RNA abundance is generally sensitive to perturbations in decay and synthesis rates, but crosstalk between RNA polymerase II transcription and cytoplasmic mRNA degradation often leads to compensatory changes in gene expression. Here, we reveal that widespread mRNA decay during early apoptosis represses RNAPII transcription, indicative of positive (rather than compensatory) feedback. This repression requires active cytoplasmic mRNA degradation, which leads to impaired recruitment of components of the transcription preinitiation complex to promoter DNA. Importin α/β-mediated nuclear import is critical for this feedback signaling, suggesting that proteins translocating between the cytoplasm and nucleus connect mRNA decay to transcription. We also show that an analogous pathway activated by viral nucleases similarly depends on nuclear protein import. Collectively, these data demonstrate that accelerated mRNA decay leads to the repression of mRNA transcription, thereby amplifying the shutdown of gene expression. This highlights a conserved gene regulatory mechanism by which cells respond to threats. Gene expression is often depicted as a unidirectional flow of discrete stages: DNA is first transcribed by RNA polymerase II (RNAPII) into messenger RNA (mRNA), which is processed and exported to the cytoplasm where it is translated and then degraded. However, there is a growing body of work that reveals complex cross talk between the seemingly distal steps of mRNA transcription and decay. For example, the yeast Ccr4-Not deadenylase complex, which instigates basal mRNA decay by removing the poly(A) tails of mRNAs , was oriThe above findings collectively support a model in which cells engage a buffering response to reduce transcription when mRNA decay is slowed, or reduce mRNA decay when transcription is slowed to preserve the steady state mRNA pool . While mA theme that unites much of the research linking mRNA decay to transcription is homeostasis; perturbations in mRNA stability are met with an opposite transcriptional response in order to maintain stable mRNA transcript levels. However, there are cellular contexts in which homeostasis is not beneficial, for example during viral infection. Many viruses induce widespread host mRNA decay  and co-oThe best-defined stimulus known to broadly accelerate cytoplasmic mRNA decay outside of viral infection is induction of apoptosis. Overall levels of poly(A) RNA are reduced rapidly after the induction of extrinsic apoptosis via accelerated degradation from the 3’ ends of transcripts . The onsHere, we used early apoptosis as a model to study the impact of accelerated cytoplasmic mRNA decay on transcription. We reveal that under conditions of increased mRNA decay, there is a coincident decrease in RNAPII transcription, indicative of positive feedback between mRNA synthesis and degradation. Using decay factor depletion experiments, we demonstrate that mRNA decay is required for the transcriptional decrease and further show that transcriptional repression is associated with reduced RNAPII polymerase occupancy on promoters. This phenotype requires ongoing nuclear-cytoplasmic protein transport, suggesting that protein trafficking may provide the signal linking cytoplasmic decay to transcription. Collectively, our findings elucidate a distinct gene regulatory mechanism by which cells sense and respond to threats.ACTB, GAPDH, EEF1A, PPIA, CHMP2A, DDX6, RPB2, and RPLP0) beginning 1.5 hr after TRAIL was applied . TRAIL treatment causes a well-characterized progression of apoptotic events including caspase cleavage and mitochondrial outer membrane permeabilization or ‘MOMP’ . It is M applied . Fold ch applied . As expeus study . These dTo monitor whether apoptosis also influenced transcription, we pulse labeled the cells with 4-thiouridine (4sU) for 20 min at the end of each TRAIL treatment. 4sU is incorporated into actively transcribing RNA and can be subsequently coupled to HPDP-biotin and purified over streptavidin beads, then quantified by RT-qPCR to measure nascent transcript levels . 4sU-labz-VAD-fmk (zVAD), a pan-caspase inhibitor, was used to confirm that TRAIL-induced mRNA decay and transcriptional arrest were associated with apoptosis and not due to an off-target effect of TRAIL. HCT116 cells were pre-treated with 40 μM zVAD or equal volume of vehicle (DMSO) for 1 hr prior to TRAIL treatment. The effectiveness of zVAD treatment was confirmed by showing it blocked the cleavage of the CASP8 target BID and blocked the degradation of the CASP3 substrate PARP1 . The decp ≅ 0, chi-squared test), while significantly more increased than decreased with TRAIL treatment in the presence of zVAD . Markedly, 71.2% of the ~28,000 unique transcripts detected were downregulated more than twofold in TRAIL-treated cells with DMSO (ACTB) in the original 4sU-labeled RNA samples were assessed by RT-qPCR using both exonic and intronic primers  that translocates into the nucleus during late stages of apoptosis  and MOMPSmall interfering RNA (siRNA) knockdowns  were perAlthough CASP3 is dispensable for apoptotic transcriptional repression, a previous report suggests that CASP8 may cleave RPB1, the largest subunit of RNAPII . We therACTB and GAPDH transcripts in the presence of TRAIL, even though CASP8 and CASP3 were still cleaved n repeats of the RPB1 C-terminal domain (CTD). To determine the stage of transcription impacted by mRNA decay, we performed RNAPII ChIP-qPCR and western blots using antibodies recognizing the different RNAPII phosphorylation states. Occupancy of hypophosphorylated RNAPII at the ACTB and GAPDH promoters was significantly reduced after 2 hr TRAIL treatment , many of which transit to the nucleus in response to virus-induced cytoplasmic mRNA decay . We therTo determine whether RBP redistribution occurs during early apoptosis, we analyzed the subcellular distribution of cytoplasmic poly(A) binding protein PABPC1, an RBP known to shuttle to the nucleus in response to virus-induced RNA decay . We perfWe next sought to directly evaluate the role of protein shuttling in connecting cytoplasmic mRNA decay to transcriptional repression. The majority of proteins ~ 60 kilodaltons (kDa) and larger cannot passively diffuse through nuclear pores; they require assistance by importins to enter the nucleus from the cytoplasm . ClassicImportantly, ivermectin did not diminish the extent of CASP3 cleavage during early apoptosis , nor didFinally, we evaluated whether importin α/β transport is also required for feedback between viral nuclease-driven mRNA decay and RNAPII transcription, as this would suggest that the underlying mechanisms involved in activating this pathway may be conserved. We used the mRNA-specific endonuclease muSOX from the gammaherpesvirus MHV68, as muSOX expression has been shown to cause widespread mRNA decay and subsequent transcriptional repression . HEK-293mRNA decay and synthesis rates are tightly regulated in order to maintain appropriate levels of cellular mRNA transcripts . It is wMultiple experiments support the conclusion that the TRAIL-induced transcriptional repression phenotype is a consequence cytoplasmic decay triggered by MOMP, rather than caspase activity. XRN1-driven 5’−3’ end decay is the major pathway involved in basal mRNA decay , but MOMA key open question is what signal conveys cytoplasmic mRNA degradation information to the nucleus to cause transcriptional repression. Our data are consistent with a model in which the signal is provided by one or more proteins imported into the nucleus in response to accelerated mRNA decay . Indeed,PABPC1 is likely among the first proteins released from mRNA transcripts undergoing 3’ end degradation, and its nuclear translocation is driven by a poly(A)-masked nuclear localization signal that is exposed upon RNA decay . Indeed,The mRNA 3’ end decay-dependent decrease in RNA synthesis is accompanied by a reduction in TBP and RNAPII occupancy at promoters, consistent with transcriptional inhibition occurring upstream of RNAPII initiation and elongation . The mecThere are several potential benefits to dampening mRNA transcription in response to accelerated mRNA turnover. Debris from apoptotic cells is usually cleared by macrophages, but inefficient clearance of dead cells can lead to the development of autoantibodies to intracellular components such as histones, DNA, and ribonucleoprotein complexes. This contributes to autoimmune conditions such as systemic lupus erythematosus . In the glaunsinger@berkeley.edu).Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Britt Glaunsinger .2. Cells were maintained in culture in 10 cm2 plates and 1 × 106 cells were seeded into six well plates for all experiments except for chromatin immunoprecipitations, in which 5 × 106 cells were seeded into 10 cm2 plates, and 4sU-sequencing, in which 5 × 106 cells were seeded into 15 cm2 plates.Wild-type HCT116, HEK293T, and HeLa cells  were obtained from the UC Berkeley Tissue Culture Facility. Cell lines were authenticated by STR analysis and determined to be free of mycoplasma by PCR screening. HEK293Ts were made to stably express doxycycline(dox)-inducible wild-type muSOX and its catalytically-dead D219A mutant by PCR amplifying the aforementioned coding sequences from Addgene plasmids 131702 and 131704 using the muSOX F/R primers (see Key Resources Table) and InFusion cloning these fragments into the Lenti-X Tet-One Inducible Expression System digested with AgeI. Lentivirus was made for both constructs by transfecting HEK293T cells with second generation packaging plasmids and spinfected onto HEK293T cells at a low multiplicity of infection (MOI) as previously described . Twenty-Protein knockdowns were performed using the Neon Transfection System with siRNA pools for the following targets: non-targeting control pool (scramble or scr siRNA), CASP3, CASP8, DFFB, DIS3L2, EXOSC4, PNPT1, XRN1, BAX, and BAK. For all siRNA pools, cells were nucleofected according to manufacturer protocols for HCT116 cells and immediately seeded into plates containing media supplemented with 10% FBS but lacking pen-strep to improve cell viability post-nucleofection. A 50 nM final siRNA concentration was used for pools targeting CASP3, CASP8, and DFFB (CAD), while 200 nM was used for individual knockdown of XRN1, DIS3L2, EXOSC4, and PNPT1, as well as the concurrent knockdowns of DIS3L2/EXOSC4/PNPT1 (66.7 nM each) and BAX/BAK (100 nM each). For all experiments involving siRNA knockdowns, cells were treated and/or harvested once 80–90% confluent in each plate or well, approximately 72 hr post-nucleofection, and protein knockdown was confirmed by western blot. Cell populations of siRNA-transfected cells were split into two wells or plates 24 hr pre-harvesting to allow for the direct comparison between apoptotic and non-apoptotic cells in the same genetic background.A total of 100 ng/mL TNF-related apoptosis-inducing ligand (TRAIL) was used to induce rapid extrinsic apoptosis in HCT116 cells. Where indicated, HCT116 cells were pre-treated for 1 hr with 40 μM caspase Inhibitor Z-VAD-FMK (zVAD) or 25 μM ivermectin before TRAIL treatment. Extrinsic apoptosis induction was confirmed by observing CASP8 and CASP3 cleavage on western blots. CASP3/8 cleavage was quantified by disappearance in intensity of the full-sized band normalized to TUBA1A or VCL using Bio-Rad ImageLab software. Intrinsic apoptosis was induced in HeLa cells with a 4 hr treatment of 10 μM raptinal, with or without 1 hr pre-treatment with 20 μM zVAD. Induction was confirmed by evidence of cytochrome c release into the cytoplasm with cell fractionation and western blot. ‘Mock’ treatments consisted of an equal volume of vehicle used to dissolve each reagent: TRAIL storage and dilution buffer, DMSO, and ethanol for TRAIL, zVAD, and ivermectin, respectively.Total RNA and protein extractions were performed according to manufacturer’s instructions after cells were harvested with Trizol reagent. RNA pellets were dissolved in DEPC water and protein pellets were dissolved in 1% SDS overnight at 50°C before spinning down insoluble material at 10,000 x g for 10 min.Terminal deoxynucleotidyl transferase Br-dUTP nick end labeling was performed on TRAIL-treated cells using a TUNEL assay kit according to manufacturer protocol. Br-dUTP incorporation was quantified by flow cytometry, analyzing the elevated peak in FL2 fluorescence on a BD Accuri Flow Cytometry System using FlowJo analysis software.18S or U6 ncRNAs, as indicated on figure axes. Primer sequences are listed in Extracted RNA was DNAse-treated treated, primed with random nonamers, and reverse-transcribed to cDNA with Avian Myeloblastosis Virus Reverse Transcriptase according to manufacturer protocols. Genes were quantified by RT-qPCR using iTaq Universal SYBR Master Mix and primers specific to each gene of interest. RNA fold change values were calculated in reference to Protein samples were quantified by Bradford assay according to manufacturer instructions. A total of 12.5–50 μg of protein was mixed with one third volume of 4X Laemelli Sample Buffer (Bio-Rad Laboratories 1610747) and boiled at 100°C before loading into a polyacrylamide gel alongside either the PageRuler or PageRuler Plus Prestained Protein Ladder. Proteins were separated by electrophoresis and transferred to a nitrocellulose membrane, which was then cut into sections surrounding the size of the protein of interest, allowing for multiple proteins to be quantified from one gel. Membranes were then blocked with 5% non-fat dry milk in TBST  at RT for 1 hr then incubated with relevant primary antibodies diluted with 1% milk in TBST overnight at 4°C. All primary antibodies were applied at a 1:1000 dilution with the exception of the following : RPB1, 1:500; TUBA1A, 1:500; RPB2, 1:500; RPB3, 1:10000; LMNB1, 1:10000; GAPDH, 1:5000; and CYTC, 1:500. After three 5 min TBST washes, species-specific secondary antibodies were diluted 1:5000 with 1% milk in TBST and incubated for 1 hr at RT. Blots were then developed, after three additional 5 min TBST washes, with Clarity Western ECL Substrate for 5 min and imaged using a ChemiDoc MP Imaging System (Bio-Rad Laboratories). Each membrane section was imaged and processed separately. Band intensity was quantified using Bio-Rad Image Lab software, and relative expression changes were calculated after normalizing to an α-tubulin (TUBA1A), vinculin (VCL), or lamin-B1 (LMNB1) loading control. For all blots that appear in figures, auto-contrast was applied in Image Lab for each membrane section before the resultant image was exported for publication. When appropriate, membrane sections were stripped with 25 mM glycine in 1% SDS, pH 2 and washed two times for 10 min with TBST before being blocked and re-probed as previously described with a primary antibody targeting a protein of similar size.Polyclonal rabbit anti-muSOX antibody was made and purified by YenZym antibodies, LLC from recombinant muSOX protein.th of the total cell lysate was reserved and diluted to the same volume of the cell and nuclear fractions for whole cell lysate samples. Protein was extracted from 200 μL of each fraction from both methods using Trizol LS reagent and analyzed by western blot as described above.For experiments performed in apoptotic HCT116 and HeLa cells, nuclear, cytoplasmic, and mitochondrial fractions were isolated using the Abcam Cell Fractionation Kit according to manufacturer’s instructions. Cytoplasmic and nuclear fractions of samples in experiments that did not involve apoptosis were separated using the REAP method . 1/5th o4sU-pulse labeling was used to measure nascent transcription concurrently with mRNA decay. Fifty μM 4sU was added to cells 20 min before harvesting lysates for RNA and/or protein extraction. Labeled transcripts contained in 25 μg of total extracted RNA were biotinylated as described by 4sU-labeled RNA from HCT116 cells treated with 100 ng/μL TRAIL, 10 μM raptinal, or their respective mock treatments was isolated as previously described. Two μL 4sU RNA from each sample was reverse transcribed into cDNA and amplified with primers targeting regions of the 18S rRNA and/or U6 snRNA using the QIAGEN OneStep RT-PCR Kit according to manufacturer instructions. Twenty-five μL of each resultant PCR product was combined with 5 μL 6X DNA loading dye and loaded onto a 1% agarose (in 1X Tris-borate EDTA) electrophoresis gel stained with SYBR Safe DNA Gel Stain alongside a DNA ladder. Gels were imaged on ChemiDoc MP Imaging System.6 HCT116 cells were seeded onto 15 cm2 plates and 24 hr later, were pre-treated with either DMSO or 20 μM zVAD, and treated with storage and dilution buffer or 100 ng/μL TRAIL for 2 hr. This process was repeated to generate two biological replicates. 50 μM 4sU was added to cells 20 min before harvesting. Cells were suspended in 2 mL Trizol and RNA extracted as previously described. Biotinylation and strepdavidin selection was performed on 200 μg of total RNA, scaling up the previously detailed protocol by 8X. 125 ng of 4sU-labeled RNA was used to synthesize rRNA-depleted sequencing libraries using KAPA-stranded RNA-Seq Kit with Ribo-Erase, HMR according to manufacturer’s instructions. ERCC RNA Spike-in Mix one was added at a 1:100 dilution to each RNA sample immediately prior to library preparation normalize read counts to RNA input across samples. Libraries were submitted for analysis on a Bioanalyzer to ensure ~400 bp fragment lengths, then submitted for sequencing on a Nova-Seq 6000 with 100 bp paired-end reads at the QB3-Berkeley Genomics Sequencing Core.5 × 10Bioinformatics analysis was conducted using the UC Berkeley High Performance Computing Cluster. Paired end sequence FASTQ files were downloaded and checked for quality using FastQC. Reads were then trimmed of adaptors using Sickle/1.33. Reads were mapped to human reference genome hg19 and ERCC spike-in list was obtained using STAR genome aligner . Differe2 plates. 72 hr post-nucleofection, cells were washed with PBS, trypsinized, washed with PBS again, and fixed in 1% formaldehyde for 2.5 min before quenching with 125 mM glycine. After an additional PBS wash, cells were lysed for 10 min at 4°C in lysis buffer  then washed with wash buffer  at 4°C for 10 min. Cells were then suspended in 1 mL shearing buffer  and sonicated in a Covaris S220 sonicator  with the following parameters: peak power, 140.0; duty factor, 5.0; cycle/burst, 200; and duration, 300 s. Insoluble material in the shearing buffer was then spun down at full speed in a 4°C benchtop centrifuge to yield the chromatin supernatant. Ten μg chromatin was rotated overnight at 4°C with 2.5 μg or 4 μg ChIP-grade primary antibodies targeting RPB1 and TBP, respectively, in 500 μL dilution buffer . Five μL of each IP was reserved as an input sample before antibody was added. 