C9orf72 gene underlie the most common form of familial ALS, and generate toxic arginine-containing dipeptide repeats (DPRs), which interfere with membraneless organelles, such as the nucleolus. Here we considered senataxin (SETX), the genetic cause of ALS4, as a modifier of C9orf72 ALS, because SETX is a nuclear helicase that may regulate RNA–protein interactions involved in ALS dysfunction. After documenting that decreased SETX expression enhances arginine-containing DPR toxicity and C9orf72 repeat expansion toxicity in HEK293 cells and primary neurons, we generated SETX fly lines and evaluated the effect of SETX in flies expressing either (G4C2)58 repeats or glycine-arginine-50 [GR(50)] DPRs. We observed dramatic suppression of disease phenotypes in (G4C2)58 and GR(50) Drosophila models, and detected a striking relocalization of GR(50) out of the nucleolus in flies co-expressing SETX. Next-generation GR(1000) fly models, that show age-related motor deficits in climbing and movement assays, were similarly rescued with SETX co-expression. We noted that the physical interaction between SETX and arginine-containing DPRs is partially RNA-dependent. Finally, we directly assessed the nucleolus in cells expressing GR-DPRs, confirmed reduced mobility of proteins trafficking to the nucleolus upon GR-DPR expression, and found that SETX dosage modulated nucleolus liquidity in GR-DPR-expressing cells and motor neurons. These findings reveal a hitherto unknown connection between SETX function and cellular processes contributing to neuron demise in the most common form of familial ALS.Identifying genetic modifiers of familial amyotrophic lateral sclerosis (ALS) may reveal targets for therapeutic modulation with potential application to sporadic ALS. GGGGCC (G4C2) repeat expansions in the The online version contains supplementary material available at 10.1186/s40478-023-01665-z. C9orf72 gene has emerged as the most common cause of familial ALS as well as a frequent occurrence in sporadic ALS [C9orf72 locus can number in the hundreds or thousands, while unaffected individuals usually carry C9orf72 G4C2 alleles ranging from 2 to 23 repeats in length [C9orf72 ALS is remarkably common, great effort has been placed on defining its mechanistic basis, and three leading models for disease pathogenesis have emerged: (1) RNA gain-of-function, (2) loss of C9orf72 normal function, and (3) production of toxic dipeptide repeat (DPR) proteins via Repeat Associated Non-ATG (RAN) translation. By studying the genetic defects and histopathology underlying many different forms of familial ALS and comparing these findings to the molecular abnormalities and cell pathology present in sporadic ALS, independent lines of investigation have coalesced on dysregulation of RNA metabolism as a potential unifying feature of disease pathogenesis. Central to this work has been the realization that toxic arginine-containing DPRs, specifically poly glycine-arginine (GR) and poly proline-arginine (PR) produced by the C9orf72 G4C2 repeat expansion, can interfere with a number of fundamental pathways in the cell, and that such arginine-containing DPRs have a proclivity to interact with and disrupt the function of low-complexity domain proteins involved in the formation of membraneless organelles, including the nucleolus [Amyotrophic lateral sclerosis (ALS) is a heterogeneous group of progressive, adult-onset motor neuron disorders that typically lead to death from respiratory collapse 2–5 years after disease onset , 40. Whiadic ALS . Pathogen length , 38. Becucleolus , 25.Amyotrophic lateral sclerosis type 4 (ALS4) is a juvenile-onset familial form of ALS characterized by dominant inheritance and slowly progressive motor neuronopathy . ALS4 isreviewed in [C9orf72 G4C2 toxicity in Drosophila [Many different studies have shown that arginine-containing DPRs interact with proteins in the nucleolus to disrupt liquid–liquid phase separation of low complexity domain proteins critical to the function of this organelle (iewed in ). With aiewed in , and veriewed in , 48. Furosophila , and helosophila .C9orf72 toxicity. To test this, we examined the effect of SETX on C9orf72 toxicity in several in vitro and in vivo model systems. We found that reduced SETX expression enhanced C9orf72 G4C2 repeat expansion toxicity and arginine-containing DPR toxicity in mammalian cells and primary neurons. We then documented that SETX expression fully rescued severe degenerative phenotypes and prevented lethality in crosses with the GR(50) Drosophila model. Furthermore, C9orf72 fly models expressing 1000 GR repeats that reveal age-related motor deficits were rescued with SETX co-expression. We found that physical interaction between SETX and arginine-containing DPRs is partially RNA-dependent, and when we performed fluorescence recovery after photobleaching (FRAP) on the nucleolus of HEK293 cells expressing Nucleolin-mCherry and GR(50), we noted that SETX significantly increased the kinetics of recovery of the mCherry signal in the nucleolus. This phenomenon was confirmed in induced pluripotent stem cell derived motor neurons from an ALS4 human patient and matched isogenic control lines. Our results indicate that SETX is a powerful modifier of arginine-containing DPR toxicity and can regulate the homeostasis of membraneless organelles whose assembly and function involves RNA-RNA and RNA–protein interactions.We thus hypothesized that the SETX helicase might protect against 30; GFP-GP30, GFP-GR30, GFP-PA30 and GFP-PR30 [30 and GFP-GA30 synthetic constructs were subcloned into the pSico lentiviral vector for efficient transduction studies, Sanger sequencing analysis was performed to confirm sequence integrity, and visual inspection of trial infections confirmed translation of GFP-fusion protein. Lentivirus particles were produced by triple transfecting pMD2.G, R8.74 and GFP-GR-30, GFP-GA30 or empty vector into HEK293 cells according to standard protocol. For siRNA knockdown experiments, HEK293 cells were transiently transfected for 48 h with a predesigned Silencer Select siRNA to SETX (s22951) or a Scramble control (si-CRL), using RNAiMAX™ Reagent (Invitrogen) at a final concentration of 10 nM.The P-LoopΔ-SETX construct was generated using the Gibson Assembly Cloning Kit (NE BioLabs™) to create an eight amino acid deletion 1963–1970) of the SETX GTP/ATP binding domain (GPPGTGKS) as described recently [0 of the GFP-PR30 . The GFPPrimary cortical neurons (PCN) were cultured from dissociated cortex of postnatal day-0 to day-2 (P0-P2) C57BL/6 J pups as described previously . DissociTo quantify cell death, we employed a Propidium Iodide (PI) exclusion assay and a lactate dehydrogenase (LDH) cell death assay. PI is a membrane impermeable stain and therefore does not enter viable cells with intact membranes. When PI does gain access to nucleic acids and intercalates, its fluorescence increases dramatically and is therefore used to identify dead cells. To perform this assay, we removed media from each sample being careful not to dislodge adherent cells and did not rinse with PBS. We then incubated in replacement media at 37 °C containing PI (1:3000) and Hoechst dye  for 15 min before imaging under a Zeiss 780 LSM confocal microscope at 10X objective magnification. Briefly 3 images were taken per well and each experiment was performed in at least triplicate. The lactate dehydrogenase (LDH) assay was conducted using LDH-Cytotoxicity Assay Kit II  according to the manufacturer’s instructions. Briefly, LDH Assay Buffer and WST Substrate Mix were added to media harvested from primary neurons. Plate readings were taken at four time points: 30, 60, 90, and 120 min. The percent of LDH released was calculated as the percent cytotoxicity normalized to the amount of total protein per well (μg/μL). Amount of protein was separately determined using BCA assay using the Pierce BCA Protein Assay Kit (Cat#23225). High control cells were treated with 10 μl of the cell lysis buffer provided with the LDH assay kit and kept in the incubator for 20 min before the media was harvested for LHD assay. For Low Control the cells/neurons were treated with complete media for neuronal growth.Protein lysates from whole brain, spinal cord tissue or cell line extracts were prepared as previously described . We loadCells were rinsed twice with ice-cold PBS and lysed in ice-cold lysis buffer  per 10 mL of lysis buffer. The soluble fractions from cell lysates were isolated by centrifugation at 12,000 rpm for 11 min in a microfuge. Protein lysates were quantified using Pierce BCA Protein Assay Kit (Thermo Scientific) following the manufactures protocol. For immunoprecipitations, primary antibodies were incubated with Dynabeads® (Invitrogen) overnight, and then washed with sterile PBS. Antibodies bound to Dynabeads were then incubated with lysates with rotation for 2 h at 4 °C. Immunoprecipitates were washed three times with lysis buffer. Immunoprecipitated proteins were denatured by the addition of 20 µl of sample buffer and heated for 10 min at 70 °C, resolved by SDS-PAGE, and analyzed via Western blot analysis.50, GFP or SETX-GFP, mCherry-Nucleolin), and grown for 48 h in 1% FBS in DMEM to slow cell division. Cells were imaged on a Nikon Confocal (A1 HD25/A1R HD25). Cells were chosen with clear expression of all three fluorophores, distinct nucleoli, and expression of GR50 in the nucleolus if