Alternative splicing in Tau exon 10 generates 3 R- and 4 R-Tau proteoforms, which have equal abundance in healthy adult human brain. Aberrant alternative splicing in Tau exon 10 leads to distortion of the balanced 3 R- and 4 R-Tau expression levels, which is a causal factor to trigger toxic Tau aggregation, neuron dysfunction and patient death in a group of neurodegenerative diseases known as tauopathies. Hence, identification of regulators upstream of the Tau exon 10 splicing events are crucial to understanding pathogenic mechanisms driving tauopathies. In this study, we used RNA Antisense Purification with Mass Spectrometry (RAP-MS) analysis to identify RNA-binding proteins (RBPs) that interact with the Tau pre-mRNA near exon 10. Among the newly identified RBP candidates, we show that knockdown of hnRNPC induces Tau exon 10 skipping whereas overexpression of hnRNPC promotes Tau exon 10 inclusion. In addition, we show that hnRNPC interacts with the poly-uridine (U-tract) sequences in introns 9 and 10 of Tau pre-mRNA. Mutation of these U-tract motifs abolished binding of hnRNPC with Tau pre-mRNA fragment and blocked its impact on Tau exon 10 inclusion. These findings indicate that hnRNPC binds and utilizes these U-tract motifs located in introns 9 and 10 of Tau pre-mRNA to promote Tau exon 10 inclusion. Intriguingly, high hnRNPC expression level is associated with progressive supranuclear palsy (PSP), a sporadic tauopathy with pathological accumulation of Tau species that contain exon 10, which suggests a putative therapeutic role of hnRNPC for PSP treatment. MAPT) gene located on chromosome 17. As a microtubule binding protein predominantly expressed in the nervous system, Tau plays a vital role in axonal transport and neuronal function [Tauopathies are a group of heterogeneous brain disorders with varying prevalence and symptoms that share the common feature of accumulation of toxic forms of the Tau protein in cytoplasmic aggregates . Human Tfunction . Tau canfunction . The molfunction .cis-element mutations around the MAPT exon 10 region that leads to 3 R-Tau or 4 R-Tau enrichment in Tau aggregates [MAPT locus [trans-acting factors may play important roles in aberrant Tau exon 10 splicing in sporadic PSP and other 4 R- and 3 R-tauopathies. While many RNA binding proteins (RBPs) were discovered to be linked to Tau exon 10 splicing regulation [At the mRNA level, Tau exon 10 alternative splicing gives rise to Tau isoforms with either 3 or 4 microtubule-binding domain repeats, termed 3 R- and 4 R-Tau, respectively  Figure ). Alterngregates ,19. Besigregates . These tPT locus . These fgulation , to datein vitro RNA pull-down, combined with mass spectrometry (MS)-based proteomic analysis. However, for many splicing regulators, their specific RNA binding motifs are not known. Although in vitro RNA pull-down approaches combined with MS-based proteomics have been widely used to discover upstream splicing factors, these methods very often fail to detect the RNA-protein interactions that occur in live cells. In vitro RNA pull-down requires prior cell lysis and re-forming of RNA-protein complexes in buffer solution, which makes it difficult to preserve the native RNA-protein interactions. Furthermore, in these published in vitro RNA pull-down proteomic methods, both direct and indirect binding proteins are detected simultaneously, along with high background proteins, making the subsequent data interpretation and validation challenging [Conventional methods to identify locus-specific splicing factors mainly rely on known splicing factor binding motifs  or in villenging . The lacin vivo RNA-protein interactions. Compared to the ChIRP-MS method, the RAP-MS method deploys longer probes (≥ 90nt) to tile the entire sequence of RNA to ensure robust capture. Importantly, the use of UV irradiation covalently links proteins to RNAs, making it possible for the subsequent probe-transcript hybridization step to occur at high temperature (> 67°C) to disrupt the RNA secondary structure for higher capture efficiency. In addition, this stringent condition with high temperature and high detergent/salt allows protein purification under denaturing conditions to remove undesired non-specific binding.Recently, two oligo-based methods, Comprehensive identification of RBPs by MS (ChIRP-MS) , and RNAGiven the promise to use RAP-MS to isolate specific RNA and its direct binding proteins in such a robust and sensitive way, in this study, we adapted and applied the RAP-MS method to detect RBPs that contribute to the Tau exon 10 alternative splicing event. Towards this end, we designed probes targeting the Tau pre-mRNA around its exon 10 region and performed RAP-MS proteomic analysis in the neuroblastoma cell line SH-SY5Y. Among the identified 15 novel RBP candidates, using loss of function assays, we identified 9 novel RBPs that function to mediate alternative splicing of Tau exon 10. Specifically, we fully validated the functional roles of hnRNPC in Tau exon 10 splicing regulation and characterized its functional binding sites in the Tau pre-mRNA sequence.pLKO.1 puro (#8453), pCDH-EF1(#72266), pCDNA3.1-FLAG-HA (#52535) were obtained from Addgene. Mouse monoclonal anti-hnRNPC  was from Abcam. Rabbit polyclonal anti-Tubulin  was from Cell Signalling Technology.HEK 293 FT and HeLa were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% foetal bovine serum (FBS). The human neuroblastoma cell lines SH-SY5Y and SK-N-SH were cultured in Ham’s F12: Minimum Essential Medium Eagle (1:1), supplemented with 2 mM GlutaMAX, 1% Non-Essential Amino Acids (NEAA) and 10% FBS.Tau minigene (4431 bp) was obtained by PCR amplification of three fragments from the genomic DNA of HEK 293 FT cells between MAPT exons 9–11 ), which Total RNA from cells was extracted using the RNeasy®mini kit  according to the manufacturer’s protocol and were used for both semiquantitative and quantitative PCR. Endogenous 3 R- and 4 R-Tau mRNA expression were detected by RT-PCR with the following primers: forward5ʹ- GCGAATTCGGTGAACCTCCAAAATCAGGGGATCG-3ʹ; reverse5ʹ-CCCTGGTTTATGTTGCCTAATGAG-3ʹ. Primers forTauminigene:forward5ʹ-GGTGGAGGTGGAGGTTCTAGAAGTG-3ʹ; reverse5ʹ- ACCTGAGCAAGG TGACCTCCA-3ʹ. RT-PCR products were detected on a 2% agarose gel: endogenous 3 R- and 4 R-Tau RT-PCR products were 359 and 452 bp, respectively. Tau minigene 3 R- and 4 R-Tau RT-PCR products were 334 and 427 bp, respectively. Gel bands were quantified with ImageJ and Significance was tested using two-tailed Student’s t test and p < 0.05 was considered significant. For quantitative real-time PCR (qRT-PCR), the RNA concentration was determined using a NanoDrop  and 2 μg RNA was reverse transcribed to cDNA using SuperScriptTM III First-Strand cDNA Synthesis. qRT-PCR was conducted with SsoAdvancedTM Universal SYBR Green Supermix ). Primers used in qRT-PCR are shown in Table S1.TM protein assay kit . After normalization, equal amounts of proteins diluted in 1x LDS loading buffer containing 0.1 M DTT were boiled for 10 minutes at 95°C prior to electrophoresis. After gel electrophoresis, proteins were then transferred onto a nitrocellulose membrane using iBlot 2 Dry Blotting System , blocked with 5% skim milk in 1x TBST for 1 hour at room temperature and then incubated overnight at 4°C with primary antibodies. Following membrane washing, secondary antibodies IRDye 800 goat-anti-rabbit  or IRDye 800 goat-anti-mouse  were added for 1 hour at room temperature and washed three times with 1x TBST. The blot was then imaged with the Odyssey Classic Imager .Cells were collected and then lysed using 8 M urea and 100 mM ammonium bicarbonate on ice. Lysate protein concentrations were measured by micro BCAPLKO.1 lentiviral vector that expresses the scramble shRNA (as the negative control) was purchased from Sigma. The sequence of shRNA oligos that target individual Tau splicing factor candidates was selected based on Sigma’s pre-validation assays with the highest target efficiencies for each target. Meanwhile the sequence specificity for each oligo was confirmed using Primer-BLAST . All theProbe design and generation: 90-mer DNA oligonucleotides spanning the target RNA with reverse complementary sequences were synthesized by Integrated DNA Technologies (IDT). Each DNA oligonucleotide probe carries a 5ʹ biotin for streptavidin capturing. Probes that may hybridize to off-target sequences or have melting temperature (Tm) below 67°C were removed. RAP oligos targeting the Tau minigene and the GAPDH mature mRNA are listed in Table S2.2 in ice cold PBS at 80% cell confluence (~10 million cells per dish). Cells were then scraped, washed twice with PBS, and pelleted at 800 g for 5 min at 4°C.UV cross linking: SH-SY5Y cells stably expressing the Tau-minigene were cultured in 150 mm dishes. Cells were UV irradiated with 600 mJ/cmTotal cell lysate preparation: About 240 million cells (12x 20 million) were lysed by completely resuspending frozen cell pellets in ice-cold detergent-based cell lysis buffer , 0.2% sodium dodecyl sulphate , 0.1% sodium deoxycholate , 1× Protease Inhibitor Cocktail  and Murine RNase Inhibitor . Samples were then incubated for 10 minutes on ice to allow lysis to proceed. During this incubation period, each cell sample was passed five times through a 26-gauge needle attached to a 1 mL syringe to disrupt the pellets and shear genomic DNA. Each sample was then sonicated at 20% pulse power for 3 times in intermittent pulses . The samples were then treated for 10 minutes at 37°C with 2.5 mM MgCl2, 0.5 mM CaCl2, and 20 U of TURBO DNase  to digest DNA. Samples were returned to ice and the reaction was immediately terminated by the addition of 10 mM EDTA and 5 mM EGTA. Disulphide bonds were reduced by addition of 2.5 mM Tris-(2-carboxyethyl) phosphine (TCEP) and samples were then mixed with twice the lysate volume of 1.5× LiCl/Urea Buffer . Lysates were incubated on ice for 10 minutes then cleared by centrifugation for 10 minutes at maximum speed. Supernatants were saved.RNA Antisense Purification of crosslinked complexes: Lysate samples from 240 million cells  were pre-cleared with 500 μl Streptavidin coated Sepharose beads  at 37°C for 30 minutes. Biotinylated 90-mer DNA oligonucleotide probes specific for the RNA target of interest (12 μg Tau probes and 12 μg GAPDH probes) were heat-denatured at 85°C for 3 minutes and then snap-cooled on ice. Probes and pre-cleared lysate were mixed and incubated at 67°C using Thermomixer R ) with intermittent shaking  for 2 hours. Hybrids of biotinylated DNA probes and target RNA were then bound to streptavidin beads at 67°C for 30 minutes with intermittent shaking as above. Beads with captured hybrids were washed 5 times with LiCl/Urea Hybridization Buffer at 67°C for 5 minutes to remove non-specifically associated proteins.Elution and analysis of RNA samples: For qPCR examination of RNA captures, beads with hybrids were centrifuged at 500 g for 2 minutes at 4°C and then the supernatant was discarded. Beads were then resuspended by pipetting in 20 μL NLS RNA Elution Buffer . To release the target RNA, beads were heated for 2 minutes at 95°C. Then eluted target RNA molecules were incubated with 1 μg Proteinase K  for 1 hour at 55°C to remove all proteins. The remaining nucleic acids were then purified by ethanol precipitation onto SILANE beads . DNA probes were removed by digestion with TURBO DNase . Further, qRT-PCR was performed to quantify RNA enrichment.Elution and analysis of protein samples: For mass spectrometry analysis of captured protein contents, on beads trypsin digestion was performed. In brief, after oligo capturing, beads were further washed six times with 50 mM ammonium bicarbonate (pH = 8.0). 