20 μL of either Dynabeads Protein G or a 1:1 mixture Protein A and Protein G beads, for anti-mouse and anti-rabbit antibodies, respectively, were added to each reaction and rotated at 4°C for at least 2 hr. The beads were then sequentially washed with low salt immune complex wash buffer , high low-salt immune complex wash buffer , LiCl immune complex buffer , and TE buffer . All washes were 5 min in duration and performed at 4°C. Beads and input samples were then suspended in 100 μL elution buffer  and incubated at 55°C for 2 hr then at 65°C for 12 hr in a thermal cycler. DNA fragments were purified with an oligonucleotide clean and concentrator kit and % input values were quantified by RT-qPCR as previously described using primers complementary to the locus of interest.HCT116 cells nucleofected with the relevant siRNAs were seeded onto 10 cmBar graphs were created using GraphPad Prism eight software and the graphical abstract was created using the Bio-Render online platform.Biological replicates were defined as experiments performed separately on biologically distinct (i.e. from cells cultured at different times in different flasks or wells) samples representing identical conditions and/or time points. See figures and figure legends for the number of biological replicates performed for each experiment and  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This manuscript by Glaunsinger and colleagues examines the link between cytoplasmic RNA decay and transcriptional repression in mammalian cells undergoing apoptosis. The authors implicate mRNA decay conducted by cytoplasmic exonucleases early in apoptosis in repression of RNA PolII transcription. This response requires mitochondrial outer membrane permeabilization but not caspase activity, likely due in part to the release of the mitochondrial exonuclease PNPT1 into the cytosol.Decision letter after peer review:eLife. Your article has been reviewed by 4 peer reviewers, and the evaluation has been overseen by Michael Green as Reviewing Editor and James Manley as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Judy Lieberman.Thank you for submitting your article "Cytoplasmic mRNA decay represses RNA polymerase II transcription during early apoptosis" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:This manuscript by Glaunsinger and colleagues examines the link between cytoplasmic RNA decay and transcriptional repression in mammalian cells undergoing apoptosis. Numerous studies have found that these two processes are linked, either involving increased transcriptional activity to buffer the effects of enhanced decay or, as shown here, reinforcement of the effects of decay by coupled transcriptional repression. For that reason, further insight into how specific cellular conditions can alter the relationship between transcription and decay are important. Here, the authors implicate mRNA decay conducted by cytoplasmic exonucleases early in apoptosis in repression of RNA PolII transcription. This response requires mitochondrial outer membrane permeabilization but not caspase activity, likely due in part to the release of the mitochondrial exonuclease PNPT1 into the cytosol. Using metabolic labeling of nascent transcripts and ChIP assays of RNAPII and TBP recruitment to promoters, the authors provide evidence that transcriptional inhibition is effected at the level of pre-initiation complex recruitment. Finally, the authors show that a similar process, dependent on importin activity and involving PABPC1 relocalization to nuclei, occurs in response to herpesvirus endonuclease expression. Overall, this is an interesting study that will be of interest to a broad audience.Essential revisions:1. Figure 1A shows the general set-up of the death pathway, and Figure 1B repeats older data with loss of proCasp8 and the processing of Casp3. Only the cleaved form or CASP3 is actually detected, so wording, "showed activation of CASP8 and caspase 3 (CASP3) by 1.5 hr" needs to include reference to TRAIL inducing rapid apoptosis in HCT116 cells – see Figure 8 in Kim et al., 2000 as well as the Thomas et al., 2015, referenced. The data in Figure 1A and B reinforces an established property of HCT116 cells, so may be more appropriate to place in the Supplemental Information.2. More problematically, the 4sU labeling scheme is not described clearly anywhere in the manuscript. The time of the 20 min pulse should have been more specifically described than "the last 20 min of TRAIL treatment", even in the Methods section, which is disconcerting. This is a powerful method that promises to reveal both patterns of mRNA decay but also altered RNA pol II incorporation. 4sU should be added for 20 min pulse steps from the start of TRAIL treatment through 1.5 or 2 h, with pulse and chase periods. The results of incorporation and chase should be shown for the assay genes to give a more complete picture right at the start of this story, and all in Figure 1.-/-BAK-/- cells."3. Further, the authors' point, "It is caspase 8 (CASP8)-induced MOMP that stimulates mRNA decay in response to an apoptosis-inducing ligand , partly by releasing the mitochondrial 3'-5' RNA exonuclease PNPT1 into the cytoplasm" seems to go in a novel direction. While the MOMP has clearly been shown to be important, and forms during intrinsic as well as extrinsic apoptosis in HCT116 cells, the point made in the introduction of Liu et al., 2018 is salient here, "mRNA decay occurs early after apoptosis triggered by diverse classical apoptotic stimuli , before membrane lipid scrambling, DNA fragmentation, and inactivation of translation initiation factors." CASP8 would be expected to be dispensable in most of these settings, although it could be activated outside of its direct role in extrinsic apoptosis as observed in other settings. With TRAIL signal transduction, CASP8 may directly activate CASP3 as well as to cleave BID and activate BAX and BAK. Emphasis on the "CASP8-induced MOMP" requires greater focus is needed here on cells deficient in steps beyond the MOMP to show that this is indeed the case, just as pursued in Thomas et al., 2015, and pointed out in Liu et al., 2018 "Apoptosis-related mRNA decay depends on MOMP-it does not occur when MOMP is blocked in BCL2-overexpressing or BAX4. (ln 155) siRNA knock down of CASP8 and CASP3 revealed "transcriptional repression  required CASP8 but not CASP3". The authors argue that this is "in agreement with the prior study showing that MOMP-induced mRNA decay occurs before DNA fragmentation begins during extrinsic apoptosis ", but that study specifically implicated the MOMP. More needs to be done here. This study demands cells that lack CASP8 as well as CASP3. The reliance on siRNA is insufficient for conclusive evidence either way. There are now not only CRISPR/Cas9 strategies that have been applied to cultured cells, but also viable mouse strains that lack CASP8 (requiring combined elimination of necroptotic machinery) and these should be used to probe the importance of this phenomenon in additional cells as well as an intact animal. Any requirement for CASP8 needs a lot more dissection, particularly with knock-out cells and animals where this protease has been eliminated without sensitizing to necroptosis (machinery that HCT116 cells apparently lack).5. (ln 184) Despite the observations on CASP8, the conclusion, "these data suggest that neither the mRNA degradation nor the concurrent transcriptional repression observed during early apoptosis are a consequence of caspase activation" is at odds with the data shown.6. Authors go on to hypothesize "that TRAIL-induced mRNA turnover was functionally linked to the concurrent transcriptional repression", pursuing previously identified targets, cytoplasmic 3'-RNA exonuclease DIS3L2 and the mitochondrial 3'-RNA exonuclease PNPT1", which contrasts "basal mRNA decay, which occurs predominantly from the 5' end by XRN1". This siRNA knock-down certainly reinforces the prior conclusions that 3' degradation by DIS3L2 and PNPT1 predominates in this setting.7. Figure 3 then extends into novel territory, showing evidence that RNA pol II loading is likely compromised by TRAIL signaling at or before the formation of the preinitiation complex (PIC) of the genes assessed. The data are carefully assembled but require more precise conditions where the death pathway does not proceed beyond certain defined points (as mentioned above). The reliance on siRNA here and in Figure 4 remains a concern. This section of the manuscript promises novel and significant insights but must bring the reader to understand what step in cell death signaling drives the RNA pol II impact on initiation. The nascent 4sU pulse is appropriate and important here.8. Figure 4 turns finally to the contributions of importans and of the viral endoribonuclease muSOX without coming to a precise synthesis of data. Complications to a simple story include the fact that mRNA degradation and RNA pol