https://orcid.org/0000-0002-5341-9329
Figures
Abstract
Cytoplasmic poly(A)-binding protein (PABPC; Pab1 in yeast) is thought to be involved in multiple steps of post-transcriptional control, including translation initiation, translation termination, and mRNA decay. To understand both the direct and indirect roles of PABPC in more detail, we have employed mass spectrometry to assess the abundance of the components of the yeast proteome, as well as RNA-Seq and Ribo-Seq to analyze changes in the abundance and translation of the yeast transcriptome, in cells lacking the PAB1 gene. We find that pab1Δ cells manifest drastic changes in the proteome and transcriptome, as well as defects in translation initiation and termination. Defects in translation initiation and the stabilization of specific classes of mRNAs in pab1Δ cells appear to be partly indirect consequences of reduced levels of specific initiation factors, decapping activators, and components of the deadenylation complex in addition to the general loss of Pab1’s direct role in these processes. Cells devoid of Pab1 also manifested a nonsense codon readthrough phenotype indicative of a defect in translation termination. Collectively, our results indicate that, unlike the loss of simpler regulatory proteins, elimination of cellular Pab1 is profoundly pleiotropic and disruptive to numerous aspects of post-transcriptional regulation.
Author summary
Many human diseases are caused by having too much or too little of certain cellular proteins. The amount of an individual protein is influenced by the level of its messenger mRNA (mRNA) and the efficiency of translation of the mRNA into a polypeptide chain by the ribosomes. Cytoplasmic poly(A)-binding protein (PABPC) plays numerous roles in the regulation of this multi-staged process, but understanding its specific role has been challenging because it is sometimes unclear whether experimental results are related to PABPC’s direct role in a specific biochemical process or to indirect effects of its other roles, leading to conflicting models of PABPC’s functions between studies. Here, we characterized defects in different stages of protein synthesis in response to loss of PABPC in yeast cells by measuring whole-cell levels of mRNAs, ribosome-associated mRNAs, and proteins. We demonstrated that defects in most steps of protein synthesis other than the last can be explained by reduced levels of mRNAs that code for proteins important for that step in addition to loss of PABPC’s direct role on that step. Our data and analyses serve as resources aiding interpretation of experiments in which PABPC levels have been manipulated and generally point to the resulting complexity of such manipulations.
Citation: Mangkalaphiban K, Ganesan R, Jacobson A (2024) Pleiotropic effects of PAB1 deletion: Extensive changes in the yeast proteome, transcriptome, and translatome. PLoS Genet 20(9): e1011392. https://doi.org/10.1371/journal.pgen.1011392
Editor: Anita K. Hopper, Ohio State University, UNITED STATES OF AMERICA
Received: November 1, 2023; Accepted: August 11, 2024; Published: September 5, 2024
Copyright: © 2024 Mangkalaphiban et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Raw sequencing data have been deposited at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under accession numbers GSE229691 and GSE229692. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD041495 and 10.6019/PXD041495. Analysis scripts and data used to generate figures are available at https://github.com/Jacobson-Lab/Pab1_deletion.
Funding: This work was supported by grants to A.J. (1R35GM122468 and 1R35GM148277) from the U.S. National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.J. is co-founder, director, and consultant for PTC Therapeutics Inc.
Introduction
Eukaryotic mRNAs are subject to complex post-transcriptional regulation by RNA binding proteins (RBPs) that control protein output. Cytoplasmic poly(A)-binding proteins (PABPCs) are RBPs that bind polyadenylated tails at mRNA 3’-ends and subsequently play roles in multiple stages of cytoplasmic mRNA regulation, from translation initiation to termination and mRNA decay [1–3]. The numerous functions of PABPC are attributed to its ability to interact with not only mRNAs but also various other proteins to form messenger ribonucleoprotein (mRNP) complexes [2]. PABPC’s conserved structure consists of three regions: i) four RNA recognition motif (RRM) domains, two of which (RRM1 and RRM2) bind 12 adenosines with high affinity [3,4], while the protein overall covers 27 adenosines [5], ii) a proline-rich (P) linker domain, and iii) a C-terminal mademoiselle (MLLE) domain that interacts with other proteins via their PABP-interacting motif 2 (PAM2) [2,3,6]. Mammals have multiple isoforms of PABPCs, of which the most ubiquitous isoform as well as the most studied is PABPC1, whereas the yeast Saccharomyces cerevisiae has only one PABPC, Pab1 [2,3,6,7].
The current model for general mRNA decay in yeast involves biphasic poly(A) tail shortening by Pan2-Pan3 and Ccr4-Not deadenylase complexes, decapping by the Dcp1/Dcp2 holoenzyme, and exonucleolytic Xrn1-mediated 5’-3’ degradation or exosome-mediated 3’-5’ degradation [3,8,9]. PABPC appears to have a paradoxical role in these processes. On the one hand, PABPC1/Pab1 stimulates deadenylation by recruiting Pan2-Pan3 to the poly(A) tail through its interaction with the PAM2 motif on Pan3 [1,3,8,10,11]. Consistent with this model, mRNAs in yeast cells harboring a deletion of PAB1 or mutations of Pab1’s C-terminal Pan3-interacting domain had longer poly(A) tails than their counterparts in wild-type cells [1–3,12–14]. On the other hand, PABPC has been shown to protect mRNAs from exonucleases [3,8].
Importantly, mRNAs with longer poly(A) tails are generally more stable and better translated than their short-tailed or un-tailed counterparts [3,15–23], observations that led us to propose a role for PABPC in the enhancement of translation initiation by the possible formation of an mRNA “closed-loop” [23–25]. Current elaborations of this model postulate that mRNAs could be circularized by a chain of interactions between poly(A)-associated PABPC and 5’ end-localized initiation factors, giving rise to a poly(A) tail-PABPC-eIF4G-eIF4E-5’cap network [2,26–30]. eIF4G has been shown to interact with PABPC’s RRM2 domain [26,31–33], and disrupting this interaction reduced translation [6,29,34]. Thus, PABPC can stabilize the cap-binding complex and aid the recruitment of the 43S pre-initiation complex to the mRNA [2,3,35]. However, this closed-loop arrangement is not required for all mRNAs or all conditions, raising the question of how else 5’-3’ communication is facilitated or whether it is indeed a universal step [30,36–39]. Depletion of PABPC1 in mammalian cells had minimal effects on transcriptome-wide translation efficiency [40,41], suggesting that the stimulatory effect of PABPC on translation initiation may be restricted to circumstances where translation initiation efficiency is rate-limited.
In addition to its interactions with initiation factors, PABPC also interacts with the release factor eRF3 via its PAM2 motif in metazoans and its P-C domains in yeast [2,6,42–45]. Translation termination involves stop codon recognition in the ribosomal A-site and nascent peptide release by eRF1, whose hydrolysis function and conformational change are stimulated by eRF3’s GTPase activity [46,47]. PABPC is thought to enhance termination efficiency by promoting the recruitment of the eRF1-eRF3 release factor complex to the stop codon. The role of PABPC in termination is also inferred from: i) the observation that tethering PABPC1 or Pab1 downstream of premature termination codons (PTCs) antagonized nonsense-mediated mRNA decay (NMD), an mRNA decay pathway thought to be activated by the reduced termination efficiency of premature translation termination [48,49] and ii) the increased termination efficiency observed with proximity of a stop codon to the mRNA 3’ end [50,51]. Direct evidence for PABPC’s ability to enhance termination includes: i) addition of PABPC1 to an in vitro termination assay improved termination efficiency [52], ii) PABP-interacting protein PAIP1 and PAIP2 competed with eRF3 for free PABPC binding, reducing termination efficiency of PTCs in vitro [53], and iii) deletion of PAB1 or Pab1’s P-C domains in vivo increased stop codon readthrough efficiency of reporter PTCs in a proximity-dependent manner [51]. However, as with initiation, a full understanding of PABPC’s role during termination of endogenous mRNAs in vivo is still lacking.
PABPC’s involvement in many major stages of mRNA regulation and translation complicate attempts to define a specific role for PABPC in vivo by deleting, depleting, overexpressing, or mutating the protein and have led to conflicting models of PABPC’s function. Therefore, to specifically assess direct and indirect consequences of deleting PABPC, we generated and analyzed mass spectrometry, RNA-Seq, and ribosome profiling [54] data from yeast cells lacking Pab1. As expected, protein and mRNA abundance changed substantially in pab1Δ cells. We found that deleting PAB1 resulted in a translation termination defect that appears to be at least partially due to reduced eRF3 protein level as well as to loss of Pab1’s stimulatory function on termination. In addition, translation initiation defects and changes in relative translation efficiency in pab1Δ cells may be confounded by reduced initiation factor levels, especially eIF4G and eIF1. Further, an analysis of decapping activator substrates revealed that increased levels of certain mRNA subgroups may be partially caused by reduced levels of a specific decapping activator or components of Ccr4-Not deadenylation complex. Together, our results catalog the consequences of PAB1 deletion and illustrate the complexity of Pab1’s pleiotropic effects on the transcriptome-wide regulation of translation and mRNA decay.
Results
Deletion of PAB1 results in significant changes in the yeast proteome and transcriptome
To investigate proteome- and transcriptome-wide abundance changes when Pab1 is absent, we performed mass spectrometry, RNA-Seq, and ribosome profiling analyses of yeast cells harboring a PAB1 deletion. Because PAB1 is an essential gene, the deletion was created in a pbp1Δ background, which suppresses pab1Δ lethality [55], and we used the PAB1/pbp1Δ strain as our wild-type PAB1 control. Proteomic and transcriptomic data obtained from three biological replicates of each strain were reproducible, as evidenced by high Pearson’s correlation coefficients between replicates (S1 Fig). Differential expression analyses were performed for mass spectrometry and RNA-Seq data for each pair of yeast strains to assess relative changes in the abundance of specific proteins and mRNAs (Figs 1–3 and S2). For proteins that were detectable by mass spectrometry, relative changes in their mRNA and protein abundance are quite consistent with each other, with Spearman’s correlation of 0.62–0.74 (Figs 1C and S2C). Similarly, relative changes in ribosome profiling reads are also consistent with protein abundance changes, with Spearman’s correlation of 0.6–0.72 (Figs 1D and S2D).
A. Volcano plot of changes in proteome (mass spectrometry data) between pab1Δpbp1Δ and pbp1Δ strains. Orange, purple, and grey dots represent proteins with higher abundance (positive log2 fold change, adjusted p-value < 0.015), lower abundance (negative log2 fold change, adjusted p-value < 0.015), and no change (adjusted p-value ≥ 0.015), respectively, in the pab1Δpbp1Δ strain. B. Volcano plot of changes in transcriptome (RNA-Seq data) between pab1Δpbp1Δ and pbp1Δ strains. Orange, purple, and grey dots represent mRNAs with higher abundance (positive log2 fold change, adjusted p-value < 0.01), lower abundance (negative log2 fold change, adjusted p-value < 0.01), and no change (adjusted p-value ≥ 0.01), respectively, in the pab1Δpbp1Δ strain. C. Comparison of log2 fold change in transcriptome (RNA-Seq reads) and proteome (mass spectrometry quantification), with Spearman’s correlation coefficient. D. Comparison of log2 fold change in ribosome profiling (Ribo-Seq) reads and proteome (mass spectrometry quantification), with Spearman’s correlation coefficient. For C and D: Grey, genes whose mRNA and protein abundance remained unchanged. Blue, genes whose protein but not mRNA abundance changed significantly. Red, genes whose mRNA but not protein abundance changed significantly. Green, genes whose mRNA and protein abundance both changed significantly.
Pbp1 is a Pab1-interacting protein that has been implicated in polyadenylation, cell growth in non-fermentable carbon source, and mitochondrial biogenesis [55–57]. Consistent with previous findings, deletion of PBP1 has a minimal impact on mRNA and protein abundance relative to isogenic wild-type (WT) cells (Figs S1 and S2A left panel, and S2B left panel) grown in YEPD media. Additionally, gene ontology analyses revealed that proteins that were down-regulated are associated with mitochondrial-related pathways (S1 Table and S3 Fig). On the other hand, as expected of Pab1’s essential and extensive role in mRNA stability regulation, relative protein and mRNA levels changed drastically when Pab1 was absent (Figs 1A, 1B, S2A right panel, and S2B right panel). Gene ontology analyses showed that up-regulated proteins belong to metabolic pathways, while down-regulated proteins are related to ribosome biogenesis, translation, and RNA-binding (S1 Table and S4 Fig). Indeed, the pab1Δ mutation led to substantial reduction in the levels of ribosomal proteins (Fig 2A, bottom left), multiple initiation factors (Fig 2A, top left), and some mRNA decay factors (Fig 2B). These results raised the question of whether the roles of Pab1 in translation and mRNA stability borne out of Pab1 perturbation experiments are at least partially attributable to this reduction in key proteins in these pathways in addition to Pab1’s direct roles in these processes. Hence, we explored the phenotypes of mRNA decay, translation initiation, translation efficiency, and translation termination upon PAB1 deletion, focusing on analyses of pab1Δpbp1Δ cells relative to pbp1Δ cells, and then considered whether the observed changes were direct or indirect effects of PAB1 deletion.
Data as in Fig 1C, with the focus on translation-related genes (A) or mRNA decay-related genes (B). The ribosomal proteins depicted here account for 94% of all ribosomal proteins that make up the 40S and 60S subunits.
