Introduction

Degradation of mRNA is a key aspect of gene expression that can be regulated in response to nutrient availability, cell stress, and developmental pathways in eukaryotic cells and further serves to eliminate defective mRNAs. A major pathway of cytoplasmic mRNA turnover involves truncating the poly(A) tail by the Ccr4/Not and Pan2/Pan3 complexes, followed by removal of the m⁷G cap by the Dcp1/Dcp2 decapping complex and 5’ to 3’ exonucleolytic degradation by Xrn1. The decapping complex is activated by factors that interact with low-complexity sequence motifs in the C-terminal tail (CTT) of the catalytic subunit Dcp2, including Edc3, Scd6, DEAD-box helicase Dhh1, and Pat1, which also interact with one another extensively. There is evidence that Pat1 is recruited to oligoadenylate tails remaining on mRNAs following partial deadenylation, in association with the Lsm1-Lsm7 complex, and activates decapping via interactions with other decapping activators and with the Dcp2 CTT itself (Parker 2012) (He and Jacobson 2023)

Genome-wide analysis of mRNA abundance (RNA-seq) in yeast mutants lacking Dhh1, Pat1, or both factors revealed functional cooperation by Pat1 and Dhh1 (He et al. 2018), with a large fraction being up-regulated only in the pat1Δdhh1Δ double mutant (Vijjamarri et al. 2023a). The majority of mRNAs up-regulated by dhh1Δ or pat1Δ mutations are likewise up-regulated in dcp2Δ cells (He et al. 2018) and exhibit greater than average proportions of decapped mRNAs in WT cells but not in dhh1Δ or pat1Δ cells (Vijjamarri et al. 2023a), consistent with Dhh1/Pat1 targeting mRNAs for degradation via decapping. While more than half of the mRNAs up-regulated by dcp2Δ are up-regulated by dhh1Δ and/or pat1Δ, a large fraction are targeted primarily by the Upf factors instead responsible for nonsense-mediated mRNA decay (NMD) (Vijjamarri et al. 2023b) (Celik et al. 2017).

The cumulative contributions of Dhh1 and Pat1 to mRNA decay is consistent with their independent interactions with distinct segments of the Dcp2 CTT (He et al. 2018), which appears to be direct for Pat1 but bridged by Edc3 or Scd6 for Dhh1. There is also evidence for distinct decapping complexes containing either Dhh1 or Pat1 in addition to Xrn1, Edc3 or Scd6 (He et al. 2022). However, Dhh1 occupancy (Miller et al. 2018), tends to be elevated for mRNAs up-regulated by either dhh1Δ (23) or pat1Δ (Vijjamarri et al. 2023a), consistent with Dhh1 contributing to degradation of mRNAs targeted by Pat1. Moreover, Pat1 binding to the Dcp2 CTT was required for degradation of certain Dhh1-targeted mRNAs when Dhh1 recruitment to the CTT was compromised (He et al. 2022).

There is evidence that Edc3 is a common constituent of decapping complexes containing Xrn1 and one or more of the decapping activators Dhh1, Pat1, Scd6, and Upf1 (He et al. 2022). It is surprising therefore that Edc3 has been implicated in targeting only two transcripts for degradation, YRA1 and RPS28B (Badis et al. 2004; Dong et al. 2007). Edc3 shares sequence similarity with decapping activator Scd6 and both proteins contain an FDF motif shown to interact competitively with the Dhh1 homolog in animal systems (Tritschler et al. 2008; Tritschler et al. 2009). Edc3 and Scd6 also share N-terminal LSm domains that compete for binding to helical-leucine-rich (HLM) motifs in the CTT of fission yeast Dcp2 (Fromm et al. 2012). Analyzing effects of deleting the Edc3 interaction site in the S. cerevisiae Dcp2-CTT on levels of several Dhh1-repressed mRNAs suggested that recruitment of Dhh1 to Dcp2 can be mediated interchangeably by Edc3 or Scd6 bound to the same site in the Dcp2 CTT (He et al. 2022). These findings, plus the fact that deleting S. cerevisiae SCD6 and EDC3 simultaneously confers a synthetic growth defect (Decourty et al. 2008), suggest that yeast Scd6 and Edc3 function redundantly in targeting specific mRNAs for decapping and degradation.

Although tethering yeast Dhh1 or Scd6 enhances degradation of reporter mRNAs via Dcp1/Dcp2 (Carroll et al. 2011; Sweet et al. 2012; Zeidan et al. 2018), it is not well understood how these factors are targeted to specific native mRNAs. Dhh1 has been implicated in accelerating degradation associated with non-optimal codons in yeast mRNAs (Presnyak et al. 2015), being required for the rapid turnover conferred by suboptimal codons inserted in reporter mRNAs (Sweet et al. 2012). A queue of slowly elongating ribosomes upstream from non-optimal codons can be recognized by Dhh1, and overexpressing or tethering Dhh1 evokes ribosome stalling at non-optimal codons. Moreover, Dhh1 association and Dhh1-dependent repression of mRNA abundance both correlate with codon non-optimality across the yeast transcriptome (Radhakrishnan et al. 2016). However, the mRNAs most highly up-regulated in dhh1Δ and pat1Δ cells are not enriched for suboptimal codons, suggesting that other features are responsible for their preferential targeting by Dhh1 or Pat1 for decapping/decay (Vijjamarri et al. 2023a).

In addition to enhancing mRNA decay, there is evidence that Pat1, Dhh1, and Scd6 can repress translation. Tethering Dhh1 or Scd6 represses translation of reporter mRNAs in dcp2Δ cells where the tethered transcripts cannot be decapped and degraded (Carroll et al. 2011; Sweet et al. 2012; Zeidan et al. 2018). Deletion of Dhh1 and Pat1 simultaneously eliminated loss of bulk polysomes evoked by nutrient starvation and also increased initiation rates of certain mRNAs (Holmes et al. 2004) (Coller and Parker 2005) (Arribere et al. 2011). Supporting a direct role in repressing translation, overexpressing Dhh1 or Pat1 in nonstarved cells evoked polysome disassembly and reduced the initiation rate of specific mRNAs; and addition of Dhh1 (Coller and Parker 2005) or N-terminally truncated Pat1 (Nissan et al. 2010) to yeast extracts inhibited bulk translation and 48S preinitiation complex (PIC) assembly in vitro. Ribosome profiling studies of dhh1Δ, pat1Δ and pat1Δdhh1Δ mutants identified hundreds of genes whose transcripts are translationally down-regulated or activated by Dhh1 or Pat1 in nutrient-replete cells (Jungfleisch et al. 2017) (Radhakrishnan et al. 2016; Zeidan et al. 2018), which frequently involves cooperation between Dhh1 and Pat1 (Vijjamarri et al. 2023a).

Recently, we showed that Pat1 and Dhh1 function with the decapping enzyme in rich medium to repress the abundance or translation of numerous mRNAs encoding proteins required specifically in media containing an alternative carbon or nitrogen source (Vijjamarri et al. 2023a; Vijjamarri et al. 2023b). These include mitochondrial proteins involved in oxidative phosphorylation (Ox. Phos.) and diverse other proteins known to be transcriptionally repressed by carbon or nitrogen catabolite repression in rich medium.

In this study, we used a multi-omics approach to determine whether Edc3 and Scd6 have largely redundant functions in targeting mRNAs for decapping and attendant degradation, and whether they functionally cooperate with Pat1 or Dhh1 in repressing the abundance or translation of specific mRNAs. We identified a large cohort of mRNAs that are up-regulated in an scd6Δedc3Δ double mutant but not in either single mutant lacking only Scd6 or Edc3, without a commensurate increase in transcription of the cognate genes. These transcripts display a strikingly similar pattern of up-regulation in the dhh1Δ mutant in the manner predicted if Edc3/Scd6 redundantly recruit Dhh1 to Dcp2 for activation of decapping (He et al. 2022). We further observed functional redundancy between Scd6 and Edc3 and extensive cooperation with Dhh1/Pat1 in repressing translation of particular mRNAs, with evidence for interdependent repression by all four decapping factors. Importantly, Edc3/Scd6 contribute to post-transcriptional repression of proteins required for catabolism of non-preferred carbon or nitrogen sources on rich medium, acting collectively to enhance glucose repression, maintain low-level mitochondrial electron transport and reduce levels of tricarboxylic acid (TCA) and glyoxylate cycle intermediates in glucose-replete cells.

Methods

Yeast strains and plasmids

Unless otherwise indicated, the following yeast strains were employed for all experiments: WT strain HFY114 (W303: MATa ade2-1 ura3-1 his3-11,15 trp1-1 leu2-3,112 can1-100) (He et al. 2003), scd6Δ strain SYY2352 (MATa ade2-1 ura3-1 his3-11,15 trp1-1 leu2-3,112 can1-100 scd6Δ::kanMX6) (He and Jacobson 2015), edc3Δ strain FZY862 (MATa ade2-1 ura3-1 his3-11,15 trp1-1 leu2-3,112 can1-100 edc3Δ::kanMX4), and scd6Δedc3Δ strain FZY858 (MATa ade2-1 ura3-1 his3-11,15 trp1-1 leu2-3,112 can1-100 scd6Δ::hphMX4 edc3Δ::kanMX4).

Strains FZY858 and FZY862 were generated by replacing chromosomal EDC3 in strains FZY855 and HFY114, respectively, with the edc3Δ::kanMX4 allele amplified by PCR from the chromosomal DNA of strain 255 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 edc3Δ::kanMX4) obtained from Research Genetics. DNA sequences up to 310 bp upstream and 340 bp downstream of the EDC3 coding sequence were included in the amplified fragment used for transformation of FZY855 and HFY114 to G418-resistance. FZY855 was derived from SYY2352 by transforming with marker-swap plasmid pAG32 (Goldstein and McCusker 1999) to replace scd6Δ::kanMX6 with scd6Δ::hphMX6, selecting for resistance to hygromycin and screening for loss of G418-resistance. The presence of all deletion alleles was verified by PCR analysis of chromosomal DNA.

Strains H5695, H5696, H5697, and H5698 strains, employed for co-immunoprecipitation experiments, were generated by PCR-amplification of a DHH1-TAP-HIS3MX fragment from strain F2262 using primers AKV224 and AKV225, containing 474 bp upstream and 450 bp downstream of the DHH1 stop codon, that was used to transform strains HFY114, FZY862, SYY2352, and FZY858, respectively, to His⁺. A complete list of yeast strains employed is given in Table S1.

Plasmids pLfz614-7 and pLfz635-5 contain the EDC3 CDS with 420 bp upstream and 490 bp downstream, on a ∼2.6 kb fragment, and plasmids pL615-5 and pLfz636-1 contain the SCD6 CDS with 500 bp upstream and 180 bp downstream on a ∼1.7 kb fragment, amplified by PCR from yeast genomic DNA and inserted between the XhoI and EcoRI sites of YCplac33 or YCplac111, respectively. Plasmid pAK133 containing DCP2-3HA was produced by a multi-step PCR amplification scheme. First, the DCP2 CDS with 920 nucleotides upstream of the start codon and 587 nucleotides downstream of the stop codon was amplified from genomic DNA of WT strain HFY114. Sequences encoding the 3XHA tag were introduced immediately upstream of the DCP2 stop codon using primer pairs AKV372/AKV373 and AKV374/AKV375 during the first round of PCR amplification; a second round of PCR followed using primers AKV372 and AKV375 to amplify a complete DCP2-3HA fragment also containing sequences homologous to the ends of plasmid YCplac33 linearized by digestion with BamHI. The PCR product was fused into BamHI-digested YCplac33 using the Takara In-Fusion cloning kit (#638956) according to the manufacturer’s instructions. Primers AKV372 and AKV375 were designed using the Takara on-line primer design tool to ensure optimal homologous recombination during the cloning process. The inserted yeast DNA fragments were verified by sequencing in their entirety. A complete list of plasmids employed is given in Table S2 and the list of primers in Table S3.