expressed. High levels of SETX are associated with diffuse Nucleolin, likely due to S-phase arrest [HEK293 cells were transfected with three constructs -wt and UAS-Flag(SETX)-L389S with a range of tissue specific and developmentally staged Gal4 drivers. Lines used in this study were obtained from the Bloomington Stock Center or as described previously . The lines tested were as follows: vGlut-GAL4/Cyo; Cha-Gal4; D42-Gal4; tim-Gal4; Elav-Gal4; Pdf-Gal4; tsh-Gal4; 24B-Gal4; Gad-Gal4/Cyo; Ppk-Gal4; OK371-Gal4, Tdc-Gal4; Ddc-Gal4; and TH-Gal4.www.thebestgene.com > for injection for directed integration into the loci 68A4–attP2 site. The results were that n = 8 founders each for of UAS-Flag(SETX)-wt, and UAS-Flag(SETX)-L389S were generated and were subsequently outcrossed into the w1118 iso31 isogenic background for at least two generations. These were bred onto the Bristle stubble inversion balancer (Tm6b) to prevent recombination. As our analysis found that there were no detectible differences (data not shown), we now maintain just single lines of the wt-and L389S-Tg flies. The (GR)1000 lines with nSYB-GAL4 controlled pan-neuronal expression were generated as previously described [We sought to generate SETX Tg-flies with the UAS/Gal4 based system which allows for tissue specific expression. We utilized existing SETX plasmids with N-terminal, flag-tags for expression of the wt and L389S human SETX cDNA sequences. Our Flag-wt-SETX and Flag-L389S-SETX expression constructs, described in detail previously , were usescribed .Drosophila embryos, RRID: AB_303395). SETX-Flag was labeled with anti-Flag . Secondary antibodies were anti-Rabbit IgG (H + L) Alexa Fluor 488  and anti-mouse IgG (H + L) Cy3 . Tissues were mounted in Vectashield Hardset mounting medium (RRID: AB-2336787). Imaging was performed using a Leica DM6000 B Microscope (low magnification) or a Leica TCS SP8 Microscope (high magnification); and using a Hamamatsu ORCA-R2 C10600-10B-H camera.Drosophila salivary gland dissections were performed as described previously . Larval Third instar wandering larvae were dissected, fixed, antibody stained, imaged and analyzed as described previously , 50. AllFlies were placed individually, without anesthetization, inside glass boiling tubes mounted on a white background. After acclimatization, the flies were banged down to the bottom of the tubes to elicit the startle-induced negative geotaxis escape behavior. Videos were recorded to determine the distance in millimeters travelled in 20 s.2 plates at 6–12 million cells/plate in NB media supplemented with 1.5 μM Retinoic Acid, 200 nM Smoothened Agonist, and 8 ng/ml each BDNF, GDNF, and CNTF. On day 24, immature MNs were re-plated for a final time in NB media supplemented with neurotrophic factors and 2 μM DAPT at a density of 250–300 × 103 cells per cm2 on poly-ornithine/laminin coated plates. On day 26, media was changed to MN maintenance media (NB with neurotrophic factors) to wash out DAPT. Half-changes of media were performed every 2 days until mature MNs were analyzed, day 31 or older.Fibroblasts were generated from dermal biopsies from ALS4 patients, upon informed consent and in compliance with IRB protocols. Fibroblasts were cultured in Dulbecco's modified Eagle's medium (DMEM) with 10% FBS and 5% penicillin and streptomycin. Low passage fibroblast cultures were reprogrammed into induced pluripotent stem cells (iPSCs) and fully characterized, as done previously in our lab . Isogeni2 during FRAP analysis. FRAP was performed using a Nikon A1R confocal microscope. FRAP conditions were empirically established such that > 85% of the mCherry-NCL signal was ablated, and neurons remained viable. After five initial scans, photobleaching was performed with the 561 nm laser at 31% power for 6 iterations. During the recovery phase, we imaged every 4 s for 100 cycles. The percentage of fluorescent recovery per minute was calculated for n ≥ 9 iPSC MNs per biological replicate (n = 3), averaged over three experiments using NIS-Elements AR analysis software.iPSC-derived motor neurons (iPSC-MNs) were plated onto a 96 well plate with a polymer bottom black frame  and transduced with lentivirus encoding mCherry-NCL and GFP-GR30 for 100 h. iPSC-MNs were maintained at 37 °C with 5% COhttp://faculty.vassar.edu/lowry/VassarStats.html), or One-Way ANOVA calculator website (https://astatsa.com/OneWay_Anova_with_TukeyHSD/). For ANOVA, if statistical significance was achieved (P < 0.05), we performed post hoc analysis to account for multiple comparisons. All t-tests were two-tailed. The level of significance  was always set at 0.05.All data were prepared for analysis with standard spread sheet software (Microsoft Excel). Statistical analysis was done using Microsoft Excel, GraphPad Prism 9.0, the VassarStats website  and UAS-FLAG-SETX(L389S) transgenic vectors, and confirmed that these vectors drive expression of full-length human SETX protein  or UAS-FLAG-SETX(L389S), we crossed these flies with a variety of GAL4 driver lines  to achieve expression in different neuronal lineages, glia, and muscle, and evaluated the resultant flies, but we did not detect any abnormalities or disease phenotypes. We also crossed the UAS-FLAG-SETX(wt) and UAS-FLAG-SETX(L389S) fly lines with GMR-GAL4 (GMR-GAL4/Cyo) to direct expression to the fly eye, and again did not observe any abnormalities. The only detectable phenotype occurred when we crossed the UAS-FLAG-SETX(wt) and UAS-FLAG-SETX(L389S) fly lines with the tsh-GAL4 driver, which promotes expression during development throughout the ventral nerve cord (and other tissues), including in motor neurons [To further assess the role of SETX in motor neuron disease and neurodegeneration, we examined  neurons , as thesC9orf72 G4C2 repeat induces length and dosage dependent degeneration in Drosophila, and these degenerative phenotypes are enhanced by loss-of-function of genes encoding nuclear exosome complex components [C9orf72 G4C2 repeat expansion toxicity in the fly eye. To test this hypothesis, we derived fly lines carrying a genomic integration of a UAS-driven (G4C2)58 repeat, co-expressed with UAS-FLAG-SETX(wt) or UAS-FLAG-SETX(L389S) under control of the GMR-GAL4 driver. We observed complete suppression of the (G4C2)58-induced rough eye phenotype in flies concomitantly expressing either normal SETX or L389S SETX -GFP [UAS-FLAG-SETX(wt) under control of the GMR-GAL4 driver. We noted total suppression of a severe degenerative eye phenotype in GR(50) flies co-expressing human SETX -GFP  and UAS-UAS-FLAG-SETX(P-loopΔ) fly line by immunoblot analysis, we confirmed strong expression of full-length SETX protein -GFP, UAS-FLAG-SETX(P-loopΔ), and GMR-GAL4, we found that GR(50) flies co-expressing human SETX with the P-loop deletion exhibited severe degenerative eye phenotypes -GFP flies are crossed with the GMR-GAL4 driver line is dramatic lethality, with near complete lethality at the pupal stage of development . When we derived fly lines containing UAS-GR(50)-GFP, UAS-FLAG-SETX(wt), and GMR-GAL4, we observed nearly complete rescue of the lethality phenotype, but for fly lines containing UAS-GR(50)-GFP, UAS-FLAG-SETX(P-loopΔ), and GMR-GAL4, were unable to rescue the pupal lethality phenotype  toxicity, we generated a transgenic fly line carrying a SETX helicase domain mutation due to a small deletion of the P-loop sequence. When we characterized the pes Fig. b. Anotheype Fig. d. These UAS-GR(50)-GFP and OK6-GAL4, which drives expression in motor neurons. When we visualized the neuromuscular junction (NMJ) in fly larvae expressing GR(50), we observed a dramatic reduction in synaptic bouton numbers and in NMJ length -GFP, UAS-FLAG-SETX(wt) or UAS-FLAG-SETX(L389S), and OK6-GAL4. NMJ staining of GR(50) fly larvae co-expressing either SETX(wt) or SETX(L389S) revealed increases in synaptic bouton numbers and NMJ length  disease phenotypes relevant to ALS motor neuron degeneration, we derived fly lines containing gth Fig. . To evalLAG-SETXL89S, and Drosophila? To address this question, we considered an effect of SETX on the subcellular localization of this arginine-containing DPR, as previous studies have documented that GR and PR repeats of this size are typically directed to the nucleolus or other liquid–liquid phase separation (LLPS) derived organelles [Drosophila model, we crossed transgenic GR(50) and SETX flies with an OK6-GAL4 fly line, since the OK6 driver directs expression to the developing salivary gland [OK6-Gal4/UAS-mCD8-GFP;UAS-Flag-SETX(wt)/ + ; 2) OK6-Gal4/UAS-mCD8-mCherry;UAS-GR(50)—GFP/ + ; and 3) OK6-Gal4/ + ;UAS-GR(50)-GFP/UAS-FLAG-SETX(wt). Transgenic flies expressing normal SETX displayed characteristic localization of SETX throughout the nucleoplasm  toxicity in ganelles , 24, andganelles , 15. To ry gland . We deriDrosophila models, it is also evident that DPR pathological properties alter with length, suggesting that shorter repeats may not recapitulate the entire disease process [Drosophila model where a GR(1000) transgene placed under the expression of the pan-neuronal driver nSYB-GAL4 develops significant motor phenotypes [UAS-GR(1000)-GFP, UAS-FLAG or UAS-FLAG-SETX(wt) or UAS-FLAG-SETX(L389S), and nSYB-GAL4. To evaluate motor performance in 14 day-old flies, we employed a negative geotaxis assay and quantified distance climbed in 20 s. Although expression of the human SETX transgene resulted in modest, but not