Substrates of decapping activators Pat1/Lsm1 and Upf1/Upf2/Upf3 tend to be more increased than decreased in response to PAB1 deletion
Among its many functions, PABPC has important regulatory roles in mRNA decay [1–3]. Hence, we asked how the absence of Pab1 impacts the levels of mRNAs that are substrates of different decapping activators, namely Dhh1, Pat1/Lsm1, and the Upf factors of the NMD pathway. Dhh1, Pat1/Lsm1, and NMD substrates are defined respectively as mRNAs whose levels were increased in dhh1Δ cells, commonly increased in pat1Δ and lsm1Δ cells, and commonly increased in upf1Δ, upf2Δ, and upf3Δ cells, relative to WT [58,59]. Approximately half of the mRNAs in each group showed significant changes in abundance in pab1Δpbp1Δ relative to pbp1Δ cells (Fig 3). Of mRNAs that showed significant changes, those that are targets of either Pat1/Lsm1 or NMD or both tend to be increased rather than decreased (Fig 3, row 2, columns 3–8, compare Up (orange) to Down (purple)). This trend does not apply to the Dhh1-only substrates, however, since there seem to be comparable proportions of Dhh1-only substrates that are increased vs. decreased (29% vs. 21%) (Fig 3, row 2, column 2, compare Up (orange) to Down (purple)).
mRNA abundance and translation efficiency changes between pab1Δpbp1Δ and pbp1Δ strains for Dhh1, Pat/Lsm1, and NMD substrates. (Row 1) Panel indicating substrate status (green) of panels below. A substrate is defined as an mRNA that is upregulated upon decapping deactivator gene deletion [9,59]. Dhh1: mRNAs upregulated in a dhh1Δ strain relative to WT [59]; Pat1/Lsm1: mRNAs commonly upregulated in pat1Δ and lsm1Δ strains relative to WT [59]; NMD: mRNAs commonly upregulated in upf1Δ, upf2Δ, and upf3Δ strains relative to WT [58]. (Row 2) Proportions of mRNA abundance changes from Fig 1B: Up (orange), Down (purple), and Unchanged (grey), separated into columns by substrate status. (Row 3) Distribution of log2 fold change in TE between pab1Δpbp1Δ and pbp1Δ strains for mRNA groups from Row 2 panel. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare median log2 fold change in TE to zero (no change, red dashed line). (Row 4) Distribution of codon optimality score for mRNA groups from Row 2 panel. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare median codon optimality score in each group to the sample’s mean score (red dashed line). Significant levels were reported as the following: (ns) not significant, (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001.
To determine whether increases in the abundance of Pat1/Lsm1 and Dhh1 substrates, which follow the canonical deadenylation-dependent pathway, were due to loss of Pab1’s role in deadenylase recruitment or dysregulation of decay pathway components, we investigated changes in mRNA and protein levels of genes involved in decapping, deadenylation, and decay (Fig 2B). We found that while decapping enzyme subunits (Dcp1 and Dcp2), Pat1, Lsm1, Dhh1, the exonuclease Xrn1, and exosome components are not significantly enriched or depleted at the protein level, two proteins that are components of the Ccr4-Not deadenylase complex, Mot2 (also commonly known as Not4) and Not5, are significantly depleted to ~77–80% of the amount in pbp1Δ cells (Fig 2B). Not4 and Not5 are thought to link slow translation elongation of non-optimal codons to deadenylation as well as deadenylation to decapping [3]. Specifically, Dhh1’s association with the ribosome requires Not5’s ribosome binding and Not4’s E3 ligase activity to ubiquitinate 40S ribosomal subunit protein eS7 [60]. Not5 has also been shown to bind Pat1 to promote decapping [61]. Thus, stabilization of Pat1/Lsm1 and some Dhh1 substrates may be partially attributable to dysregulated Ccr4-Not components, in addition to loss of Pab1’s role in deadenylase recruitment.
Because decapping of NMD substrates is usually a deadenylation-independent mechanism that is triggered by premature translation termination [9,62,63], stabilization of NMD substrates in pab1Δpbp1Δ cells is likely unrelated to Pab1’s role in protecting the poly(A) tail or deadenylase recruitment. Rather, it is likely due to decreases in translation, which lead to decreased frequency of premature termination. To test whether NMD substrates have a decreased initiation rate in the absence of Pab1, we calculated translation efficiency (TE) of each mRNA in each yeast strain by normalizing ribosome profiling reads in the protein-coding (CDS) region to mRNA level and compared relative log2 fold change in TE between pab1Δpbp1Δ and pbp1Δ strains for each group of mRNAs to the value of unchanged TE (log2 fold change = 0) (Fig 3, row 3). Consistent with our hypothesis, we found that NMD substrates that are significantly stabilized or have unchanged mRNA levels in pab1Δpbp1Δ have relatively lower TE while the minority that are depleted have relatively unchanged TE (Fig 3, row 3, column 4). However, reduced TE is probably not the only contribution to NMD substrate enrichment, as the protein level of the key NMD protein Upf1 is significantly reduced in pab1Δpbp1Δ cells to ~85% of the level in pbp1Δ cells (Fig 2B).
Since rapid mRNA decay can be triggered by slow translation elongation while mRNAs can evade deadenylation when they have optimal codons and are efficiently translated [64,65,3], we wondered whether mRNAs otherwise stabilized in WT cells would be destabilized in the absence of Pab1. We investigated whether mRNAs down-regulated in pab1Δpbp1Δ cells, such as mRNAs of ribosomal proteins, are such “optimal transcripts” by calculating a codon optimality score for each transcript (Fig 3, row 4). Except for NMD substrates which are known to be non-optimal [58], Dhh1 and Pat1/Lsm1 substrates, and non-substrates in the Down group all have higher codon optimality scores than the sample average (Fig 3, row 4), suggesting that they are more sensitive to PAB1 deletion.
Overall, we demonstrated that targets of mRNA decay pathways followed the expected phenotypes upon PAB1 deletion, namely the tendency to stabilize, suggesting direct role of Pab1 in mRNA decay regulation. However, the depletion of some factors in the pathways upon PAB1 deletion cannot be completely ruled out as an indirect effect of this stabilization.
Deletion of PAB1 leads to translation initiation defects but has minimal effects on translation efficiency
Pab1 is thought to promote translation initiation by aiding the association of the 40S ribosomal subunit and the eIF4F complex with the mRNA 5’ cap through a direct interaction with eIF4G [2,26,29,30,66,67]. Translation initiation defects may thus be expected when PAB1 is deleted. To investigate translation initiation, we analyzed ribosome profiling data of WT, pbp1Δ, and pab1Δpbp1Δ cells. Consistent with the expectation of translating ribosomes, ribosome footprints in all strains were found mostly with their P-site locations in the coding region (CDS), showed 3-nt periodicity, and consisted predominantly of footprints in the main reading frame (frame 0) (Fig 4). However, we observed subtle increased accumulation of ribosomes at the canonical AUG start codon and in the 5’-UTR region in the pab1Δpbp1Δ strain (Fig 4B), indicating a possible translation initiation defect in the absence of Pab1.
A. Ribosome footprints (replicate libraries were pooled) were counted by their P-site positions in the indicated nucleotide window around the canonical CDS’s start and stop coordinates (the first nucleotide of AUG and the last nucleotide of the last amino acid-encoding codon) of annotated ORFs. Raw footprint counts were normalized by the total footprint count in the windows. Inset: Magnified view of the 3’-UTR region (Distance from stop > 0 nt). B-C. Percentage of footprints in sequencing library belonging in different mRNA regions (B) and percentage of frame 0 footprints in each mRNA region, where grey dashed line indicates a theoretical 33% at which all 3 reading frames are equally represented (C). “Start” region includes the canonical AUG and 3 flanking nucleotides on each side. “Stop” region includes the canonical stop codon and 3 flanking nucleotides on each side. “Extension” is the region following the “Stop” until (but not including) the first in-frame stop codon in the 3’-UTR. “Distal 3’-UTR” is the 3’-UTR region following “Extension.” Percentages from individual replicate libraries (grey points) were averaged (bar plot and reported value above it). Unpaired Student’s t-test with Benjamini-Hochberg method for multiple-testing correction was used to compare values between all possible pairwise strains. Only significant comparisons were reported as the following: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001.
To determine whether deletion of PAB1 affected initiation rates of all mRNAs equally, we assessed the change in relative translation efficiency (TE) of each mRNA between pab1Δpbp1Δ and pbp1Δ strains (Fig 5A). Although ~500 mRNAs show substantive increases (“Up”) or decreases (“Down”) in relative TE, most mRNAs (>90% of the transcriptome) do not show significant changes in relative TE (Fig 5A, grey). The mostly unchanged relative TE is in line with the observation that the correlation between ribosome profiling read changes and protein abundance changes is only slightly better than that between RNA-Seq read (mRNA abundance) changes and protein abundance changes (Fig 1C and 1D). Additionally, when stratifying by TE changes (S5 Fig), the mRNAs with significant relative TE changes show more concordance in Ribo-Seq reads and protein abundance (S5B Fig, left) compared to either RNA-Seq reads and protein abundance (S5B Fig, right) or to the mRNAs with unchanged TE (S5A Fig, left). These results indicate that the absence of Pab1 affects the initiation process of most mRNAs to the same extent, such that the number of ribosomes recovered for a particular mRNA ORF remains proportional to the mRNA level. This observation is consistent with previous reports of human cells depleted of PABPC [40,41].
A. Volcano plot of changes in relative translation efficiency (TE) between pab1Δpbp1Δ and pbp1Δ strains. Orange, purple, and grey dots represent mRNAs with increased (positive log2 fold change, adjusted p-value < 0.05), decreased (negative log2 fold change, adjusted p-value < 0.05), and unchanged TE (adjusted p-value ≥ 0.05), respectively, in the pab1Δpbp1Δ strain. B-E. Distribution of the pbp1Δ strain’s mRNA abundance, log10 (RPKM) from RNA-Seq data (B), mRNA abundance changes between pab1Δpbp1Δ and pbp1Δ cells (C), TE changes between eIF4G depleted (eIF4Gd) cells and isogenic WT cells [68] (D), and fold enrichment in eIF4G or Pab1 RIP-seq [36] (E) in each mRNA TE group from A. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare values between pairwise groups. Only significant comparisons were reported as the following: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001. F. Proportion and number of mRNAs from groups in A that were enriched (positive log2 fold change, FDR < 0.05), depleted (negative log2 fold change, FDR < 0.05), or unchanged (FDR ≥ 0.05) in RIP-seq data [36]. Pairwise χ2 test with Benjamini-Hochberg method for multiple-testing correction was used to compare between Reference, Up, and Down groups. p < 0.05 in all pairwise comparisons (exact values provided in S3 Table). For B-F, Reference (Ref.) group includes all mRNAs regardless of TE changes (Up + Down + Unchanged) to recapitulate the general distribution of measured values in the transcriptome and only spliced mRNA entries were considered.
To further investigate how deletion of PAB1 impacts translation initiation, we characterized 470 completely spliced mRNAs that showed significant increases (“Up”) or decreases (“Down”) in relative TE in response to PAB1 deletion. We found that mRNAs with increased TE are those that are generally more abundant in the WT Pab1 condition (pbp1Δ cells) (Fig 5B, orange) but become relatively depleted upon PAB1 deletion (Fig 5C, orange). Gene ontology analysis revealed that these mRNAs are related to metabolism, catabolism, biosynthetic process, and ribosomes (S2 Table and S6 Fig). On the other hand, mRNAs with decreased TE have average abundance in pbp1Δ cells (Fig 5B, purple) but become relatively more abundant in pab1Δpbp1Δ cells (Fig 5C, purple). These mRNAs are related to ion transmembrane transport and ion homeostasis (S2 Table and S6 Fig). These observations imply that there may be a mechanism to translationally upregulate or downregulate these mRNAs to compensate for the relative decrease or increase in mRNA levels, respectively, to prevent too much fluctuation of protein levels.
However, it is unclear whether the translation efficiency phenotypes characterized in pab1Δpbp1Δ cells follow from the loss of Pab1’s direct role in translation initiation, as we cannot rule out an indirect effect of PAB1 deletion on translation initiation factor levels. As shown in the analysis of transcriptome and proteome changes in response to PAB1 deletion, some initiation factors were reduced at both mRNA and protein levels (Fig 2A, top left). These disproportionate initiation factor levels could potentially cause a global reduction in translation and the appearance of unchanged relative TE. Notably, among the most reduced initiation factors are the two paralogs of eIF4G (eIF4G1/Tif4631 and eIF4G2/Tif4632), a binding partner of Pab1.
Reduction of initiation factor levels confound the effects of PAB1 deletion on translation efficiency
To determine whether the reduction in eIF4G level can be ruled out as a possible explanation for the observed translation efficiency phenotypes, we again focused on 470 completely spliced mRNAs that showed significant increases (“Up”) or decreases (“Down”) in relative TE in response to PAB1 deletion and compared them with two published data sets. First, we investigated TE changes obtained from ribosome profiling and RNA-Seq data of cells depleted for eIF4G through a degron system inducible by growth media and temperature shifts [68]. We found that mRNAs in the Down group upon PAB1 deletion also have overall lowered TE upon 2 hours of eIF4G depletion compared to the Up group and the general distribution in the transcriptome (Reference “Ref.” group) (Fig 5D, 2hr). This trend is even observed before eIF4G depletion and immediately after depletion was initiated (Fig 5D, preshift and 0hr), which is unsurprising because the degron strain showed decreased eIF4G levels even in uninduced condition [68]. These results suggested that relative TE changes observed upon PAB1 deletion may be partially mediated by reduction in eIF4G level in pab1Δpbp1Δ cells. For the second analysis, we utilized RIP-seq data of mRNAs associated with immunoprecipitated (IP’d) TAP-tagged eIF4G and Pab1 [36]. If our data was indirectly influenced by the reduction of eIF4G level, we expected that mRNAs enriched in IP of eIF4G, implying their increased dependence on eIF4G for translation initiation, would be most sensitive to eIF4G reduction–they would have decreased TE (i.e., be found in our Down group) and there would be a negative correlation between fold-change in IP and fold-change in TE. Indeed, we found negative relationships between fold change in TE and fold-change in IP of both eIF4Gs, but not in IP of Pab1 (S7A Fig). We also found that mRNAs in the Down group had: i) higher enrichment fold-change for eIF4G1 and eIF4G2 IPs than those in the Up group or Reference group (Fig 5E), ii) an over-representation of mRNAs that were significantly enriched in IPs of eIF4G1 or eIF4G2 compared to the Reference group (Fig 5F, blue, and S3 Table), and iii) an under-representation of mRNAs that were significantly depleted in IPs of eIF4G1 or eIF4G2 compared to Reference group (Fig 5F, red, and S3 Table). On the other hand, mRNAs in the Up group had: i) lower enrichment fold-change for eIF4G1 or eIF4G2 than those in the Down or Reference group (Fig 5E) and had an over-representation of mRNAs that were significantly depleted in IPs of eIF4G1 or eIF4G2, implying their reduced inclination for cap-dependent initiation (Fig 5F, blue, and S3 Table). Notably, the fold-change enrichment in Pab1 IP does not differ among different TE groups (Fig 5E), although mRNAs in the Up group had an over-representation of mRNAs significantly enriched for Pab1 IP (Fig 5F and S3 Table). Overall, these results indicate that reduction in eIF4G levels cannot be ruled out as a possible explanation for the observed changes in TE when PAB1 is deleted.