II impacts require considerably more data to provide a clear picture here. The SOX MHV68, like the homologs in Kaposi's sarcoma-associated herpesvirus and in Epstein-Barr virus, as well as the classic virion host shut-off (VHS) function encoded by in herpes simplex viruses  may have an impact on RNA pol II, but this would require a bit more systematic study to be convincing.t values normalized to 18S rRNA in reference to mock-treated cells. The graph represents mean +/-SEM, used statistic – one-sample t-test, with the hypothesis that there is no deviation from 1. Thus the representation of results does not show the variability of measurements in the reference sample . Measurements should be presented as relative mRNA levels with appropriate statistical tests. More importantly, the RT-qPCR analysis of a few genes usually does not allow concluding that there is a global RNA-decay.9. The decrease in mRNA levels is measured for a few housekeeping genes only and represented as fold changes from C10. Transcription shut down is measured by RT-qPCR on 4sU labeled RNA, expected to represent nascent RNA. Surprisingly, the authors used the same primers for the analysis of 4sU labeled samples as used for standard RT-qPCRs. Those primers span the exon-exon junctions and are not suitable for the analysis of the nascent transcription. Apart from RT-qPCR, the Authors used RNAPII ChIP qPCRs (fig3D) using an antibody recognizing hypophosphorylated RNAPII, which normally is not engaged in transcription. Thus, such analyses are not optimal for studying the activity of RNAPII. In sum, the transcription shut down, and the rescue by 3' to 5' RNA decay nucleases is not sufficiently supported by the data. The best would be to perform a genome-wide analysis of RNA polymerase activity employing one of the broadly used techniques, for instance, GRO-seq.11. Although the main claim of the paper is that cytoplasmic 3' exonucleases are required for apoptotic RNAPII repression, there is no explanation of why the silencing of the main cytoplasmic 5' to 3' exonuclease, Xrn1, has no effect on transcription. Moreover, all 3' exonucleases  are always silenced together and never individually. Why is it so? What if, in reality, only one nuclease is responsible for the observed effect? Importantly, there is no rescue experiment. Thus, observed effects can be attributed to off-targets of one of theses three siRNAs, especially that the mechanism of the repression remains to be elucidated.12. A lot of attention is given to PABPC1, which upon apoptosis translocates to the nucleus . Depletion of PABPC1 and PABPC4  is supposed to rescue mRNA transcription, but keep reduced mRNA baseline reduced. PABPC1/4 depletion leads to a drastic reduction of mRNA levels. Thus it has a profound effect on cell physiology, which makes functional conclusion very difficult to draw, especially that they are not consistent with ivermectin treatment (see below). This part should be explored more thoughtfully or removed from the paper. At present, it adds very little to the story.13. Importins α/β are supposed to links mRNA decay and transcription. Treatment with ivermectin, an inhibitor of α/β transport, efficiently block nucleolin localization Figure 4B and is supposed to rescue RNAPII transcription Figure 4C. Surprisingly, there is no influence on PABPC1 localization . Thus, on the one hand, the block of import by ivermectin rescues reduced transcription but does not influence PABPC1 relocation to the nucleus. On the other hand, depletion of PABPC1 also diminishes reduced transcription, and there is a coincidence of transcriptional repression caused by apoptosis induction and PABPC1 relocation to the nucleus. This discrepancy should be discussed.14. Using the HCT116 cell line treated with TRAIL as a model, the Authors observed casp8 and 3 cleavages after 1.5h . They claim that casp8 stimulates mRNA decay inducing MOMP  partly by releasing the mitochondrial 3'-5' RNA nuclease PNPT1 (two citations). Since PNPT1 activity is important for the story, the Authors should validate this aspect in their model.15. The assays of steady-state and nascent RNA abundance that form the backbone of the paper rely on normalization to 18S (or in a few cases U6) RNA. The interpretation of these experiments relies on the assumption that levels or transcription of these ncRNAs are not affected by the cellular conditions studied, but this is not substantiated, and the rationale/validity of these controls is not discussed. The authors should provide data supporting the choice of normalization controls, such as quantification of transcripts/cell by RT-PCR or RNA-FISH.Encouraged but optional major revisions:1. The authors argue that RNA decay specifically represses polII transcription, but they observe reduced recruitment of TBP, which has a role in transcription by all three eukaryotic RNA polymerases. Does induction of apoptosis only affect TBP recruitment to polII promoters, or is recruitment to polI and polIII promoters also affected?2. Figure 4: The authors tested whether importin α/β was "required for feedback between viral nuclease-driven mRNA decay and RNAPII transcription, as this would suggest that the underlying mechanisms involved in activating this pathway are conserved." I think this overstates the evidence – import is so general that it's a stretch to say that this is evidence that the underlying mechanisms are conserved. Essential revisions:1. Figure 1A shows the general set-up of the death pathway, and Figure 1B repeats older data with loss of proCasp8 and the processing of Casp3. Only the cleaved form or CASP3 is actually detected, so wording, "showed activation of CASP8 and caspase 3 (CASP3) by 1.5 hr" needs to include reference to TRAIL inducing rapid apoptosis in HCT116 cells – see Figure 8 in Kim et al., 2000 as well as the Thomas et al., 2015, referenced. The data in Figure 1A and B reinforces an established property of HCT116 cells, so may be more appropriate to place in the Supplemental Information.We have changed the wording and included the new references as suggested (Line 118). We believe that 1A and 1B provide important context to understand the data in 1C, 1D, and in subsequent figures and therefore think that these panels should remain as part of Figure 1.2. More problematically, the 4sU labeling scheme is not described clearly anywhere in the manuscript. The time of the 20 min pulse should have been more specifically described than "the last 20 min of TRAIL treatment", even in the Methods section, which is disconcerting. This is a powerful method that promises to reveal both patterns of mRNA decay but also altered RNA pol II incorporation. 4sU should be added for 20 min pulse steps from the start of TRAIL treatment through 1.5 or 2 h, with pulse and chase periods. The results of incorporation and chase should be shown for the assay genes to give a more complete picture right at the start of this story, and all in Figure 1.ACTB pre-mRNA  as well as the observation of reduced RNAPII promoter occupancy further bolster our conclusion that transcriptional repression is occurring in addition to mRNA decay.We apologize for any confusion and have now clarified our protocol, which is 4sU pulse labeling, not a 4sU pulse-chase experiment. All 4sU pulse experiments detailed in this study were performed by adding 50 mM 4sU to cell culture media 20 minutes before cells were harvested for RNA and/or protein (i.e. the last 20 min of treatment). By measuring nascent 4sU incorporation at each apoptotic timepoint, we quantified the difference in RNAPII transcriptional output that occurs in concert with corresponding levels of mRNA depletion. Chase experiments, although informative in determining the half-lives of nascent transcripts, are not required to measure transcriptional output in the context of enhanced mRNA turnover and would require extending the time of TRAIL treatment much further past the instigation of mRNA decay at 1.5-2 h, which would likely introduce more advanced features of apoptosis  that could confound our analyses. We note that this 4sU pulse labeling protocol is regularly used by our group and others for transcriptional measurements . The observation that during early apoptosis we detect similar reduction in the 4sU-labeled mRNA when we use intron-spanning primers to amplify the -/-BAK-/- cells."3. Further, the authors' point, "It is caspase 8 (CASP8)-induced MOMP that stimulates mRNA decay in response to an apoptosis-inducing ligand , partly by releasing the mitochondrial 3'-5' RNA exonuclease PNPT1 into the cytoplasm" seems to go in a novel direction. While the MOMP has clearly been shown to be important, and forms during intrinsic as well as extrinsic apoptosis in HCT116 cells, the point made in the introduction of Liu et al., 2018 is salient here, "mRNA decay occurs early after apoptosis triggered by diverse classical apoptotic stimuli , before membrane lipid scrambling, DNA fragmentation, and inactivation of translation initiation factors." CASP8 would be expected to be dispensable in most of these settings, although it could be activated outside of its direct role in extrinsic apoptosis as observed in other settings. With TRAIL signal transduction, CASP8 may directly activate