statistically significant reductions in climbing distance, we noted that flies co-expressing normal, wild-type human SETX and GR(1000) displayed a dramatic improvement in climbing distance compared to flies co-expressing FLAG and GR(1000)  and then immunoblotted the resultant IP material. We found that SETX formed stable complexes with the arginine-containing dipeptides GR(30) and PR(30), but did not detect an interaction between SETX and the non-arginine containing dipeptides GA(30), GP(30), or PA(30)  in vivo, we next sought to determine if SETX, a RNA binding protein, engages in a physical interaction with arginine-containing DPRs. To test this hypothesis, we co-expressed each of all five possible dipeptide products of RAN translation of the 30) Fig. a. Curren30) Fig. : Fig. S330) Fig. b.Fig. 6SArginine-containing DPRs reduce the liquidity of the nucleolus, impairing its ability to modulate protein aggregation under stress conditions . As SETXP = 0.089)  line from an ALS4 patient carrying the pathogenic L389S mutation (SETX L389S), and using genome editing, we generated an isogenic corrected iPSC line (SETX WT) and an isogenic knock-out line lacking SETX expression (SETX KO). After deriving motor neurons (MNs) from these three isogenic iPSC lines, we transduced iPSC-MNs with the NCL-mCherry vector and a GFP-tagged GR(30) expression construct, and we performed FRAP in order to evaluate phase dynamics of the nucleolus in the presence of the toxic GR DPR. Fluorescence recovery for bleached nucleolus regions was visibly decreased for both SETX KO iPSC-MNs and SETX L389S iPSC-MNs  have independently identified the SETX fly orthologue as a modifier of degenerative disease phenotypes [Most forms of familial ALS are clinically and pathologically indistinguishable from sporadic ALS, suggesting that shared pathways of disease pathogenesis likely exist. Identifying genetic modifiers of the most common types of familial ALS may thus yield targets and pathways for therapeutic modulation, and it is possible that such therapies will be effective in patients with these common familial ALS subtypes; but could also be disease-modifying treatments in sporadic ALS. We have been interested in understanding the molecular genetic basis of ALS4, a rare, dominantly inherited juvenile-onset form of ALS resulting from mutations in the senataxin gene . We haveenotypes , furtherC9orf72 ALS [C9orf72 ALS, which is the most common familial form of this disease. We observed powerful SETX suppression of C9orf72 G4C2 repeat toxicity and DPR toxicity in two well-established Drosophila models. To understand the mechanistic basis for such profound SETX rescue, we considered the normal function of SETX as an RNA–DNA dependent helicase protein. We found that whereas wildtype human SETX protects against GR(50)-driven lethality in Drosophila, mutant human SETX lacking a helicase domain is not similarly protective, thus confirming that SETX helicase function is essential for countering arginine-containing DPR toxicity. Based on this result, we considered a role for SETX in regulating the localization of arginine-containing DPRs in the nucleolus. We found that in the presence of SETX, accumulation of GR(50) outside of this organelle is increased. Furthermore, via mechanisms that almost certainly require helicase activity, normal human SETX was able to rescue motor phenotypes in (GR)1000-expressing flies. Surprisingly, L389S-SETX failed in this regard. Up to this point, we had not seen a L389S-SETX linked phenotype in the fly. We suggest that this rescue failure may represent a partial loss of function, and recognize one limitation of our study is that we did not include the L389S-SETX and P-loopΔ-SETX genotypes in all experiments. However, we documented SETX modification of G4C2, GR short repeat, and GR long repeat phenotypes in diverse, complementary model systems. Indeed, to directly evaluate the effect of SETX on the liquidity of the nucleolus (in an amenable assay system), we performed FRAP on HEK293 cells expressing NCL-mCherry. We found that the recovery rate of this molecule in the nucleolus was enhanced by SETX, regardless of GR(50) expression. To determine if this modulation of nucleolus liquidity is relevant to motor neurons, we examined the mobility of NCL-mCherry in isogenic iPSC-derived motor neurons derived from an ALS4 patient, corrected control, and SETX KO line. Both SETX L389S and SETX KO motor neurons displayed impaired recovery of NCL-mCherry fluorescence in the nucleolus in FRAP experiments.Numerous studies of arginine-containing DPRs have yielded a model in which arginine-containing DPRs disrupt liquid–liquid phase separation of proteins, often RBPs containing low-complexity domains that regulate formation and stability of membraneless-organelles. In particular, the nucleolus and its many RNA regulatory functions have arisen as a central target of arginine-containing DPR toxicity in rf72 ALS , 25, 27.rf72 ALS . SETX, arf72 ALS . Here weHow does SETX promote improved liquidity of the nucleolus? As a helicase, SETX may regulate the RNA-RNA and RNA–protein interactions involved in the assembly of this membraneless organelle. Previous studies support such a model, as expression of PR(50) in U2OS cells resulted in impaired recruitment of stress granule disassembly-engaged protein regulators, including SETX, to the SG disassembly complex, indicating SETX may function as a stress-granule disassembly-engaged protein . FurtherC9orf72 ALS patients [C9orf72 gene in ALS patients may affect immune cells as well as motor neurons, and this should be a focus of future studies. Our results identify SETX as a central regulator of nucleolus homeostasis with the ability to counter the disruptive influence of arginine-containing DPRs in C9orf72 ALS and suggest that proper SETX function may be essential for maintaining motor neuron health in the face of various genetic, immune, and environmental insults.One emerging cellular function of SETX is its role in regulating the immune system, as SETX has been shown to suppress the antiviral immune response . Buildinpatients , could bpatients , 36. HenAdditional file 1. Supplementary Figures and Table.


RNA modifications have been known for many years, but their function has not been fully elucidated yet. For instance, the regulatory role of acetylation on N4-cytidine (ac4C) in RNA can be explored not only in terms of RNA stability and mRNA translation but also in DNA repair. Here, we observe a high level of ac4C RNA at DNA lesions in interphase cells and irradiated cells in telophase. Ac4C RNA appears in the damaged genome from 2 to 45 min after microirradiation. However, RNA cytidine acetyltransferase NAT10 did not accumulate to damaged sites, and NAT10 depletion did not affect the pronounced recruitment of ac4C RNA to DNA lesions. This process was not dependent on the G1, S, and G2 cell cycle phases. In addition, we observed that the PARP inhibitor, olaparib, prevents the recruitment of ac4C RNA to damaged chromatin. Our data imply that the acetylation of N4-cytidine, especially in small RNAs, has an important role in mediating DNA damage repair. Ac4C RNA likely causes de-condensation of chromatin in the vicinity of DNA lesions, making it accessible for other DNA repair factors involved in the DNA damage response. Alternatively, RNA modifications, including ac4C, could be direct markers of damaged RNAs.The online version contains supplementary material available at 10.1186/s13072-023-00501-x. In general, acetylation is a well-described cellular mechanism that regulates gene expression, especially in relation to histones . N4-acetWhen considering the function of acetylation processes as part of the DNA repair machinery, we also took into consideration the existence of N4-cytidine in RNA . It is k6A and m8A, are significantly higher in UV-damaged chromatin, which is mainly recognized by the base excision repair (BER) or nucleotide excision repair (NER) mechanisms , Biosynth; r et al. , 35. ShoFluorescence intensity values were measured by LAS X software and subsequently analyzed in Python 3 software. The obtained data were compared statistically using the ANOVA One-Way test, available in GraphPad Prism software, version 9 for Windows . Also, the Student's t-test  was used for statistical analysis. All values labeled in the graphs by the asterisk(s) differ significantly from the control values.Additional file 1: Fig. S1. Ac4C level after enzymatic treatment. A Cells were treated by RNase A, RNase H1, and DNase I to study the level of ac4C RNA  and the density of DNA in MCF7 cells. After RNase A treatment, the fluorescent intensity of ac4C RNA was significantly reduced, which was not the case in the following RNase H1 and DNase I treatments. Significant changes in the level of DAPI-stained DNA were observed when cells were treated with DNase I. Quantification of fluorescence intensities from panel A is shown in panels B, C. D Dot blot analysis of an ac4C RNA after enzymatic treatment using both ac4C antibodies . Representative anti-ac4C dot blot was performed on total RNA with methylene blue as a loading control. E Dot blot analysis demonstrates chemical deacetylation by hydroxylamine  detected by both antibodies. F Agarose gel shows fractionalization of RNA into large and small RNAs.Additional file 2:Table S1. HPLC gradient timetable and source and acquisition parameters.