Despite the apparent effect of eIF4G on different mRNA subgroups, eIF4G still does not explain the entirety of the data. For example, although the mRNAs enriched in eIF4G2 IP are proportionally over-represented in the Down group compared to Reference (36% vs. 21%), 64% of the mRNAs in the Down group are those not highly dependent on eIF4G (Fig 5F). Thus, we further characterized these mRNAs by exploring their 5’-UTR features.
First, we observed that mRNAs in the Down group tend to have longer 5’-UTRs than those in the Up or Reference groups (Fig 6A). A longer 5’-UTR increases the chance for motifs or upstream open reading frames (uORFs) that could interfere with initiation complex assembly or the scanning mechanism. The presence of uORFs is generally thought to suppress translation initiation of the main ORF [69]. We found that the proportion of mRNAs with at least one uORF is higher in the Down group than in other groups (Fig 6B, left panel, and S3 Table). We further asked if the results were due to uORFs that are completely upstream or uORFs that are overlapping with the main ORF by conducting the same analysis for the two types of uORFs separately. The presence of overlapping uORFs is quite rare in our data set and the proportions are not different between groups, but the results for upstream uORFs mimicked those of all uORFs analyzed together (Fig 6B and S3 Table). Additionally, comparison of TE changes of mRNAs with uORFs to those without uORFs revealed that mRNAs with uORFs had a greater decrease in TE in all categories (S7B Fig). The sensitivity of uORF-containing mRNAs to PAB1 deletion could be related to the relative reduction in eIF1 (Sui1) level in the pab1Δpbp1Δ strain (Fig 2A, top left). eIF1 has a role in start codon recognition, discriminating against suboptimal start sites in favor of the optimal one [70–73]. Therefore, it is not surprising that mRNAs with decreased TE tend to have uORFs (Fig 6B). Consistent with this notion, ribosome footprints in the 5’-UTR region are slightly increased in the pab1Δpbp1Δ strain relative to the other two strains (Fig 4B).
A. Distribution of 5’-UTR length of mRNAs in different TE groups from Fig 5A. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare values between pairwise groups. Only significant comparisons were reported as the following: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001. B-D. Proportion and number of mRNAs from groups in A with (“Yes”) or without (“No”) uORF (B), poly(A) tract (C), or oligo(U) (D) in the 5’-UTR. Pairwise Fisher’s exact test with Benjamini-Hochberg method for multiple-testing correction was used to compare between Reference, Up, and Down groups. p-values are provided in S3 Table. E. Motif enriched in 5’-UTR sequences of mRNAs in the Down group relative to Reference, identified by STREME. For all panels, analyses were limited to mRNAs with existing UTR annotations. Reference (Ref.) group includes all mRNAs regardless of TE changes (Up + Down + Unchanged) to recapitulate the general distribution of measured values in the transcriptome.
Next, we considered motif-dependent regulation of translation initiation. First, recruitment of Pab1 to poly(A) tracts in the 5’-UTR was reported to induce internal cap-independent initiation in yeast [74]. Therefore, mRNAs with this motif would be expected to have decreased TE in the absence of Pab1 and thus be found more often in our Down group. However, we did not find differences in the proportions of mRNAs containing poly(A) tracts in the 5’-UTR between groups (Fig 6C and S3 Table). Second, oligo(U) longer than 7 nt in the 5’-UTR was identified as eIF4G1’s preferential binding motif and can promote initiation [68,75]. However, we did not find differences in the proportions of mRNAs containing oligo(U) in the 5’-UTR between groups (Fig 6D and S3 Table). In a parallel approach, we used the motif discovery tool STREME [76] to identify motif(s) enriched in 5’-UTR sequences of either Up and Down group relative to the Reference. Neither poly(A) tract nor oligo(U) is enriched in either group compared to sequences from the Reference group, consistent with our direct analyses (Fig 6C and 6D), but the AUG motif is enriched in the Down group (Fig 6E), consistent with our uORF analysis (Fig 6B). No other novel motif was identified.
We also asked whether nucleotide context around the start codon and near the 5’ cap influence changes in TE in response to PAB1 deletion by comparing proportions of nucleotides at each position from each group relative to Reference (S7C Fig). The optimal context for translation in yeast has been determined as AA(A/G)AAUGUCU, with position -3 (3rd nucleotide upstream of AUG, which is considered as positions +1 +2 +3) being the most important and conserved [69,77,78]. We did not find mRNAs in the Up or Down group to have biases in nucleotide usage at position -3 compared to Reference or each other (S7C Fig, top). However, other positions in this window that show significant differences relative to the Reference are consistent with the consensus, namely, A/C are enriched in the Up group at position -4 (S7C Fig, top) and U is depleted in the Down group at position +4 (S7C Fig, middle). Beyond the immediate AUG context, G is enriched and U is depleted in the Down group at position +18 (S7C Fig, middle). From the 5’ cap, U is enriched and A is depleted in the Down group at position +6 (S7C Fig, bottom).
Overall, our results show that studying Pab1’s role on global translation initiation through deleting or depleting Pab1 can be confounded by the reduction in initiation factor levels, especially eIF4G and eIF1.
Properties of the mRNAs with differential TE in response to PAB1 deletion support the notion that mRNA 5’ and 3’ ends communicate
Changes in an mRNA’s poly(A) tail length can change the extent of its commitment to translation initiation, observations which led to the closed-loop model postulating that mRNA 5’ and 3’ ends communicate in translation [18,19,23,24]. Pab1 is thought to play an important role in facilitating the closed-loop mRNA structure, bridging the interaction with both the poly(A) tail and eIF4G [27–29]. Shorter mRNAs are thought to form more stable structures than longer mRNAs [28,79], but the closed-loop structure may not apply to every mRNA as not all mRNPs contain the closed-loop components [36,38,39] and for those enriched for closed-loop components, they have variable translation efficiency, not just high efficiency [39].
Efficient 5’-3’ communication allows efficient feedback of ribosomes recycled from termination to a new round of initiation, and this efficiency should be gene length-dependent, as diffusion of ribosomes between the ends would be expected to be proximity-based, i.e., more efficient for shorter mRNAs than longer mRNAs [80,81], even without Pab1 facilitating the closed-loop. This is especially relevant when the availability of ribosomes is limiting and ribosome recruitment becomes more dependent on recycled ribosomes than on limited free ribosomes [80,81], which may be the case in our data in light of the reduction in ribosomal protein levels in pab1Δpbp1Δ cells (Fig 2A). When comparing CDS and entire transcript lengths between groups, we found that mRNAs with decreased TE (Down group) tend to be longer than those with increased TE (Up group) and the Reference while those in the Up group tend be shorter (Fig 7A and 7B). These results are consistent with either the proximity-based or closed-loop 5’-3’ communication models.
To determine whether there is evidence for the closed-loop model, we stratified our transcript length analysis by whether the mRNAs were enriched or depleted in eIF4G or Pab1 RIP-seq data and whether their TEs were increased or decreased upon PAB1 deletion (S8 Fig). At baseline (Ref. group), mRNAs depleted in eIF4G or Pab1 tend to be longer while those enriched tend to be shorter, with this trend strongest for Pab1 (S8 Fig), supporting the notion that closed-loop components are more likely to be associated with shorter transcripts [28,79]. However, in the absence of Pab1, mRNAs with decreased TE (Down group) tend to be longer mRNAs regardless of whether they were enriched for the closed-loop components or not (S8 Fig). This result suggests that the TE changes are likely independent of the closed-loop structure for these mRNAs. Additionally, this result is in line with our conclusion that, while mRNAs with decreased TE tend to be eIF4G-enriched (Fig 5E and 5F), eIF4G enrichment only makes up a subset of mRNAs with decreased TE and TE changes can be influenced by other mRNA features (Figs 6 and 7) that follow the proximity-based 5’-3’ communication model.
Consistent with the notion that efficient termination and recycling of ribosomes at the 3’ end promotes efficient translation initiation at the 5’ end, we found that mRNAs with increased TE have lower readthrough efficiency (i.e., more efficient termination—see next section and Methods for readthrough efficiency calculation) while those with decreased TE have higher readthrough efficiency (Fig 7C). We limited our analysis to mRNAs with detectable readthrough due to the cyclic nature of translation and the detection limit of readthrough ribosomes. Since the amount of readthrough ribosomes depends on the amount of translation of the CDS, for mRNAs with the same readthrough efficiency, readthrough may not be detectable for mRNAs with lower TE (so readthrough efficiency appears to be zero for them) but remain or become detectable for mRNAs with higher TE. Thus, the fact that the proportion of mRNAs with detectable readthrough in the Down group is lower than the Up group (Fig 7D and S3 Table) does not necessarily mean that readthrough efficiency is lower in the Down group. In sum, we found that mRNAs with increased TE had lower readthrough efficiency and those with decreased TE had higher readthrough efficiency (Fig 7C).
A-B. Distribution of CDS length (A) or entire transcript length (B) of mRNAs in different TE groups from Fig 5A. C. Distribution of readthrough efficiency for mRNAs with detectable readthrough in the pab1Δpbp1Δ strain in each group. D. Proportion and number of mRNAs with detectable (“Yes”) or not detectable (“No”) readthrough in the pab1Δpbp1Δ strain. E. Distribution of 3’-UTR length in each mRNA group. F. Proportion and number of mRNAs with (“Yes”) or without (“No”) poly(A) tracts in the 3’-UTR. For A, B, C, and E, two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare values between pairwise groups. Only significant comparisons were reported as the following: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001. For D and F, Pairwise Fisher’s exact test with Benjamini-Hochberg method for multiple-testing correction was used to compare between groups. p-values are provided in S3 Table. G. Motif enriched in the 3’-UTR sequences of mRNAs in the Up group relative to Reference, identified by STREME. For all panels, analyses were limited to mRNAs with existing UTR annotations. Reference (Ref.) group includes all mRNAs regardless of TE changes (Up + Down + Unchanged) to recapitulate the general distribution of measured values in the transcriptome.
To see if Pab1’s function at termination influences the distinction between Up and Down TE groups in response to PAB1 deletion, we compared 3’-UTR lengths among groups, but found no significant differences (Fig 7E). We next asked whether a specific sequence motif is enriched in either Up or Down group. No differences between groups were detected in terms of the presence of poly(A) tracts in the 3’-UTR region (Fig 7F and S3 Table), which can serve as a binding site of Pab1 in addition to the poly(A) tail, and this result was confirmed by the lack of poly(A) motif in the motif discovery approach, STREME. However, STREME identified the UAKGUA motif enriched in the Up group relative to Reference (Fig 7G). Although the probabilities for sense (UAU or UAC) and nonsense codons (UAA or UAG) were comparable for this motif, it is not impossible that the UAKGUA sequence may indicate two consecutive strong stop codons, where the second stop codon (although out-of-frame with the first) acts as a fail-safe stop codon in case of failed termination or recycling at the first stop codon. Moreover, the enrichment of G following the first stop codon is consistent with the observation that mRNAs with G at this position had the lowest readthrough efficiency (S10B Fig, “+4”). This motif being enriched in mRNAs in the Up group, along with the shorter mRNA length and lower readthrough efficiency of this group, is consistent with the hypothesis that more efficient ribosome recycling promotes efficient new rounds of translation initiation.
Deletion of PAB1 promotes transcriptome-wide accumulation of ribosomes downstream of normal stop codons
Deletion of PAB1 has been shown to decrease translation termination efficiency and increase stop codon readthrough efficiency of reporter PTCs in vivo [51]. PTC readthrough occurs when a near-cognate tRNA outcompetes eRF1 in stop codon decoding, resulting in continued in-frame translation elongation and production of a C-terminally extended polypeptide [82,83]. Thus, we analyzed ribosome profiling data to determine whether decreased termination efficiency and increased readthrough efficiency could also be observed at normal termination codons (NTCs) of endogenous mRNAs when Pab1 is absent. Notably, the relative amount of ribosome footprints found in mRNA 3’-UTR regions is increased in pab1Δpbp1Δ cells compared to pbp1Δ or WT cells (Fig 4A inset and 4B), demonstrating that cells lacking Pab1 manifest an apparent termination defect. This defect occurred not only at canonical stop codons, as evidenced by a subtle increase in footprints in the “extension” region (the 3’-UTR region from the canonical stop codon to the next in-frame stop codon), but also at the first in-frame stop codons downstream of the canonical stop codon, as manifested by a significant increase in footprints in the distal 3’-UTR region (Fig 4B).
The presence of ribosomes in the 3’-UTR can arise from stop codon readthrough, ribosome frameshifting, or reinitiation. To determine the primary driver of increased ribosome footprints in the 3’-UTR of mRNAs in pab1Δpbp1Δ cells, analyses of reading frame proportions in different mRNA regions were carried out. Stop codon readthrough would yield footprints predominantly in reading frame 0 in the extension region, while frameshifting or reinitiation events would not show this bias. Indeed, the proportion of ribosome footprints in reading frame 0 increases in the extension region by 6.8% on average in pab1Δpbp1Δ cells compared to pbp1Δ or WT cells (Fig 4C) and this increase is comparable to that observed in ribosome profiling data of cells depleted of functional eRF1 (a 7% increase on average) [84,85]. Additionally, we found no correlation between 3’-UTR footprint density and fraction of out-of-frame footprints in the last 30 nt (10 codon) of the CDS (S9A Fig) as well as no significant difference in 3’-UTR footprint density between mRNAs with and without out-of-frame stop codons in the CDS (S9B Fig) in any strain, ruling out the possibility that out-of-frame translation that might have occurred in the CDS gave rise to increased footprints in the 3’UTR. Together, these results demonstrate that a notable portion of 3’-UTR footprints in pab1Δpbp1Δ cells arises from stop codon readthrough, thus suggesting that translation termination is less efficient in the absence of Pab1.
To verify that the increase in stop codon readthrough is transcriptome-wide, i.e., that the results of Fig 4 were not derived from a limited number of mRNAs, we calculated readthrough efficiency for each mRNA by dividing the density of frame 0 footprints in the extension by that in the CDS region. The number of mRNAs with detectable readthrough is increased in pab1Δpbp1Δ cells, almost double that observed in WT, and overall readthrough efficiency in pab1Δpbp1Δ cells is significantly higher than in the other two strains (Fig 8A).