CASP3 as well as to cleave BID and activate BAX and BAK. Emphasis on the "CASP8-induced MOMP" requires greater focus is needed here on cells deficient in steps beyond the MOMP to show that this is indeed the case, just as pursued in Thomas et al., 2015, and pointed out in Liu et al., 2018 "Apoptosis-related mRNA decay depends on MOMP-it does not occur when MOMP is blocked in BCL2-overexpressing or BAXWe fully agree that it is MOMP, whether induced by CASP8 or other stimuli, that is responsible for mRNA decay-induced transcriptional repression. Our qualifier that CASP8 was required “in response to an apoptosis-inducing ligand” was meant to distinguish CASP8-induced MOMP from that caused by intrinsic apoptosis inducers. To make this point more clearly, we have changed the wording of the referenced sentence to clarify that CASP8 is not necessarily required for MOMP (line 112). We also included an experiment in our initial submission using a small molecule, raptinal, that directly instigates MOMP in order to demonstrate that the mRNA decay and ensuing RNAPII transcriptional repression observed during intrinsic apoptosis and are not affected by the pan-caspase inhibitor zVAD . In order to more definitively link the transcriptional repression to MOMP-induced mRNA decay, we now include an additional set of experiments in which we show that depletion of the mitochondrial pore-forming proteins BAX and BAK inhibit TRAIL-induced mRNA decay and transcriptional repression .4. (ln 155) siRNA knock down of CASP8 and CASP3 revealed "transcriptional repression  required CASP8 but not CASP3". The authors argue that this is "in agreement with the prior study showing that MOMP-induced mRNA decay occurs before DNA fragmentation begins during extrinsic apoptosis ", but that study specifically implicated the MOMP. More needs to be done here. This study demands cells that lack CASP8 as well as CASP3. The reliance on siRNA is insufficient for conclusive evidence either way. There are now not only CRISPR/Cas9 strategies that have been applied to cultured cells, but also viable mouse strains that lack CASP8 (requiring combined elimination of necroptotic machinery) and these should be used to probe the importance of this phenomenon in additional cells as well as an intact animal. Any requirement for CASP8 needs a lot more dissection, particularly with knock-out cells and animals where this protease has been eliminated without sensitizing to necroptosis (machinery that HCT116 cells apparently lack).To clarify, we are not arguing for a direct role for CASP8 (or any other caspase) in mRNA decay or transcription, as the pathway can be activated by inducers of MOMP that bypass CASP8. Instead, our data demonstrate that CASP8 is only involved to the extent that it sets off a signaling cascade that leads to MOMP  when activated by a death-inducing signaling complex. The distinction between CASP8 and CASP3 is only made to differentiate the action of CASP8 (which cleaves a limited set of targets and instigates MOMP-induced mRNA degradation in response to TRAIL) from that of the mass proteolysis of CASP3.The data supporting this assertion are: (a) CASP3 knock down does not affect mRNA decay or transcription while knock down of CASP8 rescues both in TRAIL treated cells, (b) inducing MOMP directly with a small molecule prompts mRNA decay and RNAPII transcriptional repression even in the presence of a pan-caspase inhibitor, and (c) our new data showing that depletion of mitochondrial pore-forming proteins BAX and BAK is sufficient to attenuate both mRNA decay and RNAPII transcriptional repression, even though caspases are still activated in their absence. We believe that these experiments clearly demonstrate the necessity and sufficiency of MOMP-induced mRNA decay (and not necessarily caspase activity) in reducing mRNA output in apoptotic cells.5. (ln 184) Despite the observations on CASP8, the conclusion, "these data suggest that neither the mRNA degradation nor the concurrent transcriptional repression observed during early apoptosis are a consequence of caspase activation" is at odds with the data shown.We hope this comment (which is linked to point 4) was clarified above. We have also amended the wording of this sentence to state that “caspases are not required to drive these phenotypes outside of their role in MOMP activation.” (Lines 239-240).”6. Authors go on to hypothesize "that TRAIL-induced mRNA turnover was functionally linked to the concurrent transcriptional repression", pursuing previously identified targets, cytoplasmic 3'-RNA exonuclease DIS3L2 and the mitochondrial 3'-RNA exonuclease PNPT1", which contrasts "basal mRNA decay, which occurs predominantly from the 5' end by XRN1". This siRNA knock-down certainly reinforces the prior conclusions that 3' degradation by DIS3L2 and PNPT1 predominates in this setting.7. Figure 3 then extends into novel territory, showing evidence that RNA pol II loading is likely compromised by TRAIL signaling at or before the formation of the preinitiation complex (PIC) of the genes assessed. The data are carefully assembled but require more precise conditions where the death pathway does not proceed beyond certain defined points (as mentioned above). The reliance on siRNA here and in Figure 4 remains a concern. This section of the manuscript promises novel and significant insights but must bring the reader to understand what step in cell death signaling drives the RNA pol II impact on initiation. The nascent 4sU pulse is appropriate and important here.Having demonstrated that the action of mRNA decay factors drives the RNAPII transcriptional repression upon TRAIL treatment, we focused on the effects of the decay factors on RNAPII promoter occupancy during early apoptosis. We now include additional RNAPII ChIP experiments in cells depleted of CASP8 and CASP3 that validate concordance with the decreases in 4sU incorporation under these conditions .Our choice to use siRNAs instead of CRISPR/Cas9 in order to deplete cellular proteins, particularly mRNA decay factors, is informed by previous observations in our group that depletion for a sustained period of time (e.g. during single cell selection of CRISPR-based knockout clones) of individual mRNA decay factors can lead to compensatory changes in the expression levels of other mRNA decay factors. We favored siRNAs since they provide more acute depletion of protein levels (<72 h before harvesting) and do not elicit a compensatory increase in the abundance non-targeted decay factors . Furthermore, all siRNAs employed in this study were Horizon Discovery ON TARGETplus siRNAs, which utilize dual strand modifications reported to greatly reduce the off-target effects characteristic of unmodified siRNAs .8. Figure 4 turns finally to the contributions of importans and of the viral endoribonuclease muSOX without coming to a precise synthesis of data. Complications to a simple story include the fact that mRNA degradation and RNA pol II impacts require considerably more data to provide a clear picture here. The SOX MHV68, like the homologs in Kaposi's sarcoma-associated herpesvirus and in Epstein-Barr virus, as well as the classic virion host shut-off (VHS) function encoded by in herpes simplex viruses  may have an impact on RNA pol II, but this would require a bit more systematic study to be convincing.We certainly agree that if this were the only piece of data linking these viral mRNA degrading nucleases to transcriptional repression, we would not be able to draw strong conclusions! However, our group has published multiple mechanistic studies elucidating the link between viral endonucleases and RNAPII transcriptional repression. Briefly, KSHV and MHV68 infections reduce transcription of several RNAPII genes, as measured by both 4sU pulse labeling and RNAPII ChIP, in a manner dependent on catalytically active SOX or muSOX endonucleases . Ectopic expression of muSOX or HSV-1 vhs are sufficient to drive RNAPII transcriptional repression , demonstrating that viral mRNA decay by multiple herpesviruses directly cause this phenotype. The transcriptional repression observed during infection is widespread; MHV68 infection reduces RNAPII occupancy at 86% of promoters as measured by ChIP-seq . Finally, the widespread mRNA decay induced by muSOX causes relocalization of many RNA binding proteins from the cytoplasm to the nucleus in a manner dependent on degradation of the muSOX-cleaved mRNA fragments by Xrn1 . Here, we are adding to this significant body of work by demonstrating that the nucleo-cytoplasmic shuttling of proteins induced during mRNA decay is required for subsequent repression of RNAPII transcription. This link was hypothesized in the Gilbertson et al. publication but is experimentally demonstrated here.t values normalized to 18S rRNA in reference to mock-treated cells. The graph represents mean +/-SEM, used statistic – one-sample t-test, with the hypothesis that there is no deviation from 1. Thus the representation of results does not show the variability of measurements in the reference sample . Measurements should be presented as relative mRNA levels with appropriate statistical tests. More importantly, the RT-qPCR analysis of a few genes usually does not allow concluding that