RNA-protein interaction can be captured by crosslinking and enrichment followed by tandem mass spectrometry, but it remains challenging to pinpoint RNA-binding sites (RBSs) or provide direct evidence for RNA-binding. To overcome these limitations, we here developed pRBS-ID, by incorporating the benefits of UVA-based photoactivatable ribonucleoside  crosslinking and chemical RNA cleavage. pRBS-ID robustly detects peptides crosslinked to PAR adducts, offering direct RNA-binding evidence and identifying RBSs at single amino acid-resolution with base-specificity (U or G). Using pRBS-ID, we could profile uridine-contacting RBSs globally and discover guanosine-contacting RBSs, which allowed us to characterize the base-specific interactions. We also applied the search pipeline to analyze the datasets from UVC-based RBS-ID experiments, altogether offering a comprehensive list of human RBSs with high coverage . pRBS-ID is a widely applicable platform to investigate the molecular basis of posttranscriptional regulation. RNA-protein interactions play critical roles in post-transcriptional gene regulation. Here the authors demonstrate pRBS-ID, an updated MS/MS-based method that combines the benefits of photoactivatable ribonucleosides and the chemical cleavage of RNA. To understand their roles and action mechanisms in gene regulation, early pioneering studies focused on cataloging RBPs using RNA interactome capture methods5. RBPs were crosslinked to RNA using UV, pulled down in an RNA-dependent manner, and analyzed by liquid chromatography and tandem mass spectrometry (LC-MS/MS). Adding onto the initial protein-level mapping of RBPs, recent studies narrowed down to systematically profile RNA-binding domains (RBDs) within RBPs8. In these studies, the RBDs were inferred from the depletion of RNA-crosslinked peptides in the whole proteome7, or the enrichment of partially digested peptides adjacent to the actual RNA-crosslinked ones8. However, both methods heavily relied on quantitative enrichment analysis for RBS identification, which could only provide indirect RNA-binding evidence. Also, the resolution was low because the analyses were confined to peptide-level.RNA-binding proteins (RBPs) are integral players in post-transcriptional gene regulation14. Briefly, the crosslinked RNA adducts were shortened as much as possible using extensive nuclease digestion. Then, peptides with the crosslinked RNA fragments were detected by MS, and the mass increased owing to the adduct was used as evidence for RNA binding. In this approach, the homogeneity of the shortened RNA moiety was crucial, yet nucleolytic digestion leaves behind a rather heterogeneous pool of RNA adducts. This complexity increases MS/MS search space, making it difficult to determine RNA-binding sites (RBSs) within peptides and severely reducing the coverage.To overcome these limitations, additional LC-MS/MS-based methods were developed to identify the peptides that are directly crosslinked to RNA15). The single ribonucleoside adduct minimized MS/MS search space, thereby drastically improving the RBS identification rate. This approach using chemical cleavage instead of enzymatic digestion enabled us to reliably pinpoint individual amino acids as RBSs, providing a single amino acid-resolution.Addressing this concern, we recently implemented hydrofluoride (HF) to completely digest the crosslinked RNA adduct to a single ribonucleoside  has been widely utilized to capture RNA−protein interactions, yet it holds substantial methodological drawbacks. UVC irradiation induces a covalent bond mostly between Us and amino acids in close proximity while the other RNA bases  are crosslinked at low frequencies21. UVA irradiation (365 nm) selectively activates RNA-incorporated PARs to crosslink RBPs. The most widely utilized PARs are 4-thiouridine (4SU) and 6-thioguanosine (6SG)21, which can expand the base-interaction repertoire of RBSs from single to double base types (U and G) with higher crosslinking efficiencies compared to their respective natural RNA base counterparts17. Selective PAR labeling and efficient UVA-crosslinking would be particularly useful for the mass spectrometric detection and identification of peptides crosslinked to individual RNA base types even at low abundances. Two previous studies attempted to map RBSs crosslinked to 4SU using nucleases to cleave the RNA moiety crosslinked to peptides in yeast cells13, bringing many interesting insights to the molecular interactions between RNA and proteins. Yet, the methods utilized nucleases to digest peptide-crosslinked RNA, where the incomplete RNA cleavage may have reduced the resolution and coverage for RBS identification.Photoactivatable ribonucleoside (PAR)-specific UVA-crosslinking is a promising alternative that can relieve the aforementioned shortcomingsHere, we integrated the advantages of chemical RNA cleavage with PAR crosslinking. Initially, we uncovered the chemical features of HF-treated PAR adducts in MS/MS and built an MS/MS search pipeline specific to PAR-modification. Based on this, we developed pRBS-ID, a method that enables system-wide profiling of PAR-RBSs with a robust identification rate and accurate label-free quantification. Notably, we unraveled 4SU-RBSs in human cells with high coverage, along with enabling the identification of 6SG-RBSs. We further analyzed the domain and site-level characteristics of the PAR-RBSs and demonstrated the usefulness of pRBS-ID in the structural analysis of RNA−protein interactions.5, and digested with trypsin. Then, the peptide-RNA conjugates were enriched using a 30 kDa size-cutoff cellulose membrane filter to deplete non-crosslinked peptides smaller in size than the conjugates. The peptide-RNA conjugates were treated with hydrofluoride (HF) for complete RNA digestion15, which leaves a single nucleoside or base conjugated to peptides, and subjected to LC-MS/MS analysis  to induce crosslinking between proteins and PARs in RNA. Following cell lysis, the protein-mRNA conjugates were enriched using oligo-d(T) beadssis Fig. . The MS/22 as an open search tool, which detects the precursor mass shifts in MS1 with little dependency on MS2 fragment ions   Fig. , top. Toe”) Fig. . Among t23, which employs the adduct mass on MS2 fragment ions to find modifications in peptides . Thus, each XIC peak should be distinguished for precise RBS localization and quantification. Therefore we integrated mPE-MMR24, a tool that marks individual XIC peaks as separate unique mass classes (UMCs) and assigns label-free quantification values, prior to the MS-GF+25 closed search in pRBS-ID (see online “Methods” for details). Notably, the assignment of one RBS localization per UMC allowed precise identification of multiple RBSs from a single peptide. This expanded the RBS repertoire compared to the previous analysis pipeline in RBS-ID, where only a single RBS was chosen for each peptide15.Through careful inspection of the MS/MS data, we noticed that MS1 chromatograms from the same peptide showed multiple peaks with distinct retention times . As a result, from the previous RBS-ID data (generated from 2.5 times more cells than the pRBS-ID experiment), 2,030 RBSs were identified , but using different UV irradiation . To obtain a comparable UVC-RBS dataset, we re-analyzed our previously published oligo d(T)-enriched mRNA-RBS datasetOf note, in the protein level, the RBS-ID and pRBS-ID have a larger overlap than they do in the RBS level Fig. . In termHence, we compared the amino acid type frequencies between the 4SU- and UVC-RBSs Fig. . RNA bas26  using the 4SU-RBS and 6SG-RBS datasets. Although the number of identified 6SG-RBSs may be rather small to make highly accurate comparisons, the analysis can serve as a proof-of-principle to dissect the base-specific features of RNA−protein interactions. First, we compared the domain-level characteristics of these two types of PAR-RBSs26 Fig. . Both RBIn addition to the domain-level analysis, we further dissected the frequency of amino acid types in the two PAR-RBS datasets Fig. . Both PA30  are located close to the U bases30 Fig. . Further30 Fig. . Y298 is32, yet the resolved structures are static snapshots. Hence, dynamic intermolecular binding events, particularly those involving flexible loops and intrinsically disordered regions (IDRs), may not be captured in the conformation resolved by X-ray crystallography or cryo-EM. In theory, pRBS-ID can identify RBSs in RNP complexes at any conformational state. Thus, we examined RBSs on flexible regions to assess our hypothesis. In FAU, a component of the human 40S ribosome, F49 was identified as 4SU-RBS33 , or chemical crosslinking reagents such as formaldehyde37. A MS/MS search pipeline can then be optimized for each crosslink type, as in the pRBS-ID method development.pRBS-ID can be used as a widely applicable and amenable platform for investigating nucleic acid−protein interactions. When combined with pulse labeling and specific RNA probes, the method can profile RBSs in proteins bound to specific RNA types at various temporal stages. For instance, RBSs in proteins bound to viral genomic RNA or viral subgenomic RNAs can be selectively captured through metabolic labeling at a particular stage of the viral life cycle. In addition, RBPs acting at different stages of RNA lifetime can be distinctively captured via pRBS-ID. Importantly, the PAR-adducts provide direct evidence for RNA binding, thereby offering strong confidence in the identification. Furthermore, the single amino acid-resolution of RBS mapping can be of great value in unraveling the action mechanisms of RBPs. On the other hand, the analytical procedure of PAR adduct characterization using combinations of open search tools can be benchmarked to characterize other nucleic acid adducts. These include different nucleic acid types such as DNA32. As exemplified in Fig. 40, resulting in AlphaFold41 and RoseTTAFold42. If this approach is extended to the de novo structural determination of RNA-protein complexes, the RBS datasets can be integrated to enhance the