A. Readthrough efficiency distribution in each strain (see Methods for calculation). Footprints from replicate libraries were pooled. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare values between pairwise strains. Only significant comparisons were reported as the following: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001. B. Average feature importance scores (percent increase in mean squared error (%IncMSE)) extracted from 25 random forest models (5-fold cross-validation, repeated 5 times) trained for each strain to predict readthrough efficiency. Higher feature importance score (red) means the prediction error is high when that feature is permuted. As negative controls (NC), each mRNA was assigned arbitrary continuous and discrete values (Random number and Random factor). Features with significant importance (empirical p-value < 0.05 in at least 15 out of 25 models) are represented as bigger tiles. C. Spearman’s correlation coefficient of the relationship between readthrough efficiency and 3’-UTR length using all available data (“all”) or split data by stop codon usage. Number and dot size reflect the number of mRNAs in each correlation coefficient. For all panels, only reads belonging to genes with UTR annotations and minimally overlapping sequences (less than 18 bp overlap with another gene on the same strand) were included in the analyses (2,693 genes).
Curiously, the pbp1Δ strain also shows higher overall readthrough efficiency than the WT strain (Fig 8A), raising the question of whether Pbp1 plays a role in termination and contributes to increased readthrough in the pab1Δpbp1Δ strain. However, this observation is most likely not due to stop codon readthrough. Footprints in the 3’-UTR region in the pbp1Δ strain are only higher than in WT in the few codons immediately following the canonical stop codon, while in the pab1Δpbp1Δ strain, the increase in 3’-UTR footprints extend substantially beyond that (Fig 4A, inset). More importantly, the proportion of frame 0 footprints in the extension region in pbp1Δ cells does not differ from that in WT cells (Fig 4C). Although we limited our readthrough efficiency calculation to only frame 0 footprints to ensure as accurate a calculation as possible, we still cannot completely exclude other events, such as reinitation, that happened to also produce ribosome footprints in frame 0. Thus, despite the fact that deletion of PBP1 did not result in proportionally higher stop codon readthrough events compared to WT, a slight increase in footprints immediately following the stop codon leads to an apparent increase in the calculated readthrough efficiency values in the pbp1Δ strain.
In short, PAB1 deletion resulted in a translation termination defect, manifested as increased footprints in mRNA 3’-UTR regions. The preference for frame 0 footprints in the extension region and the higher median readthrough efficiency values calculated for individual mRNAs observed in pab1Δpbp1Δ cells compared to the two controls indicate that stop codon readthrough increases transcriptome-wide in the absence of Pab1.
Release factor depletion is not the most likely explanation for increased ribosome footprints in mRNA 3’-UTRs upon PAB1 deletion
Because PABPC plays major roles in the regulation of mRNA decay and translation, the termination defect and increased readthrough observed in pab1Δpbp1Δ cells can be due to: i) loss of Pab1’s direct function in termination via its interaction with eRF3 [45,52] or ii) changes in the stability of release factor mRNAs or changes in their translation that result in depletion of the respective proteins, which in turn affect global termination efficiency. Differential expression analyses on transcriptomic and proteomic data revealed that for the two release factors, eRF1 (Sup45) showed a slight reduction in its mRNA level but not its protein level, while eRF3 (Sup35) showed a slight but statistically significant reduction in both mRNA and protein levels in the absence of Pab1 (Fig 2A, bottom right), where the eRF3 protein level was 80% of that in pbp1Δ cells. However, the reduction in release factor levels may not necessarily result in reduced termination efficiency. If overall translation is also reduced, the normal stoichiometry of supply and demand for release factors may still be maintained or supply may even exceed demand. The latter conclusion follows from observations that PABPC depletion substantially reduces overall protein synthesis such that almost all heavy polysomes are lost [41,55] and protein synthesis is limited by the amount of free ribosomes [30,80,81,86]. Hence, since termination can only occur after initiation and elongation, the larger reductions in ribosomal proteins and initiation factors (the most reduced ribosomal protein and initiation factor are respectively reduced to 45% and 64% of their normal levels) would most likely be limiting and release factors would thus be expected to still be in excess.
We also considered whether increased ribosome footprints in the 3’-UTR arise from a reduced level of Rli1, a ribosome recycling factor. However, since recycling can only occur after termination, Rli1 may still be in excess stoichiometrically in the context of overall reduced translation in pab1Δpbp1Δ cells. More importantly, pab1Δpbp1Δ cells show a preference for frame 0 in the extension region (Fig 4C), consistent with stop codon readthrough, while Rli1 depletion cells resulted in reinitiation in the 3’-UTR in all three reading frames [87].
Termination occurs when the eRF1/eRF3 complex outcompetes the near-cognate aminoacyl-tRNA (aa-tRNA)/eEF1A complex in binding to the ribosomal A-site. Thus, in addition to the shift in equilibrium between the demand and supply of release factors, the shift in equilibrium between the levels of the eRF1/eRF3 complex and the aa-tRNA/eEF1A complex also affects termination efficiency. In pab1Δpbp1Δ cells, the relative level of eEF1A mRNA is unchanged but eEF1A protein is at 129% relative to the level in pbp1Δ cells (Fig 2A, top right). This increase in eEF1A to eRF3 ratio may increase the chance of stop codon decoding by aa-tRNA/eEF1A, reducing termination efficiency and increasing ribosome footprints in the 3’-UTR regions.
Together, previous studies and our results here suggest that a significant reduction in translation is reducing the demand for release factors and recycling factors, making them likely to still be in excess. Therefore, the observed decreased termination efficiency or increased stop codon readthrough in pab1Δpbp1Δ cells may not be because the release factor levels are limiting but could be explained by an increase in competitive advantage of aa-tRNA/eEF1A in stop codon decoding, in addition to a loss of Pab1’s stimulatory function on termination.
3’-UTR length is no longer predictive of readthrough efficiency when PAB1 is deleted
Pab1 has been shown to affect NMD-sensitivity and readthrough efficiency of PTC-containing mRNAs in a manner dependent on PTC proximity to mRNA-associated Pab1 [48,51,79]. As would be expected from this relationship, readthrough of PTCs in reporter mRNAs increased in response to 3’-UTR lengthening, but this trend was lost when PAB1 was deleted [51]. Recently, we investigated the cis-regulatory elements of transcriptome-wide stop codon readthrough using a random forest machine learning approach and found that 3’-UTR length was an important predictor of readthrough, where mRNAs with short 3’-UTRs had lower readthrough than those with long 3’-UTRs when eRF1’s functionality was compromised, but the trend was the opposite in WT cells [84]. These data led us to further assess the involvement of Pab1 and its proximity to the stop codon as a predictor of readthrough efficiency. If Pab1 is involved, we expected that the relationship between readthrough efficiency and 3’-UTR length would disappear or weaken when PAB1 is deleted. Thus, we applied the same random forest approach to identify mRNA features that influence the prediction of readthrough efficiencies in WT, pbp1Δ, pab1Δpbp1Δ strains (Figs 8B and S10A). As expected, the negative control features (NC) have no influence on readthrough efficiency prediction in any strain, while the identity of the stop codon is an important predictor of readthrough in all strains (Fig 8B). The length of the 3’-UTR is an important predictor of readthrough efficiency in WT and pbp1Δ cells but is no longer important in pab1Δpbp1Δ cells (Fig 8B). The relationship between 3’-UTR length and readthrough efficiency is slightly negatively correlated in WT and pbp1Δ strains, but it is weakened in the pab1Δpbp1Δ strain (Fig 8C, “all”). The fact that the correlation is not completely eliminated even in the absence of Pab1 could be due to technical limitations of readthrough efficiency calculation, where frame 0 non-readthrough 3’-UTR footprints were inevitably included. Nevertheless, when other mRNA features were included in the analysis and controlled for (i.e., random forest regression model), this weak correlation became insignificant in predicting readthrough efficiency in pab1Δpbp1Δ cells.
To further see how 3’-UTR length synergistically regulates readthrough with the strongest feature, stop codon identity, we grouped mRNAs by their stop codon identities and then performed the correlation analysis (Fig 8C). We found that the correlation between readthrough efficiency and 3’-UTR length is closer to zero in the absence of Pab1 than those observed in the other two strains for mRNAs with UAA as the stop codon, which happens to be the most common stop codon in the yeast transcriptome, but this trend isn’t observed for UAG and UGA (Fig 8C). This result suggests that mRNAs with UAG and UGA, although allowing higher readthrough (Fig S10B), are less sensitive to the proximity of Pab1 to the stop codon in this readthrough measurement, possibly because termination is slower and rate-limiting, unlike UAA where termination is faster. Since stop codon identity is a more important feature affecting readthrough efficiency, the effect caused by loss of Pab1 is somewhat masked.
Overall, we find that the proximity of Pab1 to the stop codon, as measured by 3’-UTR length, plays a role in readthrough efficiency prediction in combination with other known mRNA features in cells with WT Pab1 conditions (WT and pbp1Δ cells). The biological explanation for the inverse relationship between 3’-UTR length and readthrough efficiency is still unknown in WT cells. However, because 3’-UTR length was still predictive of readthrough efficiency in cells depleted of functional eRF1 [84], which also increased transcriptome-wide readthrough, the fact that 3’-UTR length no longer predicts readthrough of mRNAs in pab1Δpbp1Δ cells supports the notion that readthrough occurring in pab1Δpbp1Δ cells is likely due to loss of Pab1’s direct function in termination rather than reduction in functional release factor levels.
Discussion
Numerous biochemical, structural, in vitro, and in vivo studies of specific mRNAs have identified pleiotropic roles for PABPC in cytoplasmic mRNA deadenylation, translation initiation, and translation termination [3]. However, because of PABPC’s multiple apparent functions, defining its transcriptome-wide roles has been difficult. High-throughput approaches exploring transcriptome-wide effects of PABPC depletion have been carried out in mammalian cells [40,41], but none have been done in yeast. Consistent with observations in mammalian cells, we found that deletion of yeast PAB1 resulted in major changes in the transcriptome (Fig 1B), but only minimal changes in relative translation efficiency (TE) (Fig 5A). We showed that pab1Δ cells also drastically changed their proteome (Fig 1A). The significant reduction in ribosomal proteins suggests that global translation rates would decrease, yet we find TE of most mRNAs to be unchanged (Fig 5A), indicating that global mRNA levels are also decreased as a consequence of PAB1 deletion. Further, we provided the first evidence for a transcriptome-wide translation termination defect (Figs 4 and 8A). These results demonstrated the vast pleiotropic aspects of PAB1 deletion.
Our proteomics data have provided insights to the direct vs. indirect consequences of PAB1 deletion and suggested a new layer of complexity to interpreting genome-wide gene expression alterations in PABPC-depleted cells. PABPC’s role in translation initiation has been elusive, partly because the extent of its activity is dependent on the stoichiometry between PABPC, poly(A) tracts, and basal translation levels [40], and these experimental conditions frequently vary between studies. Moreover, Pab1 appears to have preferential association with certain mRNAs [36]. Hence, it seems counterintuitive that depleting PABPC/deleting PAB1 reduces translation overall, yet relative TE is unchanged for most mRNAs [40,41]. These observations are akin to the effects of depleting eIF4G in yeast [88]. Since PAB1 deletion also reduced eIF4G mRNA and protein levels (Fig 2A), it is possible that effects attributed to the absence of Pab1 are at least partially due to dysregulation of eIF4G mRNA stability and the consequent reduction of eIF4G, which in turn reduced global translation initiation. This sequence of events is also likely for ribosomal proteins, as PABPC depletion has been shown to cause accelerated decay of mRNAs with short poly(A) tails [40], which usually are characteristics of highly expressed, highly translated mRNAs, including those encoding ribosomal proteins [3,89]. Even when we focused our analyses on mRNAs that did have significant changes in TE, where their drastic changes may be due to specific factors outside of the global regulators of translation initiation, their properties are still related to eIF4G-dependent pre-initiation complex recruitment, eIF1-mediated start codon recognition, and efficiency of ribosome recycling (Figs 5–7). Due to their intrinsic properties, these mRNAs with significant increase or decrease in TE are respectively more or less dependent on these processes than most mRNAs and are thus more sensitive to reduction in these factors in pab1Δ cells (Fig 2A). As a result, the direct role of Pab1 in initiation is masked by the changes in these core components of initiation, i.e., reduction of initiation factor levels cannot be completely ruled out as an explanation for the translation initiation defects observed in pab1Δ cells. It is also possible that the role of Pab1 in initiation is masked by the strain background used in our study, where yeast cells may have already altered their gene expression profiles to compensate for the lack of Pab1 over extended period of time upon full gene deletion and reached a steady state, because more widespread TE changes (~2.5 times more transcripts than our study) have been observed in a yeast strain in which Pab1 was degraded over a short period of time (6 hours) by the inducible degron system [90]. Nevertheless, this recent study also found that TE changes observed under a limiting Pab1 level seem to be an indirect consequence of another phenomenon, albeit from a different aspect from our study, namely the accelerated mRNA degradation process [90].
Indirect consequences of PAB1 deletion through reduced level of key pathway components might be applied to translation termination as well, since there is a slight reduction in release factor levels in pab1Δ cells (Fig 2A). The increased stop codon readthrough observed in pab1Δ cells is unlikely to be due to the release factor level becoming limited, since reduced initiation factors and ribosomal proteins are more limiting than release factors, skewing the usual stoichiometry of demand vs. supply for release factors towards the supply, but this observation is likely due to a shift in stoichiometry of eRF1/eRF3 vs. aa-tRNA/eEF1A towards aa-tRNA/eEF1A, promoting readthrough. However, Pab1’s direct role in termination is still implied because i) 3’-UTR length, which approximates the distance of Pab1 to the stop codon, lost its ability to predict readthrough efficiency in pab1Δ cells as opposed to WT (Fig 8B) or eRF1 mutant cells [84] (i.e., 3’-UTR length should have still been predictive of readthrough efficiency if increased readthrough was a consequence of release factor depletion alone), and ii) deletion of Pab1’s eRF3 interacting domain only (pab1ΔC), which affects termination but not mRNA decay [45], yields significant stop codon readthrough of reporter mRNAs [51]. Nevertheless, it remains to be determined whether a 20% reduction in termination factors in a PAB1 strain would yield the same termination defect as a pab1Δ mutation and whether pab1ΔC cells significantly change their release factor levels. Because near cognate tRNAs compete with release factors in stop codon decoding, changes in aminoacyl-tRNA levels, synthesis, and modifications should also be considered.