1.2 μg trypsin/ 120 million cells were added to the beads and incubated overnight at 37°C. 0.8 μg trypsin/120 million cells were added the next day and incubated for another 2–4 hrs. Trypsin Supernatant was collected for mass spectrometry analysis.Peptide samples were resuspended in 0.1% Trifluoroacetic acid (TFA), loaded onto a home-made trap-column  and a home-made analytical column , and eluted with a 120 min reversed-phase gradient at 70 nl/min on a Thermo Fisher Ultimate 3000 RSLCNano UPLC (Ultra-Performance Liquid Chromatography) system coupled to a Thermo QExactive HF quadrupole-Orbitrap mass spectrometer. A parent ion scan was performed using a resolving power of 120,000 and then up to the 20 most intense peaks were selected for MS/MS. All raw data have been deposited in the ProteomeXchange Consortium via Proteomics Identification (PRIDE) . The accData were analysed using Thermo Proteome Discoverer 2.2. For protein identification, search was against the SwissProt human proteome database , while the search parameters specified a parent ion mass tolerance of 10 ppm, and an MS/MS fragment ion tolerance of 0.02 Da, with up to two missed cleavages allowed for trypsin.Among the identified RBPs, we applied a set of criteria to select proteins that are specific to Tau minigene pre-mRNA rather than GAPDH mature transcript. These criteria are: firstly, selected proteins should present in both biological replicate of the Tau RAP-MS runs; secondly, the sum of the number of peptide spectrum matches (PSM) for each selected protein in the two Tau RAP-MS runs are at least two-fold higher compared to that in the two GAPDH negative control runs; last but not the least, in some exceptional cases, RBPs with no PSM counts in both GAPDH negative control runs and very high PSM counts (higher than 5) in one of the two Tau RAP-MS runs were also selected. Based on the above criteria, we focused on the 15 novel RBPs specific to Tau minigene pre-mRNA, as showed in RIP assay was performed in SH-SY5Y cells with hnRNPC antibody  or IgG control . 10 million SH-SY5Y cells were lysed with RIP lysis buffer . After sonication, cell lysate was precleared with protein G beads for half an hour in 4°C. Meanwhile, 5 μg antibody was incubated with protein G beads for 1 hr at room temperature. Next, precleared cell lysate was incubated with antibody coupled protein G beads for overnight at 4°C. After 9 times of wash with NET-2 buffer , RNA was isolated with Trizol-LS  and was then reverse transcribed and measured by qRT-PCR, with 18S rRNA as the internal control. Primers are shown in Table S1.in vitro transcribed into RNA using the MEGAscript T7 transcription kit  incorporated with biotinylated-CTP  at a molar ratio of 1:1 with non-biotinylated CTP. RNA products were visualized in 6% or 10% TBE-Urea gel  using SYBR Gold .DNA fragments were in vitro transcribed RNA and 1.5 mg of SH-SY5Y cell lysate (in RIP lysis buffer) were used. The transcribed RNA was first incubated at 65°C for 10 mins, then cooling down slowly to 4°C. The pre-cleared cell lysate was then incubated with the RNA at 4°C for 4 hrs. After streptavidin beads capture and four washes with NET-2 buffer, proteins were eluted with the 1x LDS loading buffer and were probed with specific antibody by Western blot. Samples with beads-only and human GAPDH mRNA 3ʹUTR fragment pull-down were used as negative controls.For each reaction, 10 pmol of each PSP RNA-seq dataset was obtained from Mayo RNAseq Study  . As shown in Figure S1B-D, we confirmed the effectiveness and specificity of targeted RNA enrichment using the RAP method. Next, we worked on adapting the RAP-MS method to identify RBPs upstream of Tau exon 10 splicing in the SH-SY5Y neuroblastoma cells. Towards this end, we first confirmed that the SH-SY5Y cells express both 3 R-Tau (skipping the exon 10) and 4 R-Tau (including the exon 10) isoforms ). We optTo capture the Tau minigene pre-mRNA, we used thirty-one 90-mer biotinylated oligo probes , Table STo test the function of the 15 newly identified RBPs on Tau exon 10 splicing regulation, we knocked down each candidate gene using short-hairpin RNAs (shRNAs) in wild-type SH-SY5Y cells ). For 9 PSP is one form of sporadic 4 R-tauopathy. To date there has been no known genetic cause has been linked. Therefore, in our study, we decided to identify novel Tau splicing factors whose dysregulation may contribute to elevated 4 R-Tau expression and subsequent 4 R-Tau aggregation in PSP. For this purpose, we re-analysed large-scale RNA-seq data of post-mortem PSP patient brain samples released by Mayo Clinic  Synapse). This rTo confirm the functional role of hnRNPC in Tau exon 10 splicing regulation, we induced hnRNPC knockdown using two independent shRNAs in SH-SY5Y cells and found that 4 R-/3 R-Tau ratio was significantly decreased . Two hnRin vitro RNA pull-down in wild-type SH-SY5Y cell extracts with the seven in vitro synthesized RNA fragments (~700 nt with 150 nt overlap with each other) from the Tau minigene sequence . Pull-dsequence , S4). Assequence , S4), bosequence ). We alssequence , S6-7.FTo gain further insight into the specific hnRNPC RNA binding motifs for Tau pre-mRNA regions, we further made constructs of Tau minigene with mutated U-tracts in the E10-1, E11-2, or both regions. We then co-transfected these mutated Tau minigene constructs with hnRNPC in HEK 293 FT cells. As shown ), overexin vitro RNA pull-down and found that hnRNPC was predominantly associated with the first two out of the four tested sub-fragments within the E11-2 region  as shown ). Next, 2 region , S8). Th2 region ), we fou2 region , S9-10. 2 region , S11). OImportantly, we showed that Tau minigene exon 10 inclusion promoted by hnRNPC overexpression was largely abolished after the five U-tracts in the E10-1 region and the first four U-tracts in the E11-2 region were all mutated in the Tau minigene ). These in vitro synthesized biotin-labelled Tau probes that were complimentary to the sequence of Tau pre-mRNA. In total, we identified fifteen novel RBPs that associate with Tau pre-mRNA and nine  were found to play a role in regulating Tau exon 10 splicing. Specifically, we found the hnRNPC that we validated to promote 4 R-Tau expression is also upregulated in PSP at the transcript level, a sporadic 4 R-Tauopathy, which suggests the putative correlation between hnRNPC expression and disease progression of PSP. As a note of caution, we also wanted to point out that transcript and protein levels of a gene may not necessarily correlate in the same samples.In this study, we identified novel RBPs that contribute to alternative splicing of Tau exon 10 in SY-SY5Y cells by using RAP-MS. With this approach, RBPs were pre-covalently linked to Tau pre-mRNA with UV irradiation, and were then captured by HnRNPC is well known to regulate RNA processing and pre-mRNA splicing . Previouin vitro RNA pull-down based proteomic approaches have been widely used to detect locus specific RBPs, the high false positive rate induced by prior cell lysis, re-establishment of RNA-protein interaction and gentle purification conditions have markedly increased the work burden for subsequent validation. Instead of using in vitro transcribed RNA to pull down proteins that released by cell lysis, the recent developed RAP-MS fixed the RNA protein interaction in situ and used a groups of DNA probes to pull down RNA along with its covalently conjugated RBPs, which is available to detect the RNA-protein interaction in native state. Moreover, in denatured conditions during hybridization and purification steps, RAP-MS can be utilized to identify proteins that directly bind to RNA of interests. RAP-MS was initially developed to identify the RBPs that function to regulate lncRNA Xist. In this study, it is the first time that this method been applied to splicing field. Compared to most RNA splicing regulator studies that depend on in vitro RNA fragment pull-down in the context of cell or tissue lysate [Lack of robust approaches to study upstream splicing regulators has greatly restricted the research progress of splicing field. Even though e lysate , the RAPIn this study, one technical limitation of our approach is that we still needed to exogenously express the Tau minigene to identify regulators that specifically mediate Tau exon 10 splicing. Although we were able to efficiently capture endogenous Tau pre-mRNA in addition to the Tau minigene pre-mRNA , to directly identify RBPs that interact with the endogenous Tau pre-mRNA around its exon 10, we will need to develop a new procedure to selectively isolate and enrich for the endogenous Tau pre-mRNA sequence in that region. With this future improvement, the RAP-MS method can be applied more widely in the fields of RNA splicing and other RNA regulation mechanisms. For example, in the context of Tauopathies, RAP-MS with the endogenous Tau pre-mRNA can be applied to directly unveil the deregulated RBPs that natively interact with Tau pre-mRNA in post-mortem brain tissues collected from tauopathy patients.in situ splicing factors that mediate aberrant splicing events clinically linked with human diseases such as cancer and neurodegeneration.Altogether, in this study, we identified novel Tau splicing regulators using RAP-MS. Among the identified RBP hits, we further revealed the regulatory mechanisms of hnRNPC as a novel splicing factor to promote inclusion of Tau exon 10. In addition, our successful application of RAP-MS method in identification of Tau splicing factors indicates that RAP-MS can be routinely used as a sensitive and comprehensive tool to unveil Click here for additional data file.Click here for additional data file.