there is a global RNA-decay.9. The decrease in mRNA levels is measured for a few housekeeping genes only and represented as fold changes from CeLife from our group  and the paper that elucidated the role of PNPT1 in apoptotic mRNA decay . In addition to one sample t tests performed to determine if any given fold change between untreated and induced apoptotic cells differed from a hypothetical fold change of one, we also used multiple t tests (corrected for multiple comparisons) to compare fold changes upon apoptosis induction in the backgrounds of cells treated with different compounds or siRNAs (e.g. comparing the pairwise differences in 4sU incorporation between untreated and TRAIL-treated cells both in the presence and absence of ivermectin). These tests are indicated with brackets between the fold changes being compared and are noted as such in the figure legends. We believe that these are accurate statistical methods that suit the data but are happy to perform any additional specific statistical tests deemed more appropriate.Our primary metric of RNA decay is the change in the abundance of each transcript upon apoptosis induction, i.e. the extent of mRNA depletion from baseline levels. Similarly, we sought to measure the change in 4sU incorporation upon apoptosis induction compared to baseline as a proxy for transcriptional repression. This method of normalization has been used in similar studies, including a recent publication in In response to the comment regarding our measuring only select housekeeping genes as representative examples, we performed 4sU-sequencing to determine the extent to which mRNA synthesis is reduced during early apoptosis . These data reveal that the concurrent transcriptional repression during early apoptosis is indeed global, impacting 71.2% of human genome-aligned transcripts detected.10. Transcription shut down is measured by RT-qPCR on 4sU labeled RNA, expected to represent nascent RNA. Surprisingly, the authors used the same primers for the analysis of 4sU labeled samples as used for standard RT-qPCRs. Those primers span the exon-exon junctions and are not suitable for the analysis of the nascent transcription. Apart from RT-qPCR, the Authors used RNAPII ChIP qPCRs (fig3D) using an antibody recognizing hypophosphorylated RNAPII, which normally is not engaged in transcription. Thus, such analyses are not optimal for studying the activity of RNAPII. In sum, the transcription shut down, and the rescue by 3' to 5' RNA decay nucleases is not sufficiently supported by the data. The best would be to perform a genome-wide analysis of RNA polymerase activity employing one of the broadly used techniques, for instance, GRO-seq.Thank you for these suggestions. As described above, we have added a 4sU-seq experiment  comparing the change in 4sU labelling of nascent RNA upon TRAIL treatment both in the presence and absence of the pan-caspase inhibitor zVAD in order to better assess the breadth of transcriptional repression.While our exonic primers did amplify RNA synthesized during the short 4sU pulse, we agree that showing similar results with intronic primers would bolster confidence that the 4sU-labeled RNA represents nascent transcripts. We therefore designed intronic primers for the ACTB transcript and showed that RT-qPCR of the 4sU labeled RNA with these primers gave results similar to those with the exonic primers, in that ACTB mRNA decreased upon TRAIL treatment but was rescued in the presence of zVAD . We observed similar agreement between exonic and intronic qPCR primers used to quantify RNA isolated from a short duration 4sU pulse in a previous study from our group . This trend also held true for the next-generation sequencing data, which does not rely on gene-specific primers.The RNAPII CTD is recruited to promoters in a hypophosphorylated state , so the 8WG16 antibody is appropriate for measuring differences in RNAPII promoter recruitment. It should also be noted that this antibody does not exclusively bind hypophosphorylated RBP1, but rather exhibits a preference for this form .11. Although the main claim of the paper is that cytoplasmic 3' exonucleases are required for apoptotic RNAPII repression, there is no explanation of why the silencing of the main cytoplasmic 5' to 3' exonuclease, Xrn1, has no effect on transcription. Moreover, all 3' exonucleases  are always silenced together and never individually. Why is it so? What if, in reality, only one nuclease is responsible for the observed effect? Importantly, there is no rescue experiment. Thus, observed effects can be attributed to off-targets of one of these three siRNAs, especially that the mechanism of the repression remains to be elucidated.The reason why XRN1 depletion does not impact transcription in this system is that apoptotic mRNA decay does not occur from the 5’ end. The Lieberman lab reported that 3’ (but not 5’) mRNA decay intermediates can be detected in apoptotic cells, and our results with XRN1 depletion are consistent with their observations. This point is clarified in the discussion: “XRN1-driven 5’-3’ end decay is the major pathway involved in basal mRNA decay , but MOMP-induced mRNA decay is primarily driven by 3’ exonucleases such as PNPT1 and DIS3L2 . Accordingly, co-depletion of 3’ decay factors but not XRN1 restored RNAPII promoter occupancy and transcription during early apoptosis.” (Lines 375-377)While 5’ end decay is carried out by a single 5’-3’ exonuclease (XRN1), 3’ end decay is carried out by multiple 3’-5’ exonucleases that can function in at least partially redundant ways . For this reason, in general 3’ end decay cannot be effectively stalled by individual enzyme depletions. Indeed, we did perform individual knockdowns of the 3’ mRNA decay factors and found that no individual knock down had a reproducible effect on ACTB and GAPDH total or 4sU-labeled mRNA levels . Instead, only co-depletion of multiple 3’ end decay factors inhibited mRNA degradation and rescued mRNA transcription. The fact that none of the individual siRNAs we used altered ACTB and GAPDH levels argues against the rescue phenotype in the co-depletion experiments being due to off-target effects (as these would have manifested in the single knock downs as well).12. A lot of attention is given to PABPC1, which upon apoptosis translocates to the nucleus . Depletion of PABPC1 and PABPC4  is supposed to rescue mRNA transcription, but keep reduced mRNA baseline reduced. PABPC1/4 depletion leads to a drastic reduction of mRNA levels. Thus it has a profound effect on cell physiology, which makes functional conclusion very difficult to draw, especially that they are not consistent with ivermectin treatment (see below). This part should be explored more thoughtfully or removed from the paper. At present, it adds very little to the story.We agree that the PABPC1/4 depletion experiment adds little to the story given these caveats, so we have removed the data from the paper.Importins α/β are supposed to links mRNA decay and transcription. Treatment with ivermectin, an inhibitor of α/β transport, efficiently block nucleolin localization Figure 4B and is supposed to rescue RNAPII transcription Figure 4C. Surprisingly, there is no influence on PABPC1 localization . Thus, on the one hand, the block of import by ivermectin rescues reduced transcription but does not influence PABPC1 relocation to the nucleus. On the other hand, depletion of PABPC1 also diminishes reduced transcription, and there is a coincidence of transcriptional repression caused by apoptosis induction and PABPC1 relocation to the nucleus. This discrepancy should be discussed.We devoted much of our attention to PABPC1 since the protein has previously been shown to translocate into the nucleus in response to widespread mRNA turnover  and has been linked to the transcriptional repression elicited by expression of the muSOX exonuclease . Although we have shown that PABPC1 nuclear influx also occurs during early apoptosis, a causative link between this relocalization and the coincidental reduction in RNAPII transcription has not been definitively established. We hypothesize that the observed RNAPII transcriptional repression occurs as a result of the cumulative import of multiple factors, not all of which are blocked from entering the nucleus by inhibiting only one route of nuclear import. Our group previously reported that at least 66 proteins in addition to PABPC1 are selectively enriched in the nucleus upon transfection with the viral endonuclease muSOX , 22 of which are known to be RNA-binding proteins and 45 of which shuttle in a manner dependent on the cytoplasmic mRNA exonuclease primarily responsible for clearing cleavage fragments, XRN1 . The nuclear overexpression of PABPC1 alone only reduces RNAPII promoter recruitment when done to an extent much greater than that observed during muSOX expression or apoptosis induction, suggesting that these additional factors may be involved in regulating transcription physiological contexts. PABPC1/4 knockdown generally destabilizes mRNAs and restricts gene expression, so the loss of the link between mRNA degradation and RNAPII transcription that occurs in the absence of these factors could just as well be due to reduced expression of other key