To capture structural homology in RNAs, alignment and folding (AF) of RNA homologs has been a fundamental framework around RNA science. Learning sufficient scoring parameters for simultaneous AF (SAF) is an undeveloped subject because evaluating them is computationally expensive.We developed ConsTrain—a gradient-based machine learning method for rich SAF scoring. We also implemented ConsAlign—a SAF tool composed of ConsTrain’s learned scoring parameters. To aim for better AF quality, ConsAlign employs (1) transfer learning from well-defined scoring models and (2) the ensemble model between the ConsTrain model and a well-established thermodynamic scoring model. Keeping comparable running time, ConsAlign demonstrated competitive AF prediction quality among current AF tools.https://github.com/heartsh/consalign and https://github.com/heartsh/consprob-trained.Our code and our data are freely available at  Machine learning is applied to each significant bioinformatics subject, including structural bioinformatics. Also, machine learning has attracted computational scientists’ attention around folding single RNA sequences. In folding single RNA sequences, modern parameter training methods optimize secondary structure scoring parameters based on both training RNA sequences and training RNA secondary structures . ‘Simultad hoc combination of) the scoring parameters of (i) Turner’s (nearest neighbor physics) model for RNA secondary structures ructures . By Consructures . Scoringructures . Dowell ructures —able to ructures . ConsAliructures  accordinructures . The proructures  based onructures  between We initiate our computational methodology by introducing ConsAlign—our proposed SAF tool. Then, we turn to ConsTrain—our proposed training method for ConsAlign’s SAF parameters. ConsAlign and ConsTrain share ConsProb—our probability inference method based on sparse pairwise SAF —as theirγ-centroid estimator principle  . γM,γP aN, M are the lengths of every two RNA sequences.) However, we can perform O(L) memory usage if hyper-parameters L is i, k) satisfying the conditionand nucleotide base-pairings We need to conduct M is a set of all matched nucleotide pairs in SAF under optimization, and O(L) memory usage  satisfying the condition Here, We extend istency” ] pijP(θ)X and Y are every two aligned sets of RNA homolog sequences to be aligned. Moreover, xI is any RNA homolog x’s nucleotide stored in I. x, y; and x. We align and fold every two aligned sets keeping only their matching profiles until we reach the root of a guide tree from leaves.Here, γ-centroid estimators is that they rely on hyper-parameters (e.g. hyper-parameters γ-centroid SAF). In practice, we have to determine hyper-parameters before performing γ-centroid estimators. This study proposes a reasonable way for determining hyper-parameters in performing γ-centroid SAF. The ‘sum-of-pairs score’ (SPS) of any predicted SAF candidate C as an SPS normalization factor .Here, Align do , 2006b. We can optimize the scoring parameters of each CLLM (including our SAF pair-CLLM), given their cost functions . We extrF-dimensional space.  replacinL is the longer length of the d-th training sequence pair.)This approximated vector estimation does not change the running time Our parameter optimization goal is to find SAF scoring parameters that minimize their convex pair-CLLM cost. We can utilize general gradient-based optimization methods to complete our scoring parameter optimization. We aim to compute an optimized parameter vectorF-dimensional space) using the gradient of an approximated pair-CLLM cost  where each SAF scoring parameter fθ belongs. (We show G(f)’s settings in Section 2.4.) fθ into a cluster and penalizes each scoring parameter cluster by a shared regularization constant (https://github.com/heartsh/consprob-trained).Our approximated pair-CLLM cost constant . The merrocedure . We implBorrowing knowledge from conventional scoring parameter optimizations, we specify the concrete settings of our SAF parameters to be trained. We specify all the groups (or types) of our SAF parameters, as described in Section S2. The ‘CONTRAfold model’ is a CLLM that (from folded single RNA sequences) learns secondary structure scoring parameters based on RNA loop structures . The ‘COWe used the Rfam version 14.4  for our As a result, (i) the dataset “Rfam train” is composed of 2250 pairwise AF examples, and (ii) the dataset “Rfam test” is composed of 1565 multiple AF examples. We used the dataset “Rfam train” to learn ConsTrain. We used the dataset “Rfam test” to assess the performances of each benchmarked prediction method. Any entry in the dataset “Rfam train” and any entry in the dataset “Rfam test” are not derived from the same family to prevent structural homology from leaking between training data and test data .As another test data source, we adopted reference AF examples registered in RNAStralign . We splicmbuild. After cmbuild, we mapped each training pairwise alignment in the dataset “Rfam train” to each alignment profile using Infernal’s cmsearch. We did not perform Infernal’s cmcalibrate before cmsearch because cmcalibrate is time-consuming, and the dataset “RNAStralign processed” and BRAliBase’s dataset 1 are large.  As a result, only one alignment in each of the dataset “RNAStralign processed” and BRAliBase’s dataset 1 detected homology to the dataset “Rfam train.” We decided not to remove any alignments from the dataset “RNAStralign processed” and BRAliBase’s dataset 1. We summarized the profile of each test alignment dataset in RNAStralign and BRAliBase contain alignments derived from Rfam. Therefore, we checked the homology of RNAStralign and BRAliBase to Rfam using Infernal, an RNA homology search tool . More spUsing default command line arguments, we compared (i) the RAF version 1.00, (ii) the LocARNA version 2.0.0, (iii) the SPARSE version 2.0.0 , (iv) thi, k) into the x-th basis feature based on CONTRAlign’s posterior matching probabilities. Likewise, a function i, j) into the y-th basis feature based on CONTRAfold’s posterior base-pairing probabilities. RAF trains its weights http://eddylab.org/software/consan) because the latter predicts only pairwise SAF and because predicting multiple SAF is a more general situation than pairwise SAF.By default, DAFS turns off its pseudoknot-aware prediction based on IPknot . We inclStralign . We retrF1 score and a Matthews correlation coefficient (MCC) in each test AF dataset:Across all test AF examples, we find the numbers of true positives, true negatives, false positives, and false negatives We obtain both the SPS and the structure conservation index (SCI) of each predicted AF product to measure its matching accuracy. Each SCI is defined asgiven the minimum free energy m energy . Each SChttps://www.rust-lang.org). ConsTrain used the two gamma distribution parameters γ-centroid hyper-parameter γ-centroid hyper-parameter value showed the highest structure prediction accuracy among the possible γ-centroid hyper-parameter values Employing multi-threading to provide a more comfortable user experience, we implemented ConsTrain and ConsAlign in Rust  demonstrated  used in ConsTrain by including modified bases—such as I and Recently, deep learning-based methods for folding single RNA sequences—such as SPOT-RNA , MXfold  I and Ψ . HoweverAF: alignment and folding. The BFGS algorithm: the Broyden–Fletcher–Goldfarb–Shanno algorithm. CLLM: conditional log-linear model. CRF: conditional random field. MCC: Matthews correlation coefficient. SAF: simultaneous alignment and folding. SCFG: stochastic context-free grammar. SCI: structure conservation index. SPS: sum-of-pairs score.CLLM, CRF, and SCFG: They are probabilistic training frameworks for training data containing hidden sequential/structural states. CRFs are generalized variants of SCFGs, allowing each trained parameter not to be the (strict) form of a log probability. CLLMs generalize CRFs to loosen each learned parameter’s correspondence to a single transition/emission.It solves AF  of multiple RNA homologs in a unified scheme (e.g. a single dynamic programming scheme), not separately. One of the simultaneous alignment’s merits is that obtained alignments are globally optimum regarding any given scoring scheme compared to the case where we solve structural alignment separately.It is another view of RNA secondary structures based on structural features often found in them (e.g. hairpin loops and base pair stacking). Capturing RNA loop structures in scoring schemes involving secondary structures is significant including simultaneous alignment scoring, as Turner’s model , the CONIt computes and stores only preferable cells rather than all dynamic programming cells. We implemented our sparse dynamic programming based on γ-centroid estimator maximizes potential solutions’ expected accuracy to deal with this ‘curse of dimensionality’ rather than maximizing their likelihood. As a side effect, γ-centroid estimators deliver possible solutions’ sparsity by leaving only posterior probabilities above a threshold , maximizing likelihood (e.g. minimizing free energy) does not perform well for long input data in theory. The hreshold , as DAFSbtad255_Supplementary_DataClick here for additional data file.