accuracy and extent of RNP conformational prediction. pRBS-ID together with structural and biochemical methods will also be valuable for the development of small molecule-based therapeutics that interfere with RNA−protein interaction43 as a number of disease-associated RNAs and their protein partners are considered promising as drug targets. This integrative approach will be particularly useful for investigating the RNA-binding modes of RBPs with flexible loops and IDRs that are difficult to solve the structure. Proteins with IDRs play critical roles in complex RNA−protein interaction and condensate formation in normal development and neuronal pathology. Overall, pRBS-ID can work as a powerful, general, and amenable method to deepen our knowledge of the dynamic interactions between proteins and nucleic acids.In addition to the utility as a methodological platform, the pRBS-ID dataset, together with the previously published RBS-ID dataset, can serve as a valuable resource for the structural prediction of RNA−protein interactions44. Finding safer alternative chemicals with similar hydrolyzing activity would greatly help resolve the issue and broaden the usage of chemical RNA digestion to map RBSs.Limitations of the current study include the low coverage of 6SG-RBSs owing to its inefficient crosslinking efficiency. This can be improved by increasing the input amount or using enhanced separation technologies prior to the MS/MS analysis. Thus, for general purposes to identify and profile RBPs and RBSs, 4SU would be more useful than 6SG. However, for RBPs that interact specifically with G residues, 6SG would be a highly valuable tool, especially when applying this method to purified or enriched proteins of interest. In addition, we would like to call attention to the strong toxicity of HF which should be handled with great caution and inside a fume hood2 concentration of 5%. HeLa cells were tested negative for mycoplasma contamination, and authenticated by ATCC by short tandem repeat profiling following ISO 9001:2008 and ISO/IEC 17025:2005 quality standards. HeLa cells were generous gifts from laboratories in the School of Biological Sciences, Seoul National University.HeLa cells used in this study were 2D-cultured in Dulbecco′s modified Eagle′s medium (Welgene) supplemented with 9% FBS (Welgene) and 1× antibiotic-antimycotic (Gibco), in a 37 °C incubator with a COPAR labeling and UVA-crosslinking − HeLa cells were grown on 150 Φ dishes (SPL) to 40−50% (4-thiouridine) or 70−80% (6-thioguanosine) confluency. The cells were labeled twice with media containing 100 μM 4-thiouridine  or 6-thioguanosine  for 12 h each. After incubation, the cells were washed twice with PBS (Amresco), then placed on ice in UVP 95-0228-01 Longwave Ultraviolet Crosslinker , 12 cm away from the UV lamp. The cells were irradiated with 450 mJ of 365 nm UV.Oligo-d(T) enrichment and tryptic digestion − UVA-crosslinked HeLa cells on each 150 Φ dish were lysed on-dish using 1.2 ml lysis buffer , 1 mM EDTA (Ambion), 0.5% SDS (Merck)) supplemented with 18 μl of SUPERase•In™ RNase Inhibitor (Invitrogen). The lysates from eight dishes (twice the amount required for LC-MS/MS) were merged, homogenized with a 21 G syringe, added to 4 ml oligo-dT(25) beads (New England Biolabs) pre-equilibrated with lysis buffer, and incubated for 1 h at RT. The supernatant was removed, and the beads were washed once with 1 ml lysis buffer, then once more using 1 ml wash buffer . The beads were moved to a new tube at the initial wash with lysis buffer. Following washes, 1 ml of digest buffer (50 mM Tris pH 8.0 and 5 mM EDTA) was added. 1,4-dithiothreitol (Merck) was added to each sample to 10 mM and incubated for 1 h at 37 °C, and iodoacetamide  was added to 40 mM and incubated for 1 h at 37 °C. Then, 15 μg trypsin  was added and incubated overnight at 37 °C. The samples were eluted by heating at 95 °C for 5 min.RNA-peptide conjugate enrichment by size-cutoff filtration − A 0.5 ml Amicon Ultra 30 kDa Centrifugal Filter Unit (Merck) was equilibrated twice using 0.5 ml Equilibration buffer (10 mM Tris pH 8.0). The centrifugation steps using the Amicon filters occurred at 14,000 × g for 15 min. The eluted sample was passed through the filter and topped seven times more with a 0.5 ml Equilibration buffer. This allowed the removal of free peptides and the retention of RNA-crosslinked peptides on the filter. Another 0.5 ml Equilibration buffer was added to collect the RNA-peptide conjugates by inverting the filter and spinning briefly. This step was repeated once more.HF digestion and peptide clean-up − The sample volume was adjusted to 100 μl using Concentrator plus system (Eppendorf) set to RT. Then, 400 μl of 48% HF was added and locked into a CaCO3 trap. The sample was incubated at 4 °C overnight and dried in a Concentrator plus system supplemented with a CaCO3 trap at RT. The sample was reconstituted with triple distilled water and cleaned up using ZipTip (Millipore). The eluted peptides were dried in the Concentrator plus at RT, and reconstituted in 50 mM ABC, pH 8.5. Following centrifugation at 13,000 × g for 5 min at RT, the supernatant was collected, and a half was injected for LC-MS/MS. All experimental steps exposing HF to air were carried out inside a fume hood with great caution. Calgonate® gels  were kept readily available for emergency use, and all procedures followed the safety measures provided by the manufacturer.LC-MS/MS for pRBS-ID − LC-MS/MS analysis was performed using an Orbitrap Fusion Lumos Tribrid MS (Thermo Fisher Scientific) coupled with a nanoAcquity system (Waters) equipped with a capillary analytical column (75 μm i.d. × 100 cm) and trap column (150 μm i.d. × 3 cm), both in-house packed with 3 μm Jupiter C18 particles (Phenomenex). The LC flow rate was 300 nl min−1 with a 100 min linear gradient ranging from 95% solvent A (0.1% formic acid (Merck)) to 40% solvent B . Full MS scans  were acquired at a resolution of 60k (at m/z 200). Higher-energy collisional dissociation (HCD) fragmentation was performed under 30% of normalized collision energy (NCE) via precursor isolation within 1.4 Th window. The MS2 scans were acquired at a resolution of 15k, with a maximum precursor ion injection time (ITmax) of 30 ms and automatic gain control (AGC) of 1 × 104.Open search −MODa (v1.6.0)23 search was performed on modification sizes 0–400, using mzXML files generated from RawConverter (v.1.1.0.19)45, in a data-dependent acquisition mode of charge states 2–7. The target-decoy database was generated by MS-GF+ (v.20170127)25 from the Swiss-Prot human database (May 2019)26. The PSM-level false discovery rate (FDR) was set to <1% for 4SU, or <5% for 6SG. We analyzed the frequently modified mass using moda.ptm files. For modifications on Cys, the modified mass was adjusted by adding 57 to account for fixed modification (carbamidomethylation). An MSFragger (v.20190530)22 open search via Fragpipe was performed with raw files on modification sizes 0–400. The target-decoy database and the FDR of the PSMs sorted by hyperscore were identical to those of MODa search. We took massdiff values in the tsv files to analyze the frequently modified mass. Adjustment of modification on Cys was not performed, as MSFragger open search cannot specify the modified site within peptides.MS2 modification search − PSMs with rounded massdiff values of 94 (4SU-base), 226 (4SU-nucleoside), 133 (6SG-base), 265 (6SG-nucleoside) were collected from the FDR-filtered MSFragger search results, respectively. Then, y-ions harboring all potential 4SU or 6SG modification sites in the best_locs column (amino acids in lowercase) were collected for each PSM. Nominal modification masses ranging from 1 to 400 were iteratively given to the y-ion masses and matched against the MS2 peaks of corresponding scans from MXFiltered.mgf file generated by msconvert (v.3.0.1908)46 integrated into mPE-MMR (v.1.1.8)24. The relative MS2 peak intensities of matched ions (0.5 Da window) were binned for each modification mass. The summed relative intensities were normalized against the maximum value.Spectrum visualization − Thermo Xcalibur Qual Browser (Thermo Fisher Scientific) and its boxcar method for peak smoothing were used to visualize extracted ion chromatogram (XIC) of precursor ions. Individual MS2 spectra annotations (0.5 Da matching window) were based on peaks from MXFiltered.mgf files and visualized by custom python codes.pRBS-ID closed search with precursor mass correction − Raw files were processed with mPE-MMR24 using default settings. Followingly, each scan in the MXFiltered.mgf file was duplicated with a precursor mass correction of ribose (C5H8O4) removal. MS-GF+ search25 was performed with the corrected mgf file. Fixed modification considered was C2H3N1O1 (carbamidomethylation) on Cys at any residue. Variable modifications considered were C2H−1N1 (4SU-base minus H2S minus carbamidomethylation) on Cys and C4H2N2O1 (4SU-base minus H2S) on the remaining 19 amino acids for 4SU datasets; C3N4O−1 (6SG-base minus H2S minus carbamidomethylation) on Cys and C5H3N5 (6SG-base minus H2S) on remaining 19 amino acids, for 6SG datasets. Modification on Cys residues was altered to distinguish base-crosslinked Cys from the carbamidomethylation of uncrosslinked free Cys residues by IAA. Only one modification was allowed per peptide. The Swiss-prot human database26 (May 2019) was used, and the decoy database was generated by MS-GF+25. The output mzid file was converted to a tsv file for post-processing.Conventional closed search without precursor mass correction − Raw files were processed with mPE-MMR24, and MS-GF+ search25 was performed with the MXFiltered.mgf file. Fixed modification considered was C2H3N1O1 (carbamidomethylation) on Cys at any residue. Variable modifications considered were C7H9N1O5 (uridine minus carbamidomethylation) on Cys and C9H12N2O6 (uridine) for U mRNA-RBS datasets15; C2H−1N1 (4SU-base minus H2S minus carbamidomethylation) or C7H7N1O4 (4SU-nucleoside minus H2S minus carbamidomethylation) on Cys and C4H2N2O1 (4SU-base minus H2S) or C9H10N2O5 (4SU-nucleoside minus H2S) on remaining 19 amino acids, for 4SU datasets; C3N4O−1 (6SG-base minus H2S minus carbamidomethylation) or C8H8N4O3 (6SG-nucleoside minus H2S minus carbamidomethylation) on Cys and C5H3N5 (6SG-base minus H2S) or C10H11N5O4 (6SG-nucleoside minus H2S) on remaining 19 amino acids, for 6SG datasets. The rest were identical to pRBS-ID closed search with precursor mass correction.Further-processing for robust RBS localization and quantification − For each experiment, PSMs within the ±5 ppm window from the mean precursor isotopic error of highly scored PSMs  were collected. Then, PSMs with the lowest SpecEValue from MS-GF+ were taken per scan . Unmodified PSMs and those without unique RBS localization were discarded to remove ambiguity . Precursor modifications of each PSMs were annotated as the base  or nucleoside . Then, UMCs were assigned to each PSM using UMC_MSMS_MX.txt file from mPE-MMR24. For each UMC, RBS localization scores were calculated as  of RBS PSMs in UMC corresponding to the individual localization) divided by  of all RBS PSMs in UMC). A single RBS localization with the highest localization score was assigned for each UMC. Then, a UMC-level FDR cutoff of <0.01 was applied. MS1 intensity-based label-free quantification value for each UMC was designated as the MostAbundant value in UMC.txt file from mPE-MMR. RBS locations within proteins were mapped to the proteome databases used above. Finally, the UMC identification and label-free quantification results were merged into RBS-level or RBS-peptide-level. For each crosslink type , amino acid frequencies of RBSs were calculated as (RBS counts of each amino acid) divided by (counts of each amino acid in the entire sequence of proteins with RBS) and normalized such that the sum equals 1.47 with the dataset identifier PXD02340148. The custom Python codes central to this study have been deposited to the GitHub repository49 .The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repositoryIntegration of previous studies and databases − Identified RBSs were mapped to RBP annotations in UniProtKB (June 2019), domain annotations in UniProt PROSITE (June 2019), UniProt zinc finger and coiled-coil annotations (August 2019) if unmapped to UniProt PROSITE26. Structural data were obtained from Protein Data Bank with the PDB codes 2ADC50 (PTBP1), 2KFY51 (HNRNPF), 4V6X52 (FAU), and 6ZVH53 (EDF1), which were visualized using PyMOL (v.1.7.2.1.)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


The advent of programmable RNA editing CRISPR/Cas nucleases has made this gene-editing tool safer and more precise. Specifically, CasRx, a family member of the Cas13d family, has shown great therapeutic potential. Here, we describe the  CRISPR/Cas-type II RNA-guided nucleases, including bilities . HoweverRuminococcus flavifaciens, possesses the highest RNA cleavage activity and specificity in human cells. RNA targeting by CasRx also fared better than short hairpin RNA (shRNA) interference. Importantly, Cas13d nucleases can process CRISPR arrays, allowing for multiplex targeting  to examine the RNA knockdown efficiency of the system. An instructional manual for RNA editing in vitro with CasRx using variants of guide RNAs is described.Here, we describe three different methods to perform efficient CRISPR/CasRx-mediated RNA knockdown of vascular endothelial growth factor A (VEGFA). We examined the RNA knockdown efficiencies of the CRISPR/CasRx system using different forms of single guide RNA (sgRNA) by targeting •Human embryonic kidney (HEK) 293FT cell line •Human Müller cell line, Moorfields/Institute of Ophthalmology–Müller 1 (MIO-M1), which was obtained from the UCL Institute of Ophthalmology, London .•TM), Singapore]-gBlocksTMLacZ sgRNA  was removed at the same time. The expression cassette was then ligated with the linearized plasmid (pAAV-MCS) in vitro using Gibson assembly, as described in section “Gibson Assembly.” Three all-in-one pAAV plasmids  were generated for this study  CasRx pre-sgRNA targeting is study –11.TM, and 10% FBS in a humidified 5% CO2 atmosphere at 37°C. HEK293FT cells were passaged every 3 days. The cells were rinsed with DPBS and passaged using TryPLETM for 3 min at 37°C. The cells were neutralized using a 1:3 ratio of TryPLETM to complete DMEM and subjected to cell counting. HEK293FT cells (5 × 105) were seeded in a 75-cm2 cell culture flask for passaging. For experiments, HEK293FT cells were seeded into multiwell cell culture plates  a day before transfection. Cell densities were at 50–70% on the day of transfection.HEK293FT cells were maintained in DMEM with 1% antibiotic–antimycotic, 1% GlutaMAX(i) The cells were seeded in multiwell cell culture plates a day before transfection, for the cell density to be at 50–70% during transfection.TM), prepare the Lipofectamine 2000/DNA mixture as follows:For experiments using short fragments of the CasRx sgRNA expression cassette , prepare the Lipofectamine 2000/DNA mixture as follows:For experiments using the all-in-one pAAV–CasRx–pre-sgRNA plasmid, prepare the Lipofectamine 2000/DNA mixture as follows:(ii) The transfection steps are as follows:Step 1. Mix DNA in 150 μl (for the 6-well plate) or 50 μl (for the 12-well plate) OptiMEM.Step 2. Mix Lipofectamine 2000 in 150 μl (for the 6-well plate) or 50 μl (for the 12-well plate) OptiMEM.Step 3. Mix the DNA/OptiMEM mixture with the Lipofectamine 2000/OptiMEM mixture.Step 4. Incubate at room temperature for 10 min.Step 5. Remove the culture media from the plate wells and add 1.7 ml (for the 6-well plate) or 0.9 ml (for the 12-well plate) fresh culture media to each well.Step 6. Add the DNA/Lipofectamine 2000 mixture to each well dropwise.2.Step 7. Incubate at 37°C with 5% COStep 8. Remove the culture media from the plate wells and add 2 ml (for the 6-well plate) or 1 ml (for the 12-well plate) fresh culture media to each well the next day.2 for another 48 h.Step 9. Incubate at 37°C with 5% CO(i) Cells were seeded in multiwell cell culture plates a day before transfection, for the cell density to be at 50–70% during transfection. The ViaFect reagent/DNA mixture is prepared as follows:(ii) The transfection steps are as follows:Step 1. Mix DNA in 150 μl (for the 6-well plate) or 50 μl (for the 12-well plate) OptiMEM.Step 2. Mix ViaFect in 150 μl (for the 6-well plate) or 50 μl (for the 12-well plate) OptiMEM.Step 3. Mix the DNA/OptiMEM mixture with the ViaFect/OptiMEM mixture.Step 4. Incubate at room temperature for 10 min.Step 5. Remove the culture media from the plate wells and add 1.7 ml (for the 6-well plate) or 0.9 ml (for the 12-well plate) fresh culture media to each well.Step 6. Add the DNA/ViaFect mixture to each well dropwise.2 for 48 h.Step 7. Incubate at 37°C with 5% CO(iii) After transfection, place the samples into GENbag for 24 h.® Miniprep kit (Zymo Research) and eluted in 10 mM Tris–HCl, pH 8 (or the elution buffer from the kit) (® Miniprep kit). RNA was quantified using NanodropTM.(iv) For RNA isolation, HEK293FT or Müller cells were harvested using 300 μl RNA lysis buffer and total RNA was isolated using Quick-RNAIt should be noted that (1) cells must not reach full confluency prior to transfection and (2) as RNA is unstable, the process must be performed as quickly as possible.TM). The reverse transcription master mix was prepared as shown below:(i) After RNA extraction, 200 ng of RNA/sample was used for reverse transcription. Reverse transcription was performed using High-Capacity cDNA Reverse Transcription kit (Applied Biosystems(ii) Five microliters of the master mix was added to 5 μl of RNA (200 ng) to make a 10-μl reverse transcription reaction working solution. Reverse transcription was done with the PCR thermal cycling conditions shown below.TM Fast Advanced Master Mix (Applied BiosystemsTM). cDNA was diluted with nuclease-free water in a ratio of 1:9. The qPCR master mix was prepared as shown below.(iii) After reverse transcription, qPCR was done with TaqMan(iv) Then, 8 μl of the master mix was added to 2 μl of diluted cDNA to make 10 μl of qPCR working solution. qPCR was then performed with a quantitative chain reaction instrument with the thermal cycling conditions shown below.For troubleshooting: Low RNA knockdown rates might be due to insufficient sgRNA to target mRNA. Consider designing new sgRNAs.TM) that consisted of a promoter, sgRNA sequence, and sgRNA hairpin sequence. Knockdown efficiencies comparable to the standard plasmid DNA form of sgRNA were reported  or multiple sgRNAs (46 ± 3% reduction). Using multiple sgRNAs resulted in a slightly higher RNA knockdown. This was comparable to RNA knockdown by shRNA at 36 ± 4% , we designed and cloned VEGFA-targeting pre-sgRNAs into plasmid to compare the RNA knockdown efficiencies with varied formats of sgRNAs (VEGFA mRNA knockdown using single pre-sgRNA (90 ± 2%) and pre-sgRNA array (83 ± 3%) stood at ∼90% compared to shRNA-mediated knockdown at 56 ± 3% . In addition, without CasRx, no effect was observed in the VEGFA mRNA levels, indicating CasRx to be essential for RNA editing and efficient knockdown of target gene expression  . An EFS VEGF is predominantly expressed in certain retinal cell types, including Müller cells  of sgRNA. While using plasmids of pre-sgRNA is suitable for most applications, gBlocksTM is still a convenient, low-cost yet powerful method for the validation of RNA knockdown efficiency. To our knowledge, a complete step-by-step protocol for performing RNA knockdown using CRISPR/CasRx is not available from current literature. We also show for the first time that an array of up to three pre-sgRNAs can be incorporated into a single AAV vector with CasRx for successful knockdown of target mRNA while maintaining significant efficiency. CasRx has been previously shown to outperform siRNA-mediated RNAi knockdown . P-YW and G-SL contributed to funding acquisition and supervised the project. All authors contributed to the article and approved the submitted version.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.