Cell spotting growth assays

Yeast transformants harboring plasmids containing EDC3, SCD6, or empty vector were grown to mid-logarithmic phase at 30°C in liquid synthetic complete medium (SC) without uracil (SC-U). Cultures were diluted to OD₆₀₀ of 1.0 and 10-fold serial dilutions were spotted on agar medium of the same composition and incubated at 30°C or 37°C for 2 days.

Polysome profiling

Polysome profiling was conducted as described previously (Zeidan et al. 2018). In brief, strains were cultured in YPD medium at 30°C to mid-logarithmic phase (OD₆₀₀ of ∼0.5-0.6). Fifteen A₂₆₀ units of WCEs were resolved on a 10–50% sucrose gradient by centrifugation at 35,000 rpm. Gradients were fractionated with continuous scanning at 260 nm. Areas under the A₂₆₀ tracings of polysome and monosome peaks were calculated using ImageJ software and used to calculate polysome to monosome (P/M) ratios in mutants and WT.

Ribosome footprint profiling

Duplicate cultures of yeast strains HFY114 (WT W303), SYY2352 (scd6Δ), FZY862 (edc3Δ), and FZY858 (scd6Δedc3Δ) were cultured in YPD medium at 30°C to OD₆₀₀ ∼0.6-0.7. Yeast cells were quickly filtered, frozen in liquid nitrogen and stored at -80°C. Cells were lysed in a freezer mill and ribosome protected mRNA fragments (RPF) were isolated and used for cDNA library preparation as described previously (Vijjamarri et al. 2023b). Single-end 100 bp Illumina sequencing was performed by the National Heart, Lung and Blood Institute (NHLBI) DNA Sequencing and Genomics Core facility (Bethesda, MD).

RNA sequencing with spike-in normalization and CAGE analysis

The same lysates used for RPF library preparation were used to prepare RNA-Seq libraries after adding ERCC RNA Spike-In Control Mix 1 (Thermo Fisher Scientific, Cat. # 4456740). Total RNA was extracted from cell lysates using QIAzol Lysis reagent (Qiagen Cat. # 79306) and miRNeasy Mini Kit (Qiagen, Cat. 217004). Twenty µg of total RNA of each sample was subjected to RNase-free DNase I (Roche, Cat. # 04716728001) treatment and then processed with the RNA Clean and Concentrator Kit (Zymo, Cat. R1018). An aliquot of 2.4 µl of 1:100 diluted ERCC RNA Spike-In Control Mix 1 was added to 1.2 µg of each RNA sample and submitted to the NHLBI DNA Sequencing and Genomics Core facility (Bethesda, MD) for cDNA library preparation and Illumina sequencing. CAGE sequencing and RNA-Seq were conducted in parallel on two biological replicates of total RNA isolated from the same WT and scd6Δedc3Δ strains, cultured under the same conditions as employed for ribosomal profiling, exactly as described previously (Vijjamarri et al. 2023a).

Rpb1 ChIP-Seq with spike-in normalization

Triplicates of wild type and scd6Δedc3Δ yeast strains were cultured in rich YPD medium at 30°C to OD600=0.6∼0.8. Chromatin extracts were prepared from formaldehyde cross-linked cells as described previously (Qiu et al. 2016). S. pombe chromatin was added to each chromatin sample as a spike-in control prior to immunoprecipitation with Rpb1 antibodies, and ChIP-Seq DNA libraries were prepared as described previously (Zheng et al. 2023) subjected to 50 bp paired-end Illumina sequencing by the NHLBI DNA Sequencing and Genomics Core facility (Bethesda, MD) and analyzed as previously described (Zheng et al. 2023).

Western blot analysis

For the results in Fig. 6B-C, WCEs were prepared by trichloroacetic acid (TCA) extraction as previously described (Reid and Schatz 1982) and immunoblot analysis was conducted as described previously (Nanda et al. 2009). After electroblotting to nitrocellulose membranes (Bio-Rad 1620094), membranes were probed with antibodies against Atp20, Cox14, Pet10, Qcr8, Sdh4 (kindly provided by Dr. Nikolaus Pfanner), Cyb2 (kindly provided by Dr. Thomas Fox), Idh1 (Abnova, PAB19472), Cit2 (Antibodies-online.com, ABIN4889057), and Gcd6 (Bushman et al. 1993). Secondary antibodies employed were HRP-conjugated anti-rabbit (Cytiva, NA9340V), anti-mouse IgG (Cytiva, NA931V) and anti-goat IgG (Abnova, PAB29101). Detection was performed using enhanced chemiluminescence (ECL) Western Blotting Detection Reagent (Cytiva, RPN2016) and the Azure 200 gel imaging biosystem. NIH Image J was employed to analyze images for quantification. For analysis of Cox2 levels in Fig. 6D, total proteins were TCA-extracted as described previously (Rashida et al. 2021) and subjected to Western blot analysis as recently described (Niphadkar et al. 2024) using mouse monoclonal antibody MTCO2 (4B12A5) from Invitrogen.

TMT-MS analysis of global protein abundance

TMT-MS analysis was conducted as described previously (Vijjamarri et al. 2023a) with the following modifications. Replicate cultures of WT, edc3Δ, scd6Δ and scd6Δedc3Δ strains were cultured in YPD medium for ∼3 doublings to OD₆₀₀ of ∼0.6, and harvested by centrifugation for 5 min at 3000 x g. Cells were resuspended in nuclease-free water, collected by centrifugation, and stored at -80°C. WCEs were prepared in freshly prepared 8M Urea, 25 mM triethylammonium-bicarbonate (TEAB; Thermo Scientific, 90114) by washing the cell pellets once and resuspending again in the extraction buffer, then vortexing with glass beads in the cold room. Lysates were clarified by centrifugation at 13,000 x g for 30 min and the quality of extracted proteins was assessed following SDS-PAGE using GelCode™ Blue Stain (Thermo Scientific, 24592) and quantified with the Pierce™ BCA Protein Assay Kit (Thermo Scientific, 23225). Lysates were stored at -80°C. Sample preparation, TMT-MS/MS (Zecha et al. 2019) and data analysis were performed at the IDeA National Resource for Quantitative Proteomics.

Co-immunoprecipitation analysis

Transformants of DHH1-TAP strains were cultured in 50 ml YPD at 30°C to OD₆₀₀ ≈ 0.6–0.8, harvested by centrifugation, and washed with 1 ml lysis buffer (LB) containing 0.5 mM PMSF. Cell pellets were resuspended in 500 µl LB supplemented with 0.5 mM PMSF and 1x protease inhibitor cocktail (Roche Cat # 5056489001) and lysed by vortexing in 200 µl glass beads (8 cycles: 30 sec on, 45 sec off) on ice in a cold room. Lysates were clarified by centrifugation (15,000 rpm, 20 min, 4°C), and protein concentrations determined using the Bradford assay. Approximately 1 µg of lysate was incubated with IgG sepharose beads pre-treated with 5% BSA for 2 h at 4°C. Beads were washed five times with LB containing 0.5 mM DTT. Bound complexes were eluted with 500 µl LB + 0.5 mM DTT containing 2 µl TEV protease (1 mg/ml, 10 U/µl; Invitrogen 12575-015) at room temperature for 2 h. Eluates were precipitated with 10% cold TCA on ice for 10 min, pelleted by centrifugation (13,000 rpm, 10 min, 4°C), washed with cold acetone, air-dried, resuspended in 50 µl 2× SDS-PAGE sample buffer, neutralized with 50 µl unadjusted 1 M Tris, and boiled for 5 min. Samples were subjected to immunoblot analysis as described above using rabbit polyclonal TAP antibodies (Thermo Fisher Scientific Cat# CAB1001) and mouse monoclonal antibodies against HA (12CA5) (Roche 10522600) for detecting TAP and HA tags.

Measuring mitochondrial membrane potential

Precultures were grown in SC-Ura (to select for the URA3 plasmids) to OD₆₀₀ of ∼3.0 and used to inoculate YPD medium at OD₆₀₀ of 0.2. Cells were grown to OD₆₀₀ of ∼0.6-0.8, incubated with 500 nM TMRM for 1 h and subjected to flow cytometry to measure dye fluorescence in individual cells as described previously (Vijjamarri et al. 2023b). In control samples, 50 µM FCCP was added to dissipate the membrane potential and reveal non-specific background fluorescence. Results are presented in arbitrary fluorescence units normalized to OD₆₀₀ of the cultures.

Analysis of polar metabolites of intermediary metabolism

Yeast cell culturing

Four replicate 15 mL cultures in YEPD medium were prepared for each strain by inoculating with saturated overnight cultures to OD₆₀₀ of 0.1, culturing with shaking at 30°C, and harvesting at OD₆₀₀ of 0.6 by centrifugation in conical 15mL tubes for 3 min at 3000 x g in an Avanti J-HC centrifuge pre-cooled to -10°C. Cell pellets were resuspended in 1.8 mL of ice-cold PBS and centrifuged in 2 mL screw-cap tubes in a refrigerated microfuge for 30 s. Supernatants were decanted, tube rims blotted on tissue paper, and cell pellets frozen in dry ice/ethanol for 5 min and stored at -80C. Frozen cell pellets were shipped on dry ice to the NYU Metabolomics Core Resource Laboratory, where all subsequent analyses described below were conducted.

Extraction of metabolites

Frozen cell pellets were thawed on wet ice. Extraction buffer, consisting of 80% methanol (Fisher Scientific) and 500 nM metabolomics amino acid mix standard (Cambridge Isotope Laboratories, Inc.), was prepared and placed on dry ice. Samples were extracted by mixing cell pellets with extraction buffer at 10mg/mL (determined by sample OD measurements) in 2.0 mL screw-cap vials containing ∼100 µL of disruption beads (Research Products International, Mount Prospect, IL). Each sample was homogenized for 10 cycles on a bead blaster homogenizer (Benchmark Scientific, Edison, NJ), with each cycle consisting of 30 sec homogenization at 6 m/s followed by a 30 sec pause. Samples were centrifuged at 21,000 g for 3 min at 4°C. Aliquots of 450 µL were transferred to 1.5 mL tubes, dried in a Speedvac (Thermo Fisher, Waltham, MA), reconstituted in 50 µL of Optima LC/MS grade water (Fisher Scientific, Waltham, MA), sonicated for 2 min, and centrifuged at 21,000 g for 3 min at 4°C. Aliquots of 20 µL were transferred to LC vials containing glass inserts for analysis, and the remaining samples stored at -80°C.