Liquid-Emulsion-Assisted-Purification of RNA-Bound Protein) for the selective, quantitative recovery of UV-crosslinked RNA-protein complexes. By virtue of its high specificity and yield, LEAP-RBP distinguishes RNA-bound and RNA-free protein levels and reveals common sources of experimental noise in RNA-centric RBP enrichment methods. We introduce strategies for accurate RBP identification and signal-based metrics for quantifying protein-RNA complex enrichment, relative RNA occupancy, and method specificity. In this work, the utility of our approach is validated by comprehensive identification of RBPs whose association with mRNA is modulated in response to global mRNA translation state changes and through in-depth benchmark comparisons with current methodologies.Recent efforts towards the comprehensive identification of RNA-bound proteomes have revealed a large, surprisingly diverse family of candidate RNA-binding proteins (RBPs). Quantitative metrics for characterization and validation of protein-RNA interactions and their dynamic interactions have, however, proven analytically challenging and prone to error. Here we report a method termed LEAP-RBP ( The identification of RNA-bound proteomes is hampered by a lack of quantitative metrics for evaluating RNA binding function. Here, the authors report LEAP-RBP as a method for purification of RNA-bound proteins and introduce signal-based metrics for robust profiling of RNA-bound proteomes. Through these efforts the universe of candidate RBPs has expanded dramatically, with RNA-binding functionality now attributed to a substantial fraction of the proteome, including glycolytic enzymes, regulatory kinases, and other proteins not previously implicated in RNA biology9. With the growing catalog of candidate RBPs has come the challenge of establishing quantitative criteria for RNA-binding activity, metrics for distinguishing specific  from random (noise) protein–RNA interactions, and experimental approaches to the study of protein–RNA interaction dynamics12. Although substantial progress has been made on these fronts, significant methodological limitations in the study of RNA-protein interaction remain, and a systems-level understanding of RBP biology remains a frontier area of study17.The essential contributions of RNA-binding proteins (RBPs) to RNA biology have fostered the development of biochemical methods for the RNA-centric capture and identication of the RNA-interacting proteome13. This intrinsic biological property creates methodological hurdles to establishing biologically relevant interactions, particularly when interaction energies are weak and thus readily lost during biochemical isolation. In the case of RNA-protein complexes (RNPs), chemical- or UV cross-linking methods can capture physiologically relevant interactions though as with any cross-linking method, criteria for distinguishing specific from biologically irrelevant interactions are critical. UV cross-linking is preferred due to its high specificity, though it is also inefficient, with only a fraction of interactors forming a covalent adduct20. The generally low cross-linking efficiencies present an analytical challenge because selective, quantitative recovery of the UV-crosslinked protein–RNA complexes (clRNPs) is necessary for accurately determining RNA occupancy states in vivo. Here we report two different sources of non-specific free protein recovery during RNA-centric enrichment of clRNPs. To distinguish RNA-bound and free protein levels, we introduce signal-to-noise (S/N) metrics, where S/N is a protein-specific metric representing the ratio of RNA-bound (S) to unbound counterparts (N), and where proteins without RNA-bound counterparts (S = 0) comprise background (B). Because mass spectrometry-based proteomic analysis does not distinguish RNA-bound proteins from their unbound counterparts, S/N-based metrics provide a valuable quantitative criterion for high confidence assignment of RNA-binding function.As with protein–protein interactions, protein–RNA interactions can vary substantially in their specificities, interaction lifetimes, and apparent affinitiesS/N and use them to guide the development of a biochemical method termed LEAP-RBP (Liquid-Emulsion-Assisted-Purification of RNA-Bound Protein) for the selective isolation of total RNA-bound protein. SILAC LC–MS/MS analysis of LEAP-RBP fractions demonstrated high RNA-bound protein enrichment and through comparative analyses, revealed a key metric for evaluating method specificity for RNA-bound RBPs which we term %TPS, or RNA-bound protein abundance. High %TPS is indicative of low free protein recovery and enables the accurate study of dynamic, cell state-determined changes in RBP occupancy state. Using this signal-based analytical framework, we present perspectives and strategies for studying RNA-bound proteomes and their dynamics. The utility of this approach is established through benchmark comparisons of LEAP-RBP with current RNA-centric enrichment methods.In this work, we identify and validate experimental approaches for evaluating 21. Whereas acidic guanidinium thiocyanate–phenol–chloroform (AGPC) biphasic extraction  has been widely used for the isolation of RNA and protein from biological samples23, the finding that RBPs exhibit UV-dependent enrichment at the AGPC interphase revealed a utility for this method in RNA-protein interactome discovery  of AGPC interphase proteins using an SDS-PAGE RNase-sensitivity Assay (SRA)  Fig. .Fig. 1RNS), migrate at a higher apparent molecular weight than their unbound counterparts (noise = N) in SDS-PAGE25. RNase treatment liberates RNA-bound protein, increasing the amount of observed ( = O) protein migrating at its expected molecular weight (∆O = | S | ). Consequently, a comparison of RNase-treated (S + N) and untreated control samples (N) by SDS-PAGE reveals RNase-sensitive RBPs ( | S | > 0) and enables the determination of S/N. As described in Eq.  and non-crosslinked HeLa cells, using sequential interphase extraction to maximize RBP enrichment, as previously reported , proteins showed only modest AGPC interphase UV-enrichment and were largely RNase-insensitive , indicating that most of the proteins recovered from the AGPC interphase fraction of UV-irradiated cells were not crosslinked to RNA. These data demonstrate that UV-irradiation-dependent enrichment at the AGPC interphase alone is insufficient evidence for RNA-binding activity. To distinguish between contributions of noise (RBPs lacking crosslinked RNA) and background (non-RBPs) to this phenomenon, we compared known GO-annotated RBPs binding different RNA species with non-RBPs by SRA and immunoblot1. Here, proteins lacking GO-annotation (GO:RBP) are designated as non-RBPs, but this may not be due to a lack of RNA-binding activity (“Methods”). Protein targets were selected on the basis of prior literature, GO-RBP-annotation datasets, RNA-binding domain bioinformatic analysis, and UV cross-linking/CLIP sequencing frequency, to include canonical RBPs containing known RNA-binding domains (red font) and noncanonical RBPs (blue font) which were identified in previous studies as candidate RBPs  but which lack known RNA-binding domains  and were recovered from the AGPC interphase, whereas noncanonical RBPs and non-RBPs were either RNase-insensitive or interphase depleted following repeat extraction. In agreement with prior findings, most glycoproteins assayed (asterisks) exhibited UV-independent enrichment at the AGPC interphase4, although ribophorin I (RPN1) did not  reached an apparent ons Fig. .Isopropanol NaCl Precipitation) and LEAP-RBP (Liquid-Emulsion-Assisted-Purification of RNA-Bound Protein). Both are RNA-centric enrichment methods that enhance S/N by SRA without significant signal loss  from non-RBPs  and light SILAC-labeled non-crosslinked (nCL) cells were pooled prior to processing to accurately quantify UV-dependent free protein recovery by LC–MS/MS and evaluate S/N  ratios with sum peptide intensities (SPI) as shown in Eq. (2(CL/nCL) = 0  were UV-enriched* and undetectable by SRA in both fractions  ratio of 0 indicates equal amounts of RNA-bound (S) and unbound (N) counterparts (50% RNA-bound) while a log2(CL/nCL) S = 0, N = B or background); this relationship is described by Eq.  in response to a change in RNA-bound quantity ∆log2(SPIS). This relationship is depicted in Fig. 2(S/N) ratios; these relationships are described by Eqs.  could differ substantially. These concepts are illustrated by comparing the RNase sensitivity (S/N) of RBPs by SRA with their SILAC LC–MS/MS-derived log2(S/N) ratios. In principle, RNase sensitivity represents a change in RNA-bound quantity ( | S | ) when noise is constant (Nuntreated = NRNase); Eq.  ratios by SILAC LC–MS/MS. In contrast, RBPs displaying comparable RNase sensitivity  have comparable log2(S/N) ratios. Therefore, a change in RNA-bound quantity (S) for RBPs such as NCL, RPN1, and TRAPα will have a larger effect on their observed quantity (O) in LEAP-RBP fractions by LC–MS/MS ; Eq. . This isse); Eq.  if Sinit34.As illustrated in Fig. S) and free proteins (%TPN) as a percentage of total SPI, as described by Eqs. (By extension of Eqs.  and 4),,4), TPA  by Eqs. –11.9\docS and %TPN represent the abundance of total RNA-bound  and free protein  in the sample. By this approach, 91% of the total protein in LEAP-RBP fractions is RNA-bound compared to 47% for INP. This is consistent with differences in RNP composition (µg protein/µg RNA), though assumes equal noise-partitioning between SILAC channels , but RBPs still contribute 98.6% of total SPIS.Cumulatively, %TPels Fig. . This fiels Fig. ; not assels Fig. . Remarka. S Fig. . By compS) can be represented by %TPS). While %TPS of INP fractions (47) is less than LEAP-RBP fractions (91), both methods recover near 100% of RNA-bound protein ( contributions from RBPs (~98.5) and non-RBPs (~1.5) in both fractions is indicative of high UV cross-linking specificity. The disparity in %TPS between LEAP-RBP and INP fractions is not, however, readily illustrated by current analytical methods ) of RBPs and non-RBPs identified by INP and LEAP-RBP were evaluated as a function of their S/N ratios (log2(S/N))  of both RBPs and non-RBPs in LEAP-RBP fractions as compared to INP and higher abundance (%TP) of RBPs relative to non-RBPs , thus resulting in identification of many low-abundance proteins not observed in INP fractions .Estimating the abundance of RNA-bound proteins as a percentage of total RNA-bound protein in the sample ) Fig. . As expem RBPs ~9.5 and noS/N provided by the LEAP-RBP method, results in the UV-enrichment* of low-abundance non-RBPs. In support of this hypothesis, all non-RBPs (undetectable by SRA) were UV-enriched* but display low S/N ratios and are less abundant than the majority of RNase-sensitive RBPs . Critically, these results demonstrate that UV-enriched* RBPs can be distinguished from UV-enriched* non-RBPs in LEAP-RBP fractions by their enrichment efficiencies (S/N) and abundance (%TP).As noted above, we postulated that non-specific UV cross-linking, combined with the enhanced BPs Fig. . Indeed,hod Fig. , but the2(S/N) * log10(%TP). In practice, RCS ranking prioritizes S/N over protein abundance and places proteins with S/N ratios <1 at lower ordinal rank , representing the fold-change in RNA-bound protein quantity (S) necessary to reject the null hypothesis that ∆log2(S + N) = 0  to limit detection of ∆log2(N) and increase statistical power by reducing multiple hypothesis testing  of the most abundant proteins in the sample   fractions; Kruskal–Wallis, H(1) = 7.82, P = 0.005; Source Data Fig. To illustrate these points, we performed a comparative LEAP-RBP experiment to examine the effect of dynamic translatome remodeling on global RBP–RNA occupancy states. Using harringtonine (HT), a selective inhibitor of translation initiation, we sought to identify RBPs whose interactions with mRNAs were either sensitive to ribosome occupancy (= translation-state-dependent