TSC2 are the most common cause of tuberous sclerosis (TSC), a disorder with a high incidence of autism and intellectual disability. TSC2 regulates mRNA translation required for group 1 metabotropic glutamate receptor-dependent synaptic long-term depression (mGluR-LTD) and behavior, but the identity of mRNAs responsive to mGluR-LTD signaling is largely unknown.Mutations in Tsc2+/− mice as a mouse model of TSC and prepared hippocampal slices from these animals. We induced mGluR-LTD synaptic plasticity in slices and processed the samples for RNA-seq and ribosome profiling to identify differentially expressed genes in Tsc2+/− and following mGluR-LTD synaptic plasticity.We utilized Tsc2+/− mouse hippocampal slices, the expression of several mRNAs was dysregulated: terminal oligopyrimidine (TOP)-containing mRNAs decreased, while FMRP-binding targets increased. Remarkably, we observed the opposite changes of FMRP binding targets in Fmr1−/y hippocampi. In wild-type hippocampus, induction of mGluR-LTD caused rapid changes in the steady-state levels of hundreds of mRNAs, many of which are FMRP targets. Moreover, mGluR-LTD failed to promote phosphorylation of eukaryotic elongation factor 2 (eEF2) in TSC mice, and chemically mimicking phospho-eEF2 with low cycloheximide enhances mGluR-LTD in TSC mice.Ribosome profiling reveals that in Tsc2+/− mice.These results suggest a molecular basis for bidirectional regulation of synaptic plasticity and behavior by TSC2 and FMRP. Our study also suggests that altered mGluR-regulated translation elongation contributes to impaired synaptic plasticity in  TSC1 or TSC2; mutations in TSC2 are more common and are responsible for the most severe symptoms. The TSC1/2 proteins heterodimerize to form the tuberous sclerosis complex (TSC), a GTPase-activating protein that inhibits Ras homology enriched in brain (Rheb) [TSC1 or TSC2 leads to excessive mTOR activity, and mutations in other components of the mTOR pathway are also linked to autism in humans such as PTEN, RHEB, and MTOR [Tuberous sclerosis (TSC) is an autosomal dominant disorder characterized by benign tumor growth in multiple organs and neuropsychiatric symptoms. Individuals with TSC have an increased incidence of seizures ~ 90%), intellectual disability ~ 50%), and autism (~ 50%) . Disruptn (Rheb) , 5. Rheb0%, and an (Rheb) . mTORC1 n (Rheb) –9. The l0%, inteln (Rheb) . Completand MTOR , 11. ThuTsc2+/− mice have reduced hippocampal protein synthesis rates instead of the predicted excessive protein synthesis from mTOR hyperactivation [The mTOR pathway integrates signaling from various inputs to regulate synaptic plasticity and higher cognitive function. One example is metabotropic glutamate receptor-mediated long-term depression (mGluR-LTD), a form of synaptic plasticity that requires de novo protein synthesis . The mGltivation , 19 but tivation , 20. MGltivation , 21. Alttivation , how traTsc2+/− mouse and performed ribosome profiling, an unbiased whole transcriptome method that determines the number and positions of ribosomes on all mRNAs and thus serves as a proxy for protein synthesis [Tsc2+/− hippocampal slices under basal conditions and after induction of mGluR-LTD allows us to define posttranscriptional changes in gene expression in response to synapse activation [Tsc2+/− mouse model, because we strove to capture translational dysregulation that arises from TSC2 haploinsufficiency as in humans with TSC. This mouse model displays excessive mTOR signaling, a well-characterized deficient mGluR synaptic plasticity, and alterations in social interactions and hippocampal-dependent learning tasks that are reversed by rapamycin treatment [Tsc2+/− hippocampus as well as following mGluR-LTD signaling. We also observed that mGluR1/5 stimulation induces a rapid increase in mRNA levels, and targets of Fragile X Mental Retardation Protein (FMRP), an RNA-binding protein whose loss causes Fragile X Syndrome, are enriched in this group. mGluR stimulation also promotes phosphorylation of eukaryotic elongation factor (eEF2), which is deficient in Tsc2+/− mice. These animals have a reduced mGluR-LTD, and chemically mimicking phospho-eEF2 enhances this form of synaptic plasticity. Our results suggest a novel pathway of translational dysregulation in ribosome translocation (polypeptide elongation) that may contribute to altered translation and synaptic plasticity in TSC mice.To study the role of TSC2 in synaptic plasticity, we generated a germline ynthesis . Togethetivation . We chosreatment –26. UsinTsc2+/− mice were generated by crossing CMV-Cre (B6.C-Tg(CMV-cre)1Cgn/J) (JAX stock number: 006054) with Tsc2flox mice (JAX Stock number: 027458). F1 was then bred on the C57BL/6 J background to obtain WT and Tsc2+/− mice. Animals were given ad libitum access to food and water and reared on a 12-h light–dark cycle. All experiments were approved by the Institutional Animal Care and Use Committee at University of Texas Southwestern and conducted in accordance with the National Institutes of Health Principles of Laboratory Animal Care.Germline Tsc2+/− littermates as described previously [2PO4, 26 NaHCO3, 0.5 CaCl2, 5 MgCl2, 212 sucrose, and 10 dextrose aerated with 95% O2/5% CO2. Transverse hippocampal slices (400 µm) were obtained on a Leica VT1200S slicer. CA3 was cut off to avoid epileptogenic activity induced by DHPG. Slices were recovered for 3–4 h and maintained at 30 °C in artificial cerebral spinal fluid (ACSF) containing the following (in mM): 119 NaCl, 2.5 KCl, 2 CaCl2, 1 MgCl2, 26 NaHCO3, 1 NaH2PO4, and 11 D-glucose aerated with 95% O2/5% CO2 to pH 7.4. Slices were then treated with cycloheximide (100 µg/ml) in ACSF, 4 °C, snap-frozen on dry ice/EtOH bath, and stored at − 80 °C. Alternatively, hippocampal slices were treated with DHPG (100 µM) (Tocris) or artificial cerebrospinal fluid  for 5 min. For the cycloheximide experiments, hippocampal slices were pretreated with 75 nM cycloheximide for 45–60 min. At the end of the incubation, CA1 was dissected out, snap-frozen in a dry ice/ethanol bath and stored at − 80 °C. Some hippocampal slices were prepared without removing CA3 and used for RNA-seq and ribosome profiling experiments as indicated below (batches 1–5).Acute hippocampal brain slices were prepared from P30-40 wild-type (WT) and eviously . BrieflyFor all recordings, slices were submerged and perfused with ACSF at 2.5–3.5 ml/min (30 ± 1 °C). Field potentials (FPs) and EPSCs were evoked by stimulation of the Schaffer collateral pathway with a concentric bipolar tungsten electrode. Field potentials were recorded with a glass electrode (1 MΩ) filled with ACSF placed in the stratum radiatum of CA1. Test stimuli were delivered every 30 s, and a stable baseline was obtained at ∼50% of the maximum FP amplitude.g for 10 min at 4 °C, the pellets were discarded, and protein levels in the supernatant were measured using a Pierce BCA kit. After SDS–PAGE, proteins were transferred onto PVDF membranes. After blocking with 5% BSA in 1 × TBS, 0.05% Tween 20 for 1 h, membranes were incubated with the following primary antibodies in blocking buffer overnight at 4 °C: The following antibodies were used in this study: S6 Ribosomal Protein , Phospho-S6 Ribosomal Protein Ser235/236 , Akt , Phospho-Akt Ser473 , p44/42 MAPK (Erk1/2) , Phospho-p44/42 MAPK (Erk1/2) Thr202/Tyr204 , eEF2 , Phospho-eEF2 Thr56 , mTOR , Phospho-mTOR Ser2448 , 4E-BP1 , Phospho-4E-BP1 Thr37/46 , RPL4 , RPS25 , LARP1 , FMRP , Glyceraldehyde-3-Phosphate Dehydrogenase , actin . After three 10 min washes of 1 × TBS, 0.05% Tween 20, membranes were incubated with the appropriate HRP-conjugated secondary antibodies in 5% milk in 1 × TBS, 0.05% Tween 20 for 1 h at room temperature. After three 10 min washes of 1 × TBS, 0.05% Tween 20, the membranes were developed using ECL. For comparison of phosphorylated protein levels across conditions or genotypes, immunoreactive phosphorylated protein bands were normalized to total protein levels from the same slice homogenates , each of which was first normalized to loading control (either GAPDH or actin where indicated).Western blotting was performed on whole hippocampus homogenates or on CA1 hippocampal slices, where indicated, and as previously described . CA1 hipg for 10 min at 4 °C and the supernatant collected and centrifuged again for a 20,000g for 10 min at 4 °C. This supernatant was collected and the RNA concentration measured using the Qubit RNA HS Assay kit . One-fourth of the lysate was saved for RNA-seq libraries (~ 1 ug of RNA), and the remainder (~ 3.5 ug) was treated with 11.3 ug of RNase A  and 1410U of Rnase T1 , which corresponds to a ratio of ~ 3ug Rnase A/375U Rnase T1 per ug of input RNA. The Rnase treatment was carried out at 25 °C for 30 min with gentle shaking and the digest stopped by placement on ice and the addition of 2.5u L of SuperRNaseIn . The lysate was layered over a linear 10–50% sucrose gradient (prepared with polysome buffer supplemented with 1 mM DTT and 100 ug/mL CHX) and centrifuged at 35,000 rpm for 2.5 h at 4 °C in a SW41 rotor. The 80S monosome fractions were identified by continuous A260 monitoring and pooled for RNA extraction by TRIzol LS. To aid in RNA recovery of low concentration samples, 20 ug glycogen was added to all precipitation steps. A total of three biological replicates per genotype and treatment were made from hippocampal slices  from Tsc2+/− and their wild-type littermate mice. Wild-type slices had a total of eight biological replicates for RiboSeq and 7 for RNA-Seq .Hippocampal slices from six mice were pooled, lysed in polysome buffer , and triturated with a 25G needle ten times. Following placement on ice for 10 min, the lysate was centrifuged 20002 . Primers containing a 5 nt barcode and 8 nt unique molecular identifier (UMI) were used for RT. After RT, the RNA was hydrolyzed with a final concentration of 0.1 N NaOH for 20 min at 98 °C. The cDNA was resolved on a 10% TBU gel, and the 130–140 nt band was selected. The cDNA was eluted from crushed gel pieces overnight in DNA extraction buffer  at 25 °C with constant horizontal rotation. The cDNA was circularized using CircLigase  and depleted of tRNA and rRNA using biotinylated anti-sense probes (IDT) and Dynabeads MyOne Streptavidin C1 . To determine the optimal number of PCR cycles, a test PCR was performed using the KAPA library amplification kit . The final PCR product was run on an 8% TBE gel, and the 180–190 nt bands were selected. The PCR product was eluted overnight in DNA extraction buffer as described earlier.All RiboSeq libraries were prepared as previously described . RiboZer2CO3, 90 mM NaHCO3, pH ~ 9.3) at 95 °C for 15 min to yield ~ 140 nt fragments. The fragmented RNA was separated on a 10% TBU gel, and those migrating between 100 and 150 nt were selected. RT products were separated on a 10% TBU gel, and the 200–250 nt band was selected. The final PCR product was run on an 8% TBE gel, and the 260-300 bp band was selected. The size distribution of the final libraries was confirmed by Fragment Analyzer (UMMS Molecular Biology Core). Batch 6–8 RNA-Seq libraries were prepared using NEXTflex poly(A) beads  and the NEXTFlex Rapid Directional qRNA-Seq kit  according to manufacturer’s instructions.Batch 1–5 RNA-Seq libraries were prepared using RiboZero to deplete rRNA and then fragmented with 2X alkaline fragmentation solution . Purified libraries were quantified by qPCR using KAPA Library Quantification kit  and were pooled to equimolar ratios. Libraries were sequenced on a NextSeq 500/550 High Output Kit v2  as 75 bp single-end runs on a NextSeq500 sequencer.https://www.umassmed.edu/biocore/introducing-dolphin/). The reads were quality filtered using Trimmomatic (0.32) and then mapped to the rRNA and tRNA reference using bowtie2 (2.1.0). The rRNA and tRNA unmapped reads were kept and then mapped to mm10 using TopHat2 (2.0.9). Only uniquely mapped reads were kept, and PCR duplicates were