factors rather than a specific role of PABPC. As noted in point 12 above, we removed the PABPC1/4 depletion experiment from the paper. Future studies in which changes in the nuclear and cytoplasmic proteome upon apoptosis induction in the presence and absence of ivermectin will likely provide insight into which additional protein or proteins may play a role in connecting cytoplasmic mRNA turnover to RNAPII transcription.14. Using the HCT116 cell line treated with TRAIL as a model, the Authors observed casp8 and 3 cleavages after 1.5h . They claim that casp8 stimulates mRNA decay inducing MOMP  partly by releasing the mitochondrial 3'-5' RNA nuclease PNPT1 (two citations). Since PNPT1 activity is important for the story, the Authors should validate this aspect in their model.In response to this comment, we spent several months trying to generate at PNPT1 knockout using CRISPR/Cas9 , however were unsuccessful at generating this line despite multiple attempts. However, we note that Liu et al. comprehensively characterized the nature of mRNA degradation that occurs after TRAIL treatment in HCT116 cells and the role of PNPT1 in this process . We performed a variety of experiments to validate this model of mRNA decay , and in each case, mRNA decay was linked to and required for transcriptional repression.15. The assays of steady-state and nascent RNA abundance that form the backbone of the paper rely on normalization to 18S (or in a few cases U6) RNA. The interpretation of these experiments relies on the assumption that levels or transcription of these ncRNAs are not affected by the cellular conditions studied, but this is not substantiated, and the rationale/validity of these controls is not discussed. The authors should provide data supporting the choice of normalization controls, such as quantification of transcripts/cell by RT-PCR or RNA-FISH.t values in cDNA synthesized from equal volumes of total RNA extracted from the same number of apoptotic and non-apoptotic cells. 18S Ct values were also stable in 4sU-labeled RNA, but since this is the first time to our knowledge that nascent transcription during early apoptosis has been measured in such a way, we have added RT-PCR agarose gels to illustrate this fact . In contrast, the levels of 18S 4sU RNA seemed to decrease upon 4 hr raptinal treatment, perhaps due to the onset of 18S rRNA cleavage during later stages of apoptosis , so we instead normalized to the relatively stable U6 transcript. RT-PCR illustrating these trends have also been added .18S rRNA levels have been previously reported to be stable during early apoptosis , which was confirmed in our hands by near-identical 18S CEncouraged but optional major revisions:1. The authors argue that RNA decay specifically represses polII transcription, but they observe reduced recruitment of TBP, which has a role in transcription by all three eukaryotic RNA polymerases. Does induction of apoptosis only affect TBP recruitment to polII promoters, or is recruitment to polI and polIII promoters also affected?We believe that the defect in TBP recruitment occurs upstream of any regulation of TBP itself, perhaps at the level of chromatin availability, given that there is not a decrease in RNAPI and III transcripts known to have TBP at their promoter .2. Figure 4: The authors tested whether importin α/β was "required for feedback between viral nuclease-driven mRNA decay and RNAPII transcription, as this would suggest that the underlying mechanisms involved in activating this pathway are conserved." I think this overstates the evidence – import is so general that it's a stretch to say that this is evidence that the underlying mechanisms are conserved.We have amended this sentence to end “may be conserved.” (Line 338-339)References:Abernathy, E., Gilbertson, S., Alla, R., Glaunsinger, B., 2015. Viral nucleases induce an mRNA degradation-transcription feedback loop in mammalian cells. Cell Host Microbe 18, 243–253.Biasini, A., Marques, A.C., 2020. A Protocol for Transcriptome-Wide Inference of RNA Metabolic Rates in Mouse Embryonic Stem Cells. Front. Cell Dev. Biol. 8, 1–11.Brien, T.O., Hardlnu, S., Greenleaft, A., Lls, J.T., 1993. Phosphorylation of RNA polymerase lie-terminal elongation 75–77.Burke, J.M., Moon, S.L., Matheny, T., Parker, R., 2019. RNase L Reprograms Translation by Widespread mRNA Turnover Escaped by Antiviral mRNAs. Mol. Cell 75, 1203-1217.e5.Cheng, C., Sharp, P.A., 2003. 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Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF‐predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation. For example, in order for assisted gene flow to contribute to robust populations of harvested forests   analyses  remain unanswered, including how it is affected by neutral demography. For example, changes in allele frequencies could reflect simple genetic drift rather than adaptive signals  in allele frequencies across a gradient, GF Offset will always be positive. The underlying assumption being that a population already occupies its adaptive optimum when sampled, and therefore any subsequent change in allele frequency composition will reduce its fitness. However, because fitness could Variation in population size (N). What effect do neutral processes, operating on a cline in population size across an environmental gradient, have on GF Offset when only neutral loci are considered? We tested the hypothesis that a decrease in population size would result in an increased GF Offset at neutral loci due to increased genetic drift operating in small populations.(Q1) Relationship between GF Offset and fitness offset. Given equal deme sizes in a metapopulation, how well does GF Offset predict changes in fitness when populations experience an immediate environmental change ? We tested the prediction that GF Offset is inversely related to fitness by conducting in silico common garden experiments under monogenic and polygenic architectures.(Q2) GF Offset versus other measures of offset. Given equal deme sizes in a metapopulation, how does GF Offset perform relative to environmental distance or STF? We tested the hypothesis that GF Offset outperforms both environmental distance and genetic distance because GF appropriately weights and scales the environmental gradients to reflect their genetic importance.(Q3) To evaluate GF Offset as a potential measure of fitness offset, we asked the following questions:We tested the performance of GF Offset and other offset measures in their ability to predict fitness of genotypes when transplanted to common gardens  across the species range in silico. Using SLiM  different sampling schemes of a steep allele frequency cline along one or multiple environmental variables, (2) different slopes of allele frequency clines along an environmental variable, (3) different nonmonotonic relationships between allele frequency and an environmental variable, (4) a comparison of linear and nonmonotonic allele frequency relationships with an environmental variable, and (5) a comparison of a linear allele frequency relationship with an environmental variable and the same relationship with noise added by sampling from a normal distribution with 0 mean and variance of 0.1.2.2N) were split across a metapopulation consisting of 100 demes arranged in a 10×10 connectivity matrix  was assigned at least one environmental value that could vary over generational time . When two environmental variables were considered, they are referred to as Environment 1 and Environment 2 . Symmetric migration was simulated between adjacent demes at a per‐generation migration rate (m), and each deme contained equal proportions of males and females, with bi‐parental mating producing the subsequent generation.We generated simulations using SLiM v3.4. Ten‐thousand individuals  was 10−7 , and a base recombination rate (r) of 10−5 (T * rN = 0.1) was used to approximate a distance of 50 cM per linkage group. The population‐scaled mutation and recombination rates were chosen to approximate the resolution that would be observed from sampling SNPs from a larger genome, that is, allowing SNPs 50K bases away to be unlinked, while allowing for signatures of linkage to arise within each linkage group , and neutral variants were then overlaid on that recapitated file ; increasing dN; and decreasing Nd . Each scenario was replicated 10 times.To test the hypothesis that GF Offset at neutral loci could be influenced by variation in genetic drift, we simulated a linear environmental gradient with values from −1 to 1, in three neutral scenarios: equal deme size , then the correlation between dN and GF Offset will be negative.GF analysis, implemented in R . These simulations evolved a simple two‐dimensional isolation‐by‐distance population structure. We evaluated the relationship between GF Offset and mean deme fitness, after individuals from that deme were transplanted into another environment in the simulation, for both monogenic and polygenic scenarios. We evaluated offset measures under two genetic architectures: a single‐locus population genetic model, and a multilocus two‐trait quantitative genetic model. In both models, a migration rate of ωS) and mean allopatric fitness (ωA)  considered to be antagonistically