Reverse transcription is the first step of most analyses of gene expression, yet the quantitative biases it introduces are largely overlooked. Following a series of purpose-designed systematic experiments we cherry-pick examples of various biases introduced by reverse transcription, and alert the “gene expression community” to the pitfalls and improved practice of this fundamental technique. The analysis of gene expression underpins every aspect of modern biomedical research, which in the last two decades has progressed from single-gene to genome-wide approaches. Typically, the relative abundance of specific RNAs  is enumerated by quantitative PCR  or by massively parallel  sequencing. Substantial effort has been invested in the assurance of standards for these techniques, including their linearity, positive and negative controls, normalisation of data and the recognition of inherent biochemical and computational biases.1), the prevalent practice still assumes that manufacturers’ reverse transcription (RT) protocols ensure uniform and reliable synthesis of cDNA. Over the past decade, we have accumulated anecdotal, and often contradictory, experience regarding RT non-linearity and biases.In contrast, the initial essential step “converting” the RNA to measurable cDNA, has not been scrutinised with the same rigour, and is not subject to an accepted best practice. Although a handful of studies attempted to tackle various aspects of reverse transcription pitfalls (reviewede.g. ΔΔCq) of relative gene expression inaccurate at best and erroneous at worst. We cherry-pick examples where differential RNA integrity or saturation of the RT may falsely appear as differential expression (DE). Lastly, we propose improved practices which can easily be integrated into the MIQE guidelines2 for RT-qPCR and NGS experimental design and reporting.In a series of purpose-designed experiments, we systematically demonstrate that the bias introduced by RT is far greater than is commonly assumed. Our data  indicate that RT introduces amplicon-specific and transcriptase-specific biases which render standard calculations  and Hs578T (H). An aliquot of T RNA has been mildly chemically fragmented , and later validated across multiple breast cell lines (not shown).We examined 8 qPCR amplicons, representing coding (a-d) and non-coding (e-h) transcripts of varying abundance, approximated by relative Cq values. They are: (a) a highly abundant eEF1A1 “exon-exon” amplicon with primers on consecutive exons spanning a 366 nt intron (b) a low abundance eEFint amplicon from this eEF1A1 intron; (c) a moderately abundant OAZ1 “exon-exon” amplicon; (d) a low abundance MVP “exon-exon” amplicon; and several non-coding RNAs  18 S rRNA, (f) 5.8 S rRNA and (g) U1 snRNA), as well as (h) Alu which can be transcribed either as a stand-alone ncRNA or embedded in untranslated regions of coding genes, and which has been proposed as a measure of total RNA load instead of unreliable single-transcript RT-qPCR reference genesThe key observations from our systematic analysis are general non-linearity as well as amplicon-specific and reverse transcriptase-specific biases. In general, across the entire dataset, a 2-fold increase of cDNA input into a qPCR reaction resulted in an average decrease of 0.99 in Cq value (n = 576 2-fold dilution pairs) – in line with the theoretical decrease of 1. In contrast, a 2-fold increase of RNA input into an RT reaction led to an average decrease of only 0.39 (n = 648) – a substantially lower value than the theoretical decrease. For brevity, hereafter we will refer to Cq increase per 2-fold dilution as Cq2f.5. Figure A major decision in evaluation of gene expression is the choice of reverse transcriptase enzyme or kit. This decision is typically a matter of lab tradition and cost, and is seldom evaluated when experimental designs change6. This can represent an inherent biological truth , or can be the artifact of variable sample storage and processing . Figure 7. A more subtle effect is evident when normalising MVP or eEFint amplicon values to the eEF1A1 reference amplicon, yielding a false DE of ~2-fold up-regulation between intact and fragmented samples of the same RNA. Since SuperScript-IV RT-qPCR  analysis of the same samples was not similarly skewed, we assume that this bias is not inherent to the presented amplicons, e.g. 5'/3' bias in samples degraded by exonucleolytical activity in vivo or in preparation6.An added complication in studies of gene expression in patient tissues, is inconsistent RNA integrity across the sample cohortWe then used the data obtained in the titration experiment for a typical analysis of differential expression, as is the common practice across hundreds of research papers. Figure Lastly, we examined the potential contribution of the aforementioned biases on reverse transcription used in the generation of libraries for RNA-seq. To this end, we have set up a similar titration experiment using the SuperScript-II protocol and reagents from the Illumina’s TruSeq® Stranded Total RNA Sample Preparation Guide. To rule out the contribution of potential contaminants due to our RNA extraction techniques, we processed alongside H and T cell line RNAs similar quantities of Stratagene QPCR Human Reference Total RNA (U), which is a high-quality commercial control RNA recommended for quantitative PCR gene-expression analysis. Each 75–600 ng sample was tested with or without associated TruSeq fragmentation .Figure 8, in our experience RT biases lead to far greater errors than qPCR imperfections, especially when users adhere to MIQE-based practices for qPCR. Amplicon-dependent biases within a chosen RT system can identify non-existent DE or mask real effects. Moreover, RT is the founding step in many cutting edge technologies, such as single-cell expression analysis. Leaving its biases untested undermines such advances.These systematic data corroborate our (and colleagues’) cumulative anecdotal experience involving >100 amplicons  across thousands of samples and dozens of experimental designs. Differential dose-response – and ultimately saturation – of RT reactions is far more prevalent than is commonly assumed, even within manufacturers’ usage parameters. In line with published observations of lower reproducibility of RT compared to qPCR5, in our experience, the maximal RNA input suggested by all manufacturers is too high; and the most substantial – although by no means complete – improvement to RT biases is achieved by reducing the RNA input at the cost of higher eventual Cq values.We cannot pinpoint a single factor, either in the nature of the qPCR amplicon or the RT procedure, which can alleviate all these biases. In addition to critical evaluation of available reverse transcriptases1, secondary structures may lead to differential denaturation of RNA templates at the RT temperature. This bias can easily be minimised by avoiding one-step RT-qPCR methods1 and by using a thermostable RT enzyme at a higher temperature. Unfortunately, the effect of the same structures on differential degradation in vivo or during RNA extraction cannot be as easily moderated.Many additional hypothetical factors can play a role in differential RT efficiency or imperfect dose dependence. In addition to their implication on optimal primer designIn the absence of secondary structure, primary sequence may play an under-researched role in RT efficiency. Features such as GC-content and repeat sequences are well known to cause “difficult regions” in PCR amplification, and numerous simple or proprietary additives exist to alleviate these difficulties in PCR. It stands to reason that similar effects will take place at the RT step. The use of random hexamers for priming RT throughout the entire length of RNAs poses additional challenges. These are not only dependent on local GC-content, but certain hexamer