The targeted and conditional activation of pharmaceuticals is an increasingly important feature in modern personalized medicine. Nucleic acid nanoparticles show tremendous potential in this exploit due to their programmability and biocompatibility. Among the most powerful nucleic acid specific treatments is RNA interference-based therapeutics. RNA interference is a naturally occurring phenomenon in which specific genes are effectively silenced. Recently we have developed two different strategies based on customized multivalent nucleic acid nanoparticles with the ability to conditionally activate RNA interference in diseased cells as well as elicit detectable fluorescent responses.,2 These  Specifically, RNA has garnered attention through research exploiting its plethora of functionalities in sensing and treating various diseases. RNA interference (RNAi) which is the effective termination of specific protein production, serves as an important aspect of RNA therapeutics –13. RNAi occurs naturally through various pathways but can be taken advantage of synthetically using a number of designed RNA strands . The ability to simultaneously deliver and activate multiple functionalities (including but not limited to RNAi) would prove immensely beneficial to personalized medicine. Additionally, predicting the structure, folding, and tertiary interactions of RNA would greatly benefit and accelerate the design of therapeutics.Nucleic acid nanoparticles have become increasingly attractive in therapeutic research due to their high customizability and various functionalities while retaining biocompatibility and ease in production via introduction of split cognate strands –16. Each strand contributes half of a designed DS RNA duplex, which upon re-association and further dicing forms a functional therapeutic. The initially prepared non-functional DNA-RNA hybrids contain either ssDNA or, as in the highlighted work , ssRNA over-hangs of different lengths, called toeholds . This in silico approach supplemented in vitro experiments to confirm RNA structures and interactions.The previously mentioned functionality operates using DNA-RNA hybrids. An additional scheme has also been exploited using RNA-RNA interactions .[1] In tin vivo.The conditionally activated RNA nanoparticles, published back-to-back in Nano Letters , 2, offe. In a bacterial system, designed riboswitches that are responsive to temperature and binding of a ligand were recently shown to be able to regulate both transcriptional and translational efficiency . Such approaches are not necessarily in competition, but instead have different uses. The two aforementioned approaches are well suited for designing reporter genes or gene circuits but require the de novo design of genomic regions upstream of the translation start site of a gene. The conditional activation of RNA interference (the focus of this research highlight) on the other hand, is based on comparatively short synthetic noncoding RNAs that function in the context of human cancer cells and are thus closer to traditional small molecule therapeutics. Not only will it be interesting to compare these different novel tools for the “smart” control of gene expression: exciting opportunities abound to combine such approaches, leading to systems that allow ever more complex control of the cellular machinery by operating on different levels simultaneously.There are other approaches of designed RNA constructs that can switch between different conformations. For example, riboregulators have been developed, that enable protein translation conditional to the binding of a trigger RNA Overall, published techniques can be used in conjunction with pre-existing nucleic acid nanostructures thus increasing the control over the formulation and co-delivery of pharmaceuticals and biosensors for the treatment and detection of a large variety of diseases.


The main role proposed for IGF2BP1 in cancer cells is the stabilization of mRNAs encoding pro-oncogenic factors. Several IGF2BP1-RNA association studies, however, revealed a plethora of putative IGF2BP1-RNA targets. Thus, at present the main conserved target RNAs and pathways controlled by IGF2BP1 in cancer remain elusive. In this study, we present a set of genes and cancer hallmark pathways showing a conserved pattern of deregulation in dependence of IGF2BP1 expression in cancer cell lines. By the integrative analysis of these findings with publicly available cancer transcriptome and IGF2BP1-RNA association data, we compiled a set of prime candidate target mRNAs. These analyses confirm a pivotal role of IGF2BP1 in controlling cancer cell cycle progression and reveal novel cancer hallmark pathways influenced by IGF2BP1. For three novel target mRNAs identified by these studies, namely AURKA, HDLBP and YWHAZ, we confirm IGF2BP1 mRNA stabilization. In sum our findings confirm and expand previous findings on the pivotal role of IGF2BP1 in promoting oncogenic gene expression by stabilizing target mRNAs in a mainly 3’UTR, m Despiteand E2F1 . NotablyC00266-1 . ConsistC00266-1 . MoreoveC00266-1 . CollectC00266-1 . In addiC00266-1 . HoweverC00266-1 , we aim 22.12. Cells were transfected with control siRNAs (CEL-miR-239b) or a paraloque-specific IGF2BP1-directed siRNA pool to minimize RNAi off-target effects at a final concentration of 15 nM as previously described -C cells were cultured in EMEM:DMEM/F12 (1:1) medium supplemented with 10% FBS. All cell lines were grown at 37°C in 5% COescribed  For BE . NEBNext Ultra Directional RNA Library kit (NEB) was used for library generation. Paired-end sequencing (76 bp) of three biological replicates per condition was performed on an Illumina NextSeq 500 platform at the Deep Sequencing Facility TU Dresden.2.2.272 h upon transfection cells were harvested for RNA extraction. RNA extraction was performed using TRIzol according to manufacturer’s protocol. Poly-A RNA was enriched using oligo (dT) beads. RNAs are fragmented randomly by adding fragmentation buffer. cDNA is synthesized by using random hexamers primer followed by second strand synthesis. Strand-specific double-stranded cDNA libraries were completed by size selection (250–300 bp) and PCR enrichment. Paired-end sequencing (150 bp) of three biological replicates per condition was performed on an Illumina NovaSeq 6,000 platform at Novogene (Hong Kong).2.2.3A-549, ES-2, MV3, PANC-1 and Hep-G2 cells were harvested for RNA extraction 48 h upon seeding. BE(2)-C cells were harvested 24 h upon seeding for RNA extraction. RNA extraction was performed using TRIzol according to manufacturer’s protocol. For A-549, ES-2, MV3 and PANC-1 cells, 50 ng of total RNA served as input using the NEXTflex Small RNA Library Prep Kit v3 (Bioo Scientific). Small RNA-seq libraries for HepG2 and BE(2)-C cells were prepared by Novogene (Hong Kong). Single-end sequencing  was performed on Illumina HiSeq 2000  at the Deep Sequencing Facility TU Dresden or on an Illumina HiSeq 1500  platform at Novogene (Hong Kong). One (PANC-1) or two  biological replicates per cell line were sequenced.2.3For RNA-seq data analyses low quality read ends as well as remaining parts of sequencing adapters were clipped off using Cutadapt (v 1.14). Subsequently, the processed sequencing reads were aligned to the human genome (UCSC hg38) using HiSat2 [v 2.1.0 ]. Featur2.4p-values of 0.05, and a minimum GO-level of four for Gene Ontology categories. We used the Gene Ontology, KEGG pathways and REACTOME pathways releases from the 8th May 2020. Enriched transcription factor sites were determined via the R-package RcisTarget [v1.8.0  was performed using the R-package clusterProfiler v 3.16.1  and MSig [v1.8.0 ]. Applie2.5portal.gdc.cancer.gov]. The log-rank test and calculation of hazard ratios was implemented in an R-script according to the description in  and associated clinical data from the TCGA project  were obtption in . High an2.6www.encodeproject.org;  were removed. Overlap between CLIP-peaks and candidate genes was determined using the intersect program of the bedtools suite [v2.25.0  peak data of Hep-G2 and K-562 cells were obtained from the ENCODE portal  portal [version 20Q3 ]. Median2.106A) modification sites were identified using the RNA Modification database, RMBase [v2.0; 6A modification sites were predicted from m6A-seq or MeRIP-seq peaks via the RMBase workflow, resulting in 477452 human m6A -sites. Available sites were matched to transcripts of known human RefSeq genes (UCSC hg19). Number of identified sites were reported for each respective gene, corresponding to the transcript with