LC-MS/MS with the hybrid metabolomics method

Samples were subjected to an LC-MS analysis to detect and quantify known peaks. Extraction of polar metabolites was carried out on each sample based on a previously described method (Jones et al. 2014). LC was conducted using a Millipore™ ZIC-pHILIC (2.1 x150 mm, 5 μm) column coupled to a Dionex Ultimate 3000™ system with gradient elution conducted at 25°C with a flow rate of 100 μL/min using the following buffersA) 10 mM ammonium carbonate in water, pH 9.0, and B) neat acetonitrile. The gradient profile was as follows; 80-20% B (0-30 min), 20-80% B (30-31 min), 80-80% B (31-42 min). Injection volume was set to 2 μL for all analyses with a 42 min total run time per injection.

MS was conducted by coupling the LC system to a Thermo Q Exactive HF™ mass spectrometer operating in heated electrospray ionization mode (HESI). Method duration was 30 min with a polarity switching data-dependent Top 5 method for both positive and negative modes. Spray voltage for both positive and negative modes was 3.5kV and capillary temperature was set to 320 °C with a sheath gas rate of 35, aux gas of 10, and max spray current of 100 μA. The full MS scan for both polarities utilized 120,000 resolution with an AGC target of 3e6 and a maximum IT of 100 ms, and the scan range was from 67-1000 m/z. Tandem MS spectra for both positive and negative mode used a resolution of 15,000, AGC target of 1e5, maximum IT of 50 ms, isolation window of 0.4 m/z, isolation offset of 0.1 m/z, fixed first mass of 50 m/z, and 3-way multiplexed normalized collision energies (nCE) of 10, 35, 80. The minimum AGC target was 1e4 with an intensity threshold of 2e5. All data were acquired in profile mode.

Relative quantification and statistical analyses of polar metabolites

The resulting Thermo^TM RAW files were converted to SQLite format using an in-house python script to enable downstream peak detection and quantification. The available MS/MS spectra were first searched against the NIST17 MS/MS (Simon-Manso et al. 2013), METLIN (Smith et al. 2005) and respective Decoy spectral library databases using an in-house data analysis python script adapted from our previously described approach for metabolite identification false discovery rate control (FDR) (Wang et al. 2018; Wang et al. 2020). Metabolite peaks were extracted based on the theoretical m/z of the expected ion type, e.g., [M+H]⁺, with a 15 part-per-million (ppm) tolerance and a ± 0.2 min peak apex retention time tolerance within an initial retention time search window of ±0.5 min. For all the group-wise comparisons, t-tests were performed using the Python SciPy (1.5.4) (Virtanen et al. 2020) library to test for differences and generate statistics for the downstream analyses. For the pairwise t-tests, any metabolite with a p-value < 0.01 was considered significantly regulated (up- or down-) for prioritization in the subsequent analyses.

Coverage of a library of polar metabolites of major pathways of intermediary metabolism allowed detection of 130 of the 147 metabolites examined in at least 4 samples, and 91 detected in all 20 samples after background threshold correction. Instrument performance was assessed using the internal standards added to the samples during metabolite extraction and instrument mass accuracy was within tolerance (-2.0 ppm), LC column performance was stable (0.16 min RT range) and internal standard response variability was 13% across the samples. The resulting data were analyzed by principal components analysis (Fig. 7A), by constructing heatmaps with unsupervised hierarchical clustering of the imputed matrix values utilizing the R library pheatmap (1.0.12). GraphPad Prism 9 (9.4.1, GraphPad Software, San Diego, CA), and volcano plots (generated utilizing R library script Manhattanly (0.2.0)), in addition to the statistical comparisons summarized in Supplementary File S7.

¹³C₆-glucose metabolic flux measurements

Three replicate cultures of each strain were grown in YP medium with 2% unlabeled glucose to OD₆₀₀ of 0.6-0.7 and then shifted to YP with 1% unlabeled glucose and cultured for 20 min. ¹³C₆-labelled glucose (1%) was then added and growth was continued for 8 min. Cells were collected, and metabolites were extracted, derivatized and subjected to mass spectrometry as described previously, with the addition of additional masses for assessing the label incorporation into the specific labelled intermediates that were analyzed (Walvekar et al. 2018).

ATP Measurements

Cells were cultured in YPD to OD₆₀₀ of 0.6-0.7 and treated or untreated with 5 mM sodium azide for 30 min at 30°C. Five OD₆₀₀ units of cells were harvested and resuspended in 300 µL ice-cold 5% trichloroacetic acid (TCA), incubated on ice for 15 min, and diluted 50-fold in 20 mM Tris-HCl (pH 7.5) to 0.1 % TCA, after which 10 µL was mixed with 90 µL of ATP Determination reaction mixture (Cat. # A22066, Thermo-Fisher Scientific) and incubated at room temperature for 5 min. Units of Firefly Luciferase were measured in a Berthold Centro XS³ LB 960 Luminometer, and ATP levels were calculated from an ATP standard curve generated using the same assay.

Additional data visualization and statistical analyses

Notched box-plots were constructed using a web-based tool at http://shiny.chemgrid.org/boxplotr/. In all such plots, the upper and lower boxes contain the 2^nd and 3^rd quartiles and the band gives the median. If the notches in two plots do not overlap, there is roughly 95% confidence that their medians differ. Density scatter plots, Venn diagrams, significance testing of gene set overlaps in Venn diagrams using the hypergeometric distribution, hierarchical clustering analysis, and gene ontology (GO) analysis all were conducted as described previously (Vijjamarri et al. 2023a).

Results

Evidence that Scd6 and Edc3 functionally cooperate to control the abundance of many individual mRNAs

To determine whether Scd6 and Edc3 function redundantly in post-transcriptional control of gene expression, we constructed a scd6Δedc3Δ double mutant isogenic to the scd6Δ and edc3Δ single mutants we examined previously (Zeidan et al. 2018). Only the double mutant exhibits a marked slow-growth (Slg^-) phenotype on synthetic complete medium (SC), which was largely complemented by introducing either SCD6 or EDC3 on a single copy plasmid (Supplemental Fig. S1A). Analysis of polysome assembly revealed a ∼40% reduction in ratio of polysomes to monosomes (P/M) in the scd6Δedc3Δ double mutant, whereas the single mutants showed little (scd6Δ) or no (edc3Δ) reduction in bulk translation by this assay (Fig. S1B). These results suggest that Scd6 and Edc3 act redundantly to carry out one or more functions required for WT levels of bulk translation and cell growth in nutrient replete cells.

To examine the effects of the scd6Δ, edc3Δ, and scd6Δedc3Δ mutations on the abundance and translation of individual mRNAs, we conducted RNA-Seq and ribosome profiling (Ribo-Seq) of the mutant and WT strains following growth in liquid rich medium (YPD) at 30°C (processed data compiled in supplementary File S1). Ribo-Seq entails deep-sequencing of ribosome-protected fragments (RPFs, or ribosome footprints), and cycloheximide was added to the lysates to arrest elongating ribosomes on the mRNA following cell breakage. The ratio of RPF sequencing reads summed over the coding sequences (CDS) to the total mRNA reads from RNA-Seq for the corresponding transcript provides a measure of translational efficiency (TE) for each mRNA (Ingolia et al. 2009). The ribosome profiling and RNA-Seq results between two biological replicates for each strain were highly reproducible with Pearson correlation coefficients (r) ranging between 0.95-1.0 for different pairwise comparisons of replicates (Fig. S2A-B). We employed DESeq2 (Love et al. 2014) to identify statistically significant differences in relative mRNA abundance, RPF abundance, or TE for all expressed mRNAs between WT and mutant strains (see Methods for details).

Analysis of the RNA-Seq results identified 81 mRNAs that were significantly up-regulated in the edc3Δ mutant vs. WT by >1.5-fold at a false discovery rate (FDR) of <0.05 (dubbed mRNA_up_e3 transcripts; Fig. S3A), and 123 mRNAs reduced in abundance by edc3Δ by the same criteria (dubbed mRNA_dn_e3, Fig. S3B). Only 14 mRNAs were up-regulated and only 34 down-regulated by the scd6Δ single mutation (respectively, mRNA_up_s6 and mRNA_dn_s6, Fig. S3A-B). Importantly, many more mRNAs were dysregulated by the scd6Δedc3Δ double mutation: 741 in the mRNA_up_s6,e3 group and 793 in the mRNA_dn_s6,e3 group (Fig. S3A-B), indicating that the two factors have highly redundant functions in controlling mRNA abundance.

Many yeast mutants with Slg^- phenotypes, including pat1Δ, dhh1Δ, and dcp2Δ deletion mutants, exhibit altered expression of most mRNAs belonging to the Environmental Stress Response (ESR) (O’Duibhir et al. 2014), which includes ∼300 induced (iESR) and ∼600 down-regulated (rESR) mRNAs dysregulated in WT cells by various stresses (Gasch et al. 2000). In keeping with its Slg^- phenotype, the scd6Δedc3Δ mutation conferred a marked reduction in median expression of rESR mRNAs, and increased expression of the iESR mRNAs, which exceeded in magnitude the changes observed for the slowest growing yeast deletion mutants analyzed previously (O’Duibhir et al. 2014) (Fig. S3C-D). The two single mutations, by contrast, conferred much smaller changes in ESR mRNAs (Fig. S3C-D). (In all box plots, when notches do not overlap between adjacent boxes, their two medians differ with 95% confidence; and when notches do not overlap 0 in log₂ plots, the median differs significantly from that of all mRNAs, which is invariably close to 1.0.) Consistent with these results, the transcripts up-regulated in the double mutant are enriched for iESR mRNAs (Fig. 1A), suggesting that the 187 iESR transcripts up-regulated in this strain are responding indirectly to cell stress. The remaining 75% of mRNAs up-regulated in scd6Δedc3Δ cells are not iESR mRNAs however (Fig. 1A), suggesting that their increased abundance arises from eliminating Edc3/Scd6 functions in mRNA decay. Below, we excluded the ESR mRNAs from analyses of mRNA changes in an effort to focus on the transcripts controlled directly by Scd6 and Edc3.

Fig. 1.

Considering only mRNAs not governed by the ESR, we identified 591 non-iESR mRNAs significantly up-regulated in any of the three mutants (Fig. 1B; eg. Non-iESR mRNA_up_s6,e3 designating the 554 mRNAs up-regulated in the double mutant). Examining the ΔRNA values for the majority fraction of the Non-iESR transcripts up-regulated only in the double mutant reveals little change in median abundance in each single mutant but strong up-regulation in the double mutant (Figs. 1B & 1C, sector (iii)), as expected for redundant repressive functions of Scd6 and Edc3. MDH2, encoding an enzyme of the glyoxylate cycle, exemplifies a non-iESR transcript up-regulated in both mRNA and RPF abundance exclusively in the double mutant (Fig. 1D). The small fraction of 27 mRNAs significantly up-regulated in both the edc3Δ single mutant and double mutant exhibits only slightly elevated median abundance in the scd6Δ single mutant, and only slightly greater up-regulation in the double mutant vs. edc3Δ single mutant (Fig. 1B & 1C, sector (ii)), indicating a minimal repressive contribution by Scd6. Interestingly, the small set of 37 mRNAs significantly up-regulated only in the edc3Δ single mutant shows reduced rather than increased abundance in the scd6Δ single mutant, and lower up-regulation in the double mutant vs. the edc3Δ single mutant (Fig. 1B & C, sector (i)), suggesting that Scd6 enhances rather than represses these mRNAs, especially in edc3Δ cells.