Candida albicans, 18S and 25S ribosomal RNA components, containing more than one phosphate on their 5′-end were resistant to 5′-monophosphate requiring 5′ → 3″ exonuclease. Several lines of evidence pointed to RNAP II as the enzyme producing them.We have previously found that, in the pathogenic yeast Saccharomyces cerevisiae that have been permanently switched to RNAP II . They contain more than one phosphate at their 5′-end and an anti-cap specific antibody binds to them indicating capping of these molecules. These molecules are found in RNA isolated from nuclei, therefore are unlikely to have been modified in the cytoplasm.We now show the production of such 18S and 25S rRNAs in Saccharomyces cerevisiae indicates that they are not only a result of mutations but are part of the cells physiology. This adds another way RNAP II is involved in ribosome production in addition to their role in the production of ribosome associated proteins.Our data confirm the existence of such molecules and firmly establish RNAP II playing a role in their production. The fact that we see these molecules in wild type The online version contains supplementary material available at 10.1186/s12860-022-00417-6. Eukaryotic cells devote a large percentage of their energy resources to the production of ribosomes , the proCandida albicans, we unexpectedly found that this yeast was producing 18S and 25S rRNA components resistant to digestion as it shifted to the stationary growth phase [Since ribosomal RNA (rRNA) represents over 80% of total RNA produced by cells, a major function for rRNA has become to serve as control for quality and quantity of RNA isolation, for studies that focus on the many coding and non-coding smaller RNAs . 5′-monoth phase . Digestith phase . The sumSaccharomyces cerevisiae a role for RNAP II in rRNA production is well established [Saccharomyces cerevisiae with a cryptic RNAP II promoter has been described [UAF30 deletion also utilize both RNAP I and RNAP II for rRNA transcription [rrn9, one of the UAF components results in complete switching to RNA II, designated as a PSW phenotype [Saccharomyces cerevisiae from YGRC/NBRP in Japan, which allowed us to test the validity of RNAP II’s role in the production of exonuclease resistant 18S and 25S rRNA components.While typically RNAP II is suppressed from gaining access to the rDNA promoter site, allowing RNAP I an exclusive role in rRNA transcription, in ablished , 17. Thiablished . They arescribed . This orcription . Deletiohenotype . Additiohenotype , 22. PriSaccharomyces cerevisiae regarding 18S and 25S rRNA resistance to Terminator digestion. As can be seen in Fig. Initially, we studied the behavior of wild type Saccharomyces cerevisiae. As can be seen, both single and double mutant yeasts produce Terminator resistant 18S and 25S rRNA  with or without a cap structure or some other molecule. Data shown in Fig. To see if any cap structure is present on these molecules, we utilized the trimethyl cap monoclonal antibody H20, widely used in cap detection studies . As can Saccharomyces cerevisiae mimics the pattern of Terminator resistance we observed in Candida albicans [The wild type albicans . These dThe mechanism underlying the production of Terminator resistant molecules, is unknown. It is well established that RNAP II can be involved in rRNA production in a polycistronic fashion in PSW yeast . It has Our data indicating that Terminator resistant 18S and 25S rRNA can be made Terminator sensitive by a de-capping enzyme Fig.  establisIf indeed Terminator resistance in these molecules is related to extra phosphates with a possible cap attached then, the mechanism by which the cell achieves this is unknown. Our data is compatible with these molecules having tri-phosphates with a cap at their 5′-end. As all polymerases initiate transcription with nucleoside triphosphates (NTPs), in general an RNA molecule with three phosphates at its 5′-end can be newly transcribed, and if capped, it is by RNAP II. This is unlikely for these molecules, as there are no canonical sequences for an RNAP II associated promoter immediately upstream of either 18S or 25S.Saccharomyces cerevisiae, is carried out co-transcriptionally by the capping enzyme complex (CE) made up of the triphosphatase Cet1 and the guanylyltransferase Ceg1 [Trypanosoma brucei, a cytoplasmic guanylyltransferase with 5′-RNA kinase activity capable of transforming a pRNA to a ppRNA, allowing a GMP transfer from GTP has been described [The other modality would involve the modifying of processed 18S and 25S molecules initially transcribed in a polycistronic manner, similar to cytoplasmic recapping of decapped mRNAs . The facase Ceg1 . It is wase Ceg1 . In factase Ceg1 . As procase Ceg1 , 33. Simescribed .Candida albicans we have been able to detect Terminator resistant 18S and 25S rRNAs in ribosomes isolated from stationary yeast indicating that they are functional [In nctional . A potenSaccharomyces cerevisiae, confirms a role for RNAP II in the production of these molecules. This supports our previous published data regarding Candida albicans, where it is shown that RNAP II can produce such molecules in stationary growth phase cells, when the role of RNAP I has been downregulated. These findings point to another role for RNAP II in the production of ribosomes in addition to transcribing ribosome associated proteins.Our findings of 5′-exonulease resistant 18S and 25S ribosomal molecules in polymerase switched Saccharomyces Cerevisiae S288C (ATCC), BY27539  and BY27384  (YGRC/NBRP Japan) were maintained in 50% glycerol in YPD broth  at − 80 °C. Cells were activated in YPD broth at 30 °C and maintained on Sabouraud dextrose agar at 4 °C, passaged every 4–6 weeks up to 4–5 times. Yeasts were lifted from agar surface and grown in YPD broth for variable length of times at 30 °C. Yeast cell concentrations were established using a hemocytometer.Cells were collected by centrifugation, washed with sterile phosphate buffered saline (PBS) and were put on ice pending total RNA extraction. Cells were disrupted with RNase-free zirconia beads and RNA was isolated using Ambion RiboPure RNA Purification kit for yeast ) according to the manufacturer’s instructions.Nuclear RNA was obtained using the Yeast Nuclei Isolation kit  following the manufacturer’s instructions. Histone Acetyltransferase (HAT) Activity Assay Kit  was used to verify nuclear source of isolated RNA Fig. f. RNA quTotal RNA was treated with Terminator  following the manufacturer’s protocol using the supplied Buffer A. The ratio of enzyme to substrate employed was 1 U per 1 μg of RNA to ensure adequate cleavage. RNase inhibitors (NEB) were used in all the assays at a 1 μg/ml concentration.Terminator treated and non-treated RNA samples were loaded into an RNA 6000 Nano chip and analyzed with the Agilent 2100 bioanalyzer system . Electropherograms were used to calculate Terminator resistance percentages by measuring the areas under the peaks of untreated (uncut) RNA and dividing it by the area of treated (cut) RNA.RNA was separated on formaldehyde agarose gels (Lonza) and stained with SYBR Gold Nucleic Acid Gel Stain (Life Technologies) for 30 min. Gel images were captured with a digital camera (Canon Vixia HFS30). RNA was transferred by electro-blotting (BIO-RAD Trans-Blot Turbo Transfer System) to a positively charged nylon membrane (Millipore) in 0.5 x TBE (standard Tris/Borate/EDTA buffer). The RNA was cross-linked to the membrane using UV (Stratagene UV Crosslinker). For immunoblotting, the membrane was blocked with 10% Block Ace™ (Bio-Rad) for 30 min at 25 °C, followed by the addition of anti-m3G-cap, m7G-cap antibody clone H20 (Millipore Sigma) diluted 1:1000 in 10% Block ACE™ and incubated for 24 h at 4 °C. Goat anti-mouse conjugated to HRP was added to the membrane at 1:5000 in blocking solution for 30 min at 25 °C. The Prosignal™ (Prometheus) chemiluminescence substrate was used to detect the HRP signal. Film was developed with the SRX-101A Konica film processor. For Northern blotting, we used the North2South Chemiluminescent Hybridization and Detection Kit  following the manufacturer’s protocol. Probes specific for 25S and 18S components of the ribosomal RNA were prepared by PCR using specific 18S and 25S primers. PCR products were cloned into TOPO® pcr4 vector followed by transformation with TOP-10 chemically competent cells. Several colonies were screened for insert presence and sequenced (Laragen Inc). For probe preparation, bacteria were grown in Terrific Broth (Fisher) with ampicillin at 50 μg/ml, and plasmids were isolated with a QuickLyse Kit (Qiagen). Inserts were released with BamH I/EcoR I for 25S and BamH I/NcoI for 18S, and were purified with a QIAquick Gel Extraction Kit (Qiagen). 50–100 ng of purified) inserts were biotinylated using the EZ-Link™ Psoralen-PEG3-Biotin (ThermoFisher).https://imagej.nih.gov/ij/index.html). Each image was processed to have the same resolution. Area and pixel densities were measured from three different experiments after converting the image to gray-scale. All the images that were used to generate these results are shown in Fig. SBand quantitation of scanned images was performed by open-source ImageJ software (Cap-Clip™ acid pyrophosphatase (Cellscript) was used according to manufacturer instructions for decapping RNA samples. Verification of cap removal was done by gel electrophoresis, Northern blotting and immunoblotting using anti-cap (H20) antibody. Biotinylated probes used for Northern blot were made using the following sequences: 18S_3_Fwd (5′-GTGAAACTCC GTCGTGCTGGG-3′), 18S_3_Rev (5′-TAATGATCCTTCCGCAGGTTCAC CTAC-3′), 25S_3_Fwd (5′-AACGCGGTGATTTCTTTGCTCCAC-3′), 25S_3_Rev (5′-GGCTTAATCT CAGCAGATCGTAACAACAAGG-3′).Additional file 1: Fig S1 Full-sized images of the SYBR-gold stained gel and Northern blot shown in Fig. Fig S2 Full-sized images of the SYBR-gold stained gel and Northern blot shown in Fig. Fig S3. Full-sized images of the SYBR-gold stained gel and Immunoblot shown in Fig. Fig. S4. Full-sized images of the SYBR-gold stained gel and Northern blot shown in Fig. Fig S5. Full-sized images of the SYBR-gold stained gel and Immunoblot shown in Fig. Fig S6. Images of scanned gels and immunoblots that were used to quantitate Terminator resistance before and after Cap-Clip treatment. Area and density of each band was measured using ImageJ software. Results were obtained by determining the area under each band peak (immunoblots) or in between peaks (gels) from three different experiments and are depicted in Fig. Fig S7 Images of full blots and membranes with visible edges that were cropped in Fig S6 and were used to calculate band areas with ImageJ software.