marked based on UMI sequence and a custom script. SAMtools (0.019) was used to keep uniquely mapped reads without duplicates. For gene-level quantification of RiboSeq, RSEM/1.2.11 was used to align cleaned, unique reads to the RefSeq mm10 CDS with the first and last 30 nts removed to avoid quantifying initiation and termination peaks. For RNA-seq libraries, reads were aligned to the entire mm10 transcriptome. Counts from RSEM were batch-corrected using the Combat function in sva (3.24.4). Batch-corrected counts were used for DESeq2 (1.26.0) to identify differentially expressed genes for RNA and RiboSeq libraries. To validate our Tsc2+/−/WT differential expression results from Fig. The individual samples were separated based on 8 nt barcodes using custom scripts. Cutadapt 1.7.1) was used to remove the adapter (TGGAATTCTCGGGTGCCAAGGAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCG). The reads were then uploaded to the UMass Bioinformatics Core Dolphin platform for mapping . The genes past filtering from DE analysis were used as the background.GO term enrichment was performed using the R package clusterProfiler (3.14.0) . For GSEGene features were obtained from the UCSC Genome Browser using RefSeq annotations. For downstream transcript analysis, the most abundant isoform from the RSEM output of the RNA-Seq libraries was selected. ViennaRNA/RNAFold (2.1.6 h) was used to calculate the minimal free energy of the 5′UTR.The average CA1 translational efficiency (TE) was calculated by taking the ratio of RPF to RNA reads per kilobase per million mapped reads (RPKM) from three wild-type basal samples aligned to the mm10 CDS reference without the initiation and termination peak. For consistent TE calculations, mapping to the CDS for RPF and RNA was used, and transcripts longer than 300 nt and RPKM > 5 were included.n = # mice. Post hoc Sidak’s multiple comparison test was performed to determine statistical significance for specific comparisons, reported in the figure. For analysis of LTD results in Fig. An unpaired Wilcoxon rank-sum test with Bonferroni correction was used to compare means between groups unless indicated otherwise in the text and figures. A one-tailed Kolmogorov–Smirnov test was used as shown in Fig. Custom scripts for generating plots will be available upon request to the lead contact.Tsc2 gene, which will henceforth be referred to as the Tsc2+/− mouse.The GTPase activating protein tuberous sclerosis complex (TSC) is an upstream but indirect repressor of mTOR, a kinase which forms the multi-subunit complex mTORC1 to stimulate translation in response to a number of signaling inputs including activated group I metabotropic glutamate receptors . mGluR5 activation leads to long-term depression (LTD), a protein synthesis-dependent form of synaptic plasticity frequently examined at hippocampal Schafer collateral-CA1 synapses .To detect subtle changes in translation, we turned to ribosome profiling, a high-resolution and unbiased whole transcriptome method for determining the number and positions of ribosomes on mRNA. When combined with RNA-seq, the ratio of ribosome occupancy over input mRNA yields translational efficiency (TE) . AlthougTsc2 was the top differentially expressed mRNA at both the RNA and RPF levels, confirming the validity of our approach  conditions that pass a significance cutoff of FDR < 0.1. The differentially expressed (DE) RNAs and RPFs altered in Tsc2+/− had little overlap with each other (n = 5). The ratio of RPF to RNA yields translational efficiency (TE), but because changes in TE do not distinguish between RPF and RNA effects and because few RNAs passed statistical cutoff for changes in TE (log2FC), we considered RNA and RPF changes separately.ach Fig. c. FigureTsc2+/− brain  in CA1 pyramidal neurons, where FMRP targets were stratified into three groups based on binding strength: stringent (n = 327), high (n = 938), and low (n = 1330) binding targets [p < 5.3e−8) in the mRNAs altered in TSC , which is also correlated with stringency of FMRP binding  leads to Fragile X Syndrome (FXS). FMRP and TSC2 bidirectionally regulate mGluR-LTD and behavior, suggesting they coregulate similar mRNAs. In support of this, a combined TSC/FXS mouse rescues deficits in synaptic plasticity and learning to wild-type levels compared to the single mouse mutants [ targets . FMRP tagent n = 27, high gent n = 27, high gent n = 27, high gent n = 27, high n = 5). Our DHPG-responsive translational changes in area CA1 do not overlap with those identified by polysome RNA-seq in cultured cortical neurons, which we attribute to different sample types and methodologies [https://gene.sfari.org, score 1 or 2) are shown in Fig. Shank3, Dlg4, and Ache. Dlg4 mRNA encodes PSD-95, which has increased mRNA and protein levels following DHPG-mGluR signaling [Nrsn1 mRNA encodes a neuron-enriched regulator of vesicle transport, which is translationally repressed 5 min after a hippocampus-dependent learning task [Nrsn1 mRNA during learning may be a requirement for this cognitive process as evidenced by the observation that its overexpression leads to deficits in object location and contextual fear memory. Because we did not observe immediate early genes  in our list of differentially expressed RNAs, we infer that the rapid changes in RNA levels are likely due to alterations in RNA destruction and not transcription.mGluR-LTD is regulated by TSC, so to determine the identity of mGluR-stimulated translation of specific RNAs during LTD, we performed ribosome profiling and RNA-seq on hippocampal slices following 5 min of DHPG treatment , 18, 22.dologies . Gene seignaling . Nrsn1 ming task . Translap < 1.8e−44) and RPFs (p < 5.0e−9). As a group, the FMRP targets have increased RNA levels following mGluR signaling that correlate with stringency of FMRP binding  and RPFs  . No GO terms were enriched in the overlapping list, likely due to the small number of genes. Heat maps of the overlapped mRNAs and RPFs in Fig. Tsc2+/− slices appear to have a deficient DHPG response compared to wild-type slices , we determined whether signaling to eEF2 could be altered. In wild-type slices, DHPG treatment robustly induces eEF2 phosphorylation  and following DHPG treatment .The sequencing results of the rvations . DHPG diion Fig. c. SurprieEF2 is a GTP-dependent elongation factor that promotes transfer of peptidyl-tRNAs from the A to P site of the ribosome. Phosphorylation of eEF2 on threonine 56 prevents it from binding to ribosomes, leading to inhibition of translation elongation . SomewhaTsc2+/− slices have a deficient phospho-eEF2 response to DHPG treatment, we hypothesized that ribosome elongation is excessive during mGluR signaling in Tsc2+/− and may contribute to deficient mGluR-LTD. We used a low concentrations of cycloheximide (75 nM), which inhibits ribosome elongation by binding to the E-site of the ribosome to prevent eEF2-mediated translocation [Tsc2+/−  but not WT mice . In vehicle, mGluR-LTD in Tsc2+/− was less than WT as previously reported [Tsc2/+ mice limits LTD magnitude and this can be reinstated by pharmacological inhibition of ribosome elongation  or mTORC2. The small sample size for the Tsc2 genotype comparison is a limitation to our sequencing analysis, which has low power to identify differences from lowly expressed genes and small effect changes frequently observed in the brain and neuropsychiatric disorders. Our results do not rule out more dramatic, cell-type specific arising from Tsc2 haploinsufficiency, such as in excitatory neurons. These results also highlight the importance of maintaining appropriate gene dosage in modeling TSC and other mTORopathies such as PTEN, where only one allele is usually mutated in patients.In complete loss of TSC1 or TSC2 models, there is clearly excessive mTOR activity that leads to widespread changes in translation . Howeverhe brain . Using accupancy , 68. Alt[Tsc2+/− , 46. Theignaling , 69. OurTsc2+/− mice have decreased protein synthesis in the hippocampus and DHPG-mGluR1/5 signaling can activate near global regulators of translation, and we used the “ribosome concentration” hypothesis as an intellectual framework for interpreting our results. This model was originally proposed to explain the seemingly mRNA-specific effects arising from ribosomopathies, a group of disorders with mutations in ribosomal proteins that manifest with cell-type-specific features. Using mathematical modeling and assuming initiation as the rate-limiting step of translation, the “ribosome concentration” hypothesis predicts that efficiently and inefficiently translated mRNAs respond bidirectionally to global decreases in translation arising from reduced levels of ribosomes, and recent experimental evidence from yeast and hematopoietic cells supports this hypothesis, albeit in a direction opposite of originally predicted [Tsc2+/− mice, inefficiently translated mRNAs have decreased translation and efficiently translated mRNAs have increased translation , an FMRP target, increase after mGluR activation in cultured neurons and are regulated by interaction with a microRNA [6-methyladenosine modification of the mRNA and codon optimality [RNA-seq reveals that mGluR activation induces a rapid response in both increased and decreased mRNA levels. Because translation is often linked to RNA stabilization/destabilization, this mGluR response could be linked to this process . The decmicroRNA . Severaltimality , 74. Thetimality , 65, 75.timality , 76, 77.Tsc2+/− hippocampus, which mainly feeds into regulation of translation initiation [Tsc2+/− and a low-dose cycloheximide enhances mGluR-LTD in these mice. Increased levels of phospho-eEF2 and low-dose cycloheximide have previously been shown to paradoxically increase translation of specific mRNAs, such as Arc and alpha-CAMKII, despite globally inhibiting protein synthesis [Tsc2+/−, where some of the mRNAs and RPFs increased in Tsc2+/− mice appear to no longer respond to DHPG, consistent with the higher basal levels of eEF2 and the deficient eEF2 response following DHPG. Our work suggests a novel mechanism of translational dysregulation in Tsc2+/− and mGluR synaptic plasticity through signaling to ribosome elongation.Recent work has revealed regulation of translational elongation as critical for multiple forms of protein-synthesis-dependent synaptic plasticity, including mGluR-LTD signaling , 78–80. itiation . Insteadynthesis , 80. FurTsc2+/− and Fmr1−/y mouse rescues phenotypes in synaptic plasticity and contextual fear conditioning in each other, yet the mechanism of this rescue paradigm has remained elusive. In Tsc2+/− and following DHPG-mGluR signaling, FMRP binding targets have decreased TE from increased RNA levels; in Fmr1−/y, RNA levels of FMRP targets are instead decreased. Furthermore, DHPG-induced phospho-eEF2 is deficient in Tsc2+/−, but enhanced in Fmr1−/y mice [Tsc2+/−/Fmr1−/y mice. Because total FMRP protein levels are unchanged in Tsc2+/−, the dysregulation of FMRP binding targets may be through alternate pathways in transcription, mRNA stability, and/or translation. Furthermore, mRNAs that are dysregulated in Tsc2+/− are enriched for DHPG-mGluR-responsive mRNAs and many of these overlapping mRNAs are FMRP binding targets. These results reveal a complex interaction between TSC2 and FMRP in steady state and mGluR regulation of distinct mRNA subclasses.A combined −/y mice . This biTsc2 would result in widespread but perhaps subtle gene expression changes, consistent with other mouse models of neuropsychiatric disorders. As such, we used a FDR p-adjusted cutoff of 0.1 for the genotype comparison. One limitation of the interpretation of our ribosome profiling results is that we identified altered signaling to ribosome movement in Tsc2+/− and following mGluR-LTD signaling. Thus, changes in ribosome density may not a priori reflect changes in protein synthesis. Multiple mouse models of autism have altered mGluR-LTD