maximum number of m6A-sites.N6-Methyladenosine  based on RNA expression fold changes revealed pathways commonly deregulated in the six RNA-seq experiments. Out of the 50 hallmark gene sets included in the Molecular signatures database MSigDB; , four ge3.2The investigation of DEGs observed in all six analyses revealed 238 protein-coding genes consistently down- and 42 genes consistently upregulated. Notably, the downregulation of only one lincRNA, LINC00205, was conserved over all cell lines. The lack of substantial conservation of lincRNA regulation by IGF2BP1 may be due, at least in part, to the largely tissue- or cell type-specific expression of these non-coding RNAs . Aiming in vitro. Despite only mild differences, these analyses support the notion that IGF2BP1 tends to stabilize transcripts encoding factors promoting tumor cell vitality in vitro. This was further evaluated by the inspection of prognostic relevancies of the UNPs and DPPs. Hazard ratios (HR) between high and low RNA expression were determined for the respective genes in 31 TCGA tumor cohorts. This revealed that high RNA expression of the DPPs tended to be associated with dismal prognosis, as indicated by median log2 HR values greater then 0  in primary cancers. This supports the notion, that IGF2BP1 drives tumor progression primarily by stabilizing mRNAs encoding pro-oncogenic proteins, mostly factors serving roles in tumor cell proliferation and cell cycle progression.To investigate essentiality of deregulated genes in respect to proliferation and survival of cancer derived cell lines, pan-cancer loss-of-function CRISPR screens were analyzed. The inspection of dependency scores using data from CRISPR knockout screens in 789 different cell lines generated by the DepMap project revealed a slight tendency of the DPPs to be ”more essential” (lower median dependency score) for proliferation than the UNPs . The medr then 0 . The oppr then 0 . Despiter then 0 . Thus, i3.3p-value <10−5) were found for 117 of 179 DPP genes (65%) and five of the 12 UNPs (42%) in at least one of the CLIP-samples. In contrast, for only 191 of the 3952 NDP genes (5%) significant eCLIP-sites were identified. Considering the number of distinct CLIP samples with significant peaks for a certain gene further revealed that DPPs on average were found in more samples than UDPs and NDPs . In addition, we selected 117 of these NDPnoCLIP genes with a similar maximum 3’UTR length distribution than observed for the 117 DPPCLIP genes, in order to examine if differences between NDPnoCLIP and DPPCLIP transcripts, regarding regulatory elements, were merely attributed to longer 3’UTRs of the DPPCLIP transcripts. The set of NDPnoCLIP genes with a length-matched 3’UTR length distribution is denoted as NDPnoCLIP_LM.To determine DPPs and UNPs that might be regulated via direct binding of IGF2BP1, eCLIP [enhanced crosslinking and immunoprecipitation; and NDPs . Notablyand NDPs . For exaand NDPs . Accordi3.3.1CLIP transcripts tend to have significantly longer 3’UTRs  than the NDPnoCLIP mRNAs .The 5’ and 3’ untranslated regions (5’ or 3’UTRs) of mRNAs are essential for the regulation of mRNA fate in respect to their subcellular sorting, translation and turnover control facilitated by trans-acting factors including RBPs and non-coding RNAs, primarily miRNAs. The length of 3’UTRs has substantially expanded during the evolution of higher organisms and correlates with cellular complexity of organisms . This su3.3.26A) is the most prevalent internal modification of mRNA in eukaryotic cells  demonstrated that all 117 DPPCLIP mRNAs contain experimentally determined m6A-sites. In contrast, in only less than 40% of the 3,761 NDPnoCLIP transcripts, this modification was reported. Furthermore, the numbers of distinct sites found on the transcripts differed considerably. The median number of m6A-sites per transcript was 25 for the DPPCLIP and 0 for the NDPnoCLIP mRNAs. Among the 3’UTR length-matched NDPnoCLIP_LM transcripts, 64 (55%) had reported m6A-sites and the median number of those sites per transcript was three and thus about 8-fold lower than observed in DPPCLIP transcripts  in the respective 3’UTRs. AREs are observed in approximately 8% of human mRNAs . These mre found . Thus, a3.3.4CLIP transcripts for longer 3’UTRs and the reported role of IGF2BP1 in controlling mRNA fate in a largely miRNA-dependent manner suggested more frequent regulation by miRNAs  were predicted to be regulated by at least one of these 99 miRNAs  of the NDPnoCLIP and 18 (15%) of the NDPnoCLIP_LM transcripts. Notably, 32 DPPCLIP mRNAs (27%) were predicted targets of the let-7 miRNA-family. This finding is in agreement with previous reports of let-7 dependent regulation of HMGA2 and LIN28B expression and an aggressive tumor cell phenotype by IGF2BP1  sequence motifs are expected to occur more frequently in these sequences by chance. Accordingly, also control motifs were found more frequently in DPPCLIP 3’UTR sequences  and YWHAZ . The recently reported E2F1 mRNA served as positive control. All three novel candidate target mRNAs showed consistent downregulation in the investigated cancer cell models upon IGF2BP1 knockdown and their RNA expression was positively associated with IGF2BP1 expression across solid cancers. Moreover, for all three of these transcripts binding of IGF2BP1 was reported by CLIP studies and they contain sites of predicted miRNA targeting, AGO2 PAR-CLIP sites as well as m6A sites  content and 3’UTR length, reveal enriched features of target mRNAs stabilized by IGF2BP1. The majority these shows preferred association of IGF2BP1 in the 3’UTR close to the stop codon. In addition, most stabilized target transcripts present comparatively long 3’UTRs, m6A-modification in 5’-proximity to IGF2BP1 binding and targeting by miRNAs and AGO2 in the 3′-vicinity to IGF2BP1 association. Moreover, many of these target mRNAs are predicted to contain AREs in their 3’UTR and some encode proteins targetable by cancer therapeutics. To prove the validity of these in silico evaluations, we validate three novel target mRNAs by demonstrating that IGF2BP1 promotes the expression of AURKA, HDLBP, and YWHAZ in cancer cells by impairing decay of the respective mRNAs. In conclusion, our studies provide a comprehensive view on conserved roles of IGF2BP1-dependent mRNA stabilization in cancer. This information allows pursuing the evaluation of targetable IGF2BP1 effectors to test the potential benefit of inhibiting IGF2BP1-RNA association and combined treatment with effector inhibition in cancer therapy. In agreement with recent studies indicating a conserved role of IGF2BP1 in controlling cancer cell cycle progression by promoting E2F-driven gene expression  are key mitotic protein kinases guiding cell cycle progression by the spatiotemporal control of the onset and progression of mitotic chromosomal segregation, reviewed in 6A-, and miRNA-dependent regulation of target mRNA turnover. However, the investigation of metagene profiles of candidate target mRNAs stabilized by IGF2BP1 suggest that IGF2BP1 predominantly associates in between preferred sites of m6A-modification and miRNA targeting. This suggests that enhanced association of IGF2BP1 with m6A-modified target mRNAs does not necessarily rely on direct binding to modified nucleotides, but may rather involve m6A-dependent structural rearrangements of binding regions, favoring or stabilizing IGF2BP1-association. In respect to inhibiting miRNA-directed regulation, our studies provide further support that IGF2BP1 not necessarily directly masks miRNA-targeting sites (MTS). This is suggested by the fact that IGF2BP1 impairs a variety miRNAs with quite distinct seeds, that it associates preferentially in the 5’-proximity of MTSs and that its associates with target mRNAs in cytoplasmic mRNPs devoid of miRNAs and RISC components like AGO2  of candidate target mRNA of IGF2BP1 support a pivotal role of the protein in cancer cell cycle progression, but furthermore highlight cancer hallmark pathways influenced by IGF2BP1. The implementation of information on IGF2BP1-RNA association, mpression , our stuike AGO2 . These fike AGO2 .In conclusion, our studies reveal a potent analysis pipeline for the identification of conserved, pro-oncogenic effectors and hallmark pathways regulated by IGF2BP1 in cancer. The further evaluation of IGF2BP1’s role in modulating the expression of such effectors like AURKA will unravel novel avenues to pursue the inhibition of IGF2BP1-mRNA association in combinatorial treatment strategies to improve cancer patient outcome.