variants may be sequestered by abundant transcripts, thus causing a non-linear stoichiometry for less abundant transcripts. Manufacturers are invited to re-evaluate their protocols, perhaps using synthetic transcripts in varying mixed RNA backgrounds.9 reference “housekeeping” amplicons, as recommended by the MIQE guidelines2, can dampen the effect of some amplicon-specific problems. We further advocate restricting the use of ΔΔCq-like approaches based on reference amplicons to fine-tuning of minor fluctuations (<1.5-fold or ΔCq = 0.58) in RNA input. We strongly advise including reference RNA titration curves in the RT step, much like the DNA (or cDNA) titration curves required under the MIQE guidelines for correcting qPCR analyses of amplicons with differential amplification efficiency. Moreover, where sample availability and budgets allow, RT of key biological samples should not only be replicated to account for RT variability8, but also carried out with varying RNA inputs to ensure that perceived DE is dose-independent.The incorporation of multiple rationalisedLastly, we have used qPCR to demonstrate specific RT biases generated by kits aimed at single-gene and genome-wide readouts alike. False-positive and false-negative outcomes of biased RT may be greater on a genome-wide scale, and the RNA community will greatly benefit from an appropriately directed analysis of relevant NGS datasets. Complementary efforts from the manufacturers of RT kits are also called for.Total RNA was isolated from T-47D and Hs578T adherent breast cancer cell lines using Direct-zol (Zymo R2052), including the optional DNase treatment step. Stratagene Human Reference Total RNA (#750500), a pool of 10 human cell lines certified for use in qPCR, was purchased from Agilent. For Experiment 1, 10 µg aliquots were fragmented using RNA Fragmentation Reagents (Thermo AM8740) for 50 sec at 70 °C or 0 °C to generate matched part-fragmented or intact samples, respectively. RNA was re-purified using Direct-zol. For Experiment 2, fragmentation was carried out as part of the TruSeq Stranded Total RNA protocol (see below). Equal load and integrity of all input RNAs were confirmed by electrophoresis through an agarose gel impregnated with SYBR Safe (Thermo) followed by imaging under blue light Figure .e.g. primers, or pre-RT heat-denaturation of RNA) as consistent as possible. For each system, a pool of 200 ng of each of the RNAs was also treated alongside individual samples with the omission of the enzyme . To minimise error , all non-template reagents were combined into master mixes.Aliquots of 600, 300, 150 or 75 ng of each RNA, alongside a sample containing only water , were reverse-transcribed using one of four systems: Transcriptor (Roche), SuperScript IV (ThermoFisher), iScript (BioRad) or SuperScript II , essentially following manufacturers’ recommendations while keeping optional choices , 2 µl 10 mM dNTP and 0.5 µl Transcriptor enzyme. Reverse transcription was initiated for 10 min at 25 °C, proceeded for 30 min at 55 °C and finally terminated by incubating for 5 min at 85 °C.1 µl of 10 µM T15VN, 1.5 µl of 100 µM N6 primers and 1 µl 10 mM dNTP were added to the RNA in a final volume of 13 µl. Following denaturation for 10 min at 65 °C, samples were snap-cooled on ice and supplemented with 4 µl 5x SSIV Buffer, 1 µl RiboLock RNase Inhibitor, 1 µl dithiothreitol (DTT) and 1 µl SuperScript IV enzyme. Reverse transcription was initiated for 10 min at 25 °C, proceeded for 30 min at 55 °C and finally terminated by incubating for 5 min at 85 °C.RNA alone, in a final volume of 15 µl, was denatured for 10 min at 65 °C and snap-cooled on ice. Following the addition of 4 µl 5x iScript Mastermix and 1 µl iScript enzyme, reverse transcription was initiated for 10 min at 25 °C, proceeded for 30 min at 46 °C and finally terminated by incubating for 1 min at 95 °C.RNA alone, in a final volume of 8.5 µl, was mixed with 8.5 µl of Illumina Elute, Prime, Fragment High Mix and incubated either at 94 °C for 8 min or at 65 °C for 10 min, resulting in fragmented or intact templates, respectively. Following snap-cooling and addition of 7.2 µl of First Strand Synthesis Act D Mix and 0.8 µl SuperScript II enzyme, reverse transcription was initiated for 10 min at 25 °C, proceeded for 15 min at 42 °C and finally terminated by incubating for 15 min at 70 °C.A 15 µl aliquot of each reverse transcription reaction was diluted 20-fold. Two additional 2-fold serial dilutions were made for all samples except RT0 and RT- samples. 8 µl quantitative PCR reactions in 384-well plates contained 4 µl diluted cDNA  and 4 µl of either FAST SYBR Green Master Mix or PowerUp SYBR Green Master Mix  supplemented with 1 µM each primer  followed by imaging under blue light Figure . Alu ampe.g. containing dust or bubbles), downstream analyses were performed using the average of the two closest triplicates. Using a median or average of all three replicates introduced only insubstantial differences into the presented analyses, conclusions and recommendations. The standard deviation across triplicates was 0.19 ± 0.27 Cq, and across best duplicates −0.06 ± 0.10 Cq (representing duplicates with Cq difference of 0.09 ± 0.14), with high standard deviations mostly affecting readings with Cq >30 (single-molecule stochastic readings), such as negative controls. For clarity of presentation, error bars depicting the standard deviation were omitted throughout the manuscript.All reactions were set up in triplicate. For simplicity and consistency of removing aberrant qPCR wells  to result in a 70–110 bp amplicon suitable for fast quantitative PCR amplification.All primers were designed using the Primer3 algorithm at Supplemental Figures S1-S3.Dataset 1.


A DNA nanotechnology approach enables low-cost and programmable viral detection. Detection of viruses is critical for controlling disease spread. Recent emerging viral threats, including Zika virus, Ebola virus, and SARS-CoV-2 responsible for coronavirus disease 2019 (COVID-19) highlight the cost and difficulty in responding rapidly. To address these challenges, we develop a platform for low-cost and rapid detection of viral RNA with DNA nanoswitches that mechanically reconfigure in response to specific viruses. Using Zika virus as a model system, we show nonenzymatic detection of viral RNA with selective and multiplexed detection between related viruses and viral strains. For clinical-level sensitivity in biological fluids, we paired the assay with sample preparation using either RNA extraction or isothermal preamplification. Our assay requires minimal laboratory infrastructure and is adaptable to other viruses, as demonstrated by quickly developing DNA nanoswitches to detect SARS-CoV-2 RNA in saliva. Further development and field implementation will improve our ability to detect emergent viral threats and ultimately limit their impact. Newly emerging or reemerging viruses pose substantial challenges to health care systems, particularly as globalization has contributed to the rampant spread of these viruses  to detect immunoglobulin M (IgM) antibodies or nucleic acid testing (NAT) based on a reverse transcription polymerase chain reaction (RT-PCR) assay to detect viral RNA  upon binding a target viral RNA . The preAs a first proof of concept for detecting ZIKV, we designed DNA nanoswitches to target an already validated sequence in the ZIKV genome that has been used to bind primers in quantitative PCR (qPCR) .Having shown successful detection of ZIKV RNA using a single target sequence, we recognized that we could exploit the large genome size  to increase our detection signal through multiple targets. Once the long viral RNA is fragmented, the number of available target sequences increases markedly. Because our detection signal is proportional to the number of looped nanoswitches, a nanoswitch mixture