Evidence that Dhh1 and Pat1 functionally cooperate with Scd6/Edc3 in repressing mRNA abundance

We recently identified a group of 1018 non-iESR mRNAs up-regulated by either pat1Δ, dhh1Δ, or pat1Δdhh1Δ mutations (Vijjamarri et al. 2023a). Importantly, this group is highly enriched for the 591 mRNAs up-regulated by scd6Δ, edc3Δ, or scd6Δedc3Δ mutations (Fig. 2A). Indeed, ∼70% of the transcripts up-regulated by scd6Δ/edc3Δ are also up-regulated in one of the three pat1Δ/dhh1Δ mutants (Fig. 2A, sector (ii)), showing comparable up-regulation in the scd6Δedc3Δ, pat1Δ, and dhh1Δ mutants, but little change in the scd6Δ and edc3Δ single mutants (Fig. 2B(ii)). These mRNAs generally exhibit the greatest up-regulation in the pat1Δdhh1Δ double mutant, indicating cumulative contributions of Dhh1 and Pat1 to their repression, in contrast to the largely redundant roles played by Scd6 and Edc3 in their repression. As expected, the smaller group of 180 mRNAs significantly up-regulated exclusively by scd6Δ/edc3Δ (sector (i) of Fig. 2A) shows strong up-regulation only in the scd6Δedc3Δ double mutant; but they are appreciably up-regulated by dhh1Δ while being largely unaffected by pat1Δ (Fig. 2B, sector (i)). Finally, the majority group of 607 mRNAs significantly up-regulated by only pat1Δ or dhh1Δ display the largest increases in the two mutants lacking Pat1, but smaller and similar increases in response to the scd6Δ/edc3Δ and dhh1Δ mutations (Figs. 2A & 2B, sector (iii)). Overall, these findings suggest that Dhh1 and Pat1 both contribute to repressing the majority of mRNAs repressed redundantly by Scd6 and Edc3, with Dhh1 contributing more extensively than Pat1.

Fig. 2.

Further evidence for this last point comes from a k-means clustering analysis of mRNA changes in different mutants for the group of 431 non-iESR mRNAs up-regulated by dhh1Δ, which revealed a stronger correlation between the changes conferred by scd6Δ/edc3Δ and dhh1Δ (ρ = 0.90) than between pat1Δ with dhh1Δ (ρ = 0.74) or scd6Δ/edc3Δ (ρ = 0.71) (Fig. 2C). Consistent with this, the scd6Δ/edc3Δ mutations up-regulate a considerably larger proportion of the mRNAs significantly up-regulated by dhh1Δ (∼70%) vs. those up-regulated by pat1Δ (∼41%) (Fig. 2D). Furthermore, the majority of mRNAs up-regulated by dhh1Δ exhibit redundant repression by Scd6 and Edc3, showing marked up-regulation comparable to that given by dhh1Δ itself only in the scd6Δ/edc3Δ double mutant (Fig. 2E). These findings are consistent with the involvement of Edc3/Scd6 in the degradation of most mRNAs targeted by Dhh1, which are more variably and less extensively repressed by Pat1.

Evidence that Scd6/Edc3 repress mRNA abundance by enhancing decapping/degradation and facilitating Dhh1 association with Dcp2

We next examined whether the mRNAs significantly down-regulated by Edc3/Scd6 are also regulated by the decapping enzyme Dcp1/Dcp2. Supporting this, cluster analysis of mRNA changes revealed that the majority of non-iESR mRNAs up-regulated in the scd6Δedc3Δ double mutant are also up-regulated by dcp2Δ (Fig. 3A, blue colors in cols. 1 & 3), with a strong correlation between the abundance changes conferred by these mutations relative to WT (ρ=0.74). In addition, dcp2Δ increased the median abundance of this group of mRNAs similarly to that given by scd6Δedc3Δ (Fig. 3B, cols. 1 & 4). These findings implicate decapping by Dcp1:Dcp2 as an important driver of the repression of mRNA levels directed by Scd6/Edc3. The cluster analysis in Fig. 3A once again reveals greater similarity between the mRNA changes conferred by scd6Δedc3Δ and those given by dhh1Δ (ρ = 0.90) versus pat1Δ (ρ = 0.69); and pat1Δ also confers a smaller median reduction than dhh1Δ of the Scd6/Edc3-down-regulated mRNAs (Fig. 3B).

Fig. 3.

Additional support that Scd6/Edc3 target mRNAs for decapping came from evidence that mRNAs up-regulated in the scd6Δedc3Δ mutant tend to accumulate in WT cells as decapped isoforms. Following decapping by Dcp1/Dcp2 mRNAs frequently undergo 5’ to 3’ decay co-translationally, with Xrn1 following behind the last translating ribosome loaded prior to decapping, and such decapped intermediates account for ∼12% of all mRNAs in WT cells (Pelechano et al. 2015). We reasoned that mRNAs preferentially targeted by Scd6/Edc3 for decapping and attendant degradation by Xrn1 should exhibit a greater than average proportion of decapped intermediates in WT cells. To test this, we conducted cap analysis of gene expression (CAGE) to quantify the abundances of all capped mRNA 5’ ends and compare them to total mRNA abundances determined by RNA-Seq conducted in parallel on biological replicates prepared from the WT and scd6Δedc3Δ strains described above. Transcript numbers per million reads (TPMs) from CAGE (C) and RNA-Seq (T) were determined and C/T ratios calculated as a proxy for the proportion of capped molecules for each transcript (Vijjamarri et al. 2023a) (File S1). (Because the CAGE and RNA-Seq data were normalized separately, the C/T ratios are relative, not absolute, proportions of capped transcripts.) Importantly, the C/T ratios are lower in WT cells for the 498 non-iESR mRNAs up-regulated in the scd6Δedc3Δ double mutant compared to all expressed mRNAs (Fig. 3C, cols. 1 & 5) in the manner expected for mRNAs preferentially targeted for decapping in WT cells. By contrast, the C/T ratios are higher than average for the non-rESR mRNAs that are down-regulated in relative abundance in scd6Δedc3Δ cells, as expected for an unusually low degree of decapping in WT (Fig. 3C, cols 3 & 5). Importantly, these C/T ratios were elevated in scd6Δedc3Δ cells to nearly the same level for all three groups of mRNAs, which exceeds the ratios found in WT cells, in the manner expected from eliminating Scd6/Edc3-stimulated decapping (Fig. 3C, cf. cols. 2,4,6). Similar results were obtained for these same groups of dysregulated mRNAs by analyzing our previous CAGE data obtained for the dhh1Δ mutant (Vijjamarri et al. 2023a) (Fig. S3E). These findings are consistent with the notion that impaired decapping dependent on Scd6/Edc3 and Dhh1 is an important driver of increased mRNA abundance in the scd6Δedc3Δ mutant.

Independent evidence for this last conclusion was provided by analyzing the codon protection indices (CPI) of mRNAs up-regulated by scd6Δedc3Δ, an indicator of co-translational decay by Xrn1. Decapped degradation intermediates exhibit three-nucleotide periodicity generated by precise Xrn1 cleavage up to the last translating ribosome at the 5’ end of mRNA, and the CPI quantifies the prevalence of such intermediates for each mRNA (Pelechano et al. 2015). Importantly, the mRNA_up_s6,e3 transcripts (whether including or excluding ESR mRNAs) exhibit higher than average median CPIs, indicating a greater than average involvement of decapping and co-translational degradation by Xrn1 in their decay, whereas mRNA_dn_s6,e3 transcripts exhibit lower than average CPI values, consistent with an alternative degradation pathway controlling their abundance scd6Δedc3Δ (Fig. 3D).

To determine whether increased transcription contributes to increased abundance of mRNAs up-regulated by scd6Δedc3Δ, we performed ChIP-Seq analysis of Rpb1 to measure RNA Polymerase II (Pol II) occupancies averaged across the CDS of every gene, obtaining highly reproducible results across replicates (Fig. S2D & S4A). To quantify absolute changes in Pol II occupancies, S. pombe chromatin was added to each S. cerevisiae chromatin sample prior to immunoprecipitation. To measure absolute changes in mRNA abundance, we re-analyzed the RNA-Seq data taking into account the recovery of External RNA Controls Consortium (ERCC) transcripts that were added to each total RNA sample prior to preparation of cDNA libraries, yielding highly reproducible normalized values among replicates (Fig. S2C). Interrogating the non-ESR mRNAs up- or down-regulated by scd6Δedc3 revealed a 2.79-fold increase in median normalized mRNA abundance for the non-iESR mRNA_up_s6,e3 group that was associated with only a 1.2-fold increase in normalized Rpb1 occupancies (Fig. 3E, cols. 1 & 4). The down-regulated non-rESR mRNA_dn_s6,e3 transcripts showed 12% and 7% decreases in spike-in normalized median mRNA abundance and Rpb1 occupancies, respectively, (Fig. 3E, cols. 2 & 5). Because the majority of total mRNA is rRNA, the ERCC normalization effectively yields the abundance of each mRNA relative to rRNA, and the ribosome content per cell is expected to be reduced in the scd6Δedc3Δ mutant owing to repression of rESR mRNAs encoding ribosomal proteins and biogenesis factors (Fig. S3C). We showed previously that the isogenic dcp2Δ mutant displays an ESR response of similar magnitude and a 30% reduction in ribosomal subunits per cell compared to same WT examined here (Vijjamarri et al. 2023b). Assuming a similar reduction in ribosome abundance in scd6Δedc3Δ cells, the absolute changes in mRNA per cell conferred by scd6Δedc3Δ are expected to be 0.7-fold of the ERCC-normalized values given in Fig. 3E, yielding fold-changes of 2.0 and 0.62 for the mRNA_up_s6,e3 and mRNA_dn_s6,e3 groups, respectively. Because these predicted changes in mRNA per cell still differ substantially from the corresponding changes in normalized Rpb1 occupancies of 1.2 and 0.93, respectively, there is only a small contribution of altered transcription to the altered abundance of transcripts dysregulated in the double mutant.

Our findings above that the scd6Δedc3Δ and dhh1Δ mutations up-regulate highly similar sets of mRNAs (Fig. 2C) is consistent with a previous proposal that recruitment of Dhh1 to Dcp2 can be mediated interchangeably by Edc3 or Scd6 bound to the same site in the Dcp2 CTT (He et al. 2022). To test this model we asked whether association of Dhh1 with Dcp2 in cell extracts is impaired only when both Edc3 and Scd6 are eliminated. Indeed, co-immunoprecipitation of HA-tagged Dcp2 with TAP-tagged Dhh1 was reduced in scd6Δedc3Δ cells but not in either single mutant in replicate experiments (Fig. 3F, lanes 2-4; Fig. S5(i)-(ii)). The residual association in the double mutant suggests that Dhh1 can interact with Dcp2 either directly or via Pat1 in addition to the interaction mediated by Scd6/Edc3, which might explain why a small fraction of mRNAs are up-regulated by dhh1Δ but not by scd6Δedc3Δ (Fig. 2C).