pleiotropic . See Appendix In our “population genetic” model of environmental adaptation, a single allele had a linear relationship with fitness across an environmental cline. To avoid a scenario where maladapted individuals persist at the range edge, we modeled individual fitness for each genotype as a function of the environment, with the ancestral allele  with no dominance. When an ancestral allele mutated, the bivariate effect size of the derived allele was drawn from a multivariate normal distribution with a standard deviation of σQTN = 0.1 (without covariance) for both traits, which gives flexibility for mutations to evolve with effects on one or both traits . For each deme, the phenotypic optimum simply equaled the environmental value.In our “quantitative genetic” two‐trait, two‐environment model of environmental adaptation, QTNs were allowed to arise at a rate of i in deme j (ijω) was based on how far their P1,ij and P2,ij phenotypes fell from the bivariate optimum in that patch (Θ1jt and Θ2jt) using a multivariate normal distribution with standard deviation kσ to represent the strength of stabilizing selection in each deme:The relative fitness of individual ωmax, where ijP = Θjt. We ran a cumulative burn‐in period of 3000 generations, consisting of a 1000 generation homogeneous initial burn‐in, followed by a 1000 generation transition burn‐in  in a transplant “common garden” location j  was calculated for each deme across all contemporary environments in the simulation, resulting in a 100 × 100 matrix of pairwise relative fitness comparisons. For each common garden j, we could then assess how well an offset measure between i and j predicted the relative fitness of individuals from i when transplanted to j. We refer to this relative fitness measure as CG Fitness (common garden fitness).In order to represent the relative fitness effects of an instantaneous environmental change, we implemented a reciprocal transplant fitness assessment in our single‐ and multilocus simulations. The mean relative fitness of individuals from a home location For each common garden, the performance of each offset measure was evaluated as the correlation between the offset measure and CG fitness, with a higher correlation inferring that the given offset measure was better able to predict the relative fitness of a genotype when transplanted to the common garden environment. To capture limitations affecting most empirical studies, we used a subset of demes in the reciprocal transplant that was used to calculate the correlation for each common garden  environmental distance.In order to assess the performance of GF Offset in predicting CG Fitness relative to other distance metrics, we also calculated offset measures based on pairwise (i) ST offset(i) F. FST offset for a set of individuals from a deme was based on the pairwise FST between the source deme and transplant deme. Pairwise Weir–Cockerham FST values were calculated for every combination of demes at the end of each simulation after filtering loci for MAF > 0.01: one set of such values using all loci (FST Genome), another set using only the QTN loci .(ii) Environmental offset. Environmental offset for a set of individuals from a deme was based on the pairwise environmental distance between the source location and the transplant location. Environmental distances were calculated using both n‐dimensional Euclidean , and using just the causal environments . Cauchy,  and Maha(iii) GF offset. GF outputs an individual cumulative importance curve for each locus considered to have an environmental association and a weighted aggregate function across all such loci, for each important environmental variable. GF Offset is defined as the Euclidean distance between two locations A and B in the rescaled environmental space obtained by applying the fitted GF model to the environmental predictors. Note that the rescaling and calculation of GF Offset can be performed using the individual cumulative importance curves  we calculated GF Offset based on (i) all loci and all environments , (ii) all loci and causal environments , (iii) QTN loci and all environments , and (iv) QTN loci and causal environments .r et al. ), but mo33.1To explore the behavior of GF, we created different relationships between genetic variation and an environmental cline and used these to evaluate the relationships between the rate of allele frequency turnover and total amount of cumulative importance, as well as the shape of the cumulative importance curve. In our study, GF produced similar nonlinear cumulative importance curves for linear clines, in which the rate of turnover was highest near the middle of the cline and low elsewhere, regardless of the slope between allele frequency and the environment , where ijα was the effect of locus i on trait j, and ip was the frequency of locus i in the metapopulation postsampling), indicating that a large number of QTNs were rare alleles that individually contributed little to the total additive genetic variance.All simulated cases produced high degrees of local adaptation Figure , with CaWe evaluated if GF could identify the causal environments when all the simulated environments were input. In the cases with two linear causal environmental clines , GF was able to identify the causal environment driving adaptation Figure . HoweverFST Genome, and FST Causal  than any other method. While FST Causal was generally a better predictor of CG Fitness overall for the Edge demes, FST Genome performed similarly in the Core demes.Measures of 4Potential applications for GF Offset range from small‐scale conservation efforts in genetic rescue , GF Offset, which is based on weighting of environmental gradients given the strength of their association with adaptive variation, outperformed predictions of changes in relative fitness from the unweighted distance metrics. However, when environmental drivers of adaptation were known and only those gradients were included in the offset calculation, environmental distance performed as well as, or better, than GF Offset. Note that the demography in those simulations was simple in the sense of equal deme sizes and migration rates. We also found that neutral demography can confound GF Offset, and that GF Offset can be sensitive to sampling schemes. Additional research is needed to inform how to account for population structure and apply appropriate filtration thresholds prior to calculations of offset measures, especially if they are to be applied to real‐world applications.Whether the intent is to predict how a population will perform when moved to another location or when faced with environmental changes in its current location, GF Offset is useful only to the extent that it approximates associated reductions in fitness . Across all simulated cases with selection, GF Offset performed well at predicting CG Fitness, regardless of whether or not nonadapted loci and noncausal environments were included in the analysis. However, it is important to note that we used GF offset to predict relative fitness in our simulations, as absolute fitness was not simulated. GF Offset was representative of changes in 4.1FST, regardless of whether FST was calculated using only adapted loci or not. This has significant implications for the use of FST as a decision metric for prioritizing conservation efforts, which other evaluations have also shown has limitations  result in decreases in fitness. Furthermore, GF Offset assumes that the molecular signatures of local adaptation when multiple demes occupy the same environment at different sites are the same. These assumptions are likely met when there is high stability of both the adaptive landscape and the genomic architecture maintaining fitness. On the other hand, these assumptions are likely to be violated with shifts in the adaptive landscape potentially driven by fluctuations in climate and ecology . This distinction between absolute and relative fitness is critical when using models to inform conservation management decisions, since changes in allele frequencies (due to genetic drift or differences in relative fitness) do not necessarily impact demography as absolute fitness does. In fact, allele frequency changes can FST values , with corresponding data which are unlikely to be reflected in empirical work. All calculations using causal alleles were not dependent on those alleles being identified as outliers – it was simply assumed that they were known. In reality, even though many causal alleles showed relatively elevated s Figure , most wo5While most emergent complexities involved in applying GF Offset to realistic scenarios are still poorly understood, there is still promise in the application of this method to identifying key environmental drivers of local adaptation and for estimating fitness declines in response to rapid environmental change. This may be especially applicable to translocation assessments of at‐risk species or cultivars, and genetic rescue efforts. Key considerations of demography, genomic architecture, and the nature of environmental gradients have been highlighted here, as in earlier work (Capblancq et al., The authors have no conflict of interests to declare.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.