for different target sequences within the viral genome is expected to provide an increased signal. To test this, we developed an algorithm for choosing multiple sequence regions in the viral genome that can be targeted by the nanoswitches (note S1). First, we chose the default target length as 30 nucleotides based on results from screening nanoswitches with different detection arm lengths (fig. S3). Then, the algorithm selectively excluded target sequences that could form stable secondary structures (fig. S4) and cross-binding with nanoswitch backbone oligos (fig. S5), and enforced GC content and uniqueness of sequences. On the basis of these criteria, we chose 18 target regions along the entire ZIKV RNA for testing and designed the nanoswitches. To facilitate use of our MATLAB-based software, we have built a graphical user interface (fig. S6) and made it freely available (data file S1).We then validated quality and function of each nanoswitch in the panel of 18 nanoswitches. All nanoswitches performed well with a molar excess of positive DNA controls (20:1 DNA control to nanoswitch), although they showed more signal variation with fragmented ZIKV RNA (fig. S7). We ranked the nanoswitches from the strongest to the weakest signal and made a series of equimolar nanoswitch mixtures. Using these mixtures, we validated our inherent signal multiplication strategy using a low-concentration pool of equimolar DNA fragments to mimic the fragmented RNA. We observed that our detection signal increased steadily up to around 12 different nanoswitches  and then6 copies) in a 10-μl reaction volume  have overlapping geographical distributions and clinical symptoms, infection with either virus may result in clinical misdiagnosis . Using tIn addition to possible misdiagnosis between different viruses, there is an additional challenge in determining the specific strain of a virus. For example, in Latin America, four different DENV serotypes are known to be present and co-circulate, where misdiagnosis of the infecting strain can have substantial implications for treatment options  (5 copies/μl (0.28 pM) (5 copies/μl) (2 copies/μl) (fig. S15), and the detection time was reduced from ~13 to ~5 hours.Moving toward clinical applications, we aimed to demonstrate detection of relevant levels of ZIKV RNA from biological fluids. ZIKV is present in the serum, urine, and other body fluids of infected patients (pies/μl)  to sub-fWith the emerging outbreak of SARS-CoV-2 in January 2020, we took the opportunity to develop and test our DNA nanoswitches against the new virus. Following a similar strategy as for ZIKV, we identified a target region, developed nanoswitches, and used the NASBA strategy to detect a SARS-CoV-2 RNA in 10% human saliva. Following our ZIKV protocol, we validated nanoswitch detection of an in vitro transcript of a short segment of SARS-CoV-2 RNA in ~5 hours and cross-validated with RT-PCR (fig. S17). Further optimizing the protocol times, we achieved detection of SARS-CoV-2–positive control RNA at a concentration as low as 200 copies/μl , and the mixture was made up to 10 μl with nuclease-free water. Samples were then incubated in a thermal annealing ramp from 40° to 25°C over ~12 hours . After the incubation, samples were stained with GelRed at 3.3× concentration and incubated at room temperature for 30 min. Before loading the gel, 2 μl of 6× blue loading dye was mixed with each sample, and 10 μl of sample was loaded to each well. Samples were run in a 0.8% agarose gel at 65 to 75 V for about 70 to 90 min in a cold room.First, 500 ng of total RNA extracted from uninfected/infected cells was fragmented at 94°C for 9 min in 1× fragmentation buffer. Fragmented total RNA was then mixed with nanoswitches [100 pM, MgClFor the detection of viral RNA extracted from urine, we first added DNA/RNA shield buffer  into urine, then mixed in the RNA with blocking oligos (200 nM) into 200 μl of human urine (purchased from Innovative Research Inc.) to mimic a clinical sample, and performed the RNA extraction immediately. Then, we used Quick-RNA Viral Kit (Zymo Research) to extract the viral RNA from the urine. After RNA extraction, we added RNase Inhibitor  to the solution. We tested different amounts of ZIKV RNA . Here, t2, 40 mM tris-HCl, 42 mM KCl, 5 mM dithiotreitol, 15% (v/v) dimethyl sulfoxide. The primers were chosen from  into 1× PBS or 10% human urine (purchased from Innovative Research Inc.) to concentrations of 897, 200, and 20 pfu/μl. Subsequently, blocking oligo (200 nM) was added, and the viral RNA was released by heating the samples at 94°C for 3 min within 10-μl volume. For the human urine samples, RNase Inhibitor  was also added at a concentration of 2 U/μl before heating. After cooling down to room temperature, 0.5 μl of RNase Inhibitor at 40 U/μl  was added to 10 μl of human urine sample to protect the viral RNA. The total volume of each NASBA reaction was scaled down to 6 μl, which contains 1.25 μl of Enzyme COCKTAIL (NEC-1-24), 2 μl of 3× buffer (NECB-24), 0.48 μl of NTPs mix at 25 mM, 0.3 μl of dNTPs mix at 20 mM, 0.2 μl of two primers mix at 10 μM, 0.2 μl of RNase Inhibitor at 40 U/μl , and 1.57 μl of viral RNA. The sample was incubated at 41°C for 2 hours in the thermal cycler and followed by heating at 94°C for 5 min to deactivate all enzymes. Then, 1 μl of the NASBA sample was used in the following DNA nanoswitch detection assay in PCR tubes with 10-μl final volumes. The assay was finished by incubating at room temperature for 2 hours. After mixing with GelRed at 1× concentration and 2 μl of 6× blue loading dye, the detection samples were loaded to the 25-ml 0.8% agarose gel, which was run in 0.5× TBE buffer at 75 V at room temperature for 45 min.A gBlock gene fragment of the SARS-CoV-2 RNA segment  was heated at 94°C for 3 min to mimic the process of destroying viral capsid to release the RNA. Then, the SARS-CoV-2 RNA was spiked into diluted saliva (10%) with blocking oligos (~200 nM) and RNase Inhibitor (2 U/μl). For the NASBA-based detection of SARS-CoV-2 RNA purchased from Twist Biosciences, NASBA kits purchased from Life Sciences Advanced Technologies Inc. were used. Briefly, 3.3 μl of 3× buffer (NECB-24), 1.7 μl of 6× nucleotide mix, 0.4 μl of two primers mix at 10 μM, 0.25 μl of RNase Inhibitor at 40 U/μl , and 2 μl of viral RNA sample with different concentrations were mixed first and heated at 65°C for 2 min, and then the samples were incubated at 41°C and 2.5 μl of Enzyme COCKTAIL (NEC-1-24) was mixed. The samples were incubated at 41°C for 40 min in the thermal cycler followed by heating at 94°C for 5 min to deactivate all enzymes.Then, 1 μl of the NASBA amplified RNA sample was used in the following DNA nanoswitch detection assay in PCR tubes with 10 μl of final volumes. Two nanoswitches were developed and used to target the amplified RNA pieces on two different regions (fig. S18). The assay was finished by incubating at room temperature for 40 min. After mixing with GelRed at 1× concentration and 2 μl of 6× blue loading dye, the detection samples were loaded to the 25-ml 0.8% agarose gel, which was run in 0.5× TBE buffer at 90 V at room temperature for 25 min.The detection samples were run in 25-ml 0.8% agarose gels unless otherwise noted, cast from molecular biology grade agarose (Fisher BioReagents) dissolved in 0.5× TBE buffer. Typical running conditions were 75 V for 45 to 70 min at room temperature or cold room. Samples were mixed with a Ficoll-based blue loading dye before loading. Imaging was completed on a Bio-Rad Gel Doc XR+ imager with different exposure times based on the brightness of the detection bands. The detection efficiency was analyzed using included Image Lab software . The pro


Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps , for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at  RNA is a highly flexible and dynamic biopolymer capable of adopting complex, intricate structures ultimately dictated by its sequence. It plays fundamental roles and regulates all steps of gene expression . To compN-cyclohexyl-N-(2-morpholinoethyl)carbodiimide methyl-p-toluenesulfonate) are commonly used chemicals that react with the nitrogen base moiety of RNA in a sequence-specific manner. Both compounds modify the Watson–Crick edge of nucleotides that are free from the Watson-Crick base pairing (DMS (dimethyl sulfate) and CMCT , or applied in RNA probing in vivo 1M7 (1-methyl-7-nitroisatoic anhydride)  is ahydride) ,10, are hydride) ,10.Regardless of the type of modifying agent used in the experiment, classical chemical probing begins with the RNA folding, followed by chemical modification with structural probes, and detection of modification sites in the reverse transcription reaction. Finally, the read-out, previously by denaturing PAGE, and more recently superseded by capillary electrophoresis (CE), is performed in order to map modification positions along the RNA sequence. The resulting CE chromatograms can be subsequently analyzed using diverse software tools, such as QuShape , to obtaMany tools for RNA structure prediction have been adapted to use chemical probing data, however only a few of them, e.g., RNAfold from ViennaRNA , and FolVarious computational tools have been developed for RNA structure prediction based on the chemical probing data, e.g., RNAex , RNA Frahttps://github.com/afafbioinfo/Macro_CE) is a standalone Python script for processing QuShape data with implemented normalization protocol. IPANEMAP (https://github.com/afafbioinfo/IPANEMAP) is a set of Python scripts for predicting RNA secondary structures based on various chemical probing data (including SHAPE and DMS). It computes and outputs one or several secondary structures, corresponding to the conformers best supported by experimental data and thermodynamics. All these tools and programs may be useful at various stages of the RNA chemical probing data analysis, but they need to be installed and run locally from the command line, which is often a limiting factor for the inexperienced user.To date, there has been no fully automated and simple tool dedicated to processing RNA chemical probing data from experiments focused on studying individual RNA molecules and analyzed with capillary electrophoresis. A few components of the workflow, which may facilitate the processing of the experimental data, are available. Macro_CE  , and RNAFor sequence-only predictions, RNAProbe makes use of ViennaRNA RNAfold , RNAstruRNA sequence is required in all analysis modes. It can be uploaded either as a text file containing the RNA sequence or pasted into the ‘direct input’ window. The input sequence must be the same as it was used in the QuShape analysis (including the cassette); the sequence to be analyzed can be specified by selecting a region of interest (ROI), leaving out the cassette on 5′ and 3′ ends of the RNA molecule. The sequence should consist of only four letters corresponding to the four RNA nucleotides - A, C, G, U ; should T be present in the sequence, it will be displayed as U in the output files.The QuShape file is a tab-separated text file, generated by the QuShape software . The seqThe secondary structure input is optional. This option allows for mapping reactivities onto the provided structure (instead of or in addition to probing-based predictions generated by RNAProbe). The secondary structure in a dot-bracket notation can be uploaded either as a text file or pasted directly into the ‘direct input’ box. The 3D structure input is also optional. It allows for mapping of reactivty values on the provided PDB structure, to obtain color-coded visualization of the probing intensties on the 3D model.The RNAProbe server generates high-quality images, most of which are displayed on the results page. These include: a table with secondary structure predictions in dot-bracket format, a heatmap and a barplot displaying the reactivity profile of the molecule under study (not available in ‘Prediction mode’), and VARNA representations of the predicted secondary structures. The non-displayed results include a text file containing predictions in dot-bracket format and a normalized reactivities file. All the generated outputs can be downloaded separately, or as a zip archive.The table with all results is available in two formats: HTML viewed on the website, and XLSX file, suitable for download. The table is divided into five main parts: Name—the job name provided by the user; Method—lists the methods used to predict the secondary structure(s); Filename—a combined name of the uploaded file, along with the analysis mode; Full string—sequence/secondary structure, represented as one string of characters; Sequence/reactivity/structure—information for each nucleotide placed in a separate cell .The predicted or user-defined structures are represented using VARNA . For eacIf the user supplies an RNA 3D structure in the PDB format, the probing data can be mapped onto the B-factor fields of the respective residues. RNAProbe visualizes the mapping using JSmol , additioThe heatmap and barplot images are available for all three probing modes. The heatmap plot presents the reactivity value for each nucleotide in a separate box according to a color-range, either a default one for each probing method  or a defined one by the user from a set of available alternative coloring schemes. The barplot represents the reactivity value for each nucleotide, where the reactivities are colored as follows: 0.0 to 0.3—black, 0.3 to 0.7—orange, 0.7 to 1.0—red. With the heatmap and barplot representations, the user can easily spot differences in reactivities of different residues of a particular RNA sequence, as well as compare the reactivity profiles resulting from different experiments.The server generates two text files with results, providing interoperability with other bioinformatic workflows. All predictions file consists of the sequence and the predicted secondary structures for all applied prediction methods. The reactivities file is a text file with two columns—a nucleotide number (reflecting its position in the sequence) and its corresponding reactivity value. The format of this file is the same as the one used in SHAPE-directed predictions with RNAfold and ShapeKnots. Note that this file is not available in ‘Prediction mode’.add riboswitch aptamer domain is an allosterically regulated element, found in the 5′ UTR of the adenosine deaminase mRNA of the Gram-negative bacterium Vibrio vulnificus, and it regulates the translation of a downstream gene in response to adenine in vivo  and refolded at 37°C for 15 min. When the chemical probing was carried out in the presence of a ligand, adenine was added to the RNA sample at 1 mM concentration in the 3× folding buffer. RNA modification, initiated by the addition of 130 mM NMIA dissolved in DMSO, was carried out at 37°C for 45 min. Since NMIA undergoes hydrolysis, the reaction was self-quenching. The negative control was treated with the same volume of DMSO. After incubation with the probing reagent, RNA was precipitated in the presence of ethanol. Resulting pellets were resuspended in 0.5× TE buffer, and combined with fluorescent primers for the reverse transcription with the SuperScript III Reverse transcriptase kit, according to the manufacturer's instructions (different fluorophores are required for the positive/negative reactions and for the sequencing reaction). In addition to the standard reaction mix, sequencing reactions also contained the ddNTP of choice. Resulting cDNA samples were precipitated in ethanol, and recovered pellets were resuspended in Hi-Di formamide. After combining cDNA from positive/negative reactions with the sequencing ladder, samples were resolved by capillary electrophoresis, and