Since deletion of the PAB1 gene in yeast is lethal, we also deleted PBP1, which suppresses this lethality. Surprisingly, single deletion pbp1Δ cells showed an apparent increase in readthrough efficiency compared to WT cells (Fig 8A), raising a question whether Pbp1 plays a role in translation termination. However, this is unlikely for several reasons. First, we noticed that the amount of footprints in the 3’-UTR from pbp1Δ cells is only relatively higher than WT in the first few codons after the stop codon, and from only one of the three biological replicates, compared to pab1Δpbp1Δ cells in which the higher amount of footprints sustains throughout the 80-nt window (Fig 4A). This observation is clearer through quantifications (Fig 4B). Second, footprints in the extension region from pbp1Δ cells do not show frame 0 preference as do those from pab1Δpbp1Δ cells (Fig 4C). Despite the lack of frame 0 preference, these footprints unfortunately cannot be selectively discarded for readthrough efficiency calculation, defined as frame 0 footprint density in the extension relative to that in the CDS. Thus, by this definition, the calculation results in an apparent increase in readthrough efficiency in pbp1Δ cells (Fig 8A). It is important to note that the same apparent “increase” in “readthrough efficiency” was also observed in cells lacking recycling factor Rli1 [84], which caused random reinitiation downstream of stop codons and not readthrough [87].
We showed that the sets of mRNA substrates of Pat1/Lsm1-mediated decay and NMD both manifest relative increases in abundance and thus likely to be partially stabilized in pab1Δ cells despite their different decay mechanisms, while substrates of Dhh1-mediated decay did not have a tendency to increase or decrease (Fig 3). Stabilization of Pat1/Lsm1 substrates may be attributable to the loss of Pab1’s role in stimulating deadenylation of mRNAs usually subject to deadenylation-dependent decay. However, there is also a slight depletion of Ccr4-Not components, Not4 and Not5 (Fig 2B), and the extent of how much this depletion contributes to substrate stabilization is unknown. The consequences of Not4 and Not5 depletion may also apply to some Dhh1 substrates (i.e., those that are increased) but not all, possibly due to Dhh1’s involvement in multiple decapping complexes which can lead to alternative degradation pathways [9]. Some complexes may require Pab1’s role in deadenylase recruitment while for others, Dhh1’s communication with the Ccr4-Not complex may be enhanced by lower Pab1 level [65]. In contrast to Pat1/Lsm1 substrates, partial increases in the levels of NMD substrates most likely arises from the combination of decreased translation and decreased Upf1. The reduction in overall translation would result in fewer instances of termination and, together with the slight reduction in Upf1, would lower the likelihood that a nonsense-containing mRNA would be targeted by NMD.
Collectively, our observations of the pleiotropic effects of PAB1 deletion may help explain the discrepancies in previous studies of PABPC’s functions and our multi-omics data can be helpful resources for the design of future experiments involving genetic manipulation, depletion, or overexpression of PABPC.
Materials and methods
Yeast strain construction
Yeast strains used in this study, listed in S4 Table, are in the W303 background. Gene deletions were achieved by the PCR-based method [91] and high efficiency transformation [92] of fragments amplified by oligonucleotides listed in S5 Table, synthesized by Integrated DNA Technologies (IDT). Three biological replicates (isolates) of each mutant strain and three technical replicates of isogenic wild-type (WT) strain were used for all experiments.
The pbp1Δ strain was made by replacing the coding sequence of the PBP1 gene in a WT strain (HFY114) with the URA3 gene. The URA3 cassette was obtained from plasmid HFSE1380 [59] by PCR using URA3_5F_v2 and URA3_3R_v2 primers. Homology arms flanking the PBP1 coding sequence were amplified from genomic DNA by PCR using PBP1_5F, PBP1_5R_v2, PBP1_3F_v2, and PBP1_3R primers. DNA fragments consisting of the homology arms flanking the URA3 cassette were constructed by PCR and transformed into competent WT yeast cells. Verification of successful gene replacement was confirmed by PCR followed by Sanger sequencing using primers listed in S5 Table.
Subsequent deletion of the PAB1 gene from the pbp1Δ strain was carried out by replacing the coding sequence of PAB1 with kanMX, resulting in a pab1Δpbp1Δ strain. The kanMX cassette was obtained from the pCAS plasmid (Addgene, #60847) by PCR using PAB1_KanMX_F and PAB1_KanMX_R primers. Homology arms flanking the PAB1 coding sequence were amplified from genomic DNA by PCR using PAB1_5H_ext_F2, PAB1_5H_ext_R, PAB1_3H_ext_F, and PAB1_3H_ext_R primers. DNA fragments consisting of the homology arms flanking the kanMX cassette were constructed by PCR and transformed into competent pbp1Δ (KMY01) yeast cells. Verification of successful gene replacement was confirmed by PCR followed by DNA sequencing using primers listed in S5 Table as well as by examining growth phenotype. Doubling time for these strains grown in YEPD at 30°C were 1.5 hours for WT and pbp1Δ strains and 3.5–4.5 hours for the pab1Δpbp1Δ strain.
Cell growth and harvest
Cells were grown in 1 L of YEPD at 30°C with shaking. When the OD600 of the culture reached 0.6–0.8, cells were collected by rapid vacuum filtration, flash-frozen in liquid nitrogen in the presence of Footprinting Buffer (20 mM Tris-HCl pH 7.4, 150 mM NaCl, 5 mM MgCl2) plus 1% TritonX-100, 0.5 mM DTT, 1 mM phenylmethylsulfonyl fluoride (PMSF), and 1X protease inhibitors, and lysed in a Cryomill (Retsch) (5 Hz, 2 min; 10 Hz, 15 min). Cell lysates were clarified by ultracentrifugation in a Beckman Coulter Optima L-90K Ultracentrifuge at 18,000 rpm for 10 min at 4°C, using a 50Ti rotor. Centrifugation was repeated for the supernatant at 18,000 rpm for 15 min at 4°C. Lysates were stored at -80°C in aliquots.
Ribosome profiling library preparation and sequencing
Ribosome profiling libraries were prepared as described previously [93]. Lysates were digested with RNase I (Invitrogen, #AM2294) for 1 hour at 25°C with shaking at 700 rpm, and the reaction was stopped using SUPERase-In RNase Inhibitor (Invitrogen, #AM2694). RNase I-treated lysates were then layered onto a 1 M sucrose cushion in Footprinting Buffer plus 0.5 mM DTT and centrifuged in a Beckman Optima TLX Ultracentrifuge at 60,000 rpm for 1 hour at 4°C using a TLA100.3 rotor to isolate 80S ribosomes. Ribosome-protected fragments (RPFs) were extracted from pelleted 80S ribosomes using a miRNeasy kit (QIAgen, #217004) following the manufacturer’s protocol for enriched recovery of small RNAs (<200 nt). RNAs larger than 200 nt, which include ribosomal RNAs (rRNAs) and other large RNAs, were discarded. Small RNAs were 3’ dephosphorylated and 5’ phosphorylated with T4 polynucleotide kinase (T4PNK, NEB, #M0201S), and purified with RNA Clean and Concentrator-5 (Zymo Research, #R1013) according to the manufacturer’s instructions that separately recover small and large RNA fractions. Large RNA fractions were discarded. Approximately 1 μg of small RNAs in 8.5 μl were incubated with 2 μl of QIAseq FastSelect–rRNA Yeast Kit (Qiagen, #334215) at 75°C, 2 min; 70°C, 2 min; 65°C, 2 min; 60°C, 2 min; 55°C, 2 min; 37°C, 2 min; 25°C, 2 min; 4°C, hold. This step hybridized any remaining rRNAs in the sample to the rRNA oligonucleotides, creating duplexes that would fail to ligate to sequencing adapters or fail to be reversed-transcribed into cDNA for sequencing library preparation. Sequencing libraries were prepared from the 10.5 μl reactions using the NEXTflex Small RNA-Seq Kit v3 (Perkin Elmer/Bioo Scientific, #NOVA-512-05) according to the manufacturer’s protocol, except for the RNA denaturation step (70°C, 2 min incubation) before 3’ 4N Adenylated Adapter ligation, which was skipped. Based on the manufacturer’s instructions for optimization, adapters were undiluted, PCR was performed for 15 cycles, and the library was purified using the manufacturer’s gel-free size-selection and cleanup protocol. Extra rounds of cleanup were performed if the amount of PCR primers was still high compared to the amount of library, as analyzed on a Fragment Analyzer. Three libraries were multiplexed according to NEXTflex’s recommended combinations of barcodes (index sequences) and sequenced (single-end, 75 cycles) in-house on Illumina NextSeq 500 or NextSeq 550 sequencers.
RNA-Seq library preparation and sequencing
RNA-Seq libraries were prepared and sequenced as described previously [93]. Briefly, total RNAs were extracted from lysates using a miRNeasy kit (QIAgen, #217004) following the manufacturer’s protocol for recovery of total RNAs (standard protocol). Genomic DNA contamination was depleted using Baseline-Zero DNase (Lucigen/Epicentre, #DB0715K) according to manufacturer’s instructions. Approximately 1 μg of DNase-treated RNAs were used to prepare a sequencing library. The rRNA depletion strategy using QIAseq FastSelect–rRNA Yeast Kit (Qiagen, #334215) was integrated into the RNA fragmentation step of the TruSeq Stranded mRNA Library Prep kit (Illumina, #20020594) according to the QIAseq FastSelect’s manual. Three libraries were multiplexed using recommended combinations of TruSeq RNA Single Indexes Set A (Illumina, #20020492) and sequenced (single-end, 75 cycles) on an Illumina NextSeq 500 sequencer.
Sequence alignment
The yeast transcriptome used for sequence alignment was from https://github.com/Jacobson-Lab/yeast_transcriptome_v5, the generation of which was described previously [84]. Briefly, gene annotations were downloaded from the Saccharomyces Genome Database (https://www.yeastgenome.org; September 10, 2015). For UTR information, the longest UTR entry was chosen for mRNAs with multiple annotations across studies [94–98], which were downloaded from the YeastMine database (July 3, 2017). Reads pre-processing, alignment, and quantification were performed on the University of Massachusetts Green High Performance Computing Cluster using the following provided software packages: cutadapt v1.9, bowtie v1.0.0, fastqc v0.10.1, samtools v0.1.19, bedtools v2.26.0, UMI-tools v1.1.1, and RSEM v1.3.0.
Ribosome profiling reads were pre-processed, aligned to the transcriptome, and transcript abundance quantified as described previously, except for the PCR duplicate removal step, where the UMI-tools software package was employed [99]. The UMI-tools’ “extract” function was used to record four nucleotides at each end of a read, which were introduced during library preparation by the NEXTflex Small RNA-Seq Kit v3. UMI-tools’ “dedup” function with the default (“directional”) method was used to identify and remove PCR duplicates based on the extracted UMIs. Number of reads processed, number of PCR duplicates removed, number of remaining unique reads, and other relevant sequencing statistics for each library are provided in S6 Table.
RNA-Seq reads were aligned to the transcriptome and transcript abundance quantified using RSEM without any pre-processing.
Mass spectrometry (LC-MS/MS)
Sample preparation.
Protein concentrations in cell lysates were determined by Pierce BCA Protein Assay according to the manufacturer’s protocol (Thermo Scientific). Aliquots of cell lysates containing 50 μg total protein were snap frozen, lyophilized in a SpeedVac, then reduced, alkylated, and digested following the S-Trap digestion protocol (ProtiFi). In brief, lyophilized lysates were first resuspended in 23 μl Lysis buffer (5% SDS in 50mM Triethyl ammonium bicarbonate (TEAB)). Resuspended protein extracts were reduced by adding 1 μl 200mM TCEP and incubating at 55°C for 1 hour, then alkylated by adding 1 μl 375mM iodoacetamide (IAA) and incubating at room temperature for 30 minutes, protected from light. To further denature and trap proteins, samples were mixed with 2.5 μl of 27.5% phosphoric acid (H2PO4 in water) and 165 μl of binding/wash buffer (100mM TEAB in 90% methanol). The mixtures were applied to S-Trap columns and centrifuged at 4,000 g for 30 seconds. Columns were washed 5 times, each by 150 μl of binding/wash buffer and centrifugation at 4,000 g for 30 seconds. To digest proteins, 25 μl of digestion buffer containing 1 μg trypsin (5 μl of 0.2 μg/μl trypsin in 50mM TEAB + 20 μl 50mM TEAB) was added to each column and samples were incubated at 37°C overnight. Digested peptides were collected by 3 subsequent centrifugations at 4,000 rpm for 1 min following the addition of these elution buffers for each collection: 1) 40 μl 50mM TEAB in water, 2) 40 μl 0.2% formic acid in water, and 3) 40 μl 50% acetonitrile in water. All flowthroughs from the same sample were pooled, lyophilized in a SpeedVac, and stored at -80°C.
Samples were labeled using a TMT10plex labeling kit (Thermo Scientific). Lyophilized, digested peptides were resuspended in 50 μl 100mM TEAB and incubated with 10 μl of TMT10plex reagent (equilibrated to room temperature and resuspended in acetonitrile) at room temperature for 1 hour. Reactions were quenched with 5 μl of 5% hydroxylamine at room temperature for 15 minutes. Equal amounts (55 μl) of each sample were pooled together; 50 μl of the pooled reaction was saved for direct shotgun analysis and the rest for high-pH fractionation. For the latter, pooled samples were dried in a SpeedVac, resuspended in 300 μl of 0.1% trifluoroacetic acid (TFA) in water, and fractionated using a Pierce High pH Reversed-Phase Peptide Fractionation Kit (Thermo Scientific), collecting 1 flowthrough fraction, 1 wash fraction, and 6 step-gradient fractions.
Data acquisition.
Mass spectrometry data was acquired using an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Scientific). Dried peptides were resuspended in 18 μl of 5% acetonitrile with 0.1% formic acid in water, vortexed for 2 minutes, and centrifuged at 16,000 rpm for 16 minutes. For mass spectrometry, 3.8 μl of the resuspended peptides were injected into the Mass Spectrometer. Peptides were trapped for 4 minutes at a flow rate of 4.0 μl/min onto a 100 μm I.D. fused-silica precolumn (Kasil frit) packed with 2 cm of 5 μm ReproSil-Pur 120 C18-AQ (dr-maisch.com), and eluted and separated in 120 minutes at a flow rate of 300 nl/min by an in-house made 75 μm I.D fused silica analytical column (gravity-pulled tip) packed with 25 cm of 3 μm ReproSil-Pur 120 C18-AQ (dr-maisch.com). Mobile phases were A (water (0.1% (v/v) formic acid) and B (acetonitrile (0.1% (v/v) formic acid). The biphasic elution program was as follows: 0–100 min (10–35% B); 100–120 min (35–65% B); 120–121 min (65–95% B); 121–126 min (95% B); 126–127 min (95–5% B); 145 min (STOP).
The MS data acquisition was performed in positive electrospray ionization mode (ESI+), within the mass range of 375–1500 Da with the Orbitrap resolution of 120,000 (m/z 200) and a maximum injection time of 50 milliseconds. Data dependent acquisition (ddMS2) was carried out with a 1.2 Da isolation window, a resolution of 30,000 (m/z 200), maximum injection time of 110 milliseconds, and the customed AGC target with a 38% of HCD collision energy.
Data analysis
Raw data files were processed with Proteome Discoverer (version 2.1.1.21, Thermo Scientific) and searched against the Uniprot Saccharomyces cerevisiae database (downloaded 06/28/2021) using Mascot Server (version 2.8, Matrix Science). Search parameters included full trypsin, with variable modifications of oxidized methionine, pyroglutamic acid (from Q), and N terminal acetylation. Fixed modifications were carbamidomethylation on cysteine and TMT10plex on peptide N-terminus and lysine side chain. Assignments were made using a 10ppm mass tolerance for the precursor and 0.05 Da mass tolerance for the fragments. Peptide and protein validation and annotation was done in Scaffold (version 5, Proteome Software, Inc.) using Peptide Prophet [100] and Protein Prophet [101] algorithms. Peptides were filtered at a 1% FDR, while protein identification threshold was set to greater than 99% probability and with a minimum of two identified peptides per protein. Protein clustering analysis was applied to increase the probability of protein identification for proteins that share peptides (e.g. paralogs). Quantitative analyses, including TMT label-based quantification, median normalization of log2 intensity values, and log2 fold change calculation were carried out in Scaffold Q+S.
Data acquired from flowthrough and wash fractions were used to determine the success of high-pH fractionation. Further analysis was based on data acquired from 6 step-gradient fractions.
Bioinformatics and statistical analyses
Data analyses and visualization were performed in the R software environment versions 3.5 and 4.2 using the following R packages: data.table, dplyr, reshape2, readxl, openxlsx, caret, randomForest, rfPermute, rstatix, rcompanion, DESeq2, limma, Biostrings, seqinr, riboWaltz, ORFik, gprofiler2, scales, ggplot2, ggpubr, ggh4x, ggrepel, ggVennDiagram, ggseqlogo, patchwork, and Cairo.
Ribosome profiling analysis and readthrough efficiency calculation.
Aligned reads of ribosome profiling libraries were processed by R package riboWaltz [102] for initial diagnostic, read length filter (retain reads 20–23 nt and 27–32 nt in length), and determination of read’s P-site offsets, which were manually checked and modified for accuracy (S7 Table). mRNA regions (5’-UTR, CDS, extension, and distal 3’-UTR) and their lengths were adjusted accordingly to consider the stop codon as part of the 3’-UTR. Read counts belonging to different mRNA regions, read’s reading frame, and metagene analysis assessing periodicity were based on the read’s P-site location of mRNAs with annotated UTRs.
Readthrough efficiency was calculated for each mRNA as follows:
where the first 15 bp of the CDS region were excluded to avoid bias in ribosome accumulation over or near the start codon, and the extension region was defined as the 3’-UTR region from the canonical stop codon (inclusive) to the next in-frame stop codon (exclusive). mRNAs with RPKM of the CDS > 0.2 and RPKM of the extension > 0.1 were used for all readthrough efficiency analyses.
Random forest models.
Random forest analyses were carried out with R packages caret [103], randomForest [104,105], and rfPermute [106]. For each sample, a random forest regression approach was trained to use mRNA features (previously defined in Mangkalaphiban et al. 2021 [84]) to predict readthrough efficiency values with 5-fold cross-validation, repeated 5 times, resulting in a total of 25 models. Each model was trained with 100 trees, the default number of features to split at each tree node (square root of number of features), and 1,000 permutation replicates to empirically determine p-value for feature importance. Feature importance score, percent increase in mean squared error (%IncMSE), was an average of scores extracted from 25 models. A feature was considered significantly important predictor of readthrough efficiency if its empirical p-value was less than 0.05 in at least 15 out of 25 models. Model performance metric was reported as an average of root mean squared error normalized to the range of readthrough efficiency values (NRMSE) across 25 regression models.
Analysis of transcript abundance changes.
All analyses involving transcript abundance changes were performed with the R package DESeq2 [107]. The “expected_count” columns in the RSEM file output “isoforms.results” were used as input raw read count. Results were extracted with automatic independent filtering applied at significant cutoff (alpha) of 0.01. The false discovery rate (FDR) method was used to adjust the P-value.
For differential expression analysis of RNA-Seq or Ribo-Seq libraries, mRNAs with adjusted P-value < 0.01 were considered significantly differentially expressed between samples, regardless of magnitude of log2 fold change. For Figs 1C–1D, S2C–S2D, and S5, where RNA-Seq or Ribo-Seq log2 fold change were plotted against mass spectrometry log2 fold change, expected_count of mRNAs in the same protein cluster were added together and differential expression analysis was carried out as described.
For relative changes in translation efficiency (TE), ribosome profiling reads whose P-site locations were in the 5’-UTR, 3’-UTR, or the first 15 bp and the last 3 bp of the CDS (ribosomes paused over canonical start codon, translational ramp, and canonical stop codon) were discarded and transcript abundance for the rest of reads was re-quantified by RSEM. TE was defined as Ribo-Seq reads in CDS normalized to RNA-Seq reads. mRNAs with adjusted P-value < 0.05 were considered to have significant changes in TE between samples, regardless of the magnitude of log2 fold change.
Analysis of protein abundance changes.
Differential abundance analyses of protein levels between samples were performed with the R package limma [108,109] on log2 normalized intensity data exported from Scaffold. The Benjamini-Hochberg method was used to adjust the P-value. Proteins with adjusted P-value < 0.015 were considered to have significant changes in abundance between samples, regardless of magnitude of log2 fold change.
Gene ontology analysis.
Gene ontology analyses of proteins enriched (“Up”) or depleted (“Down”) or mRNAs with increased (“Up”) or decreased (“Down”) TE were carried out by the R package gprofiler2’s gost function with default parameters [110,111].
mRNA features.
mRNA features related to analysis of stop codon readthrough efficiency were defined as previously described [84].
Identification of uORFs in the 5’-UTR was done with the findORFs function in the R package ORFik [112], limiting uORF’s start codon to be AUG only and no minimum uORF length.
Poly(A) tracts in the 5’-UTR and 3’-UTR were defined as stretches of at least 10 consecutive adenines (the A in AUG of main ORF included), allowing at most 2 other nucleotides in the 10 A’s window.
Oligo(U) in the 5’-UTR and 3’-UTR was defined as a stretch of at least 7 consecutive uracils, allowing no other nucleotides in the window.
Codon optimality score for each transcript’s CDS was calculated as described previously [84,93]. For a given codon, a codon optimality measurement was defined as the tRNA adaptation index (tAI) derived from tRNA gene copy numbers and wobble base-pairing penalty [113,114]. The geometric mean of tAIs of all codons in a given CDS was the codon optimality score for that CDS [113].
Motif discovery.
Identification of sequence motifs enriched in 5’-UTR (excluding the start codon) or 3’-UTR (including the stop codon) sequences of mRNA TE groups compared to the Reference was carried out by the STREME algorithm from the MEME Suite 5.5.0 (https://meme-suite.org/meme/tools/streme) [76,115]. A general search for 3–15 nt-long motifs was performed as well as a focused search for shorter 3–6 nt-long motifs. Short motifs had to be identified in both searches to be regarded as significant enrichments.
Statistical analyses.
Statistical analyses were performed using the R packages rstatix, rcompanion, and ggpubr. Specific statistical parameters, multiple-testing correction method, statistical significance (p-value or symbols representing ranges of p-values), and sample size (n) are reported accordingly on the figures, in the figure legends, or in S3 Table.
Supporting information
S1 Fig. Replicate reproducibilities.
A. Correlation matrix showing Pearson correlation coefficients (r) of log2 intensity values from mass spectrometry data between pairs of samples. B. Correlation matrix showing Pearson correlation coefficients (r) of transcript abundance (non-zero RPKM values, log10-transformed) between pairs of sequencing libraries, RNA-seq (RNA) and ribosome profiling (RPF = ribosome-protected fragment) libraries.
https://doi.org/10.1371/journal.pgen.1011392.s001
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S2 Fig. Changes in transcriptomes and proteomes of pbp1Δ and pab1Δpbp1Δ strains relative to WT.
A. Volcano plots of changes in proteome (mass spectrometry data) between strains. Orange, purple, and grey dots represent proteins with higher abundance (positive log2 fold change, adjusted p-value < 0.015), lower abundance (negative log2 fold change, adjusted p-value < 0.015), and no change (adjusted p-value ≥ 0.015), respectively. B. Volcano plots of changes in transcriptome (RNA-Seq data) between strains. Orange, purple, and grey dots represent mRNAs with higher abundance (positive log2 fold change, adjusted p-value < 0.01), lower abundance (negative log2 fold change, adjusted p-value < 0.01), and no change (adjusted p-value ≥ 0.01), respectively. C. Comparison of log2 fold change in transcriptome (RNA-Seq reads) and proteome (mass spectrometry quantification), with Spearman’s correlation coefficient. D. Comparison of log2 fold change in ribosome profiling (Ribo-Seq) reads and proteome (mass spectrometry quantification), with Spearman’s correlation coefficient. For C and D: Grey, genes whose mRNA and protein abundance remained unchanged. Blue, genes whose protein but not mRNA abundance changed significantly. Red, genes whose mRNA but not protein abundance changed significantly. Green, genes whose mRNA and protein abundance both changed significantly.
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S3 Fig. Gene ontology analysis of protein abundance changes in pbp1Δ relative WT.
Manhattan plot was generated by the gostplot function in the R package gprofiler2 [111]. Each circle on the plot represents a gene ontology (GO) term. The size of the circle reflects the number of genes in the GO term. GO terms are grouped and colored by data sources on the x-axis. GO terms that are closer in hierarchy are also closer visually along the x-axis. The y-axis shows adjusted p-values in negative log10 scale. The plot is capped at 16, meaning GO terms with adjusted p-value < 10−16 are plotted at “>16” on the y-axis. All GO terms in each category (e.g., BP: Biological Process, MF: Molecular Function, etc.) are labeled and provided as a table below the plot.
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S4 Fig. Gene ontology analysis of protein abundance changes in pab1Δpbp1Δ relative pbp1Δ.
Manhattan plot was generated by the gostplot function in the R package gprofiler2 [111]. Each circle on the plot represents a gene ontology (GO) term. The size of the circle reflects the number of genes in the GO term. GO terms are grouped and colored by data sources on the x-axis. GO terms that are closer in hierarchy are also closer visually along the x-axis. The y-axis shows adjusted p-values in negative log10 scale. The plot is capped at 16, meaning GO terms with adjusted p-value < 10−16 are plotted at “>16” on the y-axis. Top 3 GO terms in each category (e.g., BP: Biological Process, MF: Molecular Function, etc.) based on the adjusted p-value are labeled and provided as a table below the plot.
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S5 Fig. Changes in RNA-Seq reads, Ribo-Seq reads, and proteomics data in pab1Δpbp1Δ relative pbp1Δ.
A. As in Fig 1C, with genes partitioned into whether relative translation efficiency (TE) significantly changes. B. As in Fig 1D, with genes partitioned into whether relative translation efficiency (TE) significantly changes.
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S6 Fig. Gene ontology analysis of translation efficiency changes in pab1Δpbp1Δ relative pbp1Δ.
Manhattan plot was generated by the gostplot function in the R package gprofiler2 [111]. Each circle on the plot represents a gene ontology (GO) term. The size of the circle reflects the number of genes in the GO term. GO terms are grouped and colored by data sources on the x-axis. GO terms that are closer in hierarchy are also closer visually along the x-axis. The y-axis shows adjusted p-values in negative log10 scale. The plot is capped at 16, meaning GO terms with adjusted p-value < 10−16 are plotted at “>16” on the y-axis. Top 3 GO terms in each category (e.g., BP: Biological Process, MF: Molecular Function, etc.) based on the adjusted p-value are labeled and provided as a table below the plot.
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S7 Fig. Influences of translation initiation features on translation efficiency (TE) changes.
A. Comparison of log2 fold change in TE upon PAB1 deletion and log2 fold enrichment in eIF4G or Pab1 RIP-seq [36], with Spearman’s correlation coefficient. B. Cumulative density plots of log2 fold change in TE upon PAB1 deletion of mRNAs with (“Yes”) or without (“No”) uORF. Two-sided Kolmogorov-Smirnov (KS) test was used to determine significant difference between groups. C. Influences of start codon context on TE changes. Relative proportions of nucleotide usage upstream (top) and downstream (middle) of main ORF’s AUG (positions +1 +2 +3) in Up and Down groups relative to Reference. Relative proportions of nucleotide usage from the mRNA 5’ cap (first 18 nucleotides of the 5’-UTR sequences) in Up and Down groups relative to Reference (bottom). In all panels, analyses were limited to mRNAs with existing UTR annotations. Reference (Ref.) group includes all mRNAs regardless of TE changes (Up + Down + Unchanged) to recapitulate the general proportions in the transcriptome. Positive (red) and negative (blue) log2 relative proportion indicates that the nucleotide is over-represented and under-represented, respectively, in the group compared to the Reference. Pairwise χ2 test with Benjamini-Hochberg method for multiple-testing correction was used to compare the nucleotide frequencies between Reference, Up, and Down groups. p < 0.05 for Up or Down vs. Reference is represented by a big tile, while non-significant results are represented by a small tile. p < 0.05 for Up vs. Down is represented by an asterisk (“*”).
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S8 Fig. Relationships between closed-loop component association and transcript length with regard to translation efficiency (TE).
Distribution of mRNA transcript lengths grouped by TE changes from Fig 5A and enrichment or depletion in eIF4G or Pab1 (RIP-seq experiments), comparing by RIP-seq status. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare values between pairwise groups. Only significant comparisons were reported as the following: (*) p < 0.05, (**) p < 0.01, (***) p < 0.001, (****) p < 0.0001. Analyses were limited to mRNAs with existing UTR annotations. Reference (Ref.) group includes all mRNAs regardless of TE changes (Up + Down + Unchanged) to recapitulate the general distribution of measured values in the transcriptome.
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S9 Fig. Out-of-frame translation in ribosome profiling data.