Slow rates of translation initiation or elongation generally do not dictate preferential decapping/decay by the decapping activators

Codon non-optimality has been linked with Dhh1-mediated mRNA decay partly by demonstrating that the sTAI values of mRNAs, which quantify their overall codon optimality (Sabi and Tuller 2014), are inversely correlated with the changes in mRNA abundance observed in dhh1Δ versus WT cells (Radhakrishnan et al. 2016). Similarly, analyzing the mRNA changes conferred by the scd6Δedc3Δ mutation for all non-ESR transcripts reveals a small but statistically significant negative correlation with sTAI values (Pearson r of -0.085, P = 2 x 10^-9) similar to that observed for the dhh1Δ mutation (r = -0.071, P = 6 x 10^-6) (Fig. S6A, cyan vs. orange), indicating a tendency for mRNAs with lower codon optimality/sTAI scores to show greater increases in relative abundance in response to scd6Δedc3Δ or dhh1Δ. However, the 591 non-iESR mRNAs up-regulated by the scd6Δ/edc3Δ mutations (from Fig. 1B) have a median sTAI value (0.35) nearly identical to that of all nonESR mRNAs, which is also the case for the group of 1018 mRNAs up-regulated by the pat1Δ or dhh1 mutations (Vijjamarri et al. 2023a) (Fig. S6B). Similar results were obtained for other metrics of codon optimality, tAI and average CSC (Fig. S6B), suggesting that poor codon optimality is not a key property defining the mRNAs repressed most extensively by these decapping activators. It has also been proposed that competition between translation initiation and mRNA decay, rather than codon optimality and elongation, is a major determinant of mRNA stability in yeast (Muhlrad et al. 1995; LaGrandeur and Parker 1999; Schwartz and Parker 1999; Chan et al. 2018). However, the mRNAs up-regulated in the scd6Δ/edc3Δ or pat1Δ/dhh1 mutants have slightly greater than average median translational efficiencies (TEs) in WT cells (Fig. S6C), as determined by ribosome profiling experiments described below. Thus, neither slow rates of translation initiation nor pausing during elongation at non-optimal codons appears to dictate preferential targeting of the mRNAs most strongly repressed by these four decapping activators.

Scd6 and Edc3 act redundantly and cooperate more extensively with Dhh1 than Pat1 in controlling translation of individual mRNAs

The changes in mRNA abundance were highly correlated with changes in RPF abundance for the scd6Δedc3Δ mutant compared to WT for all expressed transcripts, with an r value of 0.81 (Fig. S2F), providing strong mutual validation of the RNA-Seq and Ribo-Seq data for these strains. Nevertheless, the fact that this correlation is weaker than that observed between biological replicates of RNA-Seq or Ribo-Seq data (r values >0.95, Fig. S2A-B), suggests that certain mRNAs exhibit altered translational efficiencies in the double mutant. Indeed, DESeq2 analysis of the Ribo-Seq data identified 184 mRNAs showing TE increases of >1.41-fold at FDR < 0.10 in the scd6Δedc3Δ strain (dubbed TE_up_s6,e3) but only 42 and one such mRNA in the scd6Δ and edc3Δ single mutants, respectively (Fig. 4A), suggesting that Scd6 and Edc3 have overlapping functions in repressing the translation of particular mRNAs. Supporting this, a group of 169 mRNAs translationally up-regulated exclusively in the scd6Δedc3Δ double mutant exhibit only modest TE increases in the two single mutants (Fig. 4A-B, sector (iii)). SPI1, encoding a cell wall protein, is a representative transcript displaying increased mRNA abundance coupled with an even greater increase in RPF abundance to confer a TE increase of ∼3-fold only in the double mutant (Fig. 4C). As expected, the 28 mRNAs showing substantial TE increases only in scd6Δ cells show no increase in median TE in the edc3Δ strain (Fig. 4A-B, sector (i)), and thus appear to be translationally repressed by Scd6 alone.

Fig. 4.

We explored next whether the increased ribosome occupancies observed in the scd6Δedc3Δ mutant are associated with increased protein synthesis by conducting TMT mass spectrometry (TMT-MS) of total cell proteins to obtain ratios of peptide abundance in the scd6Δedc3Δ strain vs. WT for >4000 different proteins (Fig. S2E). Importantly, significant correlations exist between changes in protein abundance and RPFs across the translatome (Fig. 4D). Moreover, mRNAs showing increased or decreased RPFs in scd6Δedc3Δ vs. WT cells (>1.5-fold, FDR<0.05, Ribo_up and Ribo_dn) likewise exhibit increased or decreased median protein abundances determined by TMT-MS (Fig. 4E). These results suggest that increased ribosome occupancies measured by Ribo-Seq, which could occur by increases in mRNA, TE, or both, are generally associated with increased synthesis of the encoded proteins in scd6Δedc3Δ cells.

It was possible that translational repression by Edc3/Scd6 generally occurs by slowing elongation, leading to increased ribosome densities (RPF/mRNA ratios, ie. calculated TEs) in WT cells and decreased TE values in scd6Δedc3Δ cells that would be associated with increased protein expression. In this scenario, changes in TEs would be inversely associated with changes in protein expression. Instead, mRNAs showing increased or decreased TEs in the double mutant (TE_up_s6,e3 and TE_dn_s6,e3 transcripts defined above) also exhibit increased or decreased protein expression in scd6Δedc3Δ vs. WT cells (Fig. 4F), implying that Scd6/Edc3 generally influence translation at the initiation step.

We previously identified 274 mRNAs whose TEs are up-regulated in the pat1Δ, dhh1Δ, or pat1Δdhh1Δ mutants by the same criteria employed here (>1.41-fold at FDR < 0.10) (Vijjamarri et al. 2023a). Interestingly, these mRNAs overlap significantly with those translationally up-regulated in the single or double scd6Δ/edc3Δ mutants (Fig. 5A). The 76 mRNAs common to both groups show larger TE increases in the scd6Δedc3Δ mutant compared to the 136 transcripts up-regulated exclusively in the scd6Δ/edc3Δ mutants (Figs. 5A-B, sector (ii) vs. sector (i)). These 76 mRNAs also show marked TE increases in the dhh1Δ, pat1Δ, and pat1Δdhh1Δ mutants (Fig. 5B(ii)), indicating that all four decapping activators are required for efficient translational repression of these mRNAs in WT cells. The 136 mRNAs significantly up-regulated only in the scd6Δ/edc3Δ mutants show a moderate increase in median TE in dhh1Δ cells, but little response to pat1Δ (Fig. 5B(i)). The 198 mRNAs up-regulated only in the pat1Δ/dhh1Δ mutants show only a small TE increase in the scd6Δedc3Δ strain and cumulative TE increases in the pat1Δdhh1Δ double mutant (Fig. 5B(iii)). In summary, similar to our findings for repression of mRNA abundance, efficient translational repression of certain mRNAs requires the combined functions of Pat1, Dhh1, and either Scd6 or Edc3, whereas other mRNAs are translationally repressed by either Edc3/Scd6 or Pat1 with appreciable contributions from Dhh1.

Fig. 5.

Greater cooperation of Scd6/Edc3 with Dhh1 vs. Pat1 in controlling translation is also revealed by clustering analysis of TE changes in different mutants for the mRNAs exhibiting altered TEs in the scd6Δ/edc3Δ mutants, with greater similarity between TE changes conferred by scd6Δedc3Δ vs. dhh1Δ (ρ = 0.82) compared to scd6Δedc3Δ vs. pat1Δ (ρ = 0.68) or scd6Δedc3Δ vs. pat1Δdhh1Δ (ρ = 0.69) cells (Fig. 5C). However, as noted above, the group showing the strongest up-regulation of TEs in the scd6Δedc3Δ double mutant tends to be highly up-regulated in all three of the other mutants lacking Dhh1 or Pat1 (bracketed mRNAs at the top of Fig. 5C), as in Fig. 5D for a group of 54 mRNAs whose TEs are up-regulated by 2.0-fold or more in both the scd6Δedc3Δ and pat1Δdhh1Δ double mutants. Thus, although Scd6/Edc3 appear to cooperate more extensively with Dhh1 than Pat1 in translational control, the subset of 50-60 mRNAs exhibiting the strongest repression by Scd6/Edc3 or Pat1/Dhh1 require the concerted functions of Pat1, Dhh1, and either Scd6 or Edc3 to achieve their strong translational repression in WT cells. To assess whether mRNAs translationally repressed by the decapping activators are also targeted for degradation by these factors, we examined mRNA changes for the groups of mRNAs exhibiting TE up-regulation in the scd6Δ/edc3Δ or pat1Δ/dhh1Δ mutants (sectors (i) to (iii) of Fig. 5A). The mRNAs showing increased TEs only in the pat1Δ/dhh1Δ strains show little change in mRNA abundance in these mutants (Fig. S7A(iii)), indicating selective repression of translation vs degradation. Similarly, mRNAs translationally up-regulated only in the scd6Δ/edc3Δ mutants show little change in mRNA abundance in the scd6Δedc3Δ strain and only modest up-regulation in the pat1Δ/dhh1Δ mutants (Fig. S6A(i)). In contrast, the transcripts showing increased TEs in all of the mutants also show markedly increased abundance in the same mutants (Fig. S6A(ii)). Thus, coupled repression of translation and abundance by the decapping factors occurs only for the subset of transcripts exhibiting concerted translational repression by all four factors. In contrast, the much larger groups of transcripts defined above showing increased mRNA abundance in the mutants (described in Fig. 2A) exhibit little change in median TE in all four mutants (Fig. S6B), suggesting that most mRNAs targeted for enhanced turnover do not exhibit translational repression of the fraction of undegraded transcripts remaining in WT cells.

We next examined the translational efficiencies of mRNAs translationally repressed by Scd6/Edc3 by interrogating the Ribo-Seq data for WT cells on rich medium. The mRNAs translationally repressed by Scd6/Edc3 in concert with Pat1/Dhh1 tend to be poorly translated in WT, having a substantially lower median relative TE compared to all mRNAs (0.24 vs. 0.95) (Fig. S6C, col. 2), thus resembling the mRNAs translationally repressed exclusively by Dhh1/Pat1 (col. 3). In contrast, the transcripts translationally repressed exclusively by Edc3/Scd6 are generally well translated, exhibiting a ∼2.5-fold greater than average median TE in WT cells (Fig. S6C, col. 1). Consistent with this distinction, gene ontology (GO) analysis revealed that products of the 136 mRNAs translationally repressed exclusively by Scd6/Edc3 are enriched for cytoplasmic or mitochondrial ribosomal proteins (P = 1 x 10^-7), whereas products of the 76 transcripts repressed by Scd6/Edc3/Pat1/Dhh1 are enriched for factors involved in fermentation (P = 2 x 10^-7), carbohydrate metabolism (P = 1 x 10^-6) or metabolism of non-protein amino acids (P = 3 x 10^-6). Thus, Scd6/Edc3 translationally repress distinct groups of mRNAs depending on whether Dhh1/Pat1 participate in the repression.