signaling, but evidence of altered mGluR-LTD in humans with autism has yet to be established. For Figs. We anticipated that loss of one copy of Tsc2+/− mouse model and following induction of mGluR-LTD, we identified changes in FMRP binding targets that suggest a molecular basis for bidirectional regulation of synaptic plasticity and behavior by TSC2 and FMRP. Our study also suggests that altered mGluR-regulated translation elongation contributes to impaired synaptic plasticity in Tsc2+/− mice.By performing RNA-seq and ribosome profiling on a Additional file 1: Fig. S1. Validation of differential expression results from Tsc2+/− versus wild type from Fig. a RNA-seq and b ribosome profiling. Fig. S2. a Density plot of the length (left) and minimal free energy (MFE) (right) of the 5′UTR of Down and Up RPFs in Tsc2+/− compared to all mRNAs. ns nonsignificant, Wilcoxon rank-sum test with correction by Bonferroni method. b The ratios of phosphorylated/total mTOR, rpS6, and 4EBP1 as determined by western blots of whole hippocampal lysates are unchanged in Tsc2+/− mice as compared to wild-type littermates . c Total levels of RPL4, RPS25, and LARP1 from hippocampus of wild-type and Tsc2+/− littermates . d Pearson correlation coefficient was computed for pairs of transcript features including translation efficiency (TE), coding length, and GC content (CDS), 5′ untranslated region length and GC content (5′UTR), 3′ untranslated region length and GC content (3′UTR), and RNA abundance . Red indicates a positive correlation, and blue a negative correlation. Gray indicates a correlation of identical parameters. p < 0.05 for all correlation coefficients. Fig. S3. Gene set enrichment analysis of mGluR-responsive RNAs and translation. a, b Gene set enrichment analysis of GO terms enriched (padj < 0.05) in DHPG/Basal RNAs (A) and RPFs (B). Gene lists were ranked by wild-type DHPG/Basal expression changes (log2FC) and p value. GO terms are separated by enrichment in either upregulated (increased) or downregulated (decreased) RNAs and RPFs.Additional file 2: Table S1. Table of GO terms enriched in Tsc2+/−/WT upregulated RNA and RPFs from Fig. padj < 0.05 for cellular compartment, molecular function, and biological process).Additional file 3: Table S2. Table of GO terms enriched in wild-type DHPG/basal ranked list of RNA and RPFs expression changes from Supplemental Fig. padj < 0.05 for cellular compartment, molecular function, and biological process).Additional file 4: Table S3. Table of DESeq2 output for RNA and RPF expression changes for Tsc2+/−/WT and WT DHPG/Basal comparisons. FMRP binding targets are indicated in the last column based on binding strength . Nontargets are indicated by nonclip. Overlapping mRNAs and RPF altered in Tsc2+/− and responsive to DHPG from Fig. Additional file 5: Table S4. Table of anota2seq output for RNA, RPF, and TE expression changes for Tsc2+/−/WT. The delta tab has calculated expression changes (log2FC) for RNA, RPF, and TE. The total mRNA tab has analysis for RNA expression changes (log2FC). The translated mRNA tab has analysis for RPF expression changes (log2FC). The buffering tab has analysis for TE expression changes (log2FC) that likely leads to translational buffering. The translation tab has analysis for TE expression changes (log2FC) that likely leads to altered protein levels.Additional file 6: Table S5. Table of anota2seq output for RNA, RPF, and TE expression changes for WT DHPG/Basal. The delta tab has calculated expression changes (log2FC) for RNA, RPF, and TE. The total mRNA tab has analysis for RNA expression changes (log2FC). The translated mRNA tab has analysis for RPF expression changes (log2FC). The buffering tab has analysis for TE expression changes (log2FC) that likely leads to translational buffering. The translation tab has analysis for TE expression changes (log2FC) that likely leads to altered protein levels.


Epigenetics is a relatively new field of science that studies the genetic and non-genetic aspects related to heritable phenotypic changes, frequently caused by environmental and metabolic factors. In the host, the epigenetic machinery can regulate gene expression through a series of reversible epigenetic modifications, such as histone methylation and acetylation, DNA/RNA methylation, chromatin remodeling, and non-coding RNAs. The coronavirus disease 19 (COVID-19) is a highly transmittable and pathogenic viral infection. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in Wuhan, China, and spread worldwide, causes it. COVID-19 severity and consequences largely depend on patient age and health status. In this review, we will summarize and comparatively analyze how viruses regulate the host epigenome. Mainly, we will be focusing on highly pathogenic respiratory RNA virus infections such as coronaviruses. In this context, epigenetic alterations might play an essential role in the onset of coronavirus disease complications. Although many therapeutic approaches are under study, more research is urgently needed to identify effective vaccine or safer chemotherapeutic drugs, including epigenetic drugs, to cope with this viral outbreak and to develop pre- and post-exposure prophylaxis against COVID-19. The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a severe new disease that emerged in Wuhan, China, in December 2019 . Zhou anCoronaviridae family in the Nidovirales order. They are small in size (65–125 nm in diameter) and contain a single-stranded RNA as a nucleic material, from 26 to 32 kb long. The subgroups of the coronaviruses family are alpha (α), beta (β), gamma (γ), and delta (δ) coronavirus [. demonstrated that SARS-CoV-2 spikes bind human Angiotensin-converting enzyme 2 (ACE2) receptor [., taking advantage of a docked complex model of the SARS-CoV-2 spike glycoprotein and DPP4, showing a large interface between the proteins, suggested that the human DPP4/CD26 may interact with the S1 domain of the viral spike glycoprotein [Coronaviruses belong to the onavirus . Sars-Coonavirus . CoVs aronavirus , 18. Speonavirus . The spionavirus . The N ponavirus . The E ponavirus , and theonavirus . The mosonavirus . Specifireceptor , a cell receptor , 25. Folreceptor . A similreceptor . Noteworreceptor . As a prreceptor . Recentloprotein . This stoprotein .Fig. 1ScEpigenetics has been described as the area of life sciences that studies stably heritable phenotype resulting from changes in chromatin structural/activation states without altering the DNA primary nucleotide sequence . CurrentTracking the epigenetic changes in pathophysiological contexts might represent an exciting source of knowledge to develop novel treatments leading, for example, to the regulation of the host immune response . Most ofBesides, several reports highlighted how viruses might disrupt the epigenetic network regulation impacting on the host immune response. For example, Marazzi et al. have shown how the highly pathogenic H3N2 influenza A virus inhibits the initiation of the host innate immune response by interfering with the epigenetic control of gene expression . ExploitSimilarly, both the hepatitis C virus (HCV) and adenoviruses have proteins that interfere with epigenetic functions and alter global immune function , 58. In Another study by Schäfer and Baric indicated that SARS-CoV and MERS-CoV could delay or offset pathogen recognition and ISGs expression levels by encoding unique proteins that prevent immune signaling response . Based o. observed an increase in the H3K27me3 levels and reduced H3K4me3 levels at the promoter region of several specific ISGs subsets, which were not upregulated. These findings indicated that these viruses had developed antagonistic mechanisms to target the IFN innate immune response [On the other hand, the Histone 3-Lysine 4 trimethylation (H3K4me3) mark, commonly present in active promoters, is often enriched in Toll-like receptors (TLRs) promoter regions. Kaikkonen and coworkers have recently demonstrated that 60 min after lipopolysaccharide (LPS) stimulation of macrophages and dendritic cells, the overall histone acetylation and the binding of polymerase II (Pol II) to specific promoters was increased, suggesting a specific epigenetic regulation of the innate immune response induction . Schäferresponse .RNA type viruses, such as SARS-CoV, also show strong associations with RNA modifications. For instance, N6-methyladenosine (m6A) and N6,2′-O-dimethyladenosine (m6Am) modifications (m6A/m) have been found to play essential roles in the viral life cycle. In particular, they can affect the structure and replication of the virus, the host innate immune response, and some innate sensing pathways. The m6A RNA methylation is the most abundant epitranscriptomic modification of eukaryotic mRNAs and has been detected on cellular and viral transcripts, regulating numerous biological processes, including viral infection , 70. Ima. showed that during lytic replication, upon YTH N6-methyladenosine RNA binding protein 2 (YTHDF2) knockdown, KSHV RNA degradation is impaired. YTHDF2 binds to viral transcripts and differentially mediates their stability. Moreover, they observed that KSHV latent infection-induced 5′ untranslated region (UTR) hypomethylation and 3′ UTR hypermethylation might alter the host epitranscriptome affecting the oncogenic and epithelial-mesenchymal transition processes. At the same time, KSHV lytic replication induces a dynamic reprogramming of the viral epitranscriptome itself [Whereas, Tan and coworkers provided evidence that m6A and m6Am of messenger RNA mediate diverse cellular functions by examining the viral and cellular m6A/m epitranscriptomes during Kaposi's sarcoma-associated herpesvirus (KSHV) latent and lytic infection. KSHV transcripts are characterized by a high level of m6A/m modifications established during latent and lytic replication, conserved during the infection of different cell types . Tan et e itself .Finally, Marz's group observed a consistent 5mC methylation signature of coronavirus RNA. Specifically, analyzing 5mC content across various RNAs, they observed consistent methylation patterns in corresponding genomic positions of different RNAs, suggesting that the methylation of coronavirus RNAs is sequence-specific or controlled by RNA structural elements . Table 1In recent years, epigenetics evolved quickly, giving us better knowledge about inheritability functions, memory mechanisms, and developmental biology. The studies into the human epigenome are becoming more relevant in oncology, immunology, and infectious diseases , 75. IndNowadays, there are no specific antiviral drugs against COVID-19 infection yet, and vaccines are still under development. Even so, many potential therapeutic approaches are under investigation, and more research is urgently needed to identify effective vaccines and safe drugs for treating COVID-19 infections in order to develop pre- and post-exposure treatments against the pathogen. Although the first aim would be generating SARS-CoV-2 S-based vaccines, with conserved epitopes, able to elicit broadly neutralizing antibodies or virus-specific T cell responses, the identification, and development of safe and effective drugs to overcome SARS-CoV-2 entry and replication is essential.https://www.clinicaltrials.gov/) [Many strategies for COVID-19 treatment have been and still are under investigation: several antiviral drugs, among them Favipiravir , Umifenovir (CTI: NCT04476719) or Lopinavir/Ritonavir (CTI: NCT04386876), alone or in combination with other chemicals such as the antimalarial chloroquine/hydroxychloroquine ; biologicals, such as convalescent plasma  or mesenchymal stem cell (MSC) and MSC-derived exosomes (CTI: NCT04276987); Chinese traditional medicines  and supplementation with Vitamins C and D  –82. Epigls.gov/) . Remarkals.gov/) . Recentlls.gov/) . Indeed,ls.gov/) . Therefols.gov/) , 82, 89.Moreover, knowing that viruses depend on the host epigenetic