Total-RNA sequencing  allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches.Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program.https://github.com/hitaandrea/MGcount.MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at The online version contains supplementary material available at 10.1186/s12859-021-04544-3. Next Generation Sequencing (NGS) experiments have become the gold standard for many applications within the transcriptomics field, including gene expression profiling, novel transcript discovery and allele diversity detection , 2. AdvaWhile early NGS experiments focused on the detection of polyadenylated RNA , later RNA library preparation methods made it possible to target small regulatory RNAs  –9 and alFigure Second, reads frequently align to more than one annotated feature . These reads comprise multi-mappers, which align to multiple genomic locations Fig. b, and muDifferent strategies have been proposed to quantify multi-mappers and multi-overlappers, reviewed in . Raw couFinally, biotype-specific pipelines quantify expression levels by taking into account the genomic structure of the biotype in question. This raises a conceptual question: what is the feature output level at which transcript abundance estimation is most meaningful for total-RNA-seq? Fig. d. While Taken together, simultaneous quantification of small RNA, lncRNA and mRNA requires new strategies that simultaneously account for the diverse nature of each transcript, without relying on assumptions that could lead to biotype-dependent quantification biases.Generalizable to the simultaneous quantification of multiple RNA biotypesCompatible with any genome and annotations setCompatible with single-cell dataAvailable as a single command-line programWe generated and analysed RNA-seq libraries from 4 well-studied species, namely, A. thaliana, H. sapiens, M. musculus and C. elegans to characterize the behaviour of multi-mapping and multi-overlapping reads and the effect of MGcount and other quantification tools  over distinct classes of RNA. We further evaluated MGcount’s performance in two different human RNA templates (K562 cell line and human brain) and validated the accuracy of the estimated counts for a set of 20 small-RNA markers with RT-qPCR. Finally, we tested MGcount on a publicly available total-RNA-seq dataset with single-cell resolution. [Motivated by the above, we developed a novel RNA-seq quantification approach named MGcount (Multi-Graph count). MGcount handles multi-overlapping reads that arise from small RNAs originating from within long-RNA exons or introns, takes into account both polyadenylated and non-polyadenylated reads from long RNA, assigns multi-mapping reads with heterogeneous profiles, and defines output expression levels in an adaptive data-driven manner. MGcount is:olution. .MGcount starts with a set of genomic alignments of RNA-seq reads (one BAM file per sample/cell) and a set of RNA feature annotations stored in a single gene transfer format (GTF) file. To quantify RNA features abundance, MGcount employs two strategies, summarized in Fig. MGcount hierarchically assigns reads to annotated genomic features in three pre-defined sequential rounds, named “small”, “long_exon” and “long_intron”. First, in the “small” round, alignments are assigned to small-RNA biotypes  and thereby prioritized in situations where an alignment overlaps with a small-RNA embedded within a long-RNA (mRNA or lncRNA) feature Fig. b. As we The list of biotypes included in each round can be customly expanded or modified by the user. In addition, each round can be configured through five arguments Table , which fMGcount exploits graph structures to model resemblances between annotated features, in its potential to produce the same transcript, from real evidence coming from RNA-seq data. MGcount builds a directed weighted graph G =  where each vertex from the set of vertices V is a feature (as defined by the feature_output parameter), with a weight equal to the log-transformed number of assigned alignments Fig. c. DirectNext, highly related features, where reads systematically multi-map, are grouped together in communities by minimizing an objective function known as the map equation , 37. TheIn the small-RNA graph, although MGcount creates one graph for the total set of features, MG communities are independently detected per biotype. For long RNA, the top represented biotype is assigned to an MG community when this contains features from different biotypes. However, when a community contains both pseudogene and non-pseudogene features, a preference is made for the latter, in order to prioritize for the active biotypes.MGcount generates one expression matrix for each hierarchical assignation round and concatenates them in a single output matrix. For each read, each alignment first gets a “fractionated count” of 1/N, where N is the number of multi-mappers or multi-overlappers that survived the hierarchical assignment because they aligned to two features from biotypes in the same round. Next, counts for annotations that have been aggregated together in a community by the map equation are summed up (communities become newly defined features). In this way, the systematic ambiguity in multi-mapping reads collapses into a single MG community while the remaining signal is reported as fractionated counts.https://github.com/hitaandrea/MGcount).MGcount requires three inputs: a TXT file listing the paths to the BAM input files, a GTF file, and the output directory path. Additional optional arguments specify whether the data is from single or paired sequencing, whether the library preparation has been done in stranded or unstranded manner and assignation rounds configuration Table . FurtherA count matrix where each row corresponds to a feature as defined by feature_output  and each column corresponds to one input BAM fileA feature metadata table reporting: feature names matching row names in the count matrix, the counting round of hierarchical assignation, and its configuration parameters, a flag designing whether a feature belongs to an MG community, and the feature biotypeA sparse adjacency matrix for each multi-mapping graph generated , stored as a symmetric, integer, squared matrix. Each matrix element stores the number of alignments that multi-map to a pair of features (defined by row and column), and the diagonal contains the total number of alignments per feature.A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers. It includes both unique features (which remain unmodified) and aggregated features (which are collapsed following MG communities). Also, the table stores the total number of alignments per feature.At the end of its execution, MGcount provides the following outputs:https://github.com/hitaandrea/MGcount). MGcount is fully written in Python3. The software can be installed using the “pip” command and run as a python3 module. Alternatively, the MGcount software can be downloaded as a single, binary, compiled file and executed independently of Python. This binary has been compiled to run in the Ubuntu operating systems. To run MGcount, featureCounts [The source code is available in the MGcount Github repository  for the following species: Next, we evaluated the effect of the hierarchical assignation of reads on resolving small-RNA long-RNA multi-overlapping ambiguities. For this, we hypothesized that the level of expression of a given transcript is not influenced by the fact that other transcripts can originate from the same locus. For example, mRNAs can be expected to have a similar count distribution, whether they overlap with a small RNA or not. We compared the count distributions from the two pooled human brain libraries, for long-RNA and small-RNA features, and in presence or absence of small-RNA long-RNA overlaps Fig. a. AssumiFor long RNA, significantly similar distributions were obtained when hierarchically assigning reads with MGcount and considering only uniquely-mapping reads with featureCounts. This suggests that multi-overlapping alignments are mainly associated with small-RNA reads and that these two strategies are both adequate to quantify long-RNA biotypes. Fractionated count of multi-overlappers with featureCounts led to an inflated estimation of transcript abundance, due to the incorrect fractionated assignment of reads that originated from small-RNA transcripts to their embedding long RNA. Results from CoCo and RSEM show a slight increase of counts for the overlapping transcripts which again can be explained by the miss-assignment of a few small RNAs.For the quantification of small RNA, considering only reads aligned to a unique annotation resulted in discarding small-RNA transcripts embedded within  a long RNA. MGcount and CoCo produced significantly similar distributions. Remarkably, the use of MGcount with a custom GTF integrating annotations from other sources than Ensembl showed that missing annotations can have a strong impact in transcript quantification due to miss-assignment of multi-overlappers originated from unannotated small RNAs.In summary, the hierarchical assignment strategy from MGcount was the one producing the most equivalent count distributions with and without overlapping events for both long-RNA and small-RNA features. This supports the rationale that reads that fully map to a small RNA that overlaps a long RNA most likely belong to the small RNA, and demonstrates that MGcount’s hierarchical assignation approach can successfully resolve long-RNA small-RNA overlapping ambiguities.To investigate the scope of ambiguous alignments by biotype, we computed the fraction of multi-mappers and multi-overlappers for each biotype in total-RNA-seq libraries from human brain Fig. b, c leftFigure Figure These results demonstrate that MGcount can successfully detect small-RNA communities in a biotype-specific manner and suggests that it will be useful in species with poor annotations. Furthermore, some MG communities integrated features that did not follow the HGNC annotations system and which were associated with computationally predicted annotations  on the same and on different total-RNA-seq input datasets. We separately processed each replicate of human brain and K562 libraries twice and computed the Normalized Mutual Information (NMI) between partitions of commonly clustered