We showed previously (Zeidan et al. 2018) that mRNAs up-regulated by dhh1Δ are enriched for Dhh1 protein association in vivo as judged by RIP-Seq analysis of yeast mRNAs using antibodies against Dhh1 (Miller et al. 2018), thus providing evidence for a direct role of Dhh1 decapping/degradation of these transcripts. The same observation was made for the mRNAs up-regulated by pat1Δ, consistent with widespread cooperation between Dhh1 and Pat1 in repressing mRNA abundance (Vijjamarri et al. 2023a). Interestingly, here we observed greater than average Dhh1 occupancies for all four mRNA groups defined above (in Figs. 2A or 5A) that are up-regulated in transcript abundance or TE by the scd6Δ/edc3Δ or dhh1Δ/pat1Δ mutations (Fig. 5E). This finding is consistent with the fact that Dhh1 contributes to repressing the abundance or TE of the majority of transcripts in these groups (Figs. 2B & 5B). It is possible that Dhh1 is recruited to most of these mRNAs in a complex with Dcp1:Dcp2 and other decapping activators (He et al. 2022), but contributes differentially to their degradation or translational repression.

Scd6/Edc3 post-transcriptionally repress mRNAs encoding enzymes for respiration and catabolism of non-glucose carbon sources in rich medium

Recently, we reported that mRNAs up-regulated in abundance or TE in the dcp2Δ, pat1Δ and dhh1Δ mutants are enriched for mitochondrial proteins that function in Ox. Phos. (Vijjamarri et al. 2023a; Vijjamarri et al. 2023b). GO analysis led to the same finding here for the non-iESR mRNAs up-regulated in abundance by the scd6Δ/edc3Δ mutations, including many of the same categories of mitochondrial functions enriched among the mRNAs up-regulated by dhh1Δ/pat1Δ mutations (Fig. S8A-B, green type). GO analysis of genes showing increased ribosome occupancies (RPFs), indicating either increased mRNA abundance or TE, confirms that the scd6Δ/edc3Δ mutations derepress the translation of Ox. Phos. gene transcripts (Fig. S8C-D). Examining a collection of Ox. Phos. genes functioning in electron transport, the TCA cycle or mitochondrial ATP synthase reveals that the scd6Δedc3Δ and pat1Δdhh1Δ double mutations up-regulate expression of these genes primarily at the level of mRNA abundance, with small additional increases in TE (Fig. 6A). Western blot analysis revealed increased expression of four Ox. Phos. proteins (Qcr8, Atp20, Idh1, and Sdh4), two proteins (Cox14 and Cox20) involved in cytochrome c oxidase assembly, and mitochondrial cytochrome b2 (Cyb2) required for lactate utilization in scd6Δedc3Δ cells, relative to Gcd6 examined as loading control (Fig. 6B-C). Results similar to these were obtained for the same mitochondrial proteins in the dhh1Δ and pat1Δ mutants (Vijjamarri et al. 2023a) and for a subset in dcp2Δ cells (Vijjamarri et al. 2023b). Importantly, we also observed increased expression of Cox2, a mitochondrially encoded subunit of cytochrome c oxidase, terminal enzyme of the mitochondrial ETC, whose expression correlates with mitochondrial activity (Vengayil et al. 2024). Except for dcp2Δ, all the decapping mutants had significantly increased Cox2 protein (Fig 6D), suggesting an increase in ETC activity in these mutants. Up-regulation of mRNA and RPF abundance in scd6Δedc3Δ and pat1Δdhh1Δ double mutants also occurred for enzymes of the glyoxylate cycle (Fig. 6E), which catalyze certain reactions of the TCA cycle in the cytoplasm to support gluconeogenesis during respiratory growth on two-carbon compounds; and the increased expression of one such enzyme, Cit2, was confirmed by Western analysis (Figs. 6B-C). Both Ox. Phos. and the glyoxylate cycle normally operate at low levels in yeast growing with abundant glucose, as in our experiments, suggesting that Scd6/Edc3 cooperate with Dhh1/Pat1 to help suppress these pathways in glucose-replete cells.

Consistent with post-transcriptional control of Ox. Phos. genes, the median relative RPF levels were up-regulated in the scd6Δedc3Δ mutant substantially more than the increases in relative Pol II occupancies observed at the cognate genes by ChIP-Seq analysis of Rpb1 (Fig. S9B, cols. 1-2). Moreover, the transcription factors responsible for induction of Ox. Phos. genes, the Hap2/Hap3/Hap4/Hap5 complex, were not activated in scd6Δedc3Δ cells: expression of a CYC1-lacZ reporter activated by this complex (Forsburg and Guarente 1989) was not elevated in the scd6Δ/edc3Δ mutants in glucose-containing medium (Fig. 6F(ii)) but showed the expected induction in WT cells grown with glycerol/ethanol versus glucose (6F(i)) (Broach 2012). These findings support the notion that reduced decapping stabilizes Ox. Phos. gene transcripts in scd6Δedc3Δ cells.

In addition to Ox. Phos., GO analysis of genes up-regulated in mRNA or RPF abundance in the scd6Δ/edc3Δ mutants revealed enrichment for utilization of alternative carbon sources (Fig. S8A-D), as observed recently for dhh1Δ,pat1Δ mutants (Vijjamarri et al. 2023a). Consistent with this, 83 carbon catabolite down-regulated (CCR) genes, known to be glucose-repressed or activated by transcription factors Adr1 or Cat8 (Young et al. 2003; Tachibana et al. 2005), exhibit increased translation (RPFs) largely through increased mRNA abundance in both scd6Δedc3Δ and pat1Δdhh1Δ double mutants (Fig. S9A). These genes encode enzymes for β-oxidation of fatty acids in addition to the glyoxylate cycle, which allow cells to synthesize precursors that feed into gluconeogenesis or amino acid biosynthesis, or produce acetyl-CoA and generate NADH by respiration on non-fermentable carbon sources (Young et al. 2003). The CCR genes exhibit larger increases in RPFs compared to Pol II occupancies at the cognate genes in scd6Δedc3Δ vs. WT cells (Fig. S9B, cols. 3-4), consistent with post-transcriptional repression by Scd6/Edc3. Supporting this, an ADH2-lacZ reporter transcriptionally induced by activated Adr1 (Sloan et al. 1999) displayed the expected large induction in our WT strain cultured with glycerol/ethanol versus glucose as carbon source (Fig. S9C(i)), but was down-regulated by scd6Δedc3Δ in glucose-grown cells (Fig. S8C(ii)).

Functional evidence that Scd6/Edc3 repress oxidative phosphorylation

We examined the effects of eliminating Scd6/Edc3 on mitochondrial electron transport by measuring mitochondrial membrane potential (ΔΨ_m) generated by the ETC using the probe tetramethylrhodamine (TMRM)—a cationic fluorescent dye that accumulates in mitochondria as a function of ΔΨ_m. Quantifying dye fluorescence by flow cytometry revealed increased TMRM fluorescence in the scd6Δedc3Δ mutant containing an empty vector compared to both the isogenic WT strain and the mutant complemented by WT EDC3 (Fig. 6G). These results are consistent with increased mitochondrial ETC activity in glucose-grown scd6Δedc3Δ cells.

Fig. 6.

To determine whether up-regulation of Ox. Phos. and other glucose-repressed genes in the decapping activator mutants alters cellular metabolites, we used targeted, quantitative LC-MS/MS based approaches (see Methods) to quantify levels of 147 polar metabolites in the isogenic scd6Δedc3Δ, dhh1Δ, pat1Δ, dcp2Δ and WT strains described above, cultured in YPD medium. Principle component analysis revealed clustering of results from biological replicates in the manner expected for reproducible differences in metabolite levels among different strains, with results for the scd6Δedc3Δ mutant most closely resembling those for dhh1Δ, which in turn were more similar to the results for pat1Δ vs. the dcp2Δ mutant or WT strains (Fig. 7A). This conclusion was borne out by cluster and correlation analyses of changes in metabolites between each mutant compared to WT, with the strongest correlation observed for scd6Δedc3Δ vs. dhh1Δ, followed by dhh1Δ vs. pat1Δ (Fig. 7B). Considering the subset of 46 metabolites up-regulated in any two of the four mutants again showed greatest similarity between changes conferred by scd6Δedc3Δ vs. dhh1Δ followed by dhh1Δ vs. pat1Δ (Fig. 7C). These findings mirror the results from RNA-seq and ribosome profiling in which the up-regulation of mRNA levels or translation was most similar between the scd6Δedc3Δ and dhh1Δ mutants. Pathway analysis of the 46 up-regulated metabolites (at https://www.metaboanalyst.ca/MetaboAnalyst/Secure/pathway) revealed a significant enrichment for metabolites of both the TCA and glyoxylate cycles (Fig. 7D), with five of the six TCA cycle intermediates detected (fumarate, malate, α-ketoglutarate, cis-aconitate and citrate) being elevated in the decapping mutants (Fig. 7E). These results are consistent with the possibility that up-regulation of Ox. Phos. proteins (Fig. 6A-D) and ETC function (Fig. 6F) in the decapping mutants leads to increased flux from glucose towards the TCA cycle.

Fig. 7.

To confirm increased flux through the TCA cycle coming from glucose breakdown, WT and mutant cells were grown in high glucose, pulsed with ¹³C₆ glucose, and relative ¹³C label incorporation into TCA cycle intermediates was estimated for each carbon molecule derived from glucose (as indicated schematically in Fig. 8A) eight min following the pulse. In all mutants, ¹³C label incorporation in all TCA cycle intermediates was significantly higher than in WT cells (Fig. 8A), demonstrating increased carbon flux from glucose into the TCA cycle. These results, along with increased ETC activity (Fig. 6G), indicate that mitochondrial function is up-regulated in these mutants on glucose. If so, we might expect them to show increased ATP production from respiration versus glycolysis. Measuring total ATP levels in glucose-grown cells revealed that, while all decapping mutants except pat1Δ showed reduced ATP per cell (Fig. 8B), the proportion of ATP produced from respiration, and thus eliminated by sodium azide treatment (by inhibiting the ETC), was elevated in all of the mutants (Fig. 8C).

Fig. 8.

Interestingly, amino acids are also up-regulated in the decapping mutants (Fig. 7D), particularly in pat1Δ cells (Fig. 7F), as well as intermediates in amino acid biosynthesis and amino acid derivatives (Fig. 7D). The expression of amino acid biosynthetic enzymes is unaffected or reduced in the mutants however (Fig. S9D), suggesting that increased amino acid abundance results from metabolic reprogramming rather than increased biosynthetic capacity. One possibility is that accumulation of the TCA cycle intermediate α-ketoglutarate leads to increased production of glutamate and glutamine, precursors in all amino acid biosynthetic pathways (Ljungdahl and Daignan-Fornier 2012). The two 3-phosphotrioses generated in glycolysis (glyceraldehyde 3-phosphate and dihydroxyacetone phosphate) are also elevated in the mutants, possibly owing to increased glyoxylate shunt function, which might stimulate synthesis of amino acids serine and glycine (Ljungdahl and Daignan-Fornier 2012) (Fig. 7D, Methane metabolism category). The increases in fructose 1,6-bisphosphate, fructose 6-phosphate, glucose 6-phosphate, and UDP-glucose in all four mutants (Fig. 7D, Starch/Sucrose metabolism category) all suggest substantial metabolic rewiring indicative of glucose up-regulation and usage of alternative carbon sources. In addition to amino acids, pyrimidine nucleotides are up-regulated in the mutants (Fig. 7D, Pyrimidine metab.), which might be driven by the elevated glutamine levels or increased glycolytic flux towards the pentose phosphate pathway (Ljungdahl and Daignan-Fornier 2012).