machinery, epigenetic drugs already used in cancer therapies might be exploited for their broad-spectrum antiviral action and inflammatory control , 90. IndInterestingly, the polycomb repressive complex 2 (PRC2), which mediates transcription repression via H3K27me3 enrichment at specific IFN-stimulated genes, could also be considered a target. Pharmacologic inhibitors of PRC2 are currently in advanced clinical trials for cancer treatment and could be easily repurposed to treat COVID-19 patients .Recent studies show that innate immune cells may possess a form of memory, termed Trained Immunity (TRIM), a long-term boosting of innate immune response mainly maintained by natural killer cells and lung innate lymphoid cell group 2 through common epigenetic mechanisms , 93. TheRecent studies also propose vitamins and natural products, as epigenetic modifiers, to enhance immunity and reduce the inflammatory response in COVID-19 patients –97. For RNA-based drugs are other epigenetic tools that should be investigated for treating viral infections , 100. FoNoteworthy, thanks to sophisticated bioinformatics software, we are now able to visualize and interpret the epigenomic data, providing in-depth cell-specific knowledge about the genetic and epigenetic predispositions of an individual and explaining how the environment affects the function of our genes by leaving long-term marks on the genome. Indeed, epigenome mapping, together with EWAS and GWAS studies, provides us with tools in the diagnostics of many common human diseases, indicating that these studies could be employed for individual diagnosis and personalized therapies , 107. ThThe COVID-19 pandemic is one of the most serious global health threats of the contemporary age so far. The presence of the so-called cytokine storm induced by the virus leads to ARDS aggravation and widespread tissue damage resulting in multi-organ failure and death. SARS-CoV-2 infection, by interfering with the host epigenetic machinery, might alter the expression of proinflammatory cytokines, such as IL-1, IL-6, IL-18, IFN-γ, and TNF-α. Epigenomic studies might open new avenues for developing antiviral drugs by evaluating specific epigenetic modulators as targets and exploring new chromatin-based therapies for different virus families, including Coronaviruses, which could reveal fundamental new landscapes of virus–host interaction and their role in disease severity . Previou


The discovery of regulatory RNA has identified an underappreciated area for microbial subversion of the host. There is increasing evidence that RNA can be delivered from bacteria to host cells associated with membrane vesicles or by direct release from intracellular bacteria. Once inside the host cell, RNA can act by activating sequence-independent receptors of the innate immune system, where recent findings suggest this can be more than simple pathogen detection, and may contribute to the subversion of immune responses. Sequence specific effects are also being proposed, with examples from nematode, plant and human models providing support for the proposition that bacteria-to-human RNA signaling and the subversion of host gene expression may occur. IntroductionBacterial RNA in the extra- and intra-cellular environment of human cellsInteractions of bacterial RNA with the innate immune systemSequence-specific action of bacterial RNA in host cellsConclusionsCells do not exist in isolation and so cell-to-cell signaling is important in all biological systems; be it a simple community of single-celled organisms or a multicellular organism coordinating information flow between its own cells and those of its microbiota. Through the course of evolution multiple “languages” have been developed for communications between cells that utilize most, if not all, key biomolecules for intracellular and intercellular signaling pathways: proteins, lipids, carbohydrates, and small organic and inorganic molecules. For humans, an important interface is manifested in the cellular and molecular interactions with microorganisms such as both pathogens and symbionts. It is therefore not surprising that these diverse species have evolved mechanisms for interpreting and manipulating the various signaling systems of the other, giving rise to the phenomenon of cross-kingdom communication.1, leading to a profuse acute diarrhea that is hypothesized to help disseminate the pathogen. A more complex “conversation” has been described between Salmonella and its target cells, where the protein effectors delivered by type 3 secretion are primarily responsible for invasion, as well as niche maintenance and dampening of immune responses2.Perhaps our first appreciation of cross-kingdom communication came from the investigation of the molecular mechanisms of bacterial protein virulence factors. The cholera toxin provides a simple example whereby this protein hijacks key intracellular signaling through the second messenger cAMP3,4. confirmed by the finding that the quorum sensing signals used by bacteria to co-ordinate their pathogenic activities could also influence immune responses to the pathogen5,6. Moreover, communication is not only from bacterium to target cell, as gene expression is also influenced in bacteria that can intercept intercellular signals deriving from human cells7.The discovery that bacterial cells communicated to one another via a range of small molecule signals, such as acylated homoserine lactones, in a process termed “quorum sensing” highlighted the possibility that cross-kingdom communication could use a non-protein language8,9, and highlighted the hitherto underappreciated role that the subversion of regulatory RNAs could play in progressing infections. The discovery of microRNA (miRNA) signals produced by one cell to influence gene expression in another10 has demonstrated RNA as a language of inter-cellular communication identifying them as a potential target for bacterial pathogens.Most recently evidence has emerged for a new “language” for communication between host and bacteria that is based on the identification of regulatory RNAs in both prokaryotes and eukaryotes. Our appreciation of the potential roles of RNA is now going beyond the classical designations of messenger, ribosomal and transfer RNAs in the mechanics of translation. Studies have demonstrated that the manipulation of this “riboregulation” is central to the molecular pathogenicity of some viruses11. In parallel, bacteria can deliver their RNA to the host, for example, using membrane vesicles (MVs) that may protect their cargo while delivering to specific compartments of the host cell12,13. In this review, we discuss the current understanding of the role of RNA in human/bacteria interactions  and provide an outlook for future developments of the field.Today, bacterial RNA is well recognized as an important “pathogen associated molecular pattern” (PAMP) that is involved in human responses to infection. This has been coupled with an appreciation that bacterial RNA is not just a simple uniform trigger of the non-specific immune system, but rather a complex multifaceted signal. For example the differential availability of RNA-recognizing sensors in the host, differences in their subcellular localization and the need to differentiate between ‘self’ and ‘foreign’ RNA define a complex ability of the host to detect intruding RNA and mount a defensive response14,15. The cells of mucosal barriers and infected tissues will therefore be regularly exposed to RNA from lysed bacteria and other microorganisms. Human tissues and fluids16-19 contain high levels of RNases, in the order of several hundred nanograms per milliliter20,21, that would be expected to degrade this RNA22. However, host miRNA bound to proteins, lipids and lipoproteins is known to be protected from the external RNAses23, with the secondary and tertiary structure of some RNA molecules also likely to provide protection from degradation24. More recently, advances in small RNA sequencing technology have allowed a detailed analysis of circulating RNAs in mammalian blood, identifying a surprisingly large proportion (ranging from 0.31-11%) as microbial RNA25-29. It is therefore probable that some RNA released following bacterial cell death will remain in the immediate environment of the human host.RNA is abundant and a lysed bacterial cell will release about tenfold more RNA than DNA30-32, suggesting that active secretion of RNA may also be at play. Indeed, extracellular RNA from these bacterial populations has been found associated with MVs12,31,33-35, as well as in a ‘free’ form31. The nano-sized MVs are produced by both gram-positive36 and gram-negative bacteria37 growing in biofilms, planktonic cultures, inside eukaryotic cells and under a variety of other environmental conditions38-40. In many respects, such as size and types of carried molecular cargo, MVs are similar to human exosomes34. Human exosomes have a well-established role in RNA communication41-43, predominantly through their carriage of regulatory miRNAs, and a similar role for bacterial MVs is beginning to emerge in the literature12,13. MVs, like exosomes, protect RNA from degradation as shown by comparisons following RNAse treatment of MVs12. The specific protein or other factors that improve the stability of MV-free bacterial RNA are yet to be determined.In addition to the RNA released from lysed bacterial cells, detectable levels of RNA have been reported to be present in supernatants from cultures where most bacteria are viable44-46. An alternative mechanism for the formation of MVs via explosive cell lysis has recently been identified for Pseudomonas aeruginosa35. Strains that encode a prophage endolysin (A0629) can undergo an explosive cell lysis event in response to exogenous stress and produce MVs through vesicularization of membrane fragments. This process allows capture of cellular components released into the extracellular space, including the incorporation of RNA into the MVs. It is yet to be determined if explosive cell lysis under non-stress conditions is a programmed cell death pathway induced by “altruistic suicide”47 to release key nutrients to other bacteria as a “colony public good”, or if it is the result of stochastic expression of the A0629 endolysin or if it could be part of a regulated virulence program. It will be of interest to determine if this process occurs in other species of bacteria, as genes with high similarity to A0629 can be found in the genomes of several other bacterial genera35.A growing body of work is now available to support various mechanisms of outer membrane vesicle production by Gram-negative bacteria that involve budding from the outer membrane, induction by stress responses and some selectivity in the composition of the MV cargo32. However, the relevance of this role in an infective setting is unclear as RNA released from bacteria into the extracellular environment probably has a long way to travel before it can influence the activity of host cells. Mechanisms of bacterial MV entry into host cells have been widely studied48-56, although it has not yet been shown which mechanism(s) are involved in the uptake of RNA-carrying MVs. Bacterial MVs are a heterogeneous population and we speculate that RNA-carrying MVs may represent only a fraction of the total MV population. At the same time, the mechanism of host entry by a bacterial MV, and consequently the targeting and fate of its cargo, is likely to be different for various MV subpopulations and influenced by MV size, surface molecules etc. In cases where the intracellular delivery of bacterial RNA has been studied, the evidence obtained currently suggests it is necessary for the induction of responses associated with exogenous RNA sensing57,58 .Free RNA added to the medium of cultured cells can induce responses that favour bacterial colonization and immune evasionListeria monocytogenes is a model organism for