accepted: September 20, 2020
  3. Version of Record published: October 8, 2020 (version 1)

Copyright

© 2020, Mangano et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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