Discussion

RNA-seq analysis of single and double scd6Δ and edc3Δ mutants has revealed that Scd6 and Edc3 have largely overlapping functions in repressing mRNA abundance, as the presence of either protein alone is sufficient for nearly WT levels of most mRNAs found up-regulated in the double mutant (Fig. S3A & Fig. 1B-C(ii)-(iii)). Although the mRNAs dysregulated in the scd6Δedc3Δ mutant are enriched for ESR mRNAs; the majority are not ESR transcripts (Fig. 1A-B) and are most likely repressed more directly by Scd6/Edc3. Functional redundancy of Scd6/Edc3 is consistent with their similarities in sequence and domain structure, including shared FDF motifs that interact competitively with Dhh1 in animals(Tritschler et al. 2008; Tritschler et al. 2009) and N-terminal LSm domains that compete for binding motifs in the Dcp2 CTT of S. pombe (Fromm et al. 2012), as well as the synthetic growth defect produced by deleting both genes simultaneously (Decourty et al. 2008) (Fig. S1A). Interestingly, a set of 37 mRNAs appears to be repressed by Edc3 exclusively (Fig. 1B-C(i)), including the canonical Edc3 targets identified previously, YRA1 and RPS28B (He et al. 2022), and is enriched for genes belonging to the GO category “mitochondrion” (22/37 genes) and the Ox. Phos. categories of electron transport and mitochondrial ATP synthesis (11/37 genes: ATP3 ATP16 ATP5 TIM11 ATP17 COX4 QCR9 CYC1 COX12 ATP18 COX5A). The fact that deleting SCD6 in the double mutant frequently diminishes the up-regulation of these transcripts conferred by deleting EDC3 alone (Fig. 1C(i)) might indicate that eliminating both Scd6 and Edc3 together enables a distinct degradation pathway that compensates for loss of Edc3-stimulated decapping/decay.

Several lines of evidence support the conclusion that most of the changes in mRNA abundance in the scd6Δedc3Δ double mutant result from impaired decapping and attendant 5’-3’ degradation by Xrn1. ChIP-seq analysis of Rpb1 shows that Pol II occupancies in the coding sequences increase by much smaller amounts compared to the increased transcript levels for non-iESR mRNAs up-regulated in scd6Δ/edc3Δ cells (Fig. 3E), indicating a minor contribution of increased transcription to their up-regulation. The transcriptional activators of Ox-Phos. and CCR genes, many of whose transcripts are up-regulated by scd6Δ/edc3Δ, remain largely inert in this mutant, ruling out inappropriate transcriptional induction of their target genes in glucose-replete scd6Δ/edc3Δ cells. Consistent with mRNA turnover via decapping, most of these transcripts are up-regulated similarly by scd6Δedc3Δ and deletion of DCP2 (Fig. 3A-B), exhibit a heightened proportion of decapped isoforms in WT cells that is diminished by deleting SCD6/EDC3 or DHH1 (Fig. 3C & Fig. S3E), and show evidence of co-translational 5’-3’ decay of decapped intermediates by Xrn1 (Fig. 3D); none of which was observed for the mRNAs down-regulated in scd6Δ/edc3Δ cells. Similar findings were reported previously for sets of mRNAs up-regulated by dhh1Δ or pat1Δ (Vijjamarri et al. 2023a), consistent with the widespread involvement of Dhh1 and Pat1 in controlling the levels of mRNAs preferentially targeted by Scd6/Edc3.

Supporting this last assertion, most (∼70%) of the mRNAs repressed in abundance by Edc3/Scd6 are also repressed by Dhh1 and Pat1, and their efficient repression in WT cells involves independent contributions of similar magnitude by Pat1, Dhh1 and either Edc3 or Scd6 (Fig. 2A-B(ii)). Another large set of mRNAs is repressed predominantly by Pat1 with lesser contributions by Dhh1 and Scd6/Edc3 (Fig. 2A-B(iii)). Our finding of extensive cooperation among these four decapping factors is consistent with our recent finding that ∼55% of the mRNAs up-regulated in the dcp2Δ mutant, and thus targeted by Dcp2 for enhanced degradation, tend to be up-regulated in the pat1Δdhh1Δ and scd6Δedc3Δ mutants, whereas the remaining 45% are generally up-regulated by upf1Δ instead. This finding suggested a major bifurcation of Dcp2-activation by either Pat1/Dhh1/Scd6/Edc3 or the Upf factors responsible for NMD (Vijjamarri et al. 2023b).

Closer examination of the effects of individual mutations on mRNA levels revealed a greater overlap between the mRNAs dysregulated by dhh1Δ and scd6Δ/edc3Δ versus those altered by pat1Δ (Fig. 2C-E), consistent with the aforementioned FDF motifs in Scd6 and Edc3 that interact with Dhh1 in animal systems, and with evidence for distinct complexes of the decapping enzyme containing Dhh1 and Edc3 or Scd6 but lacking Pat1. It also supports the recent suggestion that Edc3 and Scd6 act interchangeably to recruit Dhh1 to the Edc3-interaction site in the Dcp2 CTT to stimulate turnover of several Dhh1 target mRNAs (He et al. 2022). Our RNA-seq results support this last proposal in showing that all four Dhh1 target mRNAs examined in that study (EDC1, SDS23, HXT6, and HSP12) are up-regulated in the scd6Δedc3Δ double mutant while changing little in the single mutants (Fig. S10), two of which (SDS23 and HXT6) were shown previously to have longer half-lifes in dhh1Δ vs. WT cells (He et al. 2018). Finding that selective up-regulation in the scd6Δedc3Δ double mutant applies to most mRNAs up-regulated in dhh1Δ cells (Fig. 2E), we propose that redundant Scd6/Edc3 targeting is a widespread mechanism of Dhh1-enhanced mRNA degradation in the yeast transcriptome (Fig. S11). Supporting this, our co-immunoprecipitation analysis revealed diminished Dhh1-Dcp2 association in cell extracts of the scd6Δedc3Δ double mutant but for either single mutant (Fig. 3F).

Examining our Ribo-Seq and TMT-MS data suggests that Dhh1 translation and steady-state abundance are increased ∼2-fold in the scd6Δedc3Δ strain, indicating that up-regulation of many of the same mRNAs by scd6Δedc3Δ and dhh1Δ does not result indirectly from reduced levels of Dhh1 in the scd6Δedc3Δ mutant. The increased Dhh1 expression might signify a compensatory response to the absence of Scd6/Edc3. We also observed an ∼40% reduction in Dcp2 translation and abundance in the scd6Δedc3Δ strain, which might contribute to the up-regulation of mRNAs dysregulated in this mutant. However, our immunoblot analyses revealed no significant reduction in steady-state Dcp2 levels in scd6Δedc3Δ cells (Input lanes in Figs. 3F and S4C(i)-(ii)). Moreover, our previous finding that the majority of mRNAs subject to NMD, up-regulated by both upf1Δ and dcp2Δ, are not up-regulated by scd6Δedc3Δ (Vijjamarri et al. 2023b) implies that Dcp2 abundance in scd6Δedc3Δ cells is adequate for normal levels of NMD. Accordingly, we favor a direct role for Scd6/Edc3 in accelerating degradation of most transcripts up-regulated in the scd6Δedc3Δ mutant.

Despite strong evidence for the involvement of decapping in mRNA turnover directed by either Scd6/Edc3 or Pat1/Dhh1, it appears that neither enrichment for non-optimal codons nor low translational efficiencies are principal determinants of preferential targeting of mRNAs by these proteins (Fig. S6A-C). There are probably exceptions to these generalizations among the hundreds of transcripts repressed by these factors; nor can we rule out the involvement of clusters of non-optimal codons in transcripts of average overall codon optimality that generate a queue of slowly elongating ribosomes that triggers decapping. Nevertheless, it seems likely that other sequences or properties of the transcripts most strongly repressed by Scd6/Edc3 or Dhh1/Pat1 frequently underlie their preferential targeting by these factors for decapping/decay. Sequence-specific RNA-binding proteins that actively recruit these decapping activators is an attractive potential mechanism (Fig. S11). For example, there is evidence that Dhh1 is recruited to mRNAs in association with the Ccr4-NOT deadenylase via RNA-binding protein Puf5 (Goldstrohm et al. 2006). Involvement of RNA-binding proteins would also be consistent with the observed specific targeting of mRNAs whose expression is repressed in response to nutrient abundance.

Ribosome profiling of the single and double scd6Δ and edc3Δ mutants provided evidence that Scd6 and Edc3 also have highly overlapping functions in repressing the translation of ∼200 mRNAs, whose TE values are up-regulated substantially only in the double mutant (Fig. 4A-B(iii)). The observation that changes in TE (RPF density) generally correlate with changes in protein abundance conferred by scd6Δedc3Δ (Fig. 4E) implies that Scd6/Edc3 generally repress translation at the stage of initiation rather than elongation. Many mRNAs translationally repressed by Scd6/Edc3 additionally require Dhh1, and the subset most strongly repressed also requires Pat1 for efficient translational repression (Fig. 5B(ii) and 5D). This last group of mRNAs is translated at very low levels in WT cells on rich medium (Fig. S7C), which might reflect the concerted action of all four decapping activators in suppressing translation. The transcript abundance of these mRNAs is also up-regulated in each of the mutants (Fig. S7A(ii)), indicating enhanced degradation coupled with translational repression by the four decapping activators. One possibility is that a complex of Dcp1:Dcp2 containing Scd6/Edc3, Pat1/Dhh1, or different combinations of these factors (He et al. 2022), is recruited to these mRNAs and impedes recruitment of PICs to diminish translation in parallel with stimulating decapping and 5’-3’ degradation by Xrn1 (Fig. S11(i) & (ii)-(iv)).

The additional sets of mRNAs identified here that are translationally repressed exclusively by Scd6/Edc3 or Pat1/Dhh1 (Fig. 5A(i) & (iii)) show much less evidence of transcript degradation promoted by these factors (Fig. S7A(i) & (iii)), suggesting that inhibition of translation initiation occurs without decapping, or involves decapping uncoupled from degradation by Xrn1 (Fig. S10 (ii)-(iii)). In the latter scenario, the decapped mRNAs would be unable to recruit the cap-binding initiation factors necessary for PIC recruitment and hence persist in WT cells as translationally-inert degradation intermediates. Eliminating the decapping activators would diminish decapping and eliminate these fractions of poorly translated uncapped isoforms to confer the increased TE values we observed for the cognate genes in the decapping mutants. This hypothetical mechanism of translational repression via decapping is consistent with the lower than average capped to total mRNA (C/T) ratios found in WT cells for the mRNAs translationally repressed by all four decapping activators or by Pat1/Dhh1 (Fig. S6D (ii)-(iii)), indicating accumulation of decapped isoforms in WT cells, as observed previously for a subset of mRNAs translationally repressed by Dhh1 (Vijjamarri et al. 2023b). In contrast, the mRNAs that are translationally repressed exclusively by Scd6/Edc3 display higher than average C/T ratios in WT (Fig. S7D(i)), which is more compatible with an inhibition of PIC recruitment independently of decapping (Fig. S11(iii)). Translational repression without decapping could also occur if transcripts bound by decapping complexes are sequestered from the translation initiation machinery in RNA granules where mRNA decapping occurs inefficiently (Fig S11(iv)). Finally, it is important to note that mRNA decay occurs apparently without translational control for the much larger groups of ∼600 and ∼1000 transcripts showing up-regulated mRNA abundance but little evidence of TE changes in the scd6Δedc3Δ and pat1Δdhh1Δ mutants, respectively (Fig. S7B). It is unclear whether these mRNAs escape translational repression and are targeted exclusively for decapping/degradation (Fig. S11(i)) or are simply degraded too fast to allow translational repression to be detected by ribosomal profiling of steady-state mRNAs.