features Fig. g–h. EachTo independently check the accuracy of the quantification between small RNAs of different biotypes, we compared the total-RNA-seq MGcount expression levels of human brain and K562 libraries with estimates of expression levels from RT-qPCR for 20 small-RNA markers with different multi-loci profiles. All transcript abundances were normalized by SNORD49A, which was highly expressed in both samples. The linear regression showed good adjustment in modelling the fold-change concentration relationship between human brain and K562 libraries, independently measured with total-RNA-seq libraries and with RT-qPCR  using count tables in which features were aggregated into MG communities by MGcount and count tables obtained with the reference pipeline described in [In order to evaluate its performance at single-cell resolution, we ran MGcount on a public single-cell total-RNA-seq dataset  consistiribed in . In bothRNA-seq reads frequently align to multiple places in the genome or to genomic regions that encode more than one transcript. Traditional RNA-seq pipelines commonly discard such reads, which does generally not pose a problem in the quantification of protein-coding transcripts. However, the amount of biological information encoded in ambiguous alignments in datasets with non-coding RNA can be considerable Fig. b–d. WhilHere, we propose a flexible quantification framework to interrogate heterogeneous RNA-seq datasets comprising different non-coding RNA biotypes. First, a hierarchical assignation workflow resolves overlaps of small RNAs embedded in long-RNA loci and allows to distinctly quantify spliced and unspliced features of long RNA. Then, to quantify reads that map to multiple locations in the genome (“multi-mappers”), families of features with almost identical sequences are automatically detected and aggregated in MG communities. With this approach, we gain confidence that the given read originated from a community of annotations rather than a single genomic locus. This approach defines a meaningful output level for the quantification of different biotypes in a data-driven manner and collapses repeated loci that are associated with the same RNA product. This solution may also be used to quantify poorly annotated transcripts as a community , Arabidopsis (b), Mouse (c) and Nematode (d). Dotplot presents combinations of two (blue) or three (purple) overlapping features of different biotypes whose occurrence exceeds the 5% of the total number of features from the less abundant biotype in the combination. The top barplot shows the log10 of the total number of cases per combination. The right barplot shows the relative proportion of features overlapping with any other feature by biotype. e Proportion of reads from a human total-RNA-seq library overlapping to 1, 2 or more annotations according to raw alignments assignation (left) and after MGcount assignation by hierarchical rounds (right). f Proportion of reads multi-mapping to a given number of genomic locations (up to 50), according to raw alignments assignation (left) and after MG community aggregation by MGcount (right).). g Comparison of counts when only uniquely-mapping reads are counted; all alignments are counted, all alignments are fractionally counted as 1 divided by the number of multi-assigments or quantified with MGcount. h–j Three random sub-graphs of 500 features after aggregation extracted from the long-RNA graph. Each vertex is an annotated feature. Its size is proportional to its number of aligned reads. Vertices are colored in blue for protein-coding, yellow for pseudogenes and pink for other lncRNA transcripts. Each edge connects two features with shared multi-mappers with thickness proportional to the fraction of shared multi-mappers over the total alignments. Shared grey areas delineate MG communities. k Comparison of the number of features detected by biotype with a mean count of 5 over human brain replicates. Intronic counts for MGcount are not considered. In addition to ambiguous alignment quantification approaches, softwares differ in assignation criteria: RSEM uses a probabilistic criteria; featureCounts and MGcount were configured with the same criteria defined as a full-overlap between all the nucleotides of a read and the annotation; Coco and mmquant equire a minimum number of nucleotides for assignation, which were set according to default parameters (10nt for Coco and 1nt for mmquant). The comparison is made at community-level, where mmquant merged genes are annotated here as mmquant communities; and at gene-level, where for communities algorithms a gene is detected if it belongs to a detected community. MGcount results in a lower number of features since each community is quantified as a single feature. In addition, the communities approach allows a more inclusive quantification of the individual features collapsed in communities and facilitates multi-mapping reads back-trace in case of interest, as compared to other methods. Incorporating annotations from multiple sources in the custom GTF allows to detect more transcripts and biotypes. Mmquant quantification leads to a very large number of communities since the same gene can be part of multiple merged genes, which can difficult differential feature expression analysis, as opposed to MGcount.


Affiliation 2 for author Dinggang Shen is incorrect. The correct affiliation 2 is: Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.


The final concentration of phenolic compounds in wines is usually lower than what might be expected, given their concentration in grapes. This is in part due to the interactions between cell walls from grapes and yeast with phenolics during red winemaking. Most of these aggregates are insoluble and end up precipitating, forming part of the lees. The objective of this study is to determine the capacity of ultrasounds and/or enzymes treatments (β-glucanase and a pectolytic enzyme) to release the anthocyanins and tannins adsorbed in the lees. The ultrasound (US) applied for 120 min slightly favored the extraction of anthocyanins and doubled tannin extraction. Shorter sonication times did not show any positive effect. The combination of β-glucanase and pectolytic enzyme was always more effective in the liberation of anthocyanins (both no-acylated and acylated anthocyanins) and tannins than the enzymes acting separately. The combination of US (120 min), β-glucanase and pectolytic enzyme showed an additive effect, increasing the extraction of phenolic compounds with respect to the individual treatments and also releasing a large quantity of low molecular weight polysaccharides, compounds of enological importance. These results of this study could be of enological interest, facilitating and accelerating the aging on lees process, through the liberation of polysaccharides and the recovery of the phenolic compounds lost during vinification. Wine lees are formed by the combination of dead yeasts, their metabolites and phenolic compounds, together with tartaric salts, bacteria and debris from plant cells .Saccharomyces cerevisiae cell wall is β-(1 → 3) glucan, which form its main chain, and presenting β-(1 → 6) glucan lateral ramifications and chitin [The main component of the d chitin ,3. Thesed chitin . During d chitin .Aging on lees in red wines has become a popular technique nowadays. It consists of keeping the wine in barrels or tanks to which the lees recovered after alcoholic and/or malolactic fermentations has been added. The wine is regularly stirred, with greater or lesser frequency, to promote the transference of compounds from the lees to the wine. Due to the autolysis, the yeast cell walls become less rigid, and their polysaccharides are released ; these cSaccharomyces cerevisiae cell wall is made up of mannoproteins linked to oligosaccharides, which remain exposed outside the cell. The different polarities and the hydrophilic or hydrophobic nature of these wall polymers define the ability of yeasts to retain or adsorb different wine molecules as phenolic compounds or volatile compounds [Less attention has been paid to the fact that lees can also be a source of phenolic compounds. These phenolic compounds are retained both by yeast cell walls and by grape skin and pulp cell walls. In the case of yeasts, and as previously mentioned, the ompounds ,14,15. Iompounds . Furtherompounds ,18 reporOn the other hand, at the beginning of the vinification, there is an important amount of suspended vegetal material, and their cell walls also present high affinity for phenolic compounds. The binding of phenolic compounds to the cell walls of this suspended plant material leads to high molecular weight complexes that are insoluble and end up precipitating in later stages of the vinification process and then become part of the lees . MoreoveAmong these tools, β-glucanase is commonly used. It is a hydrolytic enzyme that degrades the β-glucans, one of the main components of the cell wall of the Saccharomyces cerevisiae yeast. Enzymatic preparations with β-glucanase have been used in order to accelerate the release of polysaccharides and mannoproteins during the aging on lees . Taking On the other hand, and since plant cell walls also are present in the lees, the possible role of pectinase enzymes, whose main enzyme activities are methylesterase, polygalacturonase and pectin-lyase, on the desorption of phenolic compounds needs to be addressed. These enzymes facilitate the degradation of the pectin fraction of the grape cell wall that is part of the lees , and thiAnother technique that could help the extraction of the bound phenolic compounds during aging on lees is ultrasound (US). This non-thermal technique is being used in wine industry for its ability to accelerate the extraction of compounds from inside the grape cells, shortening maceration time in the wineries. This treatment is based on the use of mechanical waves of 16–100 kHz to produce physical-chemical changes in the matrix where they are applied . The cavAs stated before, US has been previously used during the early stages of red wine production in order to accelerate the extraction of phenolic compounds from within the cell, reducing maceration times and improving the phenolic composition of red wines ,30,31. TAs regards the recovery of phenolic compounds, enzymes and ultrasound treatments have been used before for the recovery of anthocyanins or glycoproteins from red wine lees, considering the lees as a winemaking byproduct. However, in these studies, the