the study of intracellular bacteria/host interactions59. When the RNA of live L. monocytogenes was labelled with a modified nucleotide, 5-ethynyluridine (5EU), and these bacteria were allowed to infect the human monocytic cell line THP-1 the visualization of bacterial RNA by chemically attaching a fluorophore to the 5EU-RNA revealed it in an extra-bacterial localization in the host cell cytosol60. Further, no evidence of cytoplasmic, extra-bacterial RNA was found for THP-1 cells infected with 5EU-labelled L. monocytogenes lacking the SecA2 auxiliary protein secretion system53, suggesting that the bacterial RNA is actively secreted, possibly in association with a protein chaperone, rather than being a by-product of bacterial lysis.An alternative route for bacterial RNA to enter human cells is via release from intracellular bacteria. Borrelia burgdorferi, this occurs via RNA interaction with the endosomal TLR8 to induce transcription of IFN-β61. In other instances, RNA from the phagocytosed bacteria can translocate into the cytosol from phagosomes that are intrinsically leaky62, or made leaky by the actions of bacterial pore-forming toxins63.In a related report, RNA released from bacteria that themselves remain trapped in the phagosome can also exert an effect. In the case of 30,59,64,65, endosomal and phagosomal compartments66,67 with one study reporting delivery of bacterial MV RNA into human cell nuclei13. Thus, current research has focused on the interactions of bacterial RNA with cytosolic and endosomal receptors of the innate immune system and on the investigation of possibilities for bacterial RNA to affect human gene expression via post-transcriptional mechanisms in a sequence-dependent manner. Key recent discoveries from each of these fields are discussed in the following sections of this review.Overall, the reported findings from the last several years demonstrate that bacterial RNA is a common component in the environment of many human cells. Additionally, current evidence suggests that release of RNA by bacteria is not a simple by-product of bacterial cell death, but an active, and possibly, selective process. The variety of mechanisms involved in the secretion of RNA from bacterial cells, its stability in host extracellular fluids, its association with transport systems for intra- and inter-species signaling such as MVs, and its secretion into host intracellular compartments by live intracellular bacteria suggest that RNA may be used by bacteria as a currently underappreciated virulence factor. Which pathways are engaged by bacterial RNA will likely depend at least in part, on where inside a human cell bacterial RNA is delivered. Evidence to date suggests that bacterial RNA can be delivered into human cytosol68, while TLR7 and TLR8 sense degradation products of single stranded RNA (ssRNA). All three TLRs recognize RNA in a sequence-independent manner, although there is some evidence for preferential recognition of RNA rich in some nucleotides or modified nucleotides69,70. It has been proposed this relates to the observation that some pathogens display a greater proportion of certain types of nucleotides in their RNA sequences71. Furthermore, for at least TLR8, the key descriptors for RNA recognition are not yet certain, as THP-1 cells (a cultured monocyte line) challenged with enterococcal RNA only responded (measured as IL-12 production) when 23S and 16S rRNA, but not when mRNA, were applied72.Entry of bacterial RNA into a host eukaryotic cells is sensed as a danger signal by receptors of the innate immune system. In the endosome, RNA can be recognized by TLR3, TLR7, and TLR8. TLR3 senses long (>39bp) double stranded RNA (dsRNA)73. RIG-I binds blunt ends of dsRNA displaying a 5’ triphosphate moiety whilst MDA5 molecules bind RNA independently of its terminal structures. Additionally, the NLRP3  inflammasome has recently emerged as an important cytosolic sensor of bacterial RNA57,74,75. It is a signaling complex that consists of the sensor molecule NLRP3, the adaptor protein ASC and caspase 1, which, in addition to bacterial RNA, senses a variety of endogenous and pathogen-associated molecules .In the cytosol, some of the key sensors of bacterial RNA are two helicases of the DExD/H motif family, namely RIG-I and MDA5, which detect 5’ triphosphorylated short dsRNA and long dsRNA, respectively14,15,32,58,59,61,72,77-82. However, which RNA sensors are engaged and what host effector molecules are secreted is cell type specific. For example, IFN-α secretion is induced in murine plasmacytoid dendritic cells upon DOTAP-mediated transfection of Escherichia coli RNA but not in conventional dendritic cells (cDCs)80. IFN-β is secreted by murine cDCs in response to transfection with RNA from Group B15 and Group A streptococci78. However, in bone marrow-derived macrophages IFN-ß only responded to transfection of DNA, and not RNA, of Group A streptococci. These differences in responses to bacterial RNA reflect differential availability and use of molecular RNA sensors in different cell types. Differences in host cell responses to the type of RNA  can also reflect variations in post-transcriptional modifications of a given prokaryotic RNA molecule across various species80,83-85. For example, when polyadenylated, prokaryotic mRNAs have a shorter poly-A tail than their eukaryotic host mRNAs, and as such can be readily detected by TLR769. Additionally, the 2’O-methyl guanosine modification status of tRNA at the conserved G18 residue can determine TLR7 activation. For some pathogenic bacteria, where G18 is unmodified, the induction of type I interferon and pro-inflammatory cytokines is seen in mouse dendritic and human peripheral blood mononuclear cells, whilst modified tRNA from non-pathogenic E. coli Nissle 1917 and Thermus thermophilus does not86. Finally, the cellular localization of the pathogen  determines which sensors of bacterial RNA are engaged11,22.Induction of the innate immune sensors for bacterial RNA can lead to secretion of type I and type III interferons, and pro-inflammatory cytokines such as TNF-α, IL-1α, IL-1β, IL-6, IL-8, IL-10, and IL-1274,80,87. Studies of secondary bacterial infections that develop as a complication of viral infections, could therefore provide some insights into the relevance of RNA-induced cytokines on the progression of bacterial infections. Interestingly, it has been observed that, although common host mediators are induced, antiviral and antibacterial responses can frequently be at odds with one another88. This provides an opportunity to speculate that bacteria might use interactions of its secreted RNA with host innate immune sensors to its advantage by skewing the immune system towards antiviral responses. In evolutionary terms, it is possible to imagine how such a mechanism for manipulating the human host could have evolved in bacteria. The human microbiome includes not only bacteria but multiple other types of organisms including viruses, fungi and protozoa89. Bacteria/human cross-kingdom interactions are, therefore, a product of dynamic coevolved relationship between the various organism of the microbiome and the immune system. It is therefore possible that bacteria might have evolved to utilize elements of human/virus interaction to their advantage even in the absence of viral co-infection. In this case, we hypothesize that the secretion of bacterial RNA and its detection by human cells could create a beneficial environment for bacteria, similar to that of viral-bacterial co-infection.Studies of innate immune responses to bacterial RNA have mostly focused on the identification of host cell receptors for bacterial RNA and downstream signaling cascades. Minimal research has been done to investigate how bacteria could use their RNA to manipulate innate immunity to their advantage. Notably, many of the effector molecules secreted by host cells in response to detection of bacterial RNA are also secreted in response to viral RNA. In fact, the RNA sensors discussed above were originally discovered as sensors of viral infections. In the last decade they have also been validated as sensors of bacterial RNAChlamydia trachomatis90, Salmonella enterica91, Cryptococcus neoformans92, Group B streptococci, Streptococcus pneumoniae and E. coli93, there is evidence that indicates that type I interferons can also inhibit host defense against other bacteria94,95. For example, pathogen-induced production of type I interferons has been suggested to contribute to the pathogenesis of Mycobacterium tuberculosis infections96, and the dissemination of B. burgdorferi during the early stages of infection97 where TLR7-dependent recognition of B. burgdorferi RNA is necessary for interferon-α production77.The strongest evidence for the beneficial effects of RNA-induced cytokines on a bacterial infection process comes from studies of type I interferons. Whilst induction of type I interferons can lead to host protection and elimination of the pathogen during infections with 98. IL-17 and IL-22 are hallmark cytokines of Th17 cells, and have been shown to promote clearance of bacteria through the recruitment of phagocytes and the induction of antimicrobial peptides (AMPs)99. Induction of type I interferon production by epithelial cells in response to viral infections skews the immune status towards an antiviral phenotype and attenuates type 17 immunity against such bacterial pathogens as E. coliand P. aeruginosa100, S. aureus101 and S. pneumoniae102. The identification of bacterial communications that direct immune responses away from antibacterial activity have also been a reported feature of the response to some bacterial quorum sensing signals5.Evidence that bacteria could benefit from interferon-induced skewing of the innate immunity towards antiviral responses also comes from investigations of the activation of T helper 17 (Th17) cells. The Th17 pathway has a critical role during infection with extracellular bacteria58. ROS, in turn, can disrupt epithelial barrier function. Specifically, it has been reported that, in polarized airway epithelial cells, poly I:C (a synthetic mimic of dsRNA) signals through a recently discovered cytosolic dsRNA receptor Nod-like receptor X-1 (NLRX-1) and stimulate NADPH oxidase 1 (NOX-1) and mitochondrial ROS production to cause reactive oxygen species (ROS) dependent epithelial barrier function disruption103. A poly I:C challenge also disrupted endothelial barrier function by causing downregulation of the mRNA for claudin-5, a key endothelial tight junction protein. The exact mechanism is unknown but appears to involve a TLR3-TRIF-NF-kB signaling pathway104.Interactions of bacterial RNA with innate immune sensors could also serve a beneficial function to bacteria via mechanisms not involving interferon signaling. Transfection of bacterial RNA has been shown to induce reactive oxygen species (ROS) production via mechanisms involving NADPH oxidase and the mitochondrial transport chain105. Duox2 is located in the plasma membrane and can secrete H2O2 directly into the extracellular milieu106. Biofilm bacteria, when exposed to oxidative stress, can initiate a dispersal response107 to release free-swimming planktonic bacteria. Whilst induction of H2O2 production as an immune response can be viewed as a negative event for bacteria, both biofilm-associated and planktonic bacteria express antioxidant enzymes to resist H2O2 killing108. Additionally, planktonic bacteria dispersed from biofilms are often as resistant to killing by antimicrobials as their biofilm counterparts109. An example of how this process can lead to worsening of clinical outcomes can be found in cystic fibrosis patients with chronic Pseudomonas aeruginosa infections. It has been reported in this setting that a viral infection causing mild oxidative stress via activation of Duox 2 leads to dispersal of planktonic bacteria from established lung biofilms, increased