Trichoderma reesei is an ascomycete fungus known for its capability to secrete high amounts of extracellular cellulose- and hemicellulose-degrading enzymes. These enzymes are utilized in the production of second-generation biofuels and T. reesei is a well-established host for their production. Although this species has gained considerable interest in the scientific literature, the sugar transportome of T. reesei remains poorly characterized. Better understanding of the proteins involved in the transport of different sugars could be utilized for engineering better enzyme production strains. In this study we aimed to shed light on this matter by characterizing multiple T. reesei transporters capable of transporting various types of sugars. We used phylogenetics to select transporters for expression in Xenopus laevis oocytes to screen for transport activities. Of the 18 tested transporters, 8 were found to be functional in oocytes. 10 transporters in total were investigated in oocytes and in yeast, and for 3 of them no transport function had been described in literature. This comprehensive analysis provides a large body of new knowledge about T. reesei sugar transporters, and further establishes X. laevis oocytes as a valuable tool for studying fungal sugar transporters. Trichoderma reesei is a well-known species of saprophytic fungi that is used in the enzyme industry for the production of cellulose- and hemicellulose degrading enzymes (cellulases and hemicellulases), and which has been also studied as a platform for heterologous protein production. Although T. reesei has been the subject of numerous publications since its identification in the 1950s, sugar transporters coded in its genome remain poorly characterized.Lignocellulose biomass has gained interest as a feedstock for second generation biofuels due to its high abundance and renewability. It is used as a nutrition source by saprophytic fungi, which degrade its polysaccharide components into a range of soluble sugars. 1, whose members are characterized by the 12 transmembrane domain (TMD) topology2. Few 7 TMD SWEETs  have also been identified from fungi, but they are absent from phylum Ascomycota which contains industrially important fungi such as T. reesei and S. cerevisiae3. The MFS contains both passive facilitators and sugar/H+ symporters. Passive facilitators transport sugars along their concentration gradient across the cell membrane, while symporters are able to accumulate sugars against their concentration gradient by coupling the transport process to the transport of a co-substrate which follows its concentration gradient. In the known fungal sugar symporters the co-substrates are protons, in contrast to sodium ions used by many sugar symporters from animals, that belong to a different family of transporters4.Sugar transporters are membrane proteins which translocate sugars across the cell membrane. Fungal sugar transporters belong mainly to the major facilitator superfamily (MFS)Saccharomyces cerevisiae is the most studied species of fungi, and relevant to biofuel processes as the organism responsible for fermenting the lignocellulose-derived sugars to ethanol. Its well-characterized sugar transport system relies solely on passive facilitators in the transport of hexose sugars, which are present at high concentrations in its natural habitat5. On the other hand, the natural habitat of saprophytic fungi contains wide variety of biomass-derived sugars which are available only at low concentrations6. This wider range of utilizable substrates is reflected by the high number of sugar transporter genes when compared to S. cerevisiae7. The scarcity of sugars in the natural habitat is reflected by the fact that fungal high-affinity monosaccharide transporters often have their affinities in the micromolar range11, whereas the affinities of their S. cerevisiae counterparts are in the millimolar range12. Additionally, many fungal monosaccharide transporters appear to utilize the active sugar/H+ symport mechanism, again unlike S. cerevisiae, which further suggests adaptation to low sugar environments10. Fungal sugar transporters have been proven useful for engineering S. cerevisiae to uptake biomass-derived sugars  whose transport is not otherwise optimal in yeast, and whose utilization is required for an economically competitive bioethanol production process15.The yeast T. reesei has been predicted to contain about 50–100 genes coding for sugar transporters16, only handful of them have been characterized in the literature (Reviewed in Ref.1). Transporters have thus far been identified for monosaccharides, 21. Several transporter genes have been observed to be upregulated when the fungus is grown on cellulase-inducing carbon sources, and to be regulated by transcription factors involved in cellulase induction25. As different sugars act as inducers or repressors of the cellulolytic system and since these sugars are believed to be sensed intracellularly, sugar transporters play an important role in cellulase induction in saprophytic fungi27. Indeed, the deletion of sugar transporter genes has been shown to affect cellulase production, sugar transport and growth in T. reesei28.Although the genome of T. reesei include cellobiose, sophorose, lactose and l-sorbose26. Of these, lactose is relevant to industrial applications due to its low price and solubility23. d-Glucose, which is easily metabolized, represses the production of cellulases and hemicellulases via phenomenon termed carbon catabolite repression (CCR)29. The preference for d-glucose is illustrated by the fact that many fungal species possess multiple glucose transporters with varying affinities, capacities and expression patterns12. The tight regulation of cellulase induction by different sugars could possibly enable the improvement of cellulase production via manipulation of sugar transporters. This would be desirable, as these enzymes form a significant fraction of the manufacturing cost of second generation bioethanol30. Examples would be the alleviation of CCR by deletion of d-glucose transporters, or the improvement of inducer uptake by overexpression of e.g. lactose transporters. The prerequisite for both of these approaches is the knowledge of the sugar transportome of the species in question.Inducers of the cellulase and hemicellulase gene expression in S. cerevisiae with transport experiments carried out with radiolabeled sugars. Transporters from other kingdoms of life have been characterized with electrophysiological methods (Reviewed for plant transporters in Ref.31), such as two-voltage electrode clamp (TEVC)32, but to our knowledge there are only two published studies where this method has been applied to fungal sugar transporters34. In this method, the transporter gene is expressed in Xenopus laevis oocytes and the flow of ions through their plasma membrane is monitored32. To do so, the cell membrane is clamped to a specific voltage  and the current required to keep this voltage constant is recorded. Electrophysiological studies are convenient for transporter functional characterization, since they do not require expensive and limitedly available radiolabeled sugars, and since a complete set of kinetics can be measured on a single oocyte simultaneously with multiple test voltages35. Nevertheless, since these methods measure the flow of ions into the cell, they are limited to the characterization of electrogenic transporters, e.g. sugar/H+ symporters. In this regard, many fungal sugar transporters appear to function as symporters, and thus they could be studied with this method.Fungal sugar transporters have been characterized mainly by heterologous expression in T. reesei transporters with electrophysiological methods. Our aim was to gain better understanding of T. reesei sugar transporters, and to further establish the TEVC method for the analysis of fungal sugar transporters. We used phylogenetic analysis to identify a convenient set of transporters for the functional studies, with the aim of selecting transporters for many different classes of sugars. Some of these transporters had been published before, but our analysis provided new information about their substrates and kinetics. We also identified and characterized some transporters for which no transport activity had been described before.Accordingly, we set to investigate T. reesei sugar transporters were chosen for sequence alignment with previously characterized fungal sugar transporters and a phylogenetic tree was built from the alignment  or carbon catabolite repression (CRE1)29. Many of the selected transporters had been identified to be significantly upregulated upon growth on inducing carbon sources, cellulose and lactose  were known to be symporters.Of the 16 es Table . No sugad-glucose uptake, as shown in Supplementary Figure d-glucose and grow on multiple hexose sugars . With the oocyte system we could analyze the kinetics at multiple test voltages . It clustered together with low-affinity d-glucose transporters from N. crassa and C. graminicola11 . Although both yeast 8. Their T. reesei homologs are facilitators STP146 and XLTR118 . We expressed STP1 in yeast to study its d-glucose transport kinetics, since as a passive facilitator46 it could not be studied with TEVC. As hypothesized from the phylogenetic analysis, STP1 proved out to be high-affinity d-glucose transporter  and slightly by d-mannose  provide new information about the d-glucose transport system of T. reesei. Although our kinetics analysis for STR1 and STR3 was mostly in agreement with a previous study7, as shown in Supplementary Table d-xylose transport activity had previously been described for STR1. However, we observed that the d-xylose kinetics from a previous report appeared to exhibit biphasic behavior, with high-affinity (0–5 mM) and low affinity (5–50 mM) components . Since the previously reported kinetics parameters were from a curve fitted to the whole concentration range, a separate fit for the high-affinity component would have resulted in a significantly lower Km value. The high affinity for d-xylose was further supported by the competition experiment, which showed that d-glucose transport by STR1 was heavily inhibited by d-xylose. The XYR1-dependent upregulation of STR1 in cellulase-inducing conditions 33, and its Km for protons is quite similar to that obtained for CRT1 (CRT1: pH 8.2 ± 0.47 vs Srt1: pH 7.7 ± 0.31). The observed differences in the affinity for protons are interesting, since both N. crassa and T. reesei are usually grown in slightly acidic conditions and both employ saprotrophic lifestyle. Regarding voltage kinetics, a slight increase in affinity was seen for CRT1 as the voltage became more depolarized . In Fig. 56, which is in the same clade as FRT1 in Fig. d-galacturonic acid, l-rhamnose, d-galactose, l-arabinose), although one report indicated that it functions as a sucrose transporter in yeast57. The observed specificity of FRT1 for d-fructose, an easily metabolizable monosaccharide, is interesting since FRT1 has consistently been found to be upregulated in cellulase-inducing conditions58 and to be downregulated in the presence of d-glucose in CRE1-dependent manner43. Additionally, FRT1 has also been found to be differentially phosphorylated in response to cellulase-induction59.FRT1, on the other hand, had some homology to characterized d-galacturonic and d-glucuronic acids. d-Galacturonic acid is the primary component of the plant cell wall polysaccharide pectin. Pectin-rich sidestreams from e.g. fruit processing industry have gained interest as feedstock for bioprocesses, since they are