solvents used to perform the extraction were mixtures of ethanol or methanol in water at high concentrations, which have a more efficient extraction capacity than the wine , and thus, far from the conditions of real winemaking ,37.Although the use of enzymatic and US treatments for accelerating the autolysis of yeasts, and therefore the extraction of polysaccharides and mannoproteins, have been previously studied, no attention has been paid to their possible contribution to the release of phenolic compounds, such as anthocyanins and tannins, adsorbed in the precipitated lees. Therefore, with this objective, ultrasound, pectinases and β-glucanase were applied alone or in combination to a model solution containing suspended freeze-dried lees obtained from a red wine vinification, and the release of anthocyanins, tannins and polysaccharides was investigated.Wine lees were recovered from a traditional red wine produced in the experimental cellar of the University of Murcia. At the end of alcoholic fermentation, the lees were recovered, washed twice with distilled water and centrifuged for 5 min at 3500 rpm. Then, they were stored at −18 °C until they were lyophilized and grounded to a fine powder (<0.6 mm).The desorption tests were carried out in 12 mL glass tubes with a screw cap. A total of 0.5 g of lyophilized lees and 8 mL of a hydroalcoholic solution (12% ethanol at pH 3.6 adjusted with triphloroacetic acid) were added to each tube. Then, the different treatments were applied.Control: The control sample preparation was carried out by a simulated bâtonnage as was proposed by Liu et al. Enzymatic treatments. The pectolytic enzyme (PEC) was added at a final concentration of 0.03 mL/L . The main enzymatic activities of this preparation (as provided by the supplier) are polygalacturonase (4500 U/g), pectin methyl esterase (1000 U/g) and pectin-lyase (PL) (130 U/g). The glucanase enzyme (GLUC) was added at a final concentration of 0.05 g/L . The main enzymatic activity of this preparation is β-1,3-1,6 glucanase . Enzyme preparations were added individually (PEC and GLUC) and in combination (PEC + GLUC). After its addition, the tube was stirred in an orbital shaker at 300 rpm for 24 h.Ultrasound treatment. The application of ultrasound (US) was carried out using the equipment conditions reported by Kulkarni et al. , using tThree treatment times were tested: 30, 60 and 120 min . The tubes were placed in an ultrasonic bath , which operates at a frequency of 40 kHz, a power of 280 W and at a controlled temperature of 18 ± 1 °C.Enzyme and Ultrasound treatment combinations. The treatment time that showed the highest extraction of anthocyanins and tannins (as will be later discussed) from the lees was 120 min, and, therefore, it was chosen as the treatment time in the combinations of enzymatic and ultrasound treatments. Two different combinations were assayed, with the US being applied before or after the addition of the enzymes.All treatments and combinations were performed in triplicate. After 24 h, the tubes were centrifuged for 5 min at 4000 rpm, the supernatant was recovered and filtered with 0.45 μm nylon filters. In total, 2.5 mL of the filtered supernatant was used for the analysis of tannins by liquid chromatography (HPLC) and size-exclusion chromatography (SEC), and 2 mL for the analysis of soluble polysaccharides by SEC.The composition and quantification of the tannins released from the lees were analyzed by the phloroglucinolysis reaction, according to the method described by Del Rio and Kennedy , with soIn total, 2.5 mL of filtered supernatant (0.45 µm nylon filter) was concentrated in a Centrivap vacuum concentrator  after which it was dissolved in 250 µL of methanol. Then, 50 μL of the methanol extract was mixed with 50 μL of the phloroglucinolysis reagent prepared as described by Osete-Alcaraz et al. . The mixSize exclusion chromatography (SEC) was also used to analyze tannins released from lees. This method was described by Kennedy and Taylor , with sov/v) in high purity water. A diode array detector was used. Anthocyanins were quantified at 520 nm as malvidin-3-glucoside, using malvidin-3-glucoside chloride  as an external standard.The composition and quantification of the anthocyanins released from the lees was performed by injecting 20 µL of the filtered supernatant into a Waters 2695 liquid chromatograph . The chromatographic conditions used in this study were those described in Busse-Valverde et al. . In the The mass distribution of the polysaccharides extracted from lees with the different treatments was analyzed by SEC. Two milliliters of the filtered supernatant were concentrated in a Centrivap concentrator , and then dissolved in 250 µL of high purity water. It was necessary to dilute the sample due to the high amount of polysaccharides released, therefore 20 µL of the diluted sample was diluted again with 80 µL of high purity water. The volume of injection was 20 µL, the flow of 1 mL/min of LiNO3 (0.1 M), which passed through two columns connected in series . A refractometer detector was used (Waters 2414). The molar mass was determined using a calibration curve made with a special calibration kit .The statistical analysis of the results was made with the statistical package Statgraphics Centurion. An analysis of variance (ANOVA) was made to determine differences among samples using a Test LSD to separate the means with a confidence level of 95%.The aging of red wines usually implies the loss of monomeric anthocyanins. This is because they can be easily degraded, transformed into colorless forms and/or they could have polymerized into more stable forms. It has been reported that aging on lees reduces the degradation of anthocyanins, since the polysaccharides and mannoproteins released during yeast autolysis exert a protective effect on monomeric anthocyanins, and the red-blue color for which they are responsible, therefore, lasts longer . HoweverSaccharomyces strains and GLUC could help their liberation.The use of GLUC increased the concentration of monomeric anthocyanins, both non-acylated and acylated anthocyanins in the medium, especially the acylated ones that almost doubled their concentration. Morata et al.  reported2 [Since the lees are not completely formed by yeast components, but they are also composed of a certain amount of precipitated plant cell walls, capable of binding phenolic compounds ,45, a se2  and expr2 .During the US treatments, several exposure times were tested. When looking at the sum of monomeric anthocyanin, the results showed that the ultrasound treatment reduced their concentration, especially for the longer treatments. Del Fresno et al.  observedSimilarly, to acylated anthocyanins, sonication of lees during 120 min significantly increased the concentration of vitisin A. Liu et al. , while wRegarding the sum of all the detected anthocyanins, none of the US treatments showed significant differences with the control, however, the results indicate that a 120′ sonication time exerted the same effects as stirring the lees during 24 h, indicating that the process can save time in the treatment of lees.When the different treatments were combined with US, interesting results were obtained. If the application of US was combined with stirring (as in the control sample), especially if the US was applied after a stirring period, the extraction of total anthocyanins significantly increased. Furthermore, all the combinations of US with enzymes extracted more anthocyanins than that measured in the control sample and when the ultrasound was used alone (US120′). The treatments in which the enzymes were used before the application of ultrasound extracted significantly more anthocyanins than those in which the sample was sonicated and then the enzyme was added. The better desorption capacity of the combinations in which the enzymes were used before the sonication of the sample could be due to the degradation created by the enzymes that enhanced the effect of the US on the lees. Working with wine samples, Osete-Alcaraz et al.  combinedOur results showed that both the use of PEC and GLUC significantly increased the liberation of tannins compared to the control solution, and, as reported with total anthocyanins, the combination PEC + GLUC extracted the highest concentration of tannins. It is a well-known fact that both the plant cell walls and the yeast cell walls, which are in high concentrations in wine lees, have a great capacity to absorb tannins, absorbing even more than 50% of the tannins put in contact with them in model solutions ,26,48. TOn the other hand, and regarding the use of ultrasound at different treatment times, it was clearly observed that increasing the treatment time also increased the extraction of total tannins. When US was applied for 120′, the concentration of desorbed tannins was higher than when the enzymes were used individually and similar to that found when PEC and GLUC were used in combination, although the treatment time was only 120′ instead of the 24 h needed for the enzymatic assays.When looking at the different combinations with US, interestingly, the US120′ treatment extracted more tannins than when sonication was combined with 24 h of agitation (1ºStir-2ºUS and 1ºUS-2ºStir). The combination of US with GLUC did not improve the results of US working alone. Contrary to that, the use of PEC followed by US and the combination of PEC and GLU plus sonication led to the maximum recovery of tannins, indicating that the most effective combinations were those in which the PEC enzyme was used. It has been shown that the components of plant cell walls that have the highest affinity with tannins are pectins . Our resRegarding the mean degree of polymerization, there was a slight increase in this value in the liberated tannins when PEC + GLUC were used at the same time. It was also clearly observed that increasing the treatment time of the ultrasound treatments also increased their mDP. In the combined treatments, PEC enzyme  and sonication managed to extract the most polymerized tannins. Mazauric and Salmon  determinThe % of galloylation decreases significantly with the application of ultrasound  and the enzymatic combination (PEC + GLUC). In addition, %Gal decreased (in most of the cases) with combinations of ultrasounds and enzymes with respect to the control. There is an inverse relationship between the amount of total tannins extracted and the % of galloylation of these tannins, when tannins were more efficiently