transmigration of planktonic bacteria from the apical to basolateral surface of mucociliary-differentiated airway epithelial cells, increased planktonic bacterial and therefore the acute symptom burden110.Bacterial RNA engagement with host cell RNA sensors to induce reactive oxygen production can lead to dispersion and dissemination of bacterial biofilm cells. For example, bronchial epithelial cells have been shown to express dual oxidase 2 (Duox2) in response to poly I:C and IFN-γ treatment79,85,86. Bacteria can also use non-RNA virulence factors to interfere with signaling cascades downstream of RNA-sensors. An example of this was recently reported that showed production of IFN-β induced by TLR8-mediated sensing of S. aureus ssRNA, is antagonized by TLR2 signaling activated by S. aureus lipoproteins111. Future detailed investigations of the molecular interactions between bacteria and innate immunity that involve bacterial RNA may eventually provide a better understanding of what stimulates a productive immune response and identify opportunities for developing novel therapeutic strategies. Overall, these findings confirm that detection of bacterial RNA by the innate immune system is not always as simple as an immune surveillance ‘hit’ leading to responses that eliminate bacterial pathogens.In an infection, the RNA of pathogenic bacteria may subtly manipulate the host innate immune response to activate inappropriate defense responses that can ultimately favour bacterial survival. In other instances, bacterial mechanisms for disguising their RNA using post-transcriptional modifications can become of importance112 offering a large target for bacteria to manipulate host cells to their advantage using the same endogenous RNA-inhibition (RNAi) machinery. The use of RNA as an effector molecule by directly targeting host RNA may offer the advantages of suppressing the expression of mediators of immunity before they can exert any antibacterial effects i.e. prior to the production of the protein effector itself.Bacterial RNA is seemingly not just a ligand for sequence-independent RNA receptors, but is starting to be appreciated for its potential to act in a sequence-specific manner to regulate gene expression at the post-transcriptional level. It is estimated that ∼60% of the human protein coding genes could be subject to regulation by their regulatory miRNAs113, but there are several commonalities. Specifically, regulation through direct hybridization between the regulator and the target RNA, mediated by complementary base-pairing, is employed by both kingdoms. For example, both bacterial regulatory RNAs and human miRNA are double-stranded which helps to stabilize the RNA molecule and allows correct orientation of the regulator in relation to the potential target mRNA and accessory proteins114, such as AGO2 in humans and Hfq in bacteria. Both human and bacterial regulatory RNAs tend to have a single stranded ‘seed’ region that is devoid of secondary structure115 to allow perfect antisense binding to mRNA targets. Additionally, both miRNAs and bacterial small RNAs (sRNAs) can have varied levels of complementarity with their target RNAs, which in combination with the short seed sequence allows the binding of a single miRNA/sRNA with multiple target mRNAs116.It is interesting to speculate on how a bacterial pathogen’s RNA might regulate the host in a sequence-specific manner. The first question to ask is whether a bacterial RNA could bind to a host mRNA? There are differences in the specific mechanisms of action of known non-coding RNAs between human and bacterial cells, and RNA regulation in the eukaryotic host is substantially more complex117. Furthermore, bacterial sRNA-binding Hfq is an ortholog of eukaryotic Lsm proteins118 which also act as RNA chaperones to aid in splicing and degradation. Preliminary studies also support that exogenous RNA, from viruses118 and bacteria25,119 can bind to host AGO proteins, key proteins in the RNAi machinery.The second question to ask is whether naked bacterial RNA could hijack the hosts own protein machinery to allow it to work in a regulatory manner? Interestingly the eukaryotic RNAi machinery appears to have been put together from various prokaryotic sources Arabidopsis thaliana and humans, and the nematode Heligmosomoides polygyrus and mice120,121. Bacteria, however, do not have eukaryotic-like miRNA and their known non-coding RNAs range from 40 to 500 nucleotides in length122, which is in contrast to the average length of a human miRNA of about 22 nucleotides123. It is possible that long bacteria RNAs are processed into functional fragments. Shmaryahu et al.124 developed a high throughput bioinformatics pipeline, using the assumption of RNA fragmentation, to analyze all genes in a bacterial genome for their potential to produce RNA transcripts with secondary structures containing double-stranded regions that, through processing, could give rise to miRNA-like fragments. An analysis was made of 448 bacterial genomes, identifying on average 15 putative miRNA-like sequences per organism that could bind human mRNA. The authors validated the in silico analyses by synthesizing mimics of three predicted bacterial RNA-derived ‘miRNAs’ and transfected them into the human HEK293 cell line. The mimics represented a sequence derived from Arcobacter butzleri strain RM4018, a close taxonomic relative of Campylobacter jejuni and Helicobacter pylori125, with complementarity for the human DEK oncogene mRNA, a sequence from Burkholderia vietnamiensis G4 with complementarity for the transcript variant 2 mRNA of the human tumour suppressor PTPRJ (protein tyrosine phosphatase receptor type J) gene and a putative sequence from Burkholderia mallei with complementarity for the human NFKBIL1 (nuclear factor nuclear factor kappa-light- chain-enhancer of activated B cells) mRNA. Following transfection, mRNA levels of the predicted target genes were determined by RT-qPCR as reduced in expression. This study provides support for the hypothesis that bacterial RNA sequences could potentially target human mRNA, however, the study did not investigate if these putative sequences exist in nature or if they could be transferred from bacteria into human cells to function in the predicted manner.With laboratory studies being technically challenging and to date limited, the prediction of what targets a bacterial RNA may have relied on computational modelling. These models require many assumptions to be made and as such later wet-lab validation is vital. Most such studies begin by identifying RNA molecules in the non-human organism that could function as a miRNA mimic when transferred into human cells. Such studies have had success in models for cross-kingdom RNA signaling where both organisms have endogenous miRNAs such as the plant 12 came closer to describing a bacteria-to-human RNA communication system by demonstrating natural transfer of endogenous bacterial short RNA species from Pseudomonas aeruginosa into human host cells via MVs, whilst also demonstrating a reduction in protein levels of several kinases whose mRNAs were predicted to be targeted by one of the bacterial MV sRNAs. Specifically, Koeppen and co-authors identified that sRNA52320, a 24-nucleotide tRNA fragment, is transferred into human cells upon exposure to MVs and that it decreases translation of MAP3K7 and MAP2K4  with subsequent reduction in MV-induced host cell secretion of IL-8.Koeppen et al.Caenorhabditis elegans and Arabidopsis thaliana have begun to generate interesting hypotheses and scientific debate based on the biology of RNA in the interactions of C. elegans with dietary E. coli and A. thaliana with the mould Botrytis cinerea. However, some caution in the interpretation is required here as these two hosts have systems to allow signal amplification of exogenous RNA via RNA-dependent polymerases126,127. This mechanism, overcomes a potentially contentious issue in the bacteria-to-human signaling field, concerning whetherthe amount of bacterial RNA transferred is enough to have a function in the host cells.Overall, these studies have provided early indications that bacterial RNA could act in human cells in a sequence-specific manner to exert post-transcriptional effects on gene expression. Studies of two model systems C. elegans fed with E. coli overexpressing the non-coding RNAs OxyS (an oxidative stress response regulator) and DsrA (an acid stress response regulator)128. In bacteria over-expressing OxyS, a negative foraging effect was observed in the nematodes, which preferred to feed on E. coli not over-expressing OxyS when given a choice. The nematodes did not exhibit the repulsion effect when only fed with the OxyS over-expressing strain. Computational analysis of the C. elegans genome identified the che-2 chemosensory gene as a possible target for a sequence-specific interaction with OxyS. Down regulation of CHE-2-GFP fusion expression was visibly seen in C. elegans fed on the OxyS over-expressing strain when compared to wild type K12 E. coli controls.Alterations in physiological functions were observed in C. elegans to find them. It was shown that the transferred OxyS required the host RNAi pathway, specifically proteins ALG-1 and RDE-4, to function to repress the target CHE-2 protein128. The fact that other RNAi genes were described as dispensable128 raised questions regarding the exact biological mechanism eliciting the gene expression changes and behavioural responses observed129 and this second, independent study, was unable to support the findings of the original study. By using small RNA-sequencing they were unable to find evidence of OxyS RNAs in fed C. elegans that could capably bind to the 17nt interaction site on the che-2 target mRNA and were unable to validate regulation of the target mRNA itself129. Differences between these two study’s findings128,129 could in part be explained by the use of different strains of dietary bacteria, and differences in feeding experiment protocols, such that the role of OxyS RNA might only manifest when the nematode has to make a choice about its food. Further investigations are needed to explain the action of OxyS RNA in C. elegans, and the story to date highlights the power we now have with RNA sequencing to test complex sequence-specific cross-kingdom communication hypotheses.Interpretation of these results above led the authors to a hypothesis of environmental RNAi, with OxyS from the diet bacteria suppressing the ability of B. cinerea using the model plants A. thaliana and Solanum lycopersicum130. B. cinerea sRNAs involved in pathogenicity were identified in infected plants, and a simple bioinformatic approach identified sRNAs with miRNA-like structures that were predicted to suppress 4 genes, by perfect antisense binding, all with roles in plant immunity. Ectopic expression of three of these sRNAs in A. thaliana left the plant with an increased susceptibility to infection. Importantly, and unlike the situation with C. elegans129, the microbial sRNAs were shown to bind to host AGO1 within the RNAi machinery, and A. thaliana strains in which AGO1 is mutated exhibit reduced susceptibility to Botrytis infection. Finally, they demonstrated that B. cinerea Dicer mutants lacking the ability to process sRNAs from longer transposon RNAs were less infective. The challenge now is to identify and explain sequence-specific subversion of a bacterial mammalian host by its prokaryotic pathogen.RNA sequencing was also a key technique used to determine the potential role of fungal small RNAs in the infective processes of 53, with which they could simultaneously travel, serves to add a potential extra level of complexity to unraveling host/pathogen RNA interactions and effects on inflammatory responses128,129.Over the last several years the nascent field of cross-kingdom RNA signaling has undergone significant growth. Immune stimulatory effects of