Recently, we reported that Pat1 and Dhh1 functionally collaborate with the decapping enzyme to repress genes and cellular pathways normally repressed in rich medium (Vijjamarri et al. 2023a; Vijjamarri et al. 2023b). Here we found evidence that Scd6 and Edc3 broadly cooperate with Pat1/Dhh1 in this control, acting redundantly to repress the abundance, translation, or both, of mRNAs encoding numerous proteins involved in respiration or utilization of alternative carbon sources (Figs. 6A-C, S8A-D), without affecting transcription of the cognate genes Fig. 6F, S9A-C). Evidence that up-regulation of Ox. Phos. mRNAs/proteins in the scd6Δedc3Δ mutant is sufficient to increase respiration in glucose-replete cells came from our findings of increased mitochondrially-encoded subunit II of ETC component cytochrome c oxidase (Fig. 6D) and elevated mitochondrial membrane potential (Fig. 6G) in the scd6Δedc3Δ mutant, extending similar findings we made previously for dcp2Δ, dhh1Δ and pat1Δ mutants (Vijjamarri et al. 2023a; Vijjamarri et al. 2023b). Consistent with this, our metabolomics analysis revealed elevated steady-state levels of TCA cycle intermediates in all four decapping mutants (Fig. 7D), and increased ¹³C carbon incorporation from ¹³C₆-glucose breakdown into TCA cycle intermediates in the same mutant strains (Fig. 8A). Analysis of ATP levels further revealed increased proportions of ATP derived from Ox. Phos. in the decapping mutants (Fig. 8B), which together provide strong evidence that mitochondrial respiration is inappropriately elevated in glucose-replete cells of decapping activator mutants. It is well established that the enzymes involved in respiration and catabolism of non-glucose carbon sources are repressed at the transcriptional level in glucose-replete yeast (Zaman et al. 2008). Our findings demonstrate that Scd6/Edc3 collaborate with Dhh1/Pat1 to provide an additional layer of post-transcriptional control of these enzymes through enhanced decapping/decay or translational repression to help ensure their complete suppression when glucose is abundant.

Acknowledgements

We are grateful to Drew Jones, Tori Rodrick and the NYU Langone Medical Metabolomics Lab for valuable advice, metabolomics data acquisition and analysis, Samuel Mackintosh and IDeA National Resource for Quantitative Proteomics for TMT-MS analysis, and the NHLBI DNA Sequencing and Genomics Core for all next-generation DNA sequencing. We thank Feng He and Allan Jacobson and Bertrand Séraphin for gifts of yeast strains, Nikolaus Pfanner and Thomas Fox for gifts of antibodies, and Henry Zhang for help with bioinformatics. We are grateful to Jon Lorsch for financial support of DNA sequencing and invaluable advice, and all other members of our laboratories for helpful comments and suggestions. This work was supported in part by the Intramural Research Program of the NIH. C.O. and M.L.G. were supported by NIH grants R01 HL117880 and R35 GM149271, and SL acknowledges support from a DBT-Wellcome Trust India Alliance Senior Fellowship IA/S/21/2/505922.

Additional information

Data availability

Ribosome profiling, RNA-Seq, ChIP-Seq and CAGE-Seq data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE270789 and GSE270790. TMT-MS/MS proteomics raw data have been deposited in ProteomeXchange with accession number PXD053307. Previously published datasets used in this study can be found in (Vijjamarri et al. 2023a; Vijjamarri et al. 2023b).

Additional files

File S1. Parallel RNA-Seq and Ribo-Seq analysis of decapping mutants, gene groups dysregulated in decapping mutants or belonging to specific functional categories, and capped/total mRNA ratios, codon optimality scores, codon protection indices, and Dhh1 occupancy values for all mRNAs. Sheets 1-7 labeled “mutant vs WT all comp” contain the processed data from Ribo-seq and RNA-seq analysis of 2 or 3 biological replicates of each strain listing the fold-changes in mRNA, RPFs, or TE between the relevant mutant vs. WT with the P-values and adjusted P-values (FDRs) assigned by DESeq2 analysis. The data for dhh1Δ, dcp2Δ (3), pat1Δ, and pat1Δdhh1Δ mutants (8) were reported previously. Sheet “Gene groups_mRNA analysis” contains the lists of mRNAs (identified by systematic gene name) that are up-regulated or down-regulated in abundance by ≥1.5-fold at FDR < 0.05 in the scd6Δ, edc3Δ, or scd6Δedc3Δ mutants vs. WT, either including or excluding iESR mRNAs for up-regulated transcripts or rESR mRNAs for down-regulated transcripts, as indicated. It also lists the transcripts belonging to the three sectors of Figs. 1B-C, the iESR and rESR transcripts defined previously (11), and all of the non-iESR mRNAs up-regulated by ≥1.5-fold at FDR < 0.05 in dhh1Δ, pat1Δ, or pat1Δdhh1Δ mutants, as well as those equally up-regulated by the dcp2Δ mutation. Sheet “Gene groups_TE analysis” contains the lists of mRNAs that are up-regulated or down-regulated in TE by ≥1.41-fold at FDR < 0.10 in the different mutants vs. WT. Sheet “Gene groups_RPF analysis” contains the lists of mRNAs that are up-regulated or down-regulated in RPFs by ≥1.5-fold at FDR < 0.05 in the different mutants vs. WT. Sheet “Pathway gene groups” lists the genes involved in specific pathways (eg. Ox. Phos.) analyzed for changes in expression or translation. Sheet “Codon Opt Scores nonESR mRNAs” lists the tRNA adaptation index (tAI), species-specific tRNA adaptation index (stAI), and the average codon stabilization coefficient (AvgCSC) for all non-ESR yeast genes (12,13). Sheet “Codon Protection Indices” lists the CPI values for all genes determined previously (14). Sheet “Capped to Total RNA ratios” lists the ratios of TPMs determined by CAGE sequencing of capped mRNAs (C) to TPMs determined by parallel RNA-seq of the same total RNA samples for 5129 genes, determined previously for dhh1Δ and WT cells (8). Sheet “Dhh1 occ enrich scores” lists the relative Dhh1 occupancies (enrichment scores) determined globally for yeast mRNAs by RIP-seq analysis (15) for the 3686 transcripts for which both Dhh1 enrichment score and Ribo-seq and RNA-seq data from the dhh1Δ vs. WT comparison (3) are available. Sheet “IGV tracks” lists the results of ribosome profiling of the scd6Δ, edc3Δ, and scd6Δedc3Δ mutants vs. WT for the genes selected for gene-browser depictions.

File S2. Relative and spike-in normalized Rpb1 occupancies from ChIP-seq analysis. Sheets 1-2 labeled “Relative occs._replicates” and “Relative occs._ Reps._averaged” contain the processed data from ChIP-Seq analysis of Rpb1 in three biological replicates of WT and scd6Δedc3Δ strains and the averaged data from combining the replicates, respectively, listing the relative occupancies averaged over the coding sequences for each expressed gene normalized to the average occupancy on each chromosome. Sheet “S. pombe norm. factor calcs,” lists the calculations of normalization factors obtained from total numbers of reads aligned to the S. pombe genome for each chromatin sample spiked-in with equal aliquots of S. pombe chromatin prior to immunoprecipitation with Rpb1 antibodies. Factors are calculated for each individual replicate (col. E) or for the combined replicates for each strain (col. G). Sheets “Normalized occs._replicates” and “Normalized occs._reps._avged” lists the spike-in normalized Rpb1 occupancies calculated for each replicate or the combined replicates for each strain, respectively, calculated using the respective normalization factors for individual or combined replicates determined in the previous two sheets.

File S3. ERCC spike-in normalized RNA-Seq data. Sheet 1 lists the numbers of reads for each ERCC molecule identified in each RNA sample and calculations of the normalization factors for each sample. Sheet 2 lists the un-normalized reads for each yeast gene in each RNA sample, the ERCC-normalized reads for each sample, and the density of normalized reads for each gene calculated by normalizing for CDS lengths. Sheets 2-4 lists processed data from DESeq2, including log₂Δ mRNA, P-value and adj. P-value determined for each gene in the indicated mutant vs. WT strain, obtained by setting the size factor to unity.

File S4. TMT-MS data. Processed data from TMT-MS analysis of scd6Δedc3Δ vs. WT strains. Sheet “All proteins data” lists the normalized MS intensities of peptides for each expressed gene in three replicates each of WT (L-N) and scd6Δedc3Δ (M-Q) strains and the calculated log₂ fold-changes in protein expression and statistics in the scd6Δedc3Δ vs. WT comparison for all expressed proteins. Sheet “log2FC_protein (s6,e3 v WT)” extracts the data from columns B, Q, W, and X from the previous sheet.

File S5. Source data for Western blot analysis. Sheet “Fig. 6B-C Western analysis” lists averages of band intensities normalized to the loading control (Gcd6) on the same blots and the corresponding S.E.M. values calculated from the replicates for each protein analyzed in Fig. 6B-C. Sheet “Fig. 6D Western analysis” lists the Cox2 band intensities normalized to total stained proteins and the ratios of normalized Cox2 for each mutant vs. WT.

File S6. Source data for lacZ reporter assays. Lists the specific activities of β-galactosidase determined from three biological replicates of each strain harboring the CYC1-lacZ or ADH2-lacZ reporters analyzed in Figs. 6F and S7C, respectively.

File S7. Source data for metabolomics of polar compounds of intermediary metabolism. Sheets 1-4 list the statistical analyses of changes in metabolite concentrations in four biological replicates of the indicated four mutants vs. the WT determined in parallel for all 20 samples of metabolite extracts. Sheet 5 summarizes the log₂ fold-changes in metabolite levels and corresponding P-values for all 147 metabolites detected in for each of the four mutants. Sheet 6 lists the 46 metabolites up-regulated in any two of the four mutants (analyzed in Fig. 7C), TCA cycle intermediates, and amino acids. Sheet 7 lists 93 amino acid biosynthetic genes interrogated in Fig. S8D.

File S8. Source data for glucose flux analysis. In Sheet 2 “raw intensity”, the numbers indicate the signal intensities (areas under the peaks from mass spectrometry) of the metabolite listed in column A in the three biological replicates of the indicated mutant or WT strains. In the subsequent sheets, raw data from Sheet 2 is collated for the different labeled isoforms of the indicated metabolite in the upper eight rows, and the proportion of the metabolite comprised of each isoform is given in the lower rows for the different samples. The “_N” labelling indicates the label addition in the metabolite, ie. _1 indicates mass addition of 1 labelled carbon, _2 indicates mass addition of 2 labelled carbons). Please let me know if you have any doubts regarding this.

File S9. Source data for measurements of cellular ATP content. Sheets 2-3 provide source data for measurements of ATP content in three biological replicates of the indicated five mutants vs. WT and the fraction of total ATP content that is eliminated by inhibiting respiration by sodium azide treatment. Sheet 4 shows the standard curve produced using pure ATP employed to determine the ATP content in cell extracts.