interactors) or whose mRNA association was not sensitive to ribosome occupancy status (= translation-state-independent interactors). Through inhibitory interactions at the ribosomal A-site, HT induces global polyribosome runoff, to yield monosomes bearing initiation codon-locked 80 S ribosomesnce Fig. ; comparince Fig. . Notablyria Fig. . Importa42. This last category includes the TIA-1a isoform displaying lower total abundance in rat 832/13 cells but comparable RNA-bound abundance (gold asterisk) , human embryonic kidney cells (293T), human hepatocyte-derived carcinoma cells (Huh7), and a rat pancreatic insulinoma cell line (832/13) 49.Interestingly, integral membrane ER resident RBPs  consistently displayed higher RNA-bound protein abundance in rat insulinoma (pancreatic β) cells (832/13) without a comparable change in total abundance Fig. . As highS/N or %TPS , one solid phase separation method (TRAPP), and one affinity-based separation method (RIC)50. Except for RIC, which selects for poly(A) RNA-binding proteins, these methods aim to isolate total RNA-protein interactomes. RNP fractions were isolated from UV-crosslinked and non-crosslinked cells according to each of the published methods  %TPS)( contributions during non-SILAC LC–MS/MS experiments (Supplementary Notes Comparisons of current RNA-centric approaches include overlap (Venn) analysis of UV-enriched* proteins but lack metrics such as ods Fig. ; S/N, %Tons Fig.  and the ons Fig. 3,21,50–5S/N without signal loss  and high non-specific %TPS), non-RBPs supports this assessment  for a greater number of proteins ( contributions are due to incomplete RBP annotations (GO:RBP) for ribosomal proteins  but are detected and UV-enriched*  as key metrics for evaluating RNA-bound protein enrichment and method specificity for RNA-bound RBPs. Importantly, we present practical, experimentally accessible strategies for the accurate determination of in vivo RNA-binding activity and for robust profiling of RNA-bound proteomes at steady-state and following dynamic cell state transitions.We report LEAP-RBP as a highly selective and cost-efficient method for the purification of RNA-bound protein from biological samples. We identify S/N-based comparative analysis of RBP profiling data generated by LEAP-RBP and other RNA-centric methods revealed the complexity and challenges inherent in accurate identification of direct RNA binders based on their UV-enrichment* and assessment of RNA-binding activity based on protein recovery alone56. These method-intrinsic challenges can be compounded by low method specificity and/or non-SILAC comparisons, both of which result in apparent UV-dependent enrichment of free protein. While RBP enrichment methods utilizing SILAC LC–MS/MS and stringent sample washes achieve higher %TPS, the benchmark comparisons performed here reveal both reduced yields and biases in signal recovery which were previously unrecognized. These observations provide insights into why non-poly(A) RNA binders such as ribosomal proteins can represent a large fraction of MS spectra52. The high selectivity of LEAP-RBP achieves high %TPS without the need for high-stringency washes, and thus provides a more specific, selective portrait RNA interactomes.A S/N ratios and RNA-bound abundance, which greatly simplifies study of their RNA interactome dynamics, largely independent of limitations in existing methods. A primary challenge in the field however is the study of candidate RBPs lacking canonical RNA-binding domains, known functions in RNA biology, relatively low UV cross-linking frequencies, and/or significant free protein contributions in phase separation-based RNA-centric methods, all of which can hinder interpretation as well as meta-analysis of RNA interactomes and their dynamic regulation  is an overestimation. This perspective is consistent with findings in a recent CLIP-based RNA-protein cross-linking frequency analysis, where a crosslink frequency threshold that distinguishes bona fide and low-significant protein–RNA interactions was reported11. Importantly, LEAP-RBP combined with quantitative proteomic and SRA analysis provides direct experimental evidence of RNA binding and orthogonal validation of RBP activity58. The principle behind SRA has been previously reported and performed in combination with silica-based RBP purification methods25. Yet, biases in RBP–RNA adduct recovery or low sensitivity ( | S | /µg RNA) and/or low S/N can confound detection of many bona fide RBPs by SRA analysis alone   T; Fig. . To thations ~169 is an ovS/N by SDS-PAGE RNase-sensitivity Assay (SRA); estimating RBP-specific UV cross-linking efficiencies and S/N ratios by SDS-PAGE and immunoblot; evaluating total protein and total RNA-bound protein abundance by SDS-PAGE; MS data analysis; RCS rank analysis.A description of sample types, terminologies, quantitative metrics, and analytical approaches are provided in the Supplementary Methods. Analytical approaches: evaluating UV-dependent enrichment and S/N) and abundance (%TP). However, only those displaying discernible RNase sensitivity by SRA and immunoblot were considered bona fide RNA-binding proteins. Proteins which remained RNase-insensitive by SRA or undetectable were not considered bona fide RBPs regardless of GO-annotation . However, because the inability to detect a protein by SRA and immunoblot could be due to their low RNA-bound protein abundance, negative data were not considered formal confirmation of an absence of RNA-binding activity. To this point, validation of RNA-binding activity with LEAP-RBP and SRA requires that RNA-protein interactors are susceptible to UV cross-linking.Protein displaying CL/nCL ratios >0 in LEAP-RBP fractions by SILAC LC–MS/MS were considered high or low-confidence RBPs based on their observed enrichment efficiency (The ability of LEAP-RBP to rapidly (< 5’) recover total RNA-bound protein from AGP suspensions with near 100% recovery is supported by a lack of quantifiable RNA and RNase-sensitive bands in the unprecipitated fraction by SRA and Coomassie Blue (protein) staining  staining migrating between 17 and 30 kD  and contained diverse sRNA species displaying broad genome distributions  supplemented with 2 mM l-glutamine , 1 mM Na-pyruvate , 10 mM HEPES , 0.05 mM 2-mercaptoethanol , and 10% FBS at 37 °C, 5% CO2. SILAC-labeling was done using the Pierce SILAC-protein quantitation kit , supplemented with 2 mM l-glutamine , and 10 µg/mL l-proline . Cells were passaged at least five times in their respective SILAC-labeled media (>10 doublings). For the comparative LEAP-RBP experiment, HeLa cells were maintained as described above and treated with DMSO (negative control) or 2 µg/mL Harringtonine  for 30 min at 37 °C, 5% CO2; Harringtonine (HT) was prepared as a 1000× stock in DMSO.HeLa, 293T, and Huh7 cells were maintained in Dulbecco’s Modified Eagle’s Medium  supplemented with 10% FBS  at 37 °C, 5% CO2, 1 mM DTT, 50 µg/mL CHX, 1× protease inhibitor cocktail , 40 U/mL RNase OUT, and 2% dodecylmaltoside (DDM) (w/v)). DDM Lysates were centrifuged at 5000 × g for 5 min at 4 °C and 1 mL of the clarified supernatants were resolved on a 10 mL sucrose gradients (15–40% w/v) containing DDM lysis buffer components noted above via centrifugation at 35,000 × g for 3 h at 4 °C. Gradients were fractionated on a Teledyne Isco Lincoln (NE) gradient fractionator with continuous A254 sampling59.HeLa cells were cultured in 150 mm dishes until 80–90% confluent and treated with DMSO or harringtonine as described above, were washed twice with ice-cold 1× PBS and harvested on ice with 3 mL fresh ice-cold DDM lysis buffer 2 at 254 nm. Cells were lysed on plate, scraped, and transferred to a 2.0 mL microcentrifuge tube using two 400 µL aliquots of guanidinium thiocyanate (w/o phenol) buffer23. Guanidinium thiocyanate (GT) buffer  was prepared with the following stock solutions prepared in DEPC-treated DI water: 5 M guanidinium thiocyanate , 750 mM sodium citrate pH 7.0 , 10% N-lauryl sarcosine , 0.5 M EDTA pH 8.0 . Stock solutions were filtered (0.2 µm) to remove insoluble particulates that accumulate at the AGPC interphase: GT was filtered twice using Whatman paper  or by standing incubation overnight and transferring of the clarified portion; sodium citrate and EDTA stock solutions were filtered using 0.2-µm syringe filters .Cells were cultured in 100- or 150-mm dishes until 60–90% confluent, washed twice with ice-cold 1× PBS, and UV-crosslinked on ice with 100–800 mJ/cmg for 10 min at 4 °C with slow brake setting and ~80% (v/v) of the aqueous and organic phases were removed. For repeated AGPC extraction, 800 µL of fresh acidic guanidinium thiocyanate–phenol (2:1) buffer and 160 µL chloroform were added to the AGPC interphase and the process was repeated. The final AGPC interphase was resuspended in 1.0–1.5 mL fresh acid guanidinium thiocyanate–phenol (2:1) buffer. If AGP suspensions appeared cloudy, an additional AGPC extraction was performed. Additional protocol information is included in the Supplementary Methods.400 µL of acidic phenol  were added to 800 µL GT cell extracts. Alternatively, cells were lysed in 1.2 mL Trizol reagent  and transferred to a 2 ml microcentrifuge tube. Cell lysates were prepared by passage through a 19 ga 1–1/2” needle fifteen times . For AGPC extraction, 240 µL chloroform  or ~3/5th vol of phenol were added to samples and vigorously vortexed for 10 s. Samples were centrifuged at 10,000 × g for 15 min at 4 °C with slow brake setting. Following removal of the supernatant, pellets were washed three times with ice-cold 75% ethanol (twice the volume of precipitation mixture), incubated for 5 min on ice with occasional agitation, and centrifuged at 18,000 × g for 5 min at 4 °C with slow brake setting. Pellets were air-dried and resuspended at the desired volume with DEPC-treated water or TE buffer. For long-term storage, precipitates were stored in 75% ethanol at −80 °C. Final working sample concentrations ranged from 0.2–2.0 µg of RNA/µL.Sodium chloride (5 M) was added to aqueous phase samples to a final concentration of 0.6 M and mixed by brief vortexing. One part isopropanol was added to a final concentration of 50% and samples were mixed by brief vortexing. Samples were incubated on a rotator for 15 min at 4 °C and centrifuged at 18,000 × g for 10 min at 20 °C with slow brake setting. Following removal of the supernatant fraction, precipitates were washed twice with 1.0 mL RT 95% methanol (for up to 100 µg protein). For each wash, samples were vortexed for 5 sec, incubated on a rotator for 10 min at RT, and centrifuged at 20,000 × g for 10 min at 20 °C with