bacterial RNA and the role that they play in eliciting and/or suppressing host protective responses are becoming better understood. The sequence-specific effects of regulatory RNAs add an extra dimension of possibilities for RNA signaling. The corresponding development of RNA sequencing and accompanying bioinformatic pipelines now give us powerful tools to investigate this area further. As a consequence, an appreciation that bacterial RNAs are not just a simple uniform trigger of the non-specific immune system, but rather act as complex multifaceted signals is beginning to emerge. On one side is the differential availability of RNA-recognizing sensors within cells, including in their subcellular localization and the need to distinguish between self and foreign RNA. These define the ability of the host to detect intruding RNA and mount a defensive response. On the other side, bacteria appear to utilize multiple methods for protecting and delivering RNA to the host, ranging from MVs which can deliver their cargo over distance, to the intracellular transfer of pathogen’s RNA between host cell compartments. The further coexistence of bacterial RNA with other virulence factors such as lipopolysaccharideTo advance knowledge, careful experimental design and data interpretation is required to physiologically model relevant amounts and modes of RNA delivery into human cells. Overall, many important technical and biological questions await answers in the coming years to decipher the messages conveyed to human cells by bacterial RNA. Identification of the human cellular, molecular, and genetic networks that can both interact with, and be manipulated by, bacterial RNA signals offers exciting new research directions in the study of bacterial pathogenesis.Bacterial RNA can be delivered to human cells during infection◊ Bacterial RNA can have effects beyond simple sensing as a pathogen danger signal◊ Bacterial RNA can act in sequence-independent and sequence-dependent mechanisms◊ What are the key bacterial RNAs?◊ How important is the effect of RNA when compared to protein virulence factors?◊ Do bacterial RNAs exert an effect in the same way as miRNA?◊


These efforts are further supported by the large number of constitutive and inducible mESC mutants established with the aim of assessing the contributions of different pathways and genes to cell homeostasis and gene regulation. Gene product abundance is controlled by the modulation of the rates of RNA synthesis, processing, and degradation. The ability to determine the relative contribution of these different RNA metabolic rates to gene expression control using standard RNA-sequencing approaches, which only capture steady state abundance of transcripts, is limited. In contrast, metabolic labeling of RNA with 4-thiouridine (4sU) coupled with RNA-sequencing, allows simultaneous and reproducible inference of transcriptome wide synthesis, processing, and degradation rates. Here we describe, a detailed protocol for 4sU metabolic labeling in mESCs that requires short 4sU labeling times at low concentration and minimally impacts cellular homeostasis. This approach presents a versatile method for in-depth characterization of the gene regulatory strategies governing gene steady state abundance in mESC.The relative ease of mouse Embryonic Stem Cells (mESCs) culture and the potential of these cells to differentiate into any of the three primary germ layers: ectoderm, endoderm and mesoderm (pluripotency), makes them an ideal and frequently used  Gene expression control is central to ensure appropriate responses to intrinsic and extrinsic cellular stimuli and cellular homeostasis. The steady state abundance of RNA transcripts is controlled by the rates at which the gene is transcribed (transcription), processed (processing), and degraded (degradation). Understanding how these three RNA metabolic rates change in response to different cellular cues, is paramount for in-depth characterization of how gene expression regulation, in health and disease, contributes to the maintenance or changes in cell state during development and in adulthood.D-ribofuranoside (DRB), α-Amanitin (α-Ama) or Actinomycin-D (Act-D)  . While t (Act-D) , inhibit (Act-D) . Further (Act-D) .Methods based on RNA metabolic labeling using modified nucleotides, such as 5′-Bromouridine (BrU) or 4-thiouridine(4sU), overcome these limitations . SpecifiIn its simplest form, RNA metabolic labeling experimental design relies on a single labeling time, similar to what is described here. Single labeling reduces experimental cost and complexity, likely at the expense of the accuracy of the degradation rate estimates . AlternaRNA metabolic labeling using 4sU , has bee(A)Cell culture.(1)10 cm tissue treated cell culture plate.(2)Knockout DMEM mESC growth medium :(i)15% (v/v)Fetal Bovine Serum .(ii)50 U/ml of Penicillin and 50 μg/ml of Streptomycin .(iii)1 U/μl Recombinant Mouse LIF Protein .(iv)0.06 mM β-Mercaptoethanol .(v)1% (v/v) 100 X Non-Essential Amino Acids .(3)2O.Sterile 0.2% (m/v) Gelatin in H(B)4-Thouridine labeling and total RNA extraction.(1)2O (DEPC-H2O).4-thiouridine  dissolved in DEPC-treated H1: Keep at −20NOTE°C, protected from light (4sU is light-sensitive). Discard remaining 4sU solution after thawing.(2)PBS.(3)Trizol reagent .(4)Chloroform .(5)15 ml phase lock-tubes .(6)15 ml Falcon Tubes.(7)1.5 ml tube (RNase and DNase free).(8)2O.5M NaCl in DEPC-H(9)Isopropanol.(10)2O.DEPC- H(11)2O.75% (v/v) Ethanol in DEPC- H(12)5 ml Serological Pipette.(13)Qiagen RNeasy kit .(14)RNAse Free DNAse Set .(C)Dot-blot quality control.(1)Zeta-Probe Membrane .(2)Biotin-labeled DNA oligo.(3)2O.DEPC- H(4)2 ml Phase Lock tubes .(5)EZ-link Iodoacetyl-PEG2-biotin  dissolved to 1 mg/ml in DMF .(6)2HPO4 .Na(7)20% Sodium Dodecyl Sulfonate (SDS) .(8)Phenol:Chloroform:isoamyl alcohol .(9)PBS.(10)Streptavidin-horseradish peroxidase .(11)2HPO4, 7% SDS, pH 7.2 in DEPC-H2O).Blocking Buffer: (0.5M Na(12)Wash solution 1: 40 ml PBS + 10% (v/v) SDS.(13)Wash solution 2: 40 ml PBS + 1% (v/v) SDS.(14)Wash solution 3: 40 ml PBS + 0.1% (v/v) SDS.(15)Advansta WesternBright ECL .(D)RNA biotinylation for isolation of newly transcribed RNA.(1)1.5 ml Eppendorf tubes (RNAse and DNAse free).(2)2O).10 X Biotinylation Buffer 1 mg/ml EZ-link HPDP-Biotin  in dimethylformamide (DMF) .(4)Phenol:Chloroform:isoamyl alcohol .(5)2 ml phase lock tubes .(6)2O.75% (v/v) Ethanol in DEPC-H(7)2O.DEPC-H(8)2.0 ml Eppendorf tubes (DNAse and RNAse free).(E)Bead preparation.(1)Dynabeads MyOne T1 Streptavidin Beads .(2)2O).2X Bind and Wash (B&W) Buffer 2O).Solution A 2O).Solution B (0.1M NaCl in DEPC-H(F)Separation of newly transcribed RNA.(1)2O).1X B&W Buffer RNeasy Mini kit .(3)RNase-Free DNase Set .(4)Isopropanol.(5)2O.5M NaCl in DEPC- H(6)Isopropanol.(7)2O.75% Ethanol in DEPC-H(8)2O.RNase-free DEPC-H(9)2O.70% Ethanol in DEPC-H(10)2O.100 mM Dithiothreitol (DTT)  in DEPC-H(G)RT-qPCR analysis.(1)SuperScript IV First-Strand Synthesis System .(2)FastStart Essential DNA Green Master .(3)Gene specific primers (sequences can be found in (4)2ODEPC-HEquipment.(A)Cell culture Hood .(B)Chemical Hood .(C)2 .Cell culture incubator at 37°C with 5.0% CO(D)Centrifuge with temperature control .(E)Standard tabletop centrifuge with temperature control .(F)Rotating Vertical Mixer .(G)DynaMag-2 Magnet Magnetic Rack .(H)Quantitative PCR Machine .(I)See-saw rocker .(J)Chemiluminescence detection apparatus .(K)Thermocycler (Biometra Thermocycler ThermoBlock).(L)Fluorescent Activated Cell Sorter (Synergy MX flow Cytometer Fluorescent activated Cell Sorter).(M)Nanodrop (ND-1000 Spectrophotometer).Methods.(A)Before You start:(1)g for 30 s and 1,500 × g for 60 s respectively.Centrifuge all 2 ml phase lock tubes and 15 ml phase lock tubes at 12,000 × (2)Cool ultracentrifuges and tabletop centrifuges to 4°C.(B)4-Thouridine labeling and total RNA extraction.(1)The day before labeling, seed mESCs in two gelatin-coated 10 cm plates (12 ml of growth medium). Cells in one plate will be labeled with 4sU whereas cells in the other plate will be untreated and will serve as negative control.2: mESCs should be 70-80% confluent at the time of labeling.NOTE(2)Transfer 7 ml of medium from one of the overnight mESC culture (4sU-treated mESC) to a 15 ml falcon tube, add 4sU to a final concentration of 200 μM and mix thoroughly by pipetting up and down.3: Negative control plate (containing untreated cells) should be kept in a humidified cell culture incubator at 37NOTE°2 until RNA is harvested (step 5). From that point onwards RNA from 4sU treated and untreated cells should be handled in parallel.C with 5% CO(3)Remove the remaining 5 ml of growth medium in the plate and add 7 ml of 4sU supplemented growth medium.4: When planning to use different final 4sU concentrations one should consider the following: (1) High 4sU concentrations can induce nucleolar stress responses and impact proliferation ratesNOTE  Low 4sU concentrations may result in low recovery of newly transcribed RNA.on rates ; (2) Low(4)2, for the desired pulse duration.Incubate cells in a humidified cell culture incubator at 37°C with 5% CO5: The “Representative results”, described below were obtained from a 15 min 4sU pulse.NOTE(5)Aspirate media and wash plates with 4 ml PBS.(6)Inside a chemical hood, add 5 ml of Trizol reagent to plates. Ensure Trizol covers the whole surface of the plate.(7)Incubate at room temperature for 5 min.(8)Transfer mESC cell lysate in Trizol from plates to clean 15 ml falcon tubes.6: Lysate- containing Trizol can be stored at 4°C for up to 12 h.NOTE(9)Add 1 ml of chloroform to lysate-containing Trizol and mix vigorously by pipetting.(10)Transfer mixture to the prespun 15 ml phase lock tube.(11)Incubate, at room temperature, until clear separation between organic and inorganic phase is obtained (minimum 3 min).(12)g.Centrifuge for 5 min at 4°C at 1500 × (13)Carefully transfer the upper phase (contains RNA) to clean 15 ml falcon tube.(14)Add equal volume of Isopropanol (∼3.0–3.5 ml) and mix vigorously by pipetting.(15)Incubate mixture at room temperature for 10 min.(16)g.Centrifuge for 15 min at 4°C at 11000 × 7: RNA pellet should be visible at the end of this centrifugation step.NOTE(17)2O.Remove supernatant and wash RNA pellet with 3.5 ml of 75% Ethanol in DEPC-H(18)g.Centrifuge for 5 min at 4°C at 7500 × (19)Remove supernatant carefully to avoid dislodging RNA pellet.(20)2O and transfer to fresh 1.5 ml tube.Resuspend RNA pellet in 100 μl of RNAse free DEPC-H(21)Remove genomic DNA. To this end, we use on-column DNAse I treatment  according to manufacturer’s instructions.8: On-column DNase digestion using the RNeasy Mini kit may result in significant loss of small RNA species. If enriching for small RNA species we suggest using an alternative approach [for example TURBOTM DNase ].NOTE(22)Adjust total RNA volume to 100 μl and quantify RNA concentration.9: Proceed directly to RNA Biotinylation or store RNA at −80°C.NOTE(C)Optional dot-blot for assessment of 4sU incorporation.(1)Assemble in a 1.5 ml RNAse/DNAse free tube, 300 μl biotinylation reaction by adding in the following order:(i)2O.30 μg of total RNA in 210 μl DEPC-H(ii)30 μl of 10X Biotinylation Buffer.(iii)60 μl of EZ-link iodoacetyl-Biotin dissolved to 1 mg/ml in DMF.(2)Mix vigorously, by pipetting, until solution is homogenous.10: Ensure to mix immediately after addition of EZ-link iodoacetyl-Biotin to avoid precipitation.NOTE(3)Incubate the mixture on Rotating Vertical Mixer for 2 h at room temperature.11: During incubation, precool a tabletop centrifuge to 4°C, centrifuge 2NOTE × 2 ml phase lock tubes at 12000 × g for 30 s for each sample being tested and prepare blocking solution and wash solutions 1–3.(4)Add 300 μl of phenol:chloroform:isoamyl alcohol to biotinylation reaction and mix vigorously by pipetting.(5)Add biotinylation reaction and phenol:chloroform:isoamyl mixture to prespun phase lock tubes and allow phases to separate at room temperature (minimum 3 min).