the signal alignment to the corresponding sequence was performed in QuShape.SHAPE probing was performed following an established protocol . Shortly2) was added to the RNA, the mix was incubated at 37°C for 15 min. Then, the RNA was treated with DMS dissolved in ethanol and incubated at room temperature for 10 min. At the same time, the negative control was treated with ethanol only. The reaction was quenched by the addition of 14.7 M β-mercaptoethanol and precipitated with ethanol.DMS and CMCT probing experiments, in contrast to the SHAPE experiments, were performed only for the adenine riboswitch RNA in the absence of the ligand. The previously described protocol for DMS probing  was opti2 to achieve a final concentration of 100 mM and RNA folding was completed by incubating RNA at 37°C for 15 min. RNA was then treated with 300 mM CMCT, while negative controls were mixed with water. Both kinds of samples were incubated at room temperature for 20 min. The modification was quenched simply by ethanol precipitation. For both DMS and CMCT probing experiments, sequencing reactions were prepared in an analogous manner as described for SHAPE probing. The RNA samples recovered after modification were further processed following the SHAPE probing protocol.The CMCT probing protocol was adapted from . 1 pmol The RNAProbe is a web server for automated normalization and analysis of RNA chemical probing results, and for probing-assisted secondary structure prediction with several computational tools. RNAProbe displays secondary structure predictions generated with and without experimental data. The analysis performed by the server is much faster than manual analysis, moreover, the results are visualized in an integrated manner. The server allows analyzing data from three different chemical probing experiments: SHAPE, DMS and CMCT probing. RNAProbe is also capable of predicting secondary structure based only on the sequence employing various secondary structure prediction tools. The server offers a convenient way of forwarding the RNA sequence with the predicted secondary structures to SimRNAweb, an RNA 3D structure prediction server . RNAProbadd riboswitch aptamer domain from Vibrio vulnificus, for which a wealth of experimental data is already available. SHAPE probing was performed either in the presence or in the absence of the natural ligand (adenine). DMS and CMCT probing analyses were performed in the absence of the ligand. Experiments were conducted in triplicates for independently prepared RNA samples. The experimental protocols are described in the ‘Materials and Methods’ section of this manuscript.To demonstrate the performance of the RNAProbe server, we chose the add riboswitch aptamer domain inferred from the crystal structure , which would yield not only more accurate secondary structure predictions but also decrease the time required for data analysis.https://rnaprobe.genesilico.pl. This website is free and open to all users and there is no login required.The web server is available at


Escherichia coli and other organisms in response to metabolic stress conditions, suggests an analogous function as a 5′-modification of RNA. Here, we report the synthesis of thiamine adenosine dinucleotides and the preparation of pure 5′-thiamine-capped RNAs based on phosphorimidazolide chemistry. Furthermore, we present the incorporation of thiamine-ATP and thiamine adenosine diphosphate (thiamine-ADP) as 5′-caps of RNA by T7 RNA polymerase. Transcripts containing the thiamine modification were modified specifically with biotin via a combination of thiazole ring opening, nucleophilic substitution and copper-catalyzed azide-alkyne cycloaddition. The highlighted methods provide easy access to 5′-thiamine RNA, which may be applied in the development of thiamine-specific RNA capture protocols as well as the discovery and confirmation of 5′-thiamine-capped RNAs in various organisms.RNA 5′-modifications are known to extend the functional spectrum of ribonucleotides. In recent years, numerous non-canonical 5′-modifications, including adenosine-containing cofactors from the group of B vitamins, have been confirmed in all kingdoms of life. The structural component of thiamine adenosine triphosphate (thiamine-ATP), a vitamin B1 derivative found to accumulate in  Ribonucleic acid (RNA) obtains remarkable structural and functional versatility through the combination of the four canonical ribonucleosides adenosine (A), guanosine (G), cytidine (C) and uridine (U). Numerous additional modifications occur internally as well as terminally, at the 3′‑ and 5′-end, and fine-tune the functional spectrum of RNA. Such modifications can, e.g., increase the stability of RNA against degradation processes, extend the catalytic activity of ribozymes, promote RNA interactions with other molecules or assume various regulatory roles within the cellular environment ,3,4,5,6.Transcribed RNA is generally provided with a triphosphate group at the 5′-terminus. In eukaryotes, a post-transcriptional modification of messenger RNA (mRNA) with a 7‑methylguanosine (m7G) cap takes place . The m7GEscherichia coli and Streptomyces venezuelae ‑cytidine-labeled, full-length transcript of RNA I (mixture of 5′-thiamine and 5′-pppRNA)  , which w-pppRNA) . In a se-pppRNA) .Incubation with streptavidin prior to analysis by denaturing PAGE resulted in a retardation of biotin-linked 5′-thiamine RNA I (ThATP- and ThADP-primed) D, whereaAdenosine-containing thiamine derivatives have been successfully synthesized by imidazolide-based activation of phosphate groups of the respective thiamine or adenosine species. Thiamine-ADP and the biologically abundant thiamine-ATP were obtained in high yields and successfully purified from minor amounts of side products. With both those dinucleotides and the imidazolide-activated species ImppTh, 5′-thiamine-capped RNA was prepared by in vitro methods.Despite its inactivation by hydrolysis in aqueous solutions, ImppTh was capable of capping 5′-monophosphate RNA in the presence of divalent magnesium cations. Unreacted 5′-monophosphate RNA was removed by 5′→3′ exonuclease digestion, yielding pure 5′-thiamine RNA in 50% yield. To further increase yields, recovery of unreacted 5′-monophosphate RNA and repeated treatment with ImppTh can be considered. Theoretically, any 5′-triphosphate RNA sequence, independent of length, structure or nucleotide composition, is accessible for thiamine capping by this method, provided it is previously converted to the 5′-monophosphate by, e.g., polyphosphatases.Furthermore, 5′-thiamine RNA with up to 107 nucleotides, namely the biologically relevant RNA I, was obtained by in vitro transcription with T7 RNA polymerase using thiamine-ATP and thiamine-ADP. The acceptance of thiamine-ATP as a non-canonical initiating nucleotide strongly supports the hypothesis of the existence of thiamine-capped RNA in a variety of organisms. The development of LC-MS-based methods using thiamine-modified model RNAs could lead to the confirmation of this 5′-thiamine cap in total RNA samples, which would confirm a completely new function of thiamine. The lower intracellular concentration of thiamine compared to other NCINs of the B group of vitamins will nonetheless be a major challenge .The formation of a free thiolate by thiazole ring opening was utilized to selectively biotinylate 5′-thiamine RNA next to 5′-triphosphate RNA and for their separation by gel chromatography. The chemical accessibility of the thiamine 5′-cap was thus confirmed, which also makes biochemical modifications, e.g., ribozyme-assisted , of 5′-t+ and [M]−, respectively. The calibrant ESI‑L Low-Concentration Tuning Mix  was applied for high-resolution mass spectrometric (HR‑MS) measurements. Denaturing polyacrylamide gels  were prepared using the respective amount of Rotiphorese Sequencing gel concentrate . Denaturing