available in large quantities and contain lower amounts of lignin than lignocellulose biomass14. d-Glucuronic acid, on the other hand, is present in the hemicellulose fraction of lignocellulose biomass. Both of these transporters had been identified in a previous study, but apart from identifying their transport activity in yeast, no characterization had been done on them14. Since our initial screening identified them to be functional in oocytes, we set out to investigate them more thoroughly.Our initial screening identified two transporters capable of transporting d-galacturonic acid inducing higher currents in Trire2_106330  than d-glucuronic acid. Kinetics experiments indicated that GAT1 has higher affinity for d-glucuronic acid than for D-galacturonic acid  have not yet been characterized from T. reesei, l-arabinose/d-galactose regulator ARA1 has been shown to regulate genes related to both hemicellulase and pectin metabolism, as l-arabinose is present in both polymers41.To our knowledge, only two fungal uronic acid transporters have been kinetically characterized in the literature, although kinetic parameters are only available for T. reesei. Phylogenetic analysis was used to identify a reasonably sized set of transporters for the functional analysis, which provided a substantial amount of new information about T. reesei sugar transporters. All in all, transport activity was described for the first time for three transporters , and we were also able to identify many new functionalities for transporters which had been identified previously.The purpose of our study was to gain better understanding of the sugar transporter repertoire of 63, but to our knowledge this has not been observed in fungal sugar transporters. There are also many possible substrates that we didn’t analyze (e.g. polyols). Additionally, some transporters appear to exhibit their activity only on a certain concentration range, which we might have missed64.The studied set of transporters was diverse, and we could identify transporters for all the different classes of sugars mentioned in Fig. d-glucose transporter(s) has resulted in increased protein production in both N. crassa and T. reesei19. Double deletion of high-affinity d-glucose transporters STP1 and STR3 could be attempted for this purpose, as has been done in N. crassa8. Since T. reesei has been modified to produce high amounts of protein with d-glucose as the carbon source67, overexpression of GLT1 could be also attempted to increase the protein production in these strains. Nevertheless, protein production is conventionally done on lactose-based media, and thus manipulation of 23.Besides providing information about the physiology of the fungus, these results could have practical applications. For example, deletion of N. crassa8, the d-glucose transport system of T. reesei appears to include high-affinity  and low/moderate-affinity (GLT1) transporters. In addition to these transporters, three additional d-glucose transporters have been identified from T. reesei , but their expression profile does not support their involvement in the bulk of d-glucose transport.Regarding transport kinetics, the majority of the identified transporters exhibited high or moderate affinity for their primary substrates. Exceptions were MLT1 and FRT1, which had considerably low affinity when compared to other fungal transporters capable of transporting these sugars (Supplementary Table S2). As in 34, employed X. laevis oocytes for the electrophysiological characterization of fungal sugar transporters. Although there were differences in the Km values determined in yeast and oocytes, the general trend (low or high affinity) was similar between both as shown in Supplementary Figure 68, high-throughput screening would be also possible.This study, along with two previously published studies69. Both media were supplemented with 2% agar for preparing solid media. Yeast was grown in 21.Yeast strains were cultured in YP  or synthetic complete  medium with 2% glycerol and 2% ethanol as carbon sources unless otherwise mentionedl-rhamnose and l-sorbose were obtained from Fluka . Cellotriose and cellotetraose were obtained from Carbosynth  and Chemicals were obtained from Merck KGaA  and molecular biology reagents from Thermo Fisher Scientific  unless otherwise mentioned. Lactose was obtained from VWR , and E. coli transformations were done with electroporation and yeast transformations with the LiAc/ssDNA/PEG method described by Gietz and Woods70. In vitro transcription was done with mMessage mMachine T7 kit. Primers were obtained from Thermo Fisher Scientific and they are listed in Supplementary Table Escherichia coli strain Top10 was used for cloning and for storing the plasmids.High-fidelity PCR was done with KAPA HiFi polymerase  and colony PCR with DreamTaq polymerase. Restriction digestions were performed with restriction enzymes from Thermo Fisher Scientific or NEB . T. reesei cDNA. The amino acid sequences for gene synthesis were obtained from the T. reesei QM6a genome annotation (version 2.0)71, except for Trire2_62380 (STR3), _3405 (CRT1), _67752 and _67469. For Trire2_62380, _3405 and _67752, we used the genes from the RUT-C30 genome assembly72 . RUT-C30 versions of Trire2_3405 and _67752 have been shown to be functional in previous studies21. Sequence for Trire2_67469 was manually annotated based on a homolog identified from other species of Trichoderma, as shown in Supplementary Figure For the yeast expression, synthetic genes were obtained with codon-optimization for yeast , with the exception of Trire2_69026 which was obtained from URA3, CEN6/ARS4, PGK1 promoter, ENO1 terminator) with the yeast MoClo toolkit 73. The resulting constructs were sequenced with primers SaSS-19 and -20. The gene coding for Trire2_69026 was amplified from T. reesei QM6a cDNA with primers SaSS-75–76, and ligated to B2159 vector (derived from pYX212 as described in Ref.74) which had been digested with EcoRI and BamHI. The resulting construct was sequenced with primers TPI1_P_long and TPI1_T_long.The synthetic genes were cloned to yeast expression vector . The MoClo-compatible version of Pol1 was digested with BsaI and the two resulting fragments (Pol1 contains one endogenous BsaI site) were gel purified. The backbone fragments were ligated with T4 ligase (NEB) with the genes which were liberated from the original GeneArt pMA-T plasmids with BsaI. The constructs were sequenced with primers SaSS-57 primer and T7 promoter primer.To express the synthetic genes in T. reesei cDNA for X. laevis expression. Trire2_69026 was amplified from plasmid pSS87  which had been digested with the same enzymes. Trire2_77517 and _79202 expression vectors were constructed similarly, except that for Trire2_77517 the amplicon and vector were cut with BamHI and EcoRI, and for Trire2_79202 they were cut with XbaI and HindIII. We noted that the Trire2_79202 sequence obtained from cDNA differed from the QM6a sequence in that the second intron was missing .Oocytes were obtained via surgery as described by Clemencon 2+  which was supplemented with penicillin and streptomycin. The oocyte sacs were separated to 3-4 mm pieces with forceps. Oocytes were defolliculated by washing with MBM-Ca2+ (MBM+Ca2+ without CaCl2), after which they were suspended to MBM-Ca2+ with 4 g/L collagenase  and incubated in rocking shaker for 1 hour. After the incubation, the washing and collagenase incubation steps were repeated. Finally the oocytes were washed with MBM+Ca2+ and healthy stage V-VI oocytes were picked.After the surgery, the oocytes were suspended in modified Barth’s medium with CaNheI and in vitro transcribed into mRNA. After the transcription the product was purified with LiCl precipitation according to the kit instructions. The oocytes were injected with 10–30 ng mRNA in 50 nL volume or with the same volume of water. The injection was done with Nanoject II microinjector . After injection the oocytes were suspended in MBM+Ca2+ and incubated in The pol1-based plasmids containing the transporter CDSs were linearized with 32. The measurements were done on a TEVC setup consisting of 2-channel perfusion system, OC-725C Oocyte clamp amplifier  and Axon Digidata 1440A digitizer . The system was calibrated with oocyte model cell. Microelectrodes filled with 3 M KCl and with resistance between 0.5–5 Mhttps://www.moleculardevices.com/) was used for the analysis. ND-96 buffer without sodium  was used for the experiments. Each experiment was done with at least three oocytes derived from at least two different frogs, except for some sugars in CRT1 and CDT-1 selectivity experiments where only 2 oocytes were used. Each transporter was tested at least with 11 different sugars, including monosaccharides and disaccharides  technique was used for the electrophysiological characterization. In this method, the membrane potential of the oocyte is clamped to a specific voltage and the transport is measured by recording the substrate-induced changes in the membrane currentFor the current trace recordings, the oocytes were perfused with ND-96 (pH 7.4) and clamped at − 50 mV. For each sugar, the oocyte was first perfused with ND-96 (pH 7.4) until the current stabilized, then with ND-96 (pH 5.5) until the current stabilized and finally with ND-96 (pH 5.5) containing 5 mM sugar. Afterwards the current was recovered to baseline by perfusing with ND-96 (pH 7.4). The resulting current traces were recorded with Axoscope program of the pClamp software suite.The I–V curves were recorded with Clampex program of the pClamp software suite. The oocytes were clamped at − 50 mV, perfused with the test solution until the current stabilized and then 200 ms step-wise changes of membrane potential from − 150 mV to 50 mV were applied. For each measurement, a recording was done first with buffer alone and then with the same buffer which contained the sugar to be analyzed. The steady-state currents obtained in the absence of sugar were subtracted from the currents obtained in the presence of sugar for each of the tested voltages. Between measurements the oocyte was stabilized by perfusing with pH 7.4 buffer.S] = substrate concentration and 79. The Km values are presented as mean ± standard deviation from at least three different oocytes. For the analysis of voltage-dependence of kinetics shown in Supplementary Figures The selectivity was measured by recording I–V curves with 5 mM substrate solutions prepared in ND-96 pH 5.5. Sugar acid solutions were adjusted to pH 5.5 with KOH. For the kinetics experiments I–V curve recordings were done with different sugar concentrations with pH 5.5 buffer. For the pH-dependence experiments of CDT-1 and CRT1, I–V curve recordings were done with ND-96 (with 5 mM cellobiose) adjusted to different pH values. The kinetics parameters for sugars and protons were calculated by fitting the currents as a function of substrate concentration to Michaelis-Menten equation . Rabiolabeled d-glucose  was diluted with ordinary sugar to reach the desired concentration and radiospecific activity. Aliquots of the yeast suspensions and the label were incubated 5–10 min at 28 Uptake experiments were done as previously21. d-glucose uptake kinetics were fitted to equation n=3), and the values are presented as mean ± standard deviation from these fits. The negative control strain expressed T. reesei intracellular 21.Uptake rate was determined by substracting the counts  obtained for the blank samples from the actual samples, and by dividing the blank-corrected counts with the radiospecific activity of the label (cpm/nmol), the amount of yeast used (OD) and by the reaction time (min). OD values were further converted to CDW values using a previously published correlation factor for ySS1T. reesei sugar transporters were selected for the phylogenetic analysis. A List of putative T. reesei sugar transporters was obtained from Ref.1. From this list we excluded some transporters based on their transporter classification database (TCDB) family80, if the description indicated that the predicted substrate was not sugar, polyol or quinate . Some were further exluded based on transmembrane prediction, which indicated that they didn’t possess the characteristic 12 transmembrane domain topology of major facilitator superfamily transporters . Additionally, we included putative transporter Trire2_79202 since it has been hypothesized to be a lactose transporter28.Published fungal sugar transporters and putative 6, Nc_SUT-2827), and thus these substrates were not included in the figure. Sequence alignment was done with MUSCLE81, and the phylogenetic analysis was performed with maximum likelihood method with 100 bootstrap runs with partial gap removal (95% site coverage cutoff)82. Both steps were performed with MEGA X software (version 10.1.1)83, and the tree was visualized with ape package for R84.The amino acid sequences of the transporters were obtained from GenBank or Uniprot. Their accession numbers with references for the transported substrates are listed in in Supplementary Table Supplementary Information 1