(6)g.Centrifuge for 5 min at 4°C at 12000 × (7)Transfer the organic phase (∼280 μl) into a clean 1.5 ml tube.(8)Add equal volume of phenol:chloroform:isoamyl alcohol, mix by pipetting, transfer to phase lock tube and repeat steps 6–7 once.(9)Transfer organic phase  to clean 1.5 ml tube, add equal volume of isopropanol and 1/10 volume of 5M NaCl. Mix well by pipetting.(10)g. RNA pellet should be visible following this centrifugation step.Centrifuge for 45 min at 4°C at 16000 × (11)2O.Carefully remove supernatant and resuspend RNA pellet in 300 μl 75% Ethanol in DEPC-H(12)g.Spin for 10 min at 4°C at 13000 × (13)2O.Carefully remove supernatant and resuspend pellet in 20 μl of DEPC-treated H(14)Quantify RNA concentration, and maintain on ice until use.(15)2O on a see-saw rocker for 10 min.Incubate Zeta-membane in 5-10 ml DEPC-H12: Membrane should be covered by DEPC-H2O.NOTE(16)Take Zeta-membrane and remove excess liquid by dabbing both sides gently with clean paper towels.(17)Allow membrane to air dry for 5–10 min.(18)2O.Prepare, for each RNA sample (including 4sU-untreated control), 10 μl of 1000 ng/ul dilution in DEPC-H(19)2O.Prepare four 1:2 serial dilutions  in 3 μl for each of the RNA samples, in DEPC-H(20)Place Zeta-membrane on top of clean glass surface and apply 2 μl of each dilution of RNA to the zeta membrane. Additionally, add 2 μl of 100 ng/μl Biotinylated Oligo as positive control for Strep-HRP activity.13: To ensure proper spacing between blotted samples, we suggest pipetting the RNA onto the zeta membrane through the holes of an empty pipette tip box.NOTE(21)Air dry membrane for 7 min.(22)2PO4, 7% SDS, pH 7.2, in DEPC-H2O) on a see-saw rocker.Incubate the membrane for 30 min in 30 ml of freshly prepared blocking buffer (0.5M NaH(23)Remove blocking solution and incubate the membrane with 10 ml of freshly prepared 1:1000 streptavidin-horseradish peroxidase solution for 15 min (5 ml PBS + 5 ml 20%SDS + 10 μl of streptavidin horseradish peroxidase).14: Streptavidin horseradish-peroxidase should be thawed on ice and added just before use.NOTE(24)Wash membrane twice with Wash solution 1 for 10 min.(25)Wash membrane twice with Wash solution 2 for 10 min.(26)Wash membrane twice with Wash solution 3 for 10 min.(27)Proceed with chemiluminescent detection using WesternBright ECL solutions according to manufacturer’s instructions.(D)RNA biotinylation.(1)Thaw RNA (from section B step 22), on ice.(2)Assemble in a 2.0 ml RNAse/DNAse free tube, 1.0 ml biotinylation reaction by adding in the following order:(i)2O.100 ug of RNA in 700 μl of DEPC-H(ii)100 μl of 10X Biotinylation Buffer.(iii)200 μl of EZ-link Biotin HPDP dissolved to 1 mg/ml in DMF.15: Ensure to mix immediately after addition of EZ-link HPDP-Biotin to avoid precipitation.NOTE16: To biotinylate different amounts of 4sU labeled RNA scale reagents proportionally. We do not recommend labeling less than 80 ug of RNA as in our hands and for short pulse durations this leads toNOTE < 150 ng newly synthetized RNA.(3)Mix vigorously, by pipetting, until solution is homogenous.(4)Incubate the mixture on Rotating Vertical Mixer for 2 h at room temperature.(5)Add equal volume (in this case 1.0 ml) of phenol:chloroform:isoamyl alcohol to the biotinylation reaction and mix vigorously by pipetting.(6)Add biotinylation reaction and phenol:chloroform:isoamyl mixture to prespun 2.0 ml phase lock tubes and allow phases to separate at room temperature (minimum 3 min).17: We recommend using 2.0 ml phase lock tubes as in our experience using 15 ml phase lock tubes leads to considerable loss of material. As the maximum sample volume of 2 ml phase lock tubes is 750NOTE μl, the mix obtained at the end of step 5  should be divided into 3 separate 2.0 ml phase lock tubes and processed separately until precipitation (step 9).(7)g.Centrifuge for 5 min at 4°C at 12000 × (8)Repeat steps 5–7 to ensure complete removal of unreacted biotin that could otherwise interfere with binding of biotinylated RNA to streptavidin.(9)Transfer upper phase containing RNA into clean 2.0 ml tube and mix with 1/10 volume of 5M NaCl and an equal volume (∼850 μl) of Isopropanol.(10)g. RNA pellet should be visible following this centrifugation step.Centrifuge for 45 min at 4°C at 16000 × 18: Bead preparation (section E) can be performed during this centrifugation step, assuming that on average 5–10% of RNA is lost during the biotinylation.NOTE(11)Remove supernatant and resuspend in equal volume (850ul) of 75% Ethanol.(12)g.Centrifuge for 10 min at 4°C at 16000 × (13)Remove supernatant, taking care not to dislodge the RNA pellet.(14)2O and transfer to a clean 1.5 ml tube.Resuspend RNA in 100 μl of DEPC-H(15)Quantify RNA concentration and keep RNA on ice until separation of newly transcribed RNA from preexisting RNA step.(E)Bead preparation.19: Beads preparation is based on manufacturer’s instructions. For more details please refer to the manual provided with MyOne Streptavidin T1 Dynabeads by Thermo Fisher.NOTE(1)Vortex MyOne Streptavidin T1 Dynabeads for 30 s to ensure complete beads resuspension.(2)Pipette 2 μl of MyOne Streptavidin T1 Dynabeads for each μg of precipitated biotinylated RNA into a 1.5 ml tube.(3)Add equal volume (minimum 1.0 ml) of 1X B&W Buffer to beads.(4)Incubate on Rotating Vertical Mixer for 1 min at room temperature.(5)Place tube containing resuspended beads on Dynamag Magnetic Rack and separate beads for 1 min.(6)Carefully remove supernatant and resuspend beads in equal volume of 1X B&W Buffer.(7)Repeat steps 3-6 for a total of four washes in 1X B&W Buffer.(8)Resuspend beads in 1.0 ml of Solution A and incubate on a Rotating Vertical Mixer for 2 min.(9)Place on Dynamag Magnetic rack and separate beads for 1 min.(10)Remove supernatant carefully.(11)Repeat steps 8–10 once.(12)Resuspend beads in 1.0 ml of Solution B and incubate on Rotating Vertical Mixer for 2 min.(13)Place on Dynamag Magnetic Rack and separate beads for 1 min.(14)Remove supernatant carefully.(15)Repeat steps 12–14 once to remove NaOH traces.(16)Resuspend beads in same volume of 2X B&W Buffer as the volume of beads initially taken from the vial.20: Beads concentration for optimal coupling with RNA can be optimized for specific applications and pulse durations.NOTE(F)Separation of newly transcribed RNA from preexisting RNA.(1)2O.Resuspend biotinylated RNA precipitated in section D steps 14–15 to a final concentration of 500 ng/μl in DEPC-H(2)2O by pipetting.Mix previously washed Dynabeads with equal volume of precipitated biotinylated RNA in DEPC- H(3)Place on Rotating Vertical Mixer and mix for 15 min at room temperature.21: Beads and RNA solution is viscous! If solution viscosity inhibits even mixing, it is critical to add same volume of 1X BNOTE&W to all samples being processed to uniformly reduce bead concentration and ensure even mixing.(4)Place biotinylated RNA coated beads on Dynamag Magnetic Rack for 3 min to separate beads from solution.(5)Remove and discard supernatant.(6)Resuspend beads in 500 μl of 1X B&W Buffer and mix on Rotating Vertical Mixer for 1 min.(7)Place tube on Dynamag Magnetic rack for 1 min.(8)Repeat steps 6–7 for a total of three washes with 1X B&W Buffer. Discard supernatant.(9)Resuspend beads in 100 μl freshly prepared 100 mM DTT and incubate at room temperature for 1 min.(10)Add 350 μl of Qiagen RNeasy Mini Kit Buffer RLT to DTT bead suspension and mix thoroughly by pipetting.22:If enriching for small RNAs we suggest Trizol for RNA elution from beads and Qiagen miRNeasy Mini Kit  according to manufacturer’s instructions for RNA purification.NOTE(11)Incubate at room temperature for 5 min.(12)Place mixture containing beads on Dynamag Magnetic Rack and allow to separate for 2 min.(13)Transfer supernatant to a clean 1.5 ml tube.(14)2O (450 ul) to tube and mix well by pipetting.Add equal Volume of 70% EtOH in DEPC-H(15)Proceed with RNA purification using the Qiagen RNeasy Mini Kit according to the manufacturer’s instructions.(16)2O and quantify concentration.Elute with 25 μl of DEPC-H(G)Optional reverse transcription quantitative PCR analysis of newly transcribed and total RNA ratio.(1)Reverse transcribe the same volume of total RNA and newly transcribed RNA from each sample using random hexamers.23: In the “Representative results” section we report on data obtained from reverse transcription of 2NOTE μl of RNA using the SuperScript IV cDNA synthesis kit according to the manufacturer’s instructions.(2)Ccne2, Myc, Actin-β, Rplp0, GapdH) in the newly transcribed and total RNA fractions by RT-qPCR.Measure the expression of transcripts that vary across a range of reported stabilities To calculate the 4sU enrichment of transcripts in the newly transcribed RNA fraction relative to the total RNA fraction:(i)total and Knewly–transcribed that account for the relative fraction of total volume used for reverse transcription:Determine normalization factors K–total = VtotalRNA/VqPCRtotalRNA;K–newly–transcribed = Vnewly transcribedRNA/ VqPCR newly transcribed RNA;Kwhere–total = Volume of total RNA;V–newly–transcribed = Volume of newly transcribed RNA;V–qPCRtotalRNA = Volume of total RNA reverse transcribed;V–qPCR newly transcribed RNA = Volume of newly transcribed RNA used for reverse transcription.V(ii)total = 2–Cq total∗Ktotal and Expnewly transcribed = 2–Cqnewly transcribed∗Knewly trascribedConvert Cq/Ct to normalized expression as following: Exp(iii)rel.–enr. = Expnewly transcribed/Exptotal.For each transcript, calculate the ratio of normalized 4sU relative enrichment as following: 4sU25: Gene specific 4sUrel.–enr are inversely correlated with transcript half-life.NOTEnewly–transcribed RNA(ng)/Massbiotinylated RNA(ng)∗100) in range with what was previously reported  (rel.–enr is inversely correlated (R2 = 0.80) (Subsequently we measured the relative 4sU enrichment (4sUel.–enr) , which cel.–enr) , for a sel.–enr) , followi = 0.80)  with pre = 0.80) .r < 0.94; r < 0.68, r = 0.235, The method described here can be used to estimate RNA metabolic rates at the transcript and transcriptome wide level. To illustrate the use of the approach genome wide and to gain insights into the impact of 4sU pulse duration on rate estimates, we extracted and sequenced RNA from mESCs pulsed for 15, 30, and 60 min and estimated rates using INSPecT [, see SupNanog expression after 120 min of pulse with 4sU further underlines the advantages of using short 4sU pulse durations in mESC.Furthermore, analysis of the expression of a subset of pluripotency and differentiation markers highlights that longer pulse durations lead to more pronounced differences in these markers’ expression . The smaGene expression is controlled by the rates at which genes are transcribed, processed, and degraded. Perturbations to any of these processes can result in changes in gene product abundance. Methods that capture the dynamic processes that control gene expression are therefore paramount to understand gene regulation. Approaches based on metabolic labeling of RNA using modified ribonuleosides, such as 4sU, bypass many of the limitations of common transcription inhibition-based approaches and are now the gold-standard in the field. However, the implementation of 4sU metabolic labeling in different cells has also brought to light some of its pitfalls. For example, differences in doubling times between different cell types, result in variation in 4sU incorporation and impact the methods’ sensitivity . As a coUntil recently, isolation of 4sU-incorporating newly synthetized RNA from preexisting RNA has been achieved through biochemical enrichment . This enSince its development, SLAM-Seq has been used to infer metabolic RNA rates in a variety of cellular contexts  under accession number AM and AB designed the study, wrote the manuscript, read and agreed on the last version of this manuscript. AB performed the experiments.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.