extracted, their % of galloylation was the lowest. It is likely that the galloylated tannins form the strongest unions with the plant and yeast cell walls, and by increasing the extraction of total tannins, those non-galloylated tannin increased more than the galloylated units, therefore, decreasing the galloylation percentage of the liberated tannins. In this way, Mazauric and Salmon  observedThe analysis of tannins by phloroglucinolysis only provides information regarding easily depolymerizable tannins, mainly those non-oxidized tannins. To get a more complete image of the effect of the treatments on the molecular weight distribution of the desorbed compounds, the samples were analyzed by SEC. The quantities of polysaccharides that were released to the medium due to the different treatments were also analyzed by size exclusion chromatography. This analysis was carried out to determine, beside the desorption of phenolic compounds, the capacity of the treatments, applied to the lees , to liberate polysaccharides, which in fact, is the main objective of the aging on lees process.The results shown in When only enzyme treatments were used, the pectolytic enzyme released the highest amount of polysaccharides. The capacity of this enzymatic preparation  to degrade plant cell walls and release soluble polysaccharides has been extensively studied in other studies ,26,50. IInterestingly, when the two enzymes were used together (PEC + GLUC), a less amount of polysaccharides were released than when PEC acted individually. The same phenomenon occurred in the study by Osete-Alcaraz et al. , where tThe glucanase enzyme only released slightly higher quantities of polysaccharides than the stirred control solution. Palomero et al.  analyzedThe US treatments released almost the same amount of soluble polysaccharides as the control, but in shorter times compared to the control sample . When stirring and ultrasound were combined (1ºStir-2ºUS and 1ºUS-2ºStir), the extraction of soluble polysaccharides was favored, in addition, barely any differences were observed related to the order in which each treatment was applied. Cacciola et al.  determinAll the treatments where US and enzymes were combined released more polysaccharides than the control, the sonication of samples during 120 min and the enzymatic treatments applied individually. This result indicates that the combination of US and enzymes presented an additive effect for the extraction of polysaccharides from lees, as was found for the extraction of anthocyanin and tannin.The order in which the US and enzymes were applied did not lead to significant differences, which coincided with the results obtained when studying the liberation of tannins. The treatments that led to the highest polysaccharide extraction were those in which the PEC enzyme was combined with US, with hardly any differences when used alone (1ºPEC-2ºUS and 1ºUS-2ºPEC) or together with glucanase (1ºPEC + GLUC-2ºUS and 1ºUS-2ºPEC + GLUC). This result was similar to that observed when the enzymes were used individually (without combining with the US) a, PEC + Aging on lees may not only be a process for increasing the liberation of polysaccharides for improving wine stability but also for increasing (or recovering) the wine phenolic content, by the desorption of those phenolics bound to plant and yeast cell walls. We have demonstrated that this liberation occurs and it can be boosted by techniques such as US and the addition of enzymes, which are already used for shortening the aging on the lees process.The combination of the pectolytic enzymes and β-Glucanase was the most effective treatment in the liberation of anthocyanins, whereas the use of the pectolytic enzyme largely favored the liberation of tannins and also low molecular weight polysaccharides.The application of US led, in only 120 min, to the same desorption of phenolic compounds than 24 h of sample stirring and the combination of US (120 min of treatment), GLUC and PEC showed, in general, an additive effect, increasing the extraction of phenolic compounds (tannins and anthocyanins) compared to individual treatments and also releasing a large quantity of low molecular weight polysaccharides, compounds of enological importance.The results of this study, although it is a preliminary experience carried out on a model wine solution, indicated that it is possible that aging on lees could also improve the phenolic composition of a red wine, if techniques such as ultrasound or the addition of enzymes are applied during the process. Although the application of β-glucanase is commonly used during this process, enologists should also consider the application of a pectolytic enzyme to improve the whole process, liberation of polysaccharides and phenolic compounds.


Independent of the origin, the unexpected discrepancy between absorption and fluorescence excitation spectra allows for easily accessed screening and insight into the efficiency of a fluorogenic dye/RNA system.The discovery of the GFP-type dye DFHBI that becomes fluorescent upon binding to an RNA aptamer, termed Spinach, led to the development of a variety of fluorogenic RNA systems that enable genetic encoding of living cells. In view of increasing interest in small RNA aptamers and the scarcity of their photophysical characterisation, this paper is a model study on Baby Spinach, a truncated Spinach aptamer with half its sequence. Fluorescence and fluorescence excitation spectra of DFHBI complexes of Spinach and Baby Spinach are known to be similar. Surprisingly, a significant divergence between absorption and fluorescence excitation spectra of the DFHBI/RNA complex was observed on conditions of saturation at large excess of RNA over DFHBI. Since absorption spectra were not reported for any Spinach-type aptamer, this effect is new. Quantitative modelling of the absorption spectrum based on competing dark and fluorescent binding sites could explain it. However, following reasoning of fluorescence lifetimes of bound DFHBI, femtosecond-fluorescence lifetime profiles would be more supportive of the notion that the abnormal absorption spectrum is largely caused by  This discovery paved the road for engineering a large variety of fluorogenic RNA aptamers as powerful tools for imaging genetically encoded domains in living cells5. Apart from poor photo- and low thermal stability, the applicability of the initial Spinach aptamers to living cells5 was expected to be limited also by its propensity to misfold. In the meantime, many limitations are removed either by miniaturizing the Spinach aptamer, or optimized superfolding aptamers, or by variation of the small reporter fluorophore7.One of the most challenging problems in cell biology is the control of RNA activity by employing genetically encoded fluorescent labels. To this goal the non-toxic, membrane-permeable reporter 3,5-DiFluoro-4-HydroxyBenzylidene Imadazolinone (DFHBI) mimicking the fluorophore of the Green Fluorescent Protein (GFP) has been engineered that is non-fluorescent in solution but emits green fluorescence when bound to the core G-quadruplex of the RNA aptamer Spinach9 and, the even smaller Broccoli12 as well as still shorter variants such as Corn14. Surprisingly, when genetically inserted into ribosomal RNAs, both of the small aptamers, Baby Spinach and Broccoli, have shown superior fluorescence efficiency as compared to the identical constructs under in-vitro conditions8 and as compared to several full-sequence Spinach aptamers. Thus, the early conjecture1 that the compact Baby Spinach, only half as large as the mother Spinach, may reduce live-cell artefacts when fused to ribosomal RNA8, gained confidence. Baby Spinach is also resistant to nuclease cleavage and degradation. Moreover, the smaller and more compact Baby Spinach results in higher mechanical stability as compared to a full Spinach sequence15. The structure of Baby Spinach in comparison with full sequence Spinach is shown in Fig. In search for small aptamers that are easier to integrate into arbitrary RNA sequences, the crystal structure of Spinach allowed for a rational reduction by removing non-essential regions of the sequence, thereby miniaturizing Spinach to Baby Spinach9 and the scarcity regarding their photophysical characterization, this paper is a model study on Baby Spinach. Although there is no high-resolution X-ray structure available, identical NMR spectra of Spinach (SP) and Baby Spinach (bSP) in the imino region suggest that the structure of the binding pocket is retained upon miniaturization4. Another feature, though under some debate8, is the fluorescence yield from bSP aptamers. More recently, the high fluorescence yield reported for the fluorophore-tagged Baby Spinach approaching 95% of the fluorescence intensity of Spinach is under debate8.In view of the increasing interest in small RNA aptamerscis–trans isomerisation of the DFHBI/bSP complex17 within the binding pocket as main contribution to the large amplitude of the absorption spectrum on condition of RNA excess over DFHBI and saturation of fluorescence.In this paper, we report on DFHBI/Baby Spinach studied by steady-state absorption and fluorescence spectroscopy complemented by femtosecond time-resolved fluorescence measurements. While quantitative modelling of the absorption spectrum is consistent with competitive binding to a fluorescent and dark RNA aptamer site in the case of bSP, femtosecond lifetime profiles suggest Absorption and Fluorescence. As shown in Fig. , the absorption spectrum of DFHBI anion is strongly affected by increasing amounts of bSP. At saturating bSP concentration the spectrum is broadened, and its peak is red shifted from 416 to 427 nm, while its molar extinction coefficient decreases by ≈ 30% :The binding affinity of DFHBI (D) to bSP (RNA) can be calculated by applying Eq.  to the fd = 4.4 µM has been obtained by fitting fluorescence titration data with Eq. . Note that these measurements in the literature employed only fluorescence data, and their analysis is based on 1:1 complexation of DFHBI and Spinach RNA.The dissociation constant Kwith Eq. . This hid = 4.4 µM obtained from the fluorescence titration, the equilibrium concentrations of the free dye [D] and fluorescent complex [D•RNA]FL were calculated for different ratios of [D] and [RNA]. On the assumption that a 1:1 fluorescent complex is formed, the calculated absorption spectrum of the fluorescent complex in each mixture  by Kd in Eq. . The factor p cannot be derived from the fluorescence