slow brake setting. Three 400 µL aliquots of RT 95% methanol were used to recover precipitates adhering to the sides of the tubes and combined in a 1.5 mL microcentrifuge tube. The tubes were then stored vertically at 4 °C overnight or at RT for 30 min to allow precipitates to settle at the bottom of the tube. Samples were centrifuged at 20,000 × g for 10 min at 20 °C with slow brake setting and supernatants were removed. Pellets were air-dried and resuspended at the desired concentration with 1% lithium dodecyl sulfate (LiDS)  in TE. For long-term storage, samples were stored as precipitates in 95% methanol or as 1% LiDS TE suspensions at −80 °C. Working concentrations of methanol precipitated samples in 1% LiDS TE ranged from 0.1–5.0 µg protein/µL, 0.1–8.0 µg of RNA/µL, or 0.1–2.0 µg of protein-bound RNA/µL.Samples were mixed with 19 parts room temperature (RT) 100% methanol, incubated on a rotator for 1 h at RT, and centrifuged at 20,000 × g for 15 min at 4 °C with slow brake setting. Following removal of the supernatant fraction, samples were washed three times with 1 mL ice-cold 75% ethanol, incubated for 5 min on ice, and centrifuged at 14,000 × g for 5 min at 4 °C with slow brake setting. Samples were then washed twice with 1 mL RT 95% methanol, incubated on a rotator for 10 min at RT and centrifuged at 20,000 × g for 10 min at 20 °C with slow brake setting. Supernatants were removed. Precipitates were air-dried and resuspended at the desired concentration in 1% LiDS in TE. For long-term storage, precipitates were stored in 95% methanol or as 1% LiDS TE suspensions at −80 °C. Working concentrations of INP-precipitated RNPs ranged from 1.0 to 3.0 µg of protein-bound RNA/µL.Final AGPC interphase suspensions were split between 2 mL microcentrifuge tubes (160 µL each). AGP suspensions were either stored at −80 °C or used immediately for precipitation. For precipitation, the following reagents were added to each AGP suspension in order while mixing by brief vortexing (5 sec) after each addition: 3 µL of GlycoBlue , 640 µL of 1% LiDS TE, 96 µL 5.0 M NaCl, and 899 µL isopropanol. Samples were vortexed for 5 sec and incubated on a rotator for 15 min at 4 °C. Samples were centrifuged at 14,000 × g for 5 min at 20 °C, and supernatants were removed. RNP pellets were rinsed twice with 1 mL RT 95% methanol by inverting the tube multiple times and removing the supernatant. RNP pellets were then washed with 1 mL RT 95% methanol by incubating for 5 min at RT with occasional inversion. Following removal of the final 95% methanol wash, pellets were air-dried and resuspended at the desired concentration with 1% LiDS TE. Additional protocol information is included in the Supplementary Methods. Working concentrations of LEAP-RBP isolated RNP fractions ranged from 0.1 to 4.0 µg of protein-bound RNA/µL.AGP input suspensions or final AGPC interphase suspensions were aliquoted (200 µL) across 1.5 mL microcentrifuge tube and stored at −80 °C or used immediately for precipitation. Chloroform was added to a final concentration of ~7% v/v and the sample was mixed by vortex to form an emulsion . RNP pellets containing <55 µg RNA&DNA were fully resuspended in 15 µL of TE buffer and 5 µL of a master mix containing 10× Turbo DNase buffer, TE buffer, and Turbo DNase were added to a final concentration of 1× Turbo DNase buffer and 1 µL of Turbo DNase/10 µg DNA. Samples were incubated at 37 °C for 15 min and nine parts (180 µL) fresh acid guanidinium thiocyanate–phenol (2:1) buffer were added. Samples were precipitated according to the LEAP-RBP protocol using 14 µL of chloroform and resuspended in 1% LiDS TE at the desired concentration. Additional protocol information is included as part of the Supplementary Methods.g for 20 s at 20 °C, and clarified supernatants (~90% v/v) were transferred to a new tube. Two 2 µL aliquots of the clarified sample suspensions, typically containing between 0.1 and 1.0 µg protein were added to separate wells and mixed with 200 µL working reagent  for BCA quantitation.Samples containing more than 1.5 µg RNA/µL were diluted 1:5 in their respective buffers for RNA quantitation by UV-spectrophotometry . For samples where DNA contamination is expected to impact RNA quantitation by more than 10%, “RNA&DNA” was used in place of “RNA” for figure panels. Protein concentrations were determined by BCA protein assay  using a microplate 96-well format and BSA as a protein standard. Typically, 1% LiDS TE sample suspensions were clarified prior to protein quantitation: sample suspensions were incubated at 55 °C for 20 s, mixed by brief vortex, centrifuged at 3000 × RNase digestions were performed in separate 0.2 mL thermocycler tubes (10–12 µL reactions) using a maximum of 5 µL of 1% LiDS TE sample suspensions containing <4.0 µg RNA/µL. RNase Cocktail , 10× RNase digest buffer , and 25× protease inhibitors  were added at the same time to a final concentration of 2 µL RNase Cocktail/15 µg RNA  bromophenol blue ) and diluted 3:1 with β-mercaptoethanol (v/v) for a working stock (LB WS). LB WS was added to samples to a final detergent concentration of 2% and denatured by incubating for 15 min at 65 °C. Samples were separated on a 0.75 mm, 15-well, 4–12% gradient polyacrylamide gels  resolver, 4% stacker) at constant voltage (80 V) for 1.5 h at RT  and a Trans-Blot SD semi-dry electrophoretic transfer cell . Alternatively, samples were wet transferred to nitrocellulose membranes using wet-transfer buffer  and a Bio-Rad Mini-Protean II system. Blocking and blotting conditions were performed as follows: anti-pAbPC1 antibody , anti-PABPC4 antibody , anti-TIA1 antibody , anti-HuR antibody , anti-XRN1 antibody , anti-RPL4 antibody , anti-RPL8 antibody , anti-LRRC59 antibody , anti-NCL antibody , anti-RPN1 antibody , anti-TRAPα antibody , anti-GRP94 antibody , anti-GAPDH antibody , anti-GRP78 antibody , anti-β-tubulin antibody , anti-RPS3 antibody , anti-SND1 antibody , anti-UPF1/RENT1 antibody , anti-HDLBP antibody , anti-ABCF3 antibody , anti-GEMIN5 antibody , anti-eEF2 antibody , anti-CELF1 antibody , anti-Fibrillarin/U3 RNP antibody . Signal detection was performed using WesternBright ECL HRP substrate  and an Amersham Imager 600 (see corresponding Source Data). Additional protocol information is included as part of the Supplementary Methods.2, 50% glycerol v/v) per 10 µg of protein, and incubated at 55 °C for 15 min. For isolation of RNA and/or DNA samples were mixed with four parts neutral guanidinium thiocyanate–phenol (2:1) buffer  and 1 part chloroform, vigorously vortexed for 10 s, and centrifuged at 10,000 × g for 10 min at 4 °C with slow brake setting. Aqueous phase samples were precipitated as outlined above (Precipitation of RNA from aqueous phase samples).For proteinase K digestion, samples were diluted 1:2 with 2× proteinase K buffer , mixed with 2 µL proteinase K stock , 20 mM Tris-HCl pH 7.5, 1 mM CaClRNA samples suspended in DEPC-treated water were mixed with 6× gel loading buffer , incubated at 65 °C for 2 min, and chilled for 2 min on ice before being loaded on a 1.0% or 1.5% agarose TBE gel containing 0.5–1× SYBR Safe stain. Samples were separated under constant voltage at 140 V or 140 V for 20–40 min and visualized using an Amersham Imager 600 .qPCR was performed using the Luna Universal qPCR Master Mix  on a Bio-RAD Cfx96 real-time PCR system using a 96-well format and 20 µL reactions. DNA contamination was quantified using primers targeting the coding region of GRP78:F-primer: 5’-CTTGGTATTGAAACTGTGGGAGGT-3’R-primer: 5’-AGATCTGAGACTTCTTGGTAGGCA-3’Digestion and depletion of RNA and/or DNA from input samples and RNP fractions were necessary prior to MS-based proteomic analysis  binding buffer (90% methanol/100.0 mM TEAB). Proteins were collected on the S-Trap, digested using 20 ng/µL sequencing grade trypsin (Promega) for 1 h at 47 °C, and eluted using 50 mM TEAB, followed by 0.2% FA, and lastly using 50% ACN/0.2% FA. All samples were then lyophilized to dryness and resuspended in 12 µL 1% TFA/2% acetonitrile containing 12.5 fmol/µL yeast alcohol dehydrogenase (ADH_YEAST).r = 120,000 (m/z 200) full MS scan from m/z 375 to 1500 with a target AGC value of 4e5 ions was performed. MS/MS scans were acquired in the ion trap in rapid mode from m/z 100 with a target AGC value of 2e4 and max fill time of 100 ms. The total cycle time for each CV was 1 s, with total cycle times of 3 s between like full MS scans. A 45 s dynamic exclusion was employed to increase depth of coverage. The total analysis cycle time for each fraction injection was ~2 h.Quantitative LC–MS/MS was performed on 1 µg of each sample, using a nanoAcquity UPLC system (Waters Corp) coupled to a Thermo Orbitrap Fusion Lumos high-resolution accurate mass tandem mass spectrometer (Thermo) equipped with a FAIMSPro device via a nanoelectrospray ionization source. Briefly, peptides were trapped on a Symmetry C18 20 mm × 180 µm trapping column (5 µL/min at 99.9/0.1 v/v water/acetonitrile), after which the analytical separation was performed using a 1.8 µm Acquity HSS T3 C18 75 µM × 250 mm column (Waters Corp.) with a 90-min linear gradient of 5 to 30% acetonitrile with 0.1% formic acid at a flow rate of 400 nanoliters/minute (nL/min) with a column temperature of 55 °C. Data collection on the Fusion Lumos mass spectrometer was performed for three difference compensation voltages . Within each CV, a data-dependent acquisition (DDA) mode of acquisition with a H. sapiens database (downloaded 11/2019) and an equal number of reversed sequence “decoys” for false-discovery rate determination. Mascot Distiller and Mascot Server  were utilized to produce fragment ion spectra and to perform database searches. Database search parameters included fixed modification on Cys (carbamidomethyl) and variable modifications on Meth  and Arg/Lys . Peptide Validator and Protein FDR Validator nodes in Proteome Discoverer were used to annotate the data at a maximum 1% protein false-discovery rate based on q value calculations. Note that peptide homology was addressed using razor rules in which a peptide matched to multiple different proteins was exclusively assigned to the protein that has more identified peptides. Protein homology was addressed by grouping proteins that had the same set of peptides to account for their identification. Following database searching and peptide scoring using Proteome discoverer validation, the data was annotated at a 1% protein false-discovery rate.Data were imported into Proteome Discoverer 2.5 (Thermo Scientific Inc.) and all LC–MS/MS runs were aligned based on the accurate mass retention time of detected ions (“features”) which