polyacrylamide gel electrophoresis (PAGE) was performed with Tris-borate-EDTA buffer . All experiments comprising radioactive samples were performed in a radioactivity control area.Chemicals were purchased from Sigma Aldrich , Invitrogen  and Thermo Fisher Scientific  and used without further purification. DNA templates, oligonucleotide primers and 5‘‑monophosphorylated RNA were purchased from Integrated DNA Technologies . Deionized water was filtered via a MilliQ purification system . Chemical reactions under argon atmosphere were performed in Schlenk tubes, which were evacuated, heated and flushed with argon three consecutive times. Analysis of chemical reactions was performed by thin-layer chromatography (TLC) using Polygram Sil G/UV254 pre-coated polyester sheets  and a UV hand-lamp from Krüss Optronic . Standard column chromatography was performed on silica gel . For high-performance liquid chromatography (HPLC), setups of the 1100 and 1200 series from Agilent Technologies  were used with an analytical or semi-preparative HPLC column Luna 5u C18(2) 100 Å, 250 × 4.6 mm and 250 × 15 mm, respectively . Buffered mixtures in water  and acetonitrile  were utilized as mobile phase for HPLC. HPLC chromatograms were generally recorded at 260 nm and baseline-corrected. For nuclear magnetic resonance (NMR) spectroscopy, substances were dissolved in deuterated solvents and analyzed on a Mercury plus 300 MHz or Mercury plus 500 MHz spectrometer from Varian . Chemical shifts were reported in parts per million (ppm) in reference to the deuterated solvent used. Signal multiplicity was abbreviated as s = singulet, d = doublet, t = triplet, q = quartet and m = multiplet. NMR spectra of synthesized compounds are shown in -5-(6-amino-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl (1H-imidazol-1-yl)phosphonate (ImpA)  = 8.39 , 8.13 , 7.69 , 7.31 , 7.11 , 6.87 , 5.88 , 4.58 , 4.04 , 3.94 , 3.81–3.69 . 13C‑NMR : δ [ppm] = 155.9 , 152.7 , 149.7 , 135.2 , 128.2 , 121.6 , 118.9 , 86.8 , 83.7 , 73.6 , 70.9 , 64.9 . 31P‑NMR : δ [ppm] = −9.78 (s). HR‑MS : m/z calculated for C13H16N7NaO6P, 420.0792 [M + H]+, 442.0611 [M + Na]+; found, 420.0787, 442.0603. MS : m/z calculated for C13H16N7NaO6P, 396.0827 [M − Na]−; found, 396.1.e (ImpA) . A suspe3-((4-amino-2-methylpyrimidin-5-yl)methyl)-5-(2-((hydroxy((hydroxy(1H-imidazol-1-yl)phosphoryl)-oxy)phosphoryl)oxy)ethyl)-4-methylthiazol-3-ium (ImppTh). Thiamine pyrophosphate chloride (ThDP)  was slowly added to a stirred solution of imidazole , 2,2′-dithiopyridine , triphenylphosphine  and triethylamine  in anhydrous DMF (8.5 mL). Full dissolution of ThDP was experienced within 2.5 h. The mixture was stirred at room temperature for 3.5 h and poured into a cooled solution of sodium perchlorate  in anhydrous acetone (85 mL). While cooling in a water-ice bath, the product was allowed to precipitate for 1 h. After centrifugation , the pellet was washed with acetone (3 × 10 mL) and diethyl ether (10 mL). After evaporation of residual solvent, the ImppTh disodium salt was obtained as a pale yellow solid  and stored in an argon-flushed container at −20 °C. 1H‑NMR : δ [ppm] = 8.06 , 7.96 , 7.32 , 7.08 , 5.39 , 4.06 , 3.22 , 2.55 , 2.47 . 13C‑NMR : δ [ppm] = 168.9 , 161.8 , 157.2 , 143.1 , 139.6 , 135.0 , 127.9 , 120.4 , 104.1 , 64.8 , 51.0 , 27.4 , 23.9 , 11.1 . 31P‑NMR : δ [ppm] = −11.59 , −18.89 . HR‑MS : m/z calculated for C15H21N6O6P2S+, 475.0713 [M]+, 497.0532 [M – H + Na]+, 519.0330 [M − 2H + 2Na]+; found, 475.0706, 497.0520, 519.0352.3-((4-amino-2-methylpyrimidin-5-yl)methyl)-5--5-(6-amino-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methoxy)(hydroxy)phosphoryl)oxy)(hydroxy)phosphoryl)oxy)(hydroxy)-phosphoryl)oxy)ethyl)-4-methylthiazol-3-ium . Method A: In separate, argon-purged Schlenk tubes, thiamine pyrophosphate chloride (ThDP) , anhydrous magnesium chloride  and ImpA  were dried under reduced pressure for 30 min. Magnesium chloride was suspended in anhydrous DMF (1.2 mL) and added to the ThDP, which was then stirred at room temperature for 1 h before a solution of ImpA in anhydrous DMF (0.8 mL) was added. The mixture was stirred at room temperature for 18 h. Then, the solvent was fully evaporated under reduced pressure while stirring at room temperature. Residual solid was dissolved in 0.1 M triethylammonium-acetate buffer (pH 7.0) and purified by HPLC , yielding the product as a pale-yellow solid. Method B: In separate, argon-purged Schlenk tubes, adenosine 5′‑monophosphate monohydrate (5′-AMP) , anhydrous magnesium chloride  and ImppTh  were dried under reduced pressure for 1 h. Magnesium chloride was suspended in anhydrous DMF (1.8 mL) and added to the 5′‑AMP, which was then stirred at room temperature for 1 h before a solution of ImppTh in anhydrous DMF (1.2 mL) was added. The mixture was stirred at room temperature for 18 h. Then, the solvent was fully evaporated under reduced pressure while stirring at room temperature. Residual solid was dissolved in 0.1 M triethylammonium-acetate buffer (pH 7.0) and purified by HPLC , yielding the product as a pale-yellow solid. 1H‑NMR : δ [ppm] = 9.41 , 8.49 , 8.21 , 7.92 , 6.09 , 5.36 , 4.76 , 4.54–4.51 , 4.39–4.36 , 4.23–4.20 , 4.20–4.15 , 3.26–3.22 , 2.52 , 2.47 . 13C‑NMR : δ [ppm] = 165.4 , 162.0 , 155.1 , 152.4 , 150.3 , 148.9 , 143.2 , 139.9 , 135.4 , 118.3 , 105.1 , 86.6 , 83.9 , 74.1 , 70.3 , 65.2 , 64.8 , 50.3 , 27.5 , 22.1 , 11.1 . 31P‑NMR : δ [ppm] = −10.99 , −11.23 , −22.88 . HR‑MS : m/z calculated for C22H30N9O13P3S, 752.0824 [M − H]−; found, 752.0818 (method A), 752.0839 (method B).3-((4-amino-2-methylpyrimidin-5-yl)methyl)-5--5-(6-amino-9H-purin-9-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methoxy)(hydroxy)phosphoryl)oxy)(hydroxy)phosphoryl)oxy)ethyl)-4-methylthiazol-3-ium . In separate, argon-purged Schlenk tubes, thiamine monophosphate chloride dihydrate (ThMP) , anhydrous magnesium chloride  and ImpA  were dried under reduced pressure for 30 min. Magnesium chloride was suspended in anhydrous DMF (1.2 mL) and added to the ThMP, which was then stirred at room temperature for 1 h before a solution of ImpA in anhydrous DMF (0.8 mL) was added. The mixture was stirred at room temperature for 18 h. Then, the solvent was fully evaporated under reduced pressure while stirring at room temperature. Residual solid was dissolved in 0.1 M triethylammonium-acetate buffer (pH 7.0) and purified by HPLC , yielding the product as a pale-yellow solid. HR‑MS : m/z calculated for C22H29N9O10P2S, 672.1161 [M − H]−; found, 672.1169.1-(azidomethyl)-4-(bromomethyl)benzene (L01)  = 7.33 , 7.21 , 4.41 , 4.26 . 13C‑NMR : δ [ppm] = 138.0 , 135.8 , 129.6 , 128.7 , 54.5 , 33.0 .ne (L01) . A solut2 at 50 °C for 1 h. Then, a second addition of ImppTh was performed, increasing the concentration to 20 mM, followed by further incubation at 50 °C for 1 h. Modified and unmodified RNA was purified via ethanol precipitation and 0.2 nmol of RNA were further incubated in the presence of 1 U Xrn1  in 1 X NEB buffer 3  at 37 °C for 2 h. RNA was purified from Xrn1 by phenol-chloroform and ether extraction, followed by ethanol precipitation. Samples of the reaction with ImppTh and Xrn1 digest were analyzed by 20% denaturing PAGE and RNA bands visualized on a Typhoon FLA 9500 biomolecular imager  upon staining with SYBR Gold . ESI-MS analysis was conducted to validate the identity of 5′-thiamine RNA.5′-Monophosphate RNA  and incubated at 37 °C for 3.5 h. Radioactively labeled RNA was prepared accordingly in the presence of 1 µCi/µL [α‑32P]‑CTP. RNA was purified by phenol-chloroform and ether extraction, followed by semi-preparative HPLC (<20 nt) or denaturing PAGE and isopropanol precipitation (≥20 nt).In vitro transcriptions were performed in the presence of 5 µM DNA template, 4 mM NCIN , 2 mM ATP, 2 mM CTP, 2 mM GTP, 2 mM UTP, 40 mM Tris-HCl (pH 8.1), 1 mM spermidine, 22 mM MgClv/v DMSO and