data. However, this parameter can be determined using the absorption spectra shown in Fig. ,416 and ε460 are the corresponding molecular extinction coefficients at 416 nm and 460 nm. From the absorption spectra of the mixtures given in Fig. d for the fluorescent complex, Kd = K * p = 7.9 ± 0.2 μM than the one extracted from the fluorescence titration. As compared to Kd of the fluorescing complex, the dissociation constant Kx for the dark complex, Kx = Kd/(p − 1) = 9.8 ± 0.2 μM is only slightly larger.Equation  has the entclass1pt{minimad = 7.9 µM to derive the absorption spectrum of the fluorescing complex largely removes the discrepancy between absorption and fluorescence excitation spectra in Fig. Figure FL/[D•RNA]DARK = Kd/Kx = (p − 1). Therefore, the difference of the free energies of these species can be expressed asFL is thermodynamically more stable than X (Δ < 0) when p < 2. By contrast, in systems where p > 2, the formation of the dark states becomes dominating (Δ < 0). For bSP, p = 1.8 yields Δ = -0.17 kcal/mol. Because the ratio [D•RNA]DARK/[D•RNA]FL is extremely sensitive to Δ, small changes in the structure or in energy parameters of either RNA or the dye can seriously affect the fluorescent properties of the system.The parameter p (p > 1) determines the ratio of the fluorescent to the dark complexes [D•RNA]19. The effect has been attributed to folding problems. This observation is in contrast to the strong fluorescence reported for bSP where also the knob region and the nucleotide U50 are deleted10. A direct comparison between the two variants is not possible since the SP mutant10 still contains the S3 stem which has been removed in bSP. This comparison demonstrates that a small change in the sequence—even in the region outside of the binding pocket—may lead to a minor change in the structure that can largely affect the fluorescence.It is interesting to add that for SP the deletion of both the knob region—a non-complimentary stem in SP—and the U50 nucleotide destroyed the fluorescenceFL should be identical to the excitation spectrum of its fluorescence at the low concentration of 2 μM used, the combined absorption spectra of the free dye and the dark complex can be constructed for the various DFHBI/bSP ratios as given in Fig. 10log scale and the excitation spectrum are similar although the latter based on the number of absorbed photons is directly dependent on concentration.)The absorption spectrum of the mixture 2 µM DFHBI/20 µM RNA comprises two components: the absorption of the fluorescing complex saturated in the presence of 20 µM RNA and the combined absorption of non-fluorescing species consisting of the free dye and the dark complex. The separation of the two components is achieved as follows: Since the absorption spectrum of the fluorescent complex (D•RNA)d = 7.9 μM and Kx = 9.8 μM, respectively, the mixture contains 1.3 μM fluorescing complex, 0.55 μM dark complex, and 0.15 μM free dye. The error bar of these values is about ± 8%.The combined absorption spectrum of the free dye and the dark complex Fig. B is slig5. In the present case, folding was performed at room temperature. It is added in passing that the binding efficiency in Ref.5 was estimated using a previously described assay7 that is based on the standard 1-site model. However, at the saturating conditions treated, the 1-site standard model and the multiple-site model are equivalent.In the case of bSP, the large dissociation constant of the fluorescing complex indicates poor binding, i. e. a binding efficiency of 65 ± 8% that is similar to the 55 ± 7% binding efficiency reported for DFHBI/bSP when folding was achieved upon heating the RNA aptamer and slow cooling16 is indicative of a similar complex in the case of the smaller aptamer complex with bSP.The excited state of DFHBI in complex with bSP decays almost monoexponentially within 4 ± 0.1 ns  accessible in femtosecond up-conversion experiments. The offset is caused by the 4 ns lifetime of the fluorescing complex as resolved in the TCSPC measurement  Arguing on the level of absorption spectra, quantum chemical calculations show (SI) that H-bonding of DFHBI to the neighbouring guanine G31 has little effect on the absorption spectrum. H-bonding, however, may well be the predominant binding mode of a multitude of the complexes. This speculation could gain support by the similarity of the combined absorption spectra of the free DFHBI and the dark complex  It could be that H-bonding of the dark complex accompanied by some bending of the exocyclic bridge in the DFHBI fluorophore already approaches the conical intersection and it needs only minute rotational motion to cross over to the electronic ground state. (ii) If we translate features of the GFP chromophore to the fluorinated analogue DFHBI where the same methyne-group bridges the phenol and the heterocycle, the underlying photoisomerization may also in DFHBI be a volume conserving processHowever, the very same spectral argument holds also for the assumed overlapping of cis- and trans-conformer of DFHBI. Although all these considerations can support non-specific H-bonding of DFHBI associated with fast fluorescence decay, so far no argument is compelling enough to devalue the observation that the fluorescence amplitude of the apparent dark complex is far too small to explain the large contribution (28%) of the apparent dark state to the absorption spectrum.18 of the DFHBI/Spinach complex, DFHBI is sandwiched between the upper G tetrad of a G-quadruplex stretch and the triple bases  was dissolved in DMSO to get 40 mM stock solution and stored at − 20 °C. From that, each stock of diluted solution (200 µM) is made in HEPES buffer (10 mM pH 7.5) containing DEPC treated water, 50 mM KCl and 5 mM MgCl2.2. The 2-ml Eppendorf tube containing RNA in buffer was put in a water bath (2 L), then heated to 65 °C for 3 min and left cooling overnight to room temperature to let the RNA slowly fold to the correct conformation. The tube was then stored at -30 °C fridge and used as RNA stock after thawing to room temperature. The integrity of the RNA was confirmed by gel electrophoresis experiment as shown in Fig. RNA (sequence: 5′- GGU GAA GGA CGG GUC CAG UAG UUC GCU ACU GUU GAG UAG AGU GUG AGC UCC -3′) was purchased from Dharmacon Inc in protected form. It was then de-protected following Dharmacon’s protocol using de-protection buffer, purified by HPLC, and lyophilized to dryness. The dry pellet was stored at − 80 °C and before use, suspended in HEPES buffer (10 mM pH 7.5) containing DEPC treated water, 50 mM KCl and 5 mM MgCl2 to get 2 µM DFHBI solution, which contains less than 0.1% DMSO. RNA aliquots were added from the RNA stock to the 2 µM DFHBI solution to achieve different ratios of RNA to DFHBI concentrations in the mixture. The mixture was vortexed for 1 min and left in the dark for 2 h for completion of the binding process. Spectroscopic experiments were carried out right after that. The formation and the integrity of the G-quadruplex binding pocket before and after forming the complex was confirmed by CD spectra as shown in Fig. DFHBI at 200 µM stock was diluted in HEPES buffer (10 mM pH 7.5) containing DEPC treated water, 50 mM KCl and 5 mM MgCl2. The solution was circulated between a 20-ml bottle and a flow-cell quartz cuvette by a Gilson Minipulse 3 peristaltic pump with a rotation speed of 48 rpm. Fig. 14 photons/mm2s.All our measurements were performed under flow-cell conditions to minimize photo-bleaching unless otherwise stated. A total of 20 ml of the DFHBI/RNA mixture (at different ratios) was prepared in HEPES buffer (10 mM pH 7.5) containing DEPC treated water, 50 mM KCl and 5 mM MgCl6. That method compared fluorescence (excited at 469 nm) of the mixture under 2 extreme conditions: one in which the RNA is in excess relative to the DFHBI (0.1 µM DFHBI + 10 µM RNA), and one in which the DFHBI is in excess relative to the RNA (10 µM DFHBI + 0.1 µM RNA). For each condition, the signal from DFHBI without RNA was subtracted from each signal. The signal from the first condition (limiting RNA) was divided by the signal from the second condition (limiting dye) to determine the fraction folded. From Fig. To determine the folding efficiency of the RNA, we employed a method which was reported in Folding assay section in Online Methods in Ref.10 photons/mm2 s) used in our absorption measurements, no photo-bleaching has been observed.Absorption measurements were performed on a Varian CARY-100 spectrophotometer at room temperature using a 10 × 2 mm quartz cuvette with 10-mm path length. At the low light intensity  was frequency doubled (SHG) to obtain a 416 (469)-nm excitation and focused onto the sample (average power ~ 10 µW). A portion of the excitation light was used as the start signal for the measurement cycle controlled by a histogram accumulating real-time processor (PicoHarp 300). A time-resolving spectrometer FluoTime 200 with wavelength resolution of 1 nm or better was used to collect the fluorescence signal. This signal was then recorded by a Multi-Channel-Plate Photomultiplier Tube (MCP-PMT) with an overall IRF (Instrument Response Function) FWHM of 40 ps. The samples were held in a quartz flow cuvette (1 mm).The time-resolved fluorescence decay with time constants > 15 ps was measured using a-time-correlated single photon counting (TCSPC) set-up from PicoQuant as described elsewhereFemtosecond time-resolved fluorescence was measured using our fluorescence up-conversion spectrometer . The samples were excited with the second harmonic of a titanium-sapphire laser  at 400 nm . The fundamental beam of the laser at 800 nm travelled through an optical delay line. The fluorescence of the samples was collected and focused onto a 1 mm BBO crystal together with the delayed fundamental beam. The sum frequency (upconverted fluorescence) beam was focused into the entrance of a double-monochromator. The samples were held in a 1 mm quartz flow cuvette.To ensure photostability all steady-state and time-resolved fluorescence measurements were performed in a flow-cell Fig. .28 was used. Vertical excitation energies were calculated using TDA formalism29 with the long-range corrected functional CAM-B3LYP30. The standard 6-31G* basis set was used. The equilibrium and non-equilibrium solvation energy in a medium with dielectric constant ε was estimated using a COSMO-like polarizable continuum model in the monopole approximation31.For all calculation the program Gaussian 09 (rev. E01)Supplementary Information.