contained MS/MS spectra using Minora Feature Detector algorithm in Proteome Discoverer. Relative peptide abundance was calculated based on area-under-the-curve (AUC) of the selected ion chromatograms of the aligned features across all runs. A filter was applied which required each peptide to be measured in at least 2 unique samples and in at least 50% of at least one of the unique biological groups. The MS/MS data was searched against the SwissProt nCL values equal to 0 were replaced with the average non-zero SPInCL value of the same protein ID. Proteins only detected in UV-crosslinked samples were scored as UV-enriched*, ommitted from statistical analysis, and given the following pseudo-value: −log10 = 10, log2(CL/nCL) = 10. For the remaining proteins, log2(CL/nCL), ratios were generated with SPICL values and average SPInCL values according to Eq. (2(CL/nCL) ratio equals zero using a heteroscedastic upper-tailed t test. Correction for multiple hypothesis testing was performed using the Benjamini–Hochberg approach and a false-discovery rate of 5%. A summary of statistical testing for INP and LEAP-RBP is provided in Supplementary Data Initial data processing for the identification of UV-enriched proteins and generation of sum peptide intensities were done separately for each method (INP vs LEAP-RBP). Peptide intensities of common contaminants and spike-ins  were manually curated from protein lists. The remaining peptide intensities were sorted by SILAC label and used to generate sum peptide intensities (SPI). Proteins not detected in all three UV-crosslinked samples were excluded from downstream sample normalization procedures and data analysis. Replicate samples were mean-normalized to total SPI and SPI3, OOPs51, Ptex21, and TRAPP50 were downloaded from the ProteomeXchange using the identifiers PXD010520, PXD026716, PXD009571, and PXD011071 respectively. Protein identifiers, unique peptide counts, and sum peptide intensities were obtained from their respective proteingroup.txt file; proteins marked as potential contaminants were removed. MS datasets for RIC and eRIC, including protein identifiers, unique peptide counts, and sum peptide intensities, were obtained from52; Supplementary Data 52. Because XRNAX was performed without replicates, samples were first mean-normalized and the average non-zero SPI values of 12 different samples were used for MS data analyses . For the remaining MS datasets, proteins not detected in all UV-crosslinked samples were excluded from downstream sample normalization procedures and data analysis. Replicate samples were mean-normalized to total SPI and SPInCL values equal to 0 were replaced with the average non-zero SPInCL value of the same protein ID.Maxquant output files (.txt) for XRNAXPrior to LC–MS/MS analysis, samples were supplemented with 50 µL 8.0 M and subjected to two rounds of probe sonication. Next, samples were spiked with either a total of 120 or 240 fmol of bovine casein, supplemented with 7.9 µL 20% SDS, reduced with 10 mM dithiolthreitol for 30 min at 32 °C and alkylated with 20 mM iodoacetamide for 45 min at RT. Then, samples were supplemented with a final concentration of 1.2% phosphoric acid and 472 µL of S-Trap (Protifi) binding buffer (90% methanol/100.0 mM TEAB). Proteins were collected on the S-Trap, digested using 4 or 20 ng/µL  sequencing grade trypsin (Promega) for 1 h at 47 °C, and eluted using 50 mM TEAB, followed by 0.2% FA, and lastly using 50% ACN/0.2% FA. All samples were then lyophilized to dryness and resuspended in 12 or 60 µL  of 1% TFA/2% acetonitrile containing 12.5 fmol/µL yeast alcohol dehydrogenase (ADH_YEAST).r = 120,000 (m/z 200) full MS scan from m/z 375 to 1500 with a target AGC value of 4e5 ions was performed. MS/MS scans were acquired in the Orbitrap at r = 50,000 (m/z 200) from m/z 100 with target AGC value of 1e5 and max fill time of 35 ms. The total cycle time for each CV was 1 s, with total cycle times of 3 sec between like full MS scans. A 45 s dynamic exclusion was employed to increase depth of coverage. The total analysis cycle time for each fraction injection was approximately 2 h.Quantitative LC–MS/MS was performed on 3 µL (1 µg) of each sample, using a nanoAcquity UPLC system (Waters Corp) coupled to a Thermo Orbitrap Fusion Lumos high-resolution accurate mass tandem mass spectrometer (Thermo) equipped with a FAIMSPro device via a nanoelectrospray ionization source. Briefly, peptides were trapped on a Symmetry C18 20 mm × 180 µm trapping column (5 µL/min at 99.9/0.1 v/v water/acetonitrile), after which the analytical separation was performed using a 1.8 µm Acquity HSS T3 C18 75 µM × 250 mm column (Waters Corp.) with a 90-min linear gradient of 5 to 30% acetonitrile with 0.1% formic acid at a flow rate of 400 nanoliters/minute (nL/min) with a column temperature of 55 °C. Data collection on the Fusion Lumos mass spectrometer was performed for three different compensation voltages . Within each CV, a data-dependent acquisition (DDA) mode of acquisition with a H. sapiens database (downloaded 08/2022), a common contaminant/spiked protein database , and an equal number of reversed sequence “decoys” for false-discovery rate determination. Sequest was utilized to produce fragment ion spectra and to perform the database searches. Database search parameters included fixed modification on Cys (carbamidomethyl) and variable modification on Met (oxidation). Search tolerances were 2 ppm and 0.8 Da product ion with full trypsin enzyme rules. Peptide Validator and Protein FDR Validator nodes in Proteome Discoverer were used to annotate the data at a maximum 1% protein false-discovery rate based on q value calculations. Note that peptide homology was addressed using razor rules in which a peptide matched to multiple different proteins was exclusively assigned to the protein that has more identified peptides. Protein homology was addressed by grouping proteins that had the same set of peptides to account for their identification. A master protein within a group was assigned based on % coverage.Following 15 total UPLC-MS/MS analyses , data were imported into Proteome Discoverer 3.0 (Thermo Scientific Inc.), and individual LC–MS data files were aligned based on the accurate mass retention time of detected precusor ions (“features”) using Minora Feature Detector algorithm in Proteome Discoverer. Relative peptide abundance was measured based on peak intensities of the selected ion chromatograms of the aligned features across all runs. The MS/MS data was searched against the SwissProt 2 normalized SPI values were used to test for differences in protein recovery between samples groups (DMSO vs HT) for each fraction (input or clRNP) using independent two-tailed homoscedastic t tests (Supplementary Data S/N limit) or only those which displayed S/N ratios >3 in LEAP-RBP (clRNP) fractions by SILAC LC–MS/MS analysis.Initial data processing and generation of sum peptide intensities were done separately for each fraction and each sample group (input or clRNP and DMSO or HT). Peptide intensities of common contaminants and spike-ins  were manually curated from protein lists. Proteins not detected in all three replicates of both sample groups (DMSO and HT) for a given fraction (input or clRNP) and containing at least two unique peptide matches were excluded from downstream sample normalization and data analysis. Samples were mean-normalized to total SPI and log60. For additional analyses of GO-annotated groups, lists were exported and assigned primary Uniprot IDs as described in Supplementary Data GO-enrichment analyses were performed for UV-enriched* proteins identified by INP and LEAP-RBP using PANTHER V17.02 (254 nm). clRNPs were isolated from the final (6th) AGPC interphase suspension by LEAP-RBP and resuspended in TE buffer. Ca. 6 µg of protein-bound RNA was treated with Turbo DNase as outlined above (LEAP-RBP DNA depletion step) without performing the second LEAP step. Then, 20 µL of 2× proteinase K buffer and 3 µL proteinase K stock (20 mg/mL) were added, and samples were processed as described above (Proteinase K digestion).Two independent samples (HeLa) were UV-crosslinked with 0.4 J/cmFor sRNA library construction, 3’ and 5’ adapters were ligated to 3’ and 5’ ends of small RNAs, respectively. First-strand cDNA was synthesized after hybridization with a reverse transcription primer and double-stranded cDNA libraries generated via PCR enrichment. After purification and size selection, libraries with insertions between 18–40 bp were selected. Library concentrations and QC was performed via Qubit and real-time PCR for quantitation and Bioanalyzer for size distribution analysis. Quantified libraries were pooled and sequenced on Illumina platforms in SE50 mode.61, without mismatch. Mapped small RNA tags were examined for known miRNA homologies using miRDeep2 version 0.0.562. To remove tags originating from protein-coding genes, repeat sequences, rRNA, tRNA, snRNA, and snoRNA, small RNA tags were mapped with RepeatMasker version 4.0.3 and Rfam version 11.064. Novel miRNA predictions were performed using miRDeep2 version 0.0.5 modified with miREvo version 1.1 and ViennaRNA version 2.1.1 through exploration of secondary structure, Dicer cleavage sites, and the minimum free energy of the small RNA tags unannotated in the former steps66. For alignment and annotations, some small RNA tags may map to more than one category. To ensure that small RNAs mapped to only one annotation, the following priority rules were used: known miRNA > rRNA > tRNA > snRNA > snoRNA > repeat > gene > NAT-siRNA > gene > novel miRNA > ta-siRNA. miRNA expression levels were estimated by TPM (transcript per million) through the following criteria67: Normalization formula: Normalized expression = mapped reads * 1,000,000.Raw data (raw reads) in fastq format were processed through custom (Novogene) perl and python scripts to remove read sequences containing poly-N, 5’ adapter contaminants, lacking 3’ adapter or the insert tag, containing polyA, T, G or C, and low quality reads. Small RNA read data were mapped to reference sequence using Bowtie version 0.12.9All statistical analyses were performed using JMP Pro 14.0, exported test results included as part of the provided Source Data.Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Supplementary Dataset 8Supplementary Dataset 9Supplementary Dataset 10Supplementary Dataset 11Supplementary Dataset 12Supplementary Dataset 13Supplementary Dataset 14Supplementary Dataset 15Supplementary Dataset 16Supplementary Dataset 17Supplementary Dataset 18Supplementary Dataset 19Supplementary Dataset 20Source Data