Over the years, these four-stranded structural motifs have been demonstrated to have significant regulatory roles in diverse biological processes, but challenges remain in detecting them globally and reliably. Compared to DNA G4s (dG4s), the study of RNA G4s (rG4s) has received less attention until recently. In this review, we will summarize the innovative high-throughput methods recently developed to detect rG4s on a transcriptome-wide scale, highlight the many novel and important functions of rG4 being discovered  Around t Figure  18). Th. Th1-7GGs Figure . The G4-a+ > Li+ . Similara+ > Li+  20). In. In1-7GGa+ > Li+ . G4s cana+ > Li+ . rG4s pra+ > Li+ , most ofa+ > Li+ . Based oa+ > Li+  18,25)..1-7GGGN1a+ > Li+  and untra+ > Li+ . Moreovea+ > Li+ , which f+ adequately stabilizes both types of G4, whereas Na+ sometimes has a strong effect only on dG4s but not rG4s .,41.+ adenot rG4s ,42. In 2not rG4s  43,44).,44.+ adenot rG4s . Shortly in situ ,49, tran in situ  50,51).,51.+ ade in situ , but thequestion . Several technology, which has greatly enhanced their throughput from one to hundreds/thousands of rG4s per experiment. Importantly, the structural features of rG4s detected with high-throughput methods are coherent with our understanding of their biophysics and the results from their low-throughput counterparts. In the second part of this section, we review several high-throughput rG4 detection methods available to date, summarize their capabilities and notable features and discuss their general significance in rG4 research.Nevertheless, the emergence of high-throughput rG4 detection methods has introduced not only new opportunities in rG4 genomic research, but also new challenges. These include their high sample requirements and sensitivity to transcript abundance in RNA samples, as well as the large number of rG4 detections, which can be overwhelming for human interpretation. In the last part of this section, we discuss our perspectives on how the challenges imposed by high-throughput methods may be overcome, and how their findings could be utilized to answer pending questions in different facets of rG4 research.The general objective of rG4 detection methods is to determine the presence/absence of rG4 structures within an RNA region-of-interest. Low-throughput assays are most applicable to genomic regions that have already been suggested to harbour PQSs, but whose rG4 formation capabilities are yet to be experimentally confirmed. These methods typically evaluate one short RNA sequence (<100 nt) at a time. The rG4 detection methods developed to date can be broadly classified into three major types based on their working principles Figure :Biophysical assays are the earliest type of assay available for detecting rG4s. These techniques either evaluate changes in biophysical metrics, such as RNA melting temperature  footprinting assays are a genre of techniques that exploit the intrinsic behaviour of reverse transcriptases, where cDNA synthesis can be blocked by covalently modified RNA residues  and rG4s, and could be ambiguous in situations where an rG4 competes with an alternative RNA structure, e.g. dsRNA, to form at locus-of-interest.The major innovation underpinning the detection of RNA structure by RT footprinting involves the development of chemicals that selectively modify unstructured nucleotide residues. Structured and unstructured regions on RNA molecules can then be identified based on the differences in chemical modification rate, which are indicated by the observed frequency of RTS events. Methods that see applications in rG4 detection include selective 2′-hydroxyl acylation analysed by primer extension (SHAPE) ,66 and kin vitro rG4 folding step: when supplied with an rG4-stabilizing buffer solution of physiologically relevant K+ concentration and optionally in the presence of rG4-stabilizing ligands, rG4 structures can fold stably to block reverse transcription at their 3′ end. The reaction is then repeated in a non-rG4-stabilizing buffer, typically of Li+ concentration of the same ionic strength, which prevents rG4s from folding. RTS events that are specific to rG4-stabilizing conditions can then be identified to infer the presence of rG4 structures. Because the formation of dsRNA structures is not known to be sensitive to monovalent cations and rG4 ligands, the method can effectively differentiate rG4s from dsRNA , which compares the in vivo and in vitro SHALiPE data of PQSs to identify rG4s that can fold in both conditions from their characteristic SHALiPE footprints  to methylate guanine tracts of unfolded in vivo rG4s at N7 positions, where N7-methyl-guanine (m7G) does not support rG4 refolding under in vitro rG4-stabilizing conditions  via Watson–Crick base pairing, and thus inhibit rG4 formation. The hypothesis was first experimentally proven and established as a scoring system in a 2014 study . The relIn outline, G4 prediction methods utilize our understanding of G4 formation propensity to conduct searches for PQSs within a specific nucleotide sequence or in the entire genome, and optionally report prediction score derived from biophysical factors that influence the G4 formation propensity ,89. ExceMeanwhile, G4 prediction methods are commonly classified as either prediction algorithms or Web-based prediction platforms based on their user interfaces. Prediction algorithms are typically mathematical formulas or computer software that implement the PQSs screening logics, whereas web-based prediction platforms incorporating these algorithms serve as a more accessible interface for researchers Figure . At the quadparser algorithm into a command-line program  and Oryza sativa  , especially RNA helicases, bind rG4s on specific genes with functional implications . By compFinally, recent studies have suggested that some naturally occurring RNA modifications such as m6As may co-localize with rG4 structures with functional implications ,111. AltThe bioinformatic discovery of the widespread occurrence of PQSs in the human transcriptome , initialet al. first reported the formation of an rG4 in the coding region of FMR1 transcript, and when this specific FMR1 rG4 motif was inserted into the 5′-UTR in a reporter gene system, it was demonstrated to suppress translation in rabbit reticulocyte lysate 30 repeats  is an RNA helicase that unwinds structured 5′-UTRs to help with 43S pre-initiation complex recruitment, and a subsequent mRNA 5′-UTR scanning step is to look for the translation start codon. In one study, transcriptome-wide ribosome footprinting was performed under eIF4A inhibition with silvestrol reduced the translation efficiency of mRNAs with longer 5′-UTRs, and such mRNAs were found to be enriched in the (CGG)l assays . These rl assays , supportl assays ,125, altl assays ,86, or rl assays  to modulet al. conducted a deletion analysis on human fibroblast growth factor 2 (FGF-2) mRNA and found a 176-nt region containing two stem-loops and an rG4 motif to be a ‘structure determinant’ for the internal ribosome recruitment by IRES-containing cellular mRNAs  . Bonnal ar mRNAs . Anothernslation    and resolving rG4s (by DHX36)  . Interesy DHX36) ,159,160.et al. identified two rG4s in an FMR1 mRNA-coding sequence (exon 15) and showed that they interacted with FMRP, with no effect on translation , which is linked to Alzheimer’s disease  transcript to function as an intronic splicing silencer and thereby affect the hTERT alternative splicing in cells, and the effect was exacerbated by titrating G4-stabilizing ligands , an important process that drives cancer  components, and studies have reported that the 5′-terminal of human hTERC contains an rG4 that interferes with the formation of a critical structural element, P1 helix, in defining the template boundary for reverse transcription (hTERC rG4s in the presence of ATP (TERRA), was also reported to form rG4s and be involved in the regulation of telomerase activity and DNA telomere length , to be localized in the cytoplasm and upregulated in colorectal cancer cells  ,181,182,in vitro biophysical characterization of rG4 formation in mature miRNAs  of translational repressor YB1 protein  Figure . Ivanova protein . Lyons etructure . The bionslation . These ret al. first identified G-tracts in expansion segment (ES) tentacles in rRNA large ribosomal subunits (LSUs), and found them to be conserved in chordates. They verified the thermostable rG4 formation in ES7 and ES27 in vitro, and further identified RNA-binding proteins such as FIP1, FUS, DDX3 and hnRNP H, some of which had been previously reported as rG4-binding proteins  was reported to form in vivo and regulates the replication of PRV by enhancing the expression of IE180  is a highly contagious human viruses which was identified in 2003 . Since 2COVID-19 . It was COVID-19 . Recentlficiency ,219. CumA. thaliana, rG4s were predicted to be over-represented in protein-coding genes, and under-represented in ncRNAs such as rRNAs and tRNAs , suggesting that similar molecular mechanisms may apply to control 5′-UTR rG4-mediated translation processes across plants and mammals.In nd tRNAs . In addithaliana . Kwok etnslation . Comparale forms . Notablyet al. performed a comparative transcriptome analysis of the phloem–cambium region of three plant species and identified an uncharacterized zinc-finger (ZnF) protein referred to as JULG1. It was shown to specifically bind and induce a 5′-UTR rG4 of SMXL4 and SMXL5, which are key regulators of phloem formation, and suppress their translation. Reduced production of JULG1 restricted phloem differentiation and strikingly increased sink strength per seed , rG4s may be evolutionarily selected against in bacteria . Collectively, these fundamental insights will facilitate us to explore the origin and evolution of rG4s, and enable us to decipher the missing link between rG4s and diseases. The outcomes of these will undoubtedly be highly valuable in the design and development of tools to target rG4s and rG4-associated pathways for diverse biological, biomedical and biotechnological applications. We look forward with enthusiasm to new suite of methodologies and biological breakthroughs to be unraveled in the near future.