A. Comparison of 3’-UTR footprint density (RPKM) and fraction of out-of-frame footprints in the last 30 nt (10 codon) of the CDS, with Spearman’s correlation coefficient. mRNAs were required to have UTR annotations, RPKM of the CDS > 0.2, at least 30 footprints across the last 30 nt of CDS, and RPKM of the 3’-UTR > 0.1 to be included in the analysis. B. Distribution of 3’-UTR footprint density (RPKM) of mRNAs with (“Yes”) or without (“No”) out-of-frame stop codon(s) within the CDS region. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg was used to compare values between groups. mRNAs were required to have UTR annotations, RPKM of the CDS > 0.2, and RPKM of the 3’-UTR > 0.1 to be included in the analysis.
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S10 Fig. Influences of mRNA features on readthrough efficiency.
A. Average ± standard deviation of performance metrics (normalized root mean squared error (NRMSE)) extracted from 25 random forest models (5-fold cross-validation, repeated 5 times) trained for each strain to predict readthrough efficiency. B-C. Heatmaps of median readthrough efficiency of mRNA groups, grouped by the identity of the stop codon or the identity of nucleotide at positions near the stop codon (B) or the identity of P-site codon (C), relative to median readthrough efficiency of all mRNAs in the sample. Positive (red) and negative (blue) values indicate that the group has higher and lower readthrough efficiency than the sample median, respectively. Two-sided Wilcoxon’s rank sum test with Benjamini-Hochberg method for multiple-testing correction was used to compare a group’s median readthrough efficiency to the sample median. Significant results (p < 0.05) are represented as bigger tiles.
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S1 Table. Gene ontology analysis results of proteins with significant changes in abundance in pab1Δpbp1Δ vs. pbp1Δ and pbp1Δ vs. WT.
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S2 Table. Gene ontology analysis results of mRNAs with significant changes in translation efficiency (TE) in pab1Δpbp1Δ vs. pbp1Δ.
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S3 Table. Statistical test results of pairwise comparison of mRNA features between Reference, Up, and Down TE groups.
Related to Figs 5F, 6B, 6C, 6D, 7D and 7F.
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S4 Table. List of yeast strains and genotypes used in this study.
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S5 Table. List of oligonucleotides used in this study.
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S6 Table. Ribosome profiling reads processing and alignment statistics.
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S7 Table. Ribosome footprint P-site offsets from footprint’s 5’ end and 3’ ends.
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Acknowledgments
We thank Feng He, Chan Wu, Jill Moore, Zhiping Weng, Andrei Korostelev, Sean Ryder, Poonam Poonia, and Alan Hinnebusch for helpful discussions. We thank members of the UMass Chan Medical School Mass Spectrometry Core Facility for proteomics data acquisition, the UMass Chan Medical School Molecular Biology Core Labs for Fragment Analyzer service, and the UMass Chan Medical School Bioinformatics Core and Information Technology for the high-performance computing platform.
References
- 1. Mangus DA, Evans MC, Agrin NS, Smith M, Gongidi P, Jacobson A. Positive and Negative Regulation of Poly(A) Nuclease. Mol Cell Biol. 2004 Jun 15;24(12):5521–33. pmid:15169912
- 2. Brambilla M, Martani F, Bertacchi S, Vitangeli I, Branduardi P. The Saccharomyces cerevisiae poly (A) binding protein (Pab1): Master regulator of mRNA metabolism and cell physiology. Yeast. 2019 Jan;36(1):23–34. pmid:30006991
- 3. Passmore LA, Coller J. Roles of mRNA poly(A) tails in regulation of eukaryotic gene expression. Nat Rev Mol Cell Biol. 2022 Feb;23(2):93–106. pmid:34594027
- 4. Sachs AB, Davis RW, Kornberg RD. A single domain of yeast poly(A)-binding protein is necessary and sufficient for RNA binding and cell viability. Mol Cell Biol. 1987 Sep;7(9):3268–76. pmid:3313012
- 5. Baer BW, Kornberg RD. The protein responsible for the repeating structure of cytoplasmic poly(A)-ribonucleoprotein. Journal of Cell Biology. 1983 Mar 1;96(3):717–21. pmid:6833379
- 6. Qi Y, Wang M, Jiang Q. PABPC1——mRNA stability, protein translation and tumorigenesis. Front Oncol. 2022 Dec 1;12:1025291. pmid:36531055
- 7. Sachs AB, Bond MW, Kornberg RD. A single gene from yeast for both nuclear and cytoplasmic polyadenylate-binding proteins: Domain structure and expression. Cell. 1986 Jun;45(6):827–35. pmid:3518950
- 8. Liu J, Lu X, Zhang S, Yuan L, Sun Y. Molecular Insights into mRNA Polyadenylation and Deadenylation. IJMS. 2022 Sep 20;23(19):10985. pmid:36232288
- 9. He F, Jacobson A. Eukaryotic mRNA decapping factors: molecular mechanisms and activity. The FEBS Journal. 2022 Sep 30;febs.16626. pmid:36098474
- 10. Siddiqui N, Mangus DA, Chang TC, Palermino JM, Shyu AB, Gehring K. Poly(A) Nuclease Interacts with the C-terminal Domain of Polyadenylate-binding Protein Domain from Poly(A)-binding Protein. Journal of Biological Chemistry. 2007 Aug;282(34):25067–75. pmid:17595167
- 11. Schäfer IB, Yamashita M, Schuller JM, Schüssler S, Reichelt P, Strauss M, et al. Molecular Basis for poly(A) RNP Architecture and Recognition by the Pan2-Pan3 Deadenylase. Cell. 2019 May;177(6):1619–1631.e21. pmid:31104843
- 12. Sachs AB, Davis RW. The poly(A) binding protein is required for poly(A) shortening and 60S ribosomal subunit-dependent translation initiation. Cell. 1989 Sep;58(5):857–67. pmid:2673535
- 13. Caponigro G, Parker R. Multiple functions for the poly(A)-binding protein in mRNA decapping and deadenylation in yeast. Genes Dev. 1995 Oct 1;9(19):2421–32. pmid:7557393
- 14. Brown CE, Sachs AB. Poly(A) Tail Length Control in Saccharomyces cerevisiae Occurs by Message-Specific Deadenylation. Mol Cell Biol. 1998 Nov;18(11):6548–59. pmid:9774670
- 15. Doel MT, Carey NH. The translational capacity of deadenylated ovalbumin messenger RNA. Cell. 1976 May;8(1):51–8. pmid:954092
- 16. Deshpande AK, Chatterjee B, Roy AK. Translation and stability of rat liver messenger RNA for alpha 2 mu-globulin in Xenopus oocyte. The role of terminal poly(A). Journal of Biological Chemistry. 1979 Sep;254(18):8937–42. pmid:90045
- 17. Rosenthal ET, Tansey TR, Ruderman JV, Gottesman M. Sequence-specific adenylations and deadenylations accompany changes in the translation of maternal messenger RNA after fertilization of Spisula oocytes. Journal of Molecular Biology. 1983 May;166(3):309–27. pmid:6854649
- 18. Jacobson A, Favreau M. Possible Involvement of poly(A) in protein synthesks. Nucl Acids Res. 1983;11(18):6353–68.
- 19. Palatnik CM, Wilkins C, Jacobson A. Translational control during early Dictyostelium development: Possible involvement of poly(A) sequences. Cell. 1984 Apr;36(4):1017–25. pmid:6142768
- 20. Drummond DR, Armstrong J, Colman A. The effect of capping and polyadenylation on the stability, movement and translation of synthetic messenger RNAs in Xenopus oocytes. Nucl Acids Res. 1985;13(20):7375–94. pmid:3932972
- 21. Galili G, Kawata EE, Smith LD, Larkins BA. Role of the 3’-poly(A) sequence in translational regulation of mRNAs in Xenopus laevis oocytes. Journal of Biological Chemistry. 1988 Apr;263(12):5764–70. pmid:3356706
- 22. Gallie DR, Lucas WJ, Walbot V. Visualizing mRNA expression in plant protoplasts: factors influencing efficient mRNA uptake and translation. Plant Cell. 1989 Mar;1(3):301–11. pmid:2535505
- 23. Munroe D, Jacobson A. mRNA poly(A) tail, a 3’ enhancer of translational initiation. Mol Cell Biol. 1990 Jul;10(7):3441–55. pmid:1972543
- 24. Munroe D, Jacobson A. Tales of poly(A): a review. Gene. 1990 Jul;91(2):151–8. pmid:1976572
- 25.
Jacobson A. Poly(A) metabolism and translation: the closed-loop model. In: Hershey JW, Mathews MB, Sonenberg N, editors. Translational control. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press; 1996. p. 451–80.
- 26. Tarun SZ, Sachs AB. Association of the yeast poly(A) tail binding protein with translation initiation factor eIF-4G. The EMBO Journal. 1996 Dec;15(24):7168–77. pmid:9003792
- 27. Wells SE, Hillner PE, Vale RD, Sachs AB. Circularization of mRNA by Eukaryotic Translation Initiation Factors. Molecular Cell. 1998 Jul;2(1):135–40. pmid:9702200
- 28. Amrani N, Ghosh S, Mangus DA, Jacobson A. Translation factors promote the formation of two states of the closed-loop mRNP. Nature. 2008 Jun;453(7199):1276–80. pmid:18496529
- 29. Borman AM. Biochemical characterisation of cap-poly(A) synergy in rabbit reticulocyte lysates: the eIF4G-PABP interaction increases the functional affinity of eIF4E for the capped mRNA 5’-end. Nucleic Acids Research. 2000 Nov 1;28(21):4068–75. pmid:11058101
- 30. Dever TE, Kinzy TG, Pavitt GD. Mechanism and Regulation of Protein Synthesis in Saccharomyces cerevisiae. Genetics. 2016 May 1;203(1):65–107. pmid:27183566
- 31. Imataka H. A newly identified N-terminal amino acid sequence of human eIF4G binds poly(A)-binding protein and functions in poly(A)-dependent translation. The EMBO Journal. 1998 Dec 15;17(24):7480–9. pmid:9857202
- 32. Kessler SH, Sachs AB. RNA Recognition Motif 2 of Yeast Pab1p Is Required for Its Functional Interaction with Eukaryotic Translation Initiation Factor 4G. Mol Cell Biol. 1998 Jan;18(1):51–7. pmid:9418852
- 33. Melamed D, Young DL, Miller CR, Fields S. Combining Natural Sequence Variation with High Throughput Mutational Data to Reveal Protein Interaction Sites. PLoS Genet. 2015 Feb 11;11(2):e1004918. pmid:25671604
- 34. Michel YM, Poncet D, Piron M, Kean KM, Borman AM. Cap-Poly(A) Synergy in Mammalian Cell-free Extracts. Journal of Biological Chemistry. 2000 Oct;275(41):32268–76.
- 35. Merrick WC, Pavitt GD. Protein Synthesis Initiation in Eukaryotic Cells. Cold Spring Harb Perspect Biol. 2018 Dec;10(12):a033092. pmid:29735639
- 36. Costello J, Castelli LM, Rowe W, Kershaw CJ, Talavera D, Mohammad-Qureshi SS, et al. Global mRNA selection mechanisms for translation initiation. Genome Biol. 2015 Dec;16(1):10. pmid:25650959
- 37. Thompson MK, Gilbert WV. mRNA length-sensing in eukaryotic translation: reconsidering the “closed loop” and its implications for translational control. Curr Genet. 2017 Aug;63(4):613–20. pmid:28028558
- 38. Vicens Q, Kieft JS, Rissland OS. Revisiting the Closed-Loop Model and the Nature of mRNA 5′–3′ Communication. Molecular Cell. 2018 Dec;72(5):805–12. pmid:30526871
- 39. Kershaw CJ, Nelson MG, Castelli LM, Jennings MD, Lui J, Talavera D, et al. Translation factor and RNA binding protein mRNA interactomes support broader RNA regulons for post-transcriptional control. Journal of Biological Chemistry. 2023 Aug;105195. pmid:37633333
- 40. Xiang K, Bartel DP. The molecular basis of coupling between poly(A)-tail length and translational efficiency. eLife. 2021 Jul 2;10:e66493. pmid:34213414
- 41. Kajjo S, Sharma S, Chen S, Brothers WR, Cott M, Hasaj B, et al. PABP prevents the untimely decay of select mRNA populations in human cells. The EMBO Journal. 2022 Mar 15;41(6):e108650. pmid:35156721
- 42. Hoshino S, Hosoda N, Araki Y, Kobayashi T, Uchida N, Funakoshi Y, et al. Novel Function of the Eukaryotic Polypeptide-Chain Releasing Factor 3 (eRF3/GSPT) in the mRNA Degradation Pathway. Biochemistry (Mosc). 1999;64(12). pmid:10648960
- 43. Cosson B, Berkova N, Couturier A, Chabelskaya S, Philippe M, Zhouravleva G. Poly(A)-binding protein and eRF3 are associated in vivo in human and Xenopus cells. Biology of the Cell. 2002 Sep;94(4–5):205–16. pmid:12489690
- 44. Cosson B, Couturier A, Chabelskaya S, Kiktev D, Inge-Vechtomov S, Philippe M, et al. Poly(A)-Binding Protein Acts in Translation Termination via Eukaryotic Release Factor 3 Interaction and Does Not Influence [PSI +] Propagation. Mol Cell Biol. 2002 May 15;22(10):3301–15.