incubated at 25 °C for 1 h. The copper-catalyzed azide-alkyne cycloaddition was performed in the presence of 25 µM azide-modified thiamine-ATP or 4mer RNA, 500 µM biotin alkyne (PEG4 carboxamide-propargyl biotin), 100 µM CuSO4, 500 µM Tris(3-hydroxypropyl-triazolylmethyl)amine (THPTA ligand), 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) (pH 7.5), 200 mM NaCl, 200 mM KCl and 2.5% v/v DMSO and incubated at 25 °C for 30 min. Both reactions were analyzed via HPLC, followed by ESI-MS measurements.The nucleophilic substitution using 5′‑thiamine derivatives was performed in the presence of 10 µM thiamine-ATP or 4mer RNA , 200 µM linker L01, 50 mM buffer  and 2.0% v/v DMSO at 25 °C for 1 h. RNA was purified via isopropanol precipitation. For copper-catalyzed azide-alkyne cycloaddition (CuAAC), the redissolved RNA was incubated in the presence of 500 µM biotin alkyne, 100 µM CuSO4, 500 µM THPTA ligand, 1 mM sodium ascorbate, 50 mM HEPES (pH 7.5), 200 mM NaCl, 200 mM KCl and 2.5% v/v DMSO at 25 °C for 30 min. RNA was purified by phenol-chloroform and ether extraction and the aqueous solvent was removed to dryness under reduced pressure. RNA was redissolved, incubated in the presence of 0.4 µg/µL streptavidin, 25 mM HEPES (pH 7.5), 100 µM NaCl and 100 µM KCl at 25 °C for 5 min and analyzed by 10% denaturing polyacrylamide gel electrophoresis. Radioactive RNA bands were visualized on a Typhoon FLA 9500 biomolecular imager  using storage phosphor screens .Radioactively labeled RNA I  was incubated in the presence of 4 µM 1-(azidomethyl)-4-(bromomethyl)benzene (L01), 50 mM CAPS (pH 11.0) and 0.4%


Candida albicans resistant to processive 5′ → 3′ exonuclease, appearing as cells approached stationary growth phase. Initial analysis pointed to extra phosphate(s) at their 5′- end raising the possibility that they were newly transcribed. Here we report on additional experiments exploring this possibility and try to establish which of the RNA polymerases may be transcribing them.We have previously reported 18S and 25S ribosomal RNA molecules in Oligo-ligation and primer extension again showed the presence of extra phosphate at the 5′-end of the reported processing sites for both 18S and 25S ribosomal RNA components. Inhibition of Pol I with BMH-21 increased the presence of the molecules. Quantitation with an Agilent Bioanalyzer showed that resistant 18S and 25S molecules are primarily produced in the nucleus. Utilizing an RNA cap specific antibody, a signal could be detected on these molecules via immunoblotting; such signal could be eliminated by decapping reaction. Both the cap specific antibody and eIF4E cap-binding protein, increased fold enrichment upon quantitative amplification. Antibodies specific for the RNA Polymerase II c-terminal domain and TFIIB initiator factor showed the presence of Pol II on DNA sequences for both 18S and 25S molecules in chromatin precipitation and qPCR assays. Rapamycin inhibition of TOR complex also resulted in an increase of resistant 18S and 25S molecules.These data raise the possibility of a role for RNA Polymerase II in the production of 18S and 25S molecules and indicate that efforts for more direct proof may be worthwhile. If definitively proven it will establish an additional role for RNA Polymerase II in ribosomal production. Saccharomyces cerevisiae, requires a multistep process that includes ribosomal RNA (rRNA) transcription, pre-ribosomal RNA processing, ribosome assembly and export. While three RNA polymerases are involved in ribosome production, the 18S, 5.8S and 25S ribosomal RNA (rRNA) components are thought to be products of polycistronic transcription by RNA polymerase I (Pol I) followed by processing -12-oxo-2H-benzo [g]pyrido quinazoline-4-carboxamide subunit . To asseAs seen in Fig. 7G)-cap analogue (designated as M7AB or H20). These antibodies have the most avidity for the 5′-terminal cap but do partially cross react with m7G within RNA.To see if there are any Terminator resistant molecules that have a 7-methylguanosine cap, and thus likely to be a Pol II product, we used monoclonal antibodies that were generated by a carrier protein-conjugated 7-methylguanosine  was used to see if it would precipitate any 18S and 25S molecules. eIF4F has special affinity for 5’cap and is unlikely to be cross reacting with parts of an RNA molecule other than the 5’cap. RNA from stationary cells indeed contained such molecules and none could be seen in RNA from mid-log organisms Fig. d, lane 57G within the RNAs, but clearly there was a significant fold enrichment on RNA from stationary organisms. These PCR amplifications also point toward the presence of a cap in the 5′ position in stationary cells since RT-qPCR was performed on RNA molecules precipitated by either a cap specific antibody or a cap binding protein.The eIF4E precipitated RNA was also subjected to qPCR amplification with 18S and 25S specific primers. As controls we used primers for ITS1, clearly a product of Pol I and processing. The results can be seen in Fig. C. albicans and some of the 18S and 25S molecules are transcribed by something other than Pol I.Figure As we have previously indicated, rapamycin inhibition leads to cells producing Terminator-resistant 18S and 25S . Using iThe inhibitor BMH21 pointed to Pol I not being the source producing the Terminator-resistant molecules. The possibility of caps that are present on these molecules suggested Pol II as the source for these products. Therefore, we wanted to see if we could detect an increased presence of Pol II on the 18S and 25S regions of the ribosomal genes during periods when Terminator-resistant molecules are produced. To this end we performed chromatin precipitation with monoclonal antibody CTD4H8 widely used for Pol II recognition -12-oxo-12H-benzo [g]pyrido quinazoline-4-carboxamide (BMH21) (Tocris), shown to be inhibiting for yeast was used at a 50 μM concentration. mTOR inhibitor, Rapamycin (Sigma) was used at the ratio of 1 μg per 1 × 106 cells. Incubation time for both inhibitors was 60 min at 30 °C with constant shaking. After incubation cells were washed with PBS and used for the appropriate assay.RNA polymerase I inhibitor RT-qPCR was carried out using 18S and 25S specific primers. Actin primers were used as negative controls for pol I whereas ITS-2 and 5′-ETS were used as positive controls (Table SCap-Clip™ acid pyrophosphatase (Cellscript) was used according to manufacturer instructions for decapping RNA samples. Verification of cap removal was done by gel electrophoresis, immunoblotting and immuno-precipitation with cap binding protein.RNA immunoprecipitation (RIP) was performed on mid-log and stationary RNA with anti-m7G-Cap mAb and protein A/G magnetic beads to purify the antibody-capped RNA complex. RNA was extracted with Direct-zol™ (Zymo Research). RT-qPCR was carried out using 18S and 25S specific primers. Positive and negative controls were actin and ITS-1 respectively  were grown at 30 °C for 16 h (stationary) in a 500 mL YPD. Crosslinking was done by adding formaldehyde to the culture and incubated at RT for 20 min with gently swirling. After that, 37.5 mL of 3 M glycine, 20 mM Tris was added and incubated for 5 min. Cells were pelleted at 2000 rpm for 5 min and washed twice with 200 mL cold TBS  and once with 10 mL cold FA lysis buffer /0.1% SDS. Pellets were resuspended in 1 mL cold FA lysis buffer/ 0.5% SDS. Cells were broken up by Zirconia bead (Ambion) vortexing. Chromatin isolation and shearing were done following Keogh and Buratowski [ratowski . Isolatip values were calculated using two tailed unpaired t test and were considered statistically significant when they were less than 0.05.Statistical analysis was done using GraphPad Prism software (version 8.2). Additional file 1 Fig. S1. Representative electropherograms used to calculate Terminator resistance percentages in 18S and 