- 45. Roque S, Cerciat M, Gaugué I, Mora L, Floch AG, Zamaroczy M de, et al. Interaction between the poly(A)-binding protein Pab1 and the eukaryotic release factor eRF3 regulates translation termination but not mRNA decay in Saccharomyces cerevisiae. RNA. 2015 Jan 1;21(1):124–34. pmid:25411355
- 46. Hellen CUT. Translation Termination and Ribosome Recycling in Eukaryotes. Cold Spring Harb Perspect Biol. 2018 Oct;10(10):a032656. pmid:29735640
- 47. Salas-Marco J, Bedwell DM. GTP Hydrolysis by eRF3 Facilitates Stop Codon Decoding during Eukaryotic Translation Termination. Molecular and Cellular Biology. 2004 Sep 1;24(17):7769–78. pmid:15314182
- 48. Amrani N, Ganesan R, Kervestin S, Mangus DA, Ghosh S, Jacobson A. A faux 3′-UTR promotes aberrant termination and triggers nonsense- mediated mRNA decay. Nature. 2004 Nov;432(7013):112–8. pmid:15525991
- 49. Ivanov PV, Gehring NH, Kunz JB, Hentze MW, Kulozik AE. Interactions between UPF1, eRFs, PABP and the exon junction complex suggest an integrated model for mammalian NMD pathways. EMBO J. 2008 Mar 5;27(5):736–47. pmid:18256688
- 50. Swart EC, Serra V, Petroni G, Nowacki M. Genetic Codes with No Dedicated Stop Codon: Context-Dependent Translation Termination. Cell. 2016 Jul 28;166(3):691–702. pmid:27426948
- 51. Wu C, Roy B, He F, Yan K, Jacobson A. Poly(A)-Binding Protein Regulates the Efficiency of Translation Termination. Cell Reports. 2020 Nov;33(7):108399. pmid:33207198
- 52. Ivanov A, Mikhailova T, Eliseev B, Yeramala L, Sokolova E, Susorov D, et al. PABP enhances release factor recruitment and stop codon recognition during translation termination. Nucleic Acids Res. 2016 Sep 19;44(16):7766–76. pmid:27418677
- 53. Ivanov A, Shuvalova E, Egorova T, Shuvalov A, Sokolova E, Bizyaev N, et al. Polyadenylate-binding protein–interacting proteins PAIP1 and PAIP2 affect translation termination. J Biol Chem. 2019 May 24;294(21):8630–9. pmid:30992367
- 54. Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS. Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling. Science. 2009 Apr 10;324(5924):218–23. pmid:19213877
- 55. Mangus DA, Amrani N, Jacobson A. Pbp1p, a Factor Interacting withSaccharomyces cerevisiae Poly(A)-Binding Protein, Regulates Polyadenylation. Mol Cell Biol. 1998 Dec 1;18(12):7383–96. pmid:9819425
- 56. Tuong Vi DT, Fujii S, Valderrama AL, Ito A, Matsuura E, Nishihata A, et al. Pbp1, the yeast ortholog of human Ataxin-2, functions in the cell growth on non-fermentable carbon sources. PLoS ONE. 2021 May 13;16(5):e0251456. pmid:33984024
- 57. Van De Poll F, Sutter BM, Acoba MG, Caballero D, Jahangiri S, Yang YS, et al. Pbp1 associates with Puf3 and promotes translation of its target mRNAs involved in mitochondrial biogenesis. PLoS Genet. 2023 May 22;19(5):e1010774. pmid:37216416
- 58. Celik A, Baker R, He F, Jacobson A. High-resolution profiling of NMD targets in yeast reveals translational fidelity as a basis for substrate selection. RNA. 2017 May;23(5):735–48. pmid:28209632
- 59. He F, Celik A, Wu C, Jacobson A. General decapping activators target different subsets of inefficiently translated mRNAs. eLife. 2018 Dec 6;7:e34409. pmid:30520724
- 60. Buschauer R, Matsuo Y, Sugiyama T, Chen YH, Alhusaini N, Sweet T, et al. The Ccr4-Not complex monitors the translating ribosome for codon optimality. Science. 2020 Apr 17;368(6488):eaay6912. pmid:32299921
- 61. Alhusaini N, Coller J. The deadenylase components Not2p, Not3p, and Not5p promote mRNA decapping. RNA. 2016 May;22(5):709–21. pmid:26952104
- 62. Muhlrad D, Parker R. Premature translational termination triggers mRNA decapping. Nature. 1994 Aug;370(6490):578–81. pmid:8052314
- 63. He F, Jacobson A. Upf1p, Nmd2p, and Upf3p Regulate the Decapping and Exonucleolytic Degradation of both Nonsense-Containing mRNAs and Wild-Type mRNAs. Molecular and Cellular Biology. 2001 Mar 1;21(5):1515–30. pmid:11238889
- 64. Presnyak V, Alhusaini N, Chen YH, Martin S, Morris N, Kline N, et al. Codon Optimality Is a Major Determinant of mRNA Stability. Cell. 2015 Mar;160(6):1111–24. pmid:25768907
- 65. Webster MW, Chen YH, Stowell JAW, Alhusaini N, Sweet T, Graveley BR, et al. mRNA Deadenylation Is Coupled to Translation Rates by the Differential Activities of Ccr4-Not Nucleases. Molecular Cell. 2018 Jun;70(6):1089–1100.e8. pmid:29932902
- 66. Wakiyama M, Imataka H, Sonenberg N. Interaction of eIF4G with poly(A)-binding protein stimulates translation and is critical for Xenopus oocyte maturation. Current Biology. 2000 Sep;10(18):1147–50. pmid:10996799
- 67. Wakiyama M, Honkura N, Miura K i. Interference with Interaction between Eukaryotic Translation Initiation Factor 4G and Poly(A)-Binding Protein in Xenopus Oocytes Leads to Inhibition of Polyadenylated mRNA Translation and Oocyte Maturation. Journal of Biochemistry. 2001 Dec 1;130(6):737–40. pmid:11726272
- 68. Zinshteyn B, Rojas-Duran MF, Gilbert WV. Translation initiation factor eIF4G1 preferentially binds yeast transcript leaders containing conserved oligo-uridine motifs. RNA. 2017 Sep;23(9):1365–75. pmid:28546148
- 69. Hinnebusch AG, Ivanov IP, Sonenberg N. Translational control by 5′-untranslated regions of eukaryotic mRNAs. Science. 2016 Jun 17;352(6292):1413–6. pmid:27313038
- 70. Jackson RJ, Hellen CUT, Pestova TV. The mechanism of eukaryotic translation initiation and principles of its regulation. Nat Rev Mol Cell Biol. 2010 Feb;11(2):113–27. pmid:20094052
- 71. Hinnebusch AG. The Scanning Mechanism of Eukaryotic Translation Initiation. Annu Rev Biochem. 2014 Jun 2;83(1):779–812.
- 72. Hinnebusch AG. Structural Insights into the Mechanism of Scanning and Start Codon Recognition in Eukaryotic Translation Initiation. Trends in Biochemical Sciences. 2017 Aug;42(8):589–611. pmid:28442192
- 73. Zhou F, Zhang H, Kulkarni SD, Lorsch JR, Hinnebusch AG. eIF1 discriminates against suboptimal initiation sites to prevent excessive uORF translation genome-wide. RNA. 2020 Apr;26(4):419–38. pmid:31915290
- 74. Gilbert WV, Zhou K, Butler TK, Doudna JA. Cap-Independent Translation Is Required for Starvation-Induced Differentiation in Yeast. Science. 2007 Aug 31;317(5842):1224–7. pmid:17761883
- 75. Niederer RO, Rojas-Duran MF, Zinshteyn B, Gilbert WV. Direct analysis of ribosome targeting illuminates thousand-fold regulation of translation initiation. Cell Systems. 2022 Mar;13(3):256–264.e3. pmid:35041803
- 76. Bailey TL. STREME: accurate and versatile sequence motif discovery. Bioinformatics. 2021 Sep 29;37(18):2834–40. pmid:33760053
- 77. Hamilton R, Watanabe CK, de Boer HA. Compilation and comparison of the sequence context around the AUG startcodons in Saccharomyces cerevisiae mRNAs. Nucl Acids Res. 1987;15(8):3581–93. pmid:3554144
- 78. Li K, Kong J, Zhang S, Zhao T, Qian W. Distance-dependent inhibition of translation initiation by downstream out-of-frame AUGs is consistent with a Brownian ratchet process of ribosome scanning. Genome Biol. 2022 Dec 12;23(1):254. pmid:36510274
- 79. Amrani N, Dong S, He F, Ganesan R, Ghosh S, Kervestin S, et al. Aberrant termination triggers nonsense-mediated mRNA decay. Biochemical Society Transactions. 2006;34:4. pmid:16246174
- 80. Rogers DW, Böttcher MA, Traulsen A, Greig D. Ribosome reinitiation can explain length-dependent translation of messenger RNA. PLoS Comput Biol. 2017 Jun 9;13(6):e1005592. pmid:28598992
- 81. Fernandes LD, Moura APS de, Ciandrini L. Gene length as a regulator for ribosome recruitment and protein synthesis: theoretical insights. Sci Rep. 2017 Dec;7(1):17409. pmid:29234048
- 82. Dabrowski M, Bukowy-Bieryllo Z, Zietkiewicz E. Translational readthrough potential of natural termination codons in eucaryotes–The impact of RNA sequence. RNA Biology. 2015 Sep 2;12(9):950–8. pmid:26176195
- 83. Rodnina MV, Korniy N, Klimova M, Karki P, Peng BZ, Senyushkina T, et al. Translational recoding: canonical translation mechanisms reinterpreted. Nucleic Acids Research. 2020 Feb 20;48(3):1056–67. pmid:31511883
- 84. Mangkalaphiban K, He F, Ganesan R, Wu C, Baker R, Jacobson A. Transcriptome-wide investigation of stop codon readthrough in Saccharomyces cerevisiae. PLoS Genet. 2021 Apr 20;17(4):e1009538. pmid:33878104
- 85. Wu CCC, Zinshteyn B, Wehner KA, Green R. High-Resolution Ribosome Profiling Defines Discrete Ribosome Elongation States and Translational Regulation during Cellular Stress. Molecular Cell. 2019 Mar;73(5):959–970.e5. pmid:30686592
- 86. Shah P, Ding Y, Niemczyk M, Kudla G, Plotkin JB. Rate-Limiting Steps in Yeast Protein Translation. Cell. 2013 Jun;153(7):1589–601. pmid:23791185
- 87. Young DJ, Guydosh NR, Zhang F, Hinnebusch AG, Green R. Rli1/ABCE1 Recycles Terminating Ribosomes and Controls Translation Reinitiation in 3′UTRs In Vivo. Cell. 2015 Aug;162(4):872–84. pmid:26276635
- 88. Park EH, Zhang F, Warringer J, Sunnerhagen P, Hinnebusch AG. Depletion of eIF4G from yeast cells narrows the range of translational efficiencies genome-wide. BMC Genomics. 2011 Dec;12(1):68. pmid:21269496
- 89. Lima SA, Chipman LB, Nicholson AL, Chen YH, Yee BA, Yeo GW, et al. Short poly(A) tails are a conserved feature of highly expressed genes. Nat Struct Mol Biol. 2017 Dec;24(12):1057–63. pmid:29106412
- 90. Poonia P, Valabhoju V, Li T, Iben J, Niu X, Lin Z, et al. Yeast poly(A)-binding protein (Pab1) controls translation initiation in vivo primarily by blocking mRNA decapping and decay. bioRxiv [Preprint]. 2024 Apr 23; Available from:
- 91. Longtine MS, Mckenzie A III, Demarini DJ, Shah NG, Wach A, Brachat A, et al. Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae. Yeast. 1998 Dec 4;14(10):953–61. pmid:9717241
- 92. Schiestl RH, Gietz RD. High efficiency transformation of intact yeast cells using single stranded nucleic acids as a carrier. Curr Genet. 1989 Dec;16(5–6):339–46. pmid:2692852
- 93. Ganesan R, Mangkalaphiban K, Baker R, He F, Jacobson A. Ribosome-bound Upf1 forms distinct 80S complexes and conducts mRNA surveillance. RNA. 2022 Oct 3;rna.079416.122. pmid:36192133
- 94. Zhang Z, Dietrich FS. Mapping of transcription start sites in Saccharomyces cerevisiae using 5’ SAGE. Nucleic Acids Res. 2005;33(9):2838–51. pmid:15905473
- 95. Miura F, Kawaguchi N, Sese J, Toyoda A, Hattori M, Morishita S, et al. A large-scale full-length cDNA analysis to explore the budding yeast transcriptome. Proc Natl Acad Sci U S A. 2006 Nov 21;103(47):17846–51. pmid:17101987
- 96. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. Science. 2008 Jun 6;320(5881):1344–9. pmid:18451266
- 97. Xu Z, Wei W, Gagneur J, Perocchi F, Clauder-Münster S, Camblong J, et al. Bidirectional promoters generate pervasive transcription in yeast. Nature. 2009 Feb 19;457(7232):1033–7. pmid:19169243
- 98. Yassour M, Kaplan T, Fraser HB, Levin JZ, Pfiffner J, Adiconis X, et al. Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proc Natl Acad Sci U S A. 2009 Mar 3;106(9):3264–9. pmid:19208812
- 99. Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 2017 Mar;27(3):491–9. pmid:28100584
- 100. Keller A, Nesvizhskii AI, Kolker E, Aebersold R. Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search. Anal Chem. 2002 Oct 1;74(20):5383–92. pmid:12403597
- 101. Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry. Anal Chem. 2003 Sep 1;75(17):4646–58. pmid:14632076
- 102. Lauria F, Tebaldi T, Bernabò P, Groen EJN, Gillingwater TH, Viero G. riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data. PLOS Computational Biology. 2018 Aug 13;14(8):e1006169. pmid:30102689
- 103. Kuhn M. Building Predictive Models in R Using the caret Package. J Stat Soft. 2008;28(5).
- 104. Breiman L. Random Forests. Machine Learning. 2001 Oct 1;45(1):5–32.
- 105. Liaw A, Wiener M. Classification and Regression by randomForest. R News. 2002;2(3):18–22.
- 106.
Archer E. rfPermute: version 2.5 [Internet]. Zenodo; 2021 [cited 2023 Mar 9]. Available from: https://zenodo.org/record/5532822
- 107. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014 Dec;15(12):550. pmid:25516281
- 108. Smyth GK. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology. 2004 Jan 12;3(1):1–25. pmid:16646809
- 109. Kammers K, Cole RN, Tiengwe C, Ruczinski I. Detecting significant changes in protein abundance. EuPA Open Proteomics. 2015 Jun;7:11–9. pmid:25821719
- 110. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Research. 2019 Jul 2;47(W1):W191–8. pmid:31066453
- 111. Kolberg L, Raudvere U, Kuzmin I, Vilo J, Peterson H. gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Res. 2020 Nov 17;9:709. pmid:33564394
- 112. Tjeldnes H, Labun K, Torres Cleuren Y, Chyżyńska K, Świrski M, Valen E. ORFik: a comprehensive R toolkit for the analysis of translation. BMC Bioinformatics. 2021 Dec;22(1):336. pmid:34147079
- 113. Reis M dos, Savva R, Wernisch L. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res. 2004;32(17):5036–44. pmid:15448185
- 114. Tuller T, Waldman YY, Kupiec M, Ruppin E. Translation efficiency is determined by both codon bias and folding energy. Proceedings of the National Academy of Sciences. 2010 Feb 23;107(8):3645–50. pmid:20133581
- 115. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME Suite. Nucleic Acids Res. 2015 Jul 1;43(W1):W39–49. pmid:25953851