25S. The areas under each peak were obtained using the Bioanalyzer Expert software. These areas were used to calculate the percentage of RNA resistance by obtaining the ratio between cut (Terminator treated) and uncut (untreated) RNA. a total RNA from mid-log organisms untreated and b treated with Terminator. c nuclear RNA from mid-log organisms untreated and d treated with Terminator. Fig. S2. Representative electropherograms used to calculate Terminator resistance percentages in 18S and 25S. The areas under each peak were obtained using the Bioanalyzer Expert software. These areas were used to calculate the percentage of RNA resistance by obtaining the ratio between cut (Terminator treated) and uncut (untreated) RNA under different conditions. a total RNA from stationary organisms untreated and b treated with Terminator. c nuclear RNA from stationary organisms untreated and d treated with Terminator. Fig. S3. Representative electropherograms used to calculate Terminator resistance percentages in 18S and 25S. The areas under each peak were obtained using the Bioanalyzer Expert software. These areas were used to calculate the percentage of RNA resistance by obtaining the ratio between cut (Terminator treated) and uncut (untreated) RNA under different conditions. a total RNA from BMH21 exposed organisms untreated and b treated with Terminator. c nuclear RNA from BMH21 exposed organisms untreated and d treated with Terminator. Fig. S4. Histone Acetyltransferase Activity Assay (HAT) results. Nuclear extracts from C. albicans were compared at three different concentrations to a positive extract (control). HAT assays were carried out in order to verify that the nuclear RNA source was indeed the nucleus. Fig. S5. Evidence for the role of RNA Pol II in the transcription of 18S and 25S molecules in stationary C. albicans. Chromatin Immunoprecipitation (ChIP) with polymerase II specific antibody. PCR fragments amplified from stationary organisms. Cells were cross-linked and chromatin was sheared by sonification. RNA Polymerase II mAb CTD4H8 (Epigentek) was used to precipitate DNA-protein complex. PCR was performed using three different sets of specific primers for 18S  and 25S  (+). See Supporting Table Table S1. List of primers used in all the experiments


Global sea-level rise (SLR) is projected to increase water depths above coral reefs. Although the impacts of climate disturbance events on coral cover and three-dimensional complexity are well documented, knowledge of how higher sea levels will influence future reef habitat extent and bioconstruction is limited. Here, we use 31 reef cores, coupled with detailed benthic ecological data, from turbid reefs on the central Great Barrier Reef, Australia, to model broad-scale changes in reef habitat following adjustments to reef geomorphology under different SLR scenarios. Model outputs show that modest increases in relative water depth above reefs (Representative Concentration Pathway (RCP) 4.5) over the next 100 years will increase the spatial extent of habitats with low coral cover and generic diversity. More severe SLR (RCP8.5) will completely submerge reef flats and move reef slope coral communities below the euphotic depth, despite the high vertical accretion rates that characterize these reefs. Our findings suggest adverse future trajectories associated with high emission climate scenarios which could threaten turbid reefs globally and their capacity to act as coral refugia from climate change. LocalHere, we address this knowledge gap using an unprecedented dataset of reef cores  and reef benthic cover data collected from nearshore turbid reefs on the central Great Barrier Reef (GBR), Australia, to project future changes in reef morphology and habitat extent under different sea-level rise (SLR) scenarios. This is possible because recent work on these reefs has shown: (i) the long-term  persistence of coral communities under high terrestrial sedimentation ,13; (ii)Prior knowledge of the spatio-temporal dynamics of turbid reefs is applied here to construct a depth/habitat-controlled predictive reef growth model. The model allows for reef growth to be simulated, beginning from the present-day morphology, through time under different scenarios of SLR. Model outputs are used to quantify changes in habitat extent following adjustments of the underlying reef morphology as it grows vertically. Based on detailed spatial data on the living coral communities, we can then infer changes in key ecological metrics . Reverse-time model runs also allow us to validate outputs against observed age/depth histories from reef cores, giving new broad-scale insights into the early onset of turbid reefs and their coral communities. Our results show the long-term ecological dynamics of turbid reefs and provide robust projections of future reef trajectories. This is important because turbid reefs have been identified as potential climate change refugia for corals –20, and 2.(a)−1) and low-light conditions because of the wave and tidal resuspension  of fine seafloor material that was reworked onshore during the last post-glacial transgression [The Paluma Shoals reef complex (PSRC) is located less than 3 km off the Queensland coast within Halifax Bay  on the central GBR, Australia . PSRC is a series of linear shore-perpendicular coral reefs comprising: (i) shore-attached reefs ) located within shallower water (less than 4 m depth) and adjoining intertidal sand flats at the coast. The reefs have recently reached sea level and become emergent under low tidal conditions ; and (ii) shore-detached reefs  A–D) which remain submerged in deeper water further offshore (3–6 m LAT). PSRC experiences high turbidity (up to 385 mg lgression . Light agression .(b)n = 4420) were automatically extracted from the video at 8 s intervals. Each frame was depth-calibrated using the DEM and a digital 9-point grid overlay was added to frames to calculate benthic community composition (%) and make a qualitative estimate  of reef rugosity (R) [Goniastrea reef flat (>LAT); H2: rubble and encrusting coral (0–0.5 m); H3: Montipora and Acropora framework (0.5–1.5 m); H4: massive Porites and sand (1.5–2 m); H5: Turbinaria carpets (2–3 m); H6: rubble and sediment-tolerant coral (3–4 m); H7: sand/mud (greater than 4 m)).To inform our model, we collected field data on the spatial distribution of coral communities, the morphology of present-day reefs and their Holocene growth histories –16. The sity (R) . The depIn situ coral material was selected for radiocarbon dating (142 radiometric dates from 31 cores) to establish the age and accretionary history of reefs. Rates of vertical reef accretion (mm yr−1) were calculated between consecutive dated corals in the core, and then pooled to determine average rates of vertical reef accretion for each 0.5 m depth interval relative to the LAT . Palaeoecological analysis of coral material shows a direct comparison between living and historical coral communities within similar depth ranges [To constrain rates of vertical accretion in our model, we used data from previously published core records (16 reef cores from submerged OPS–OPSD and 15 cores from the sea-level attained PS) collected using percussion coring techniques ,24. This(c)2; 10 × 10 m grid cells) was used to model past and future reef growth (±500 years) and to quantify habitat extent. We first assigned each grid cell a label corresponding to the absence or presence of reef . A small number of grid cells exceeded 0.5 m LAT (i.e. above sea level) and below the 4 m LAT depth contour . For simplicity, we forced all grid cell heights into −4.0 to +0.5 m depth/elevation. The thickness of Holocene reef  was determined as the termination point at which reverse-time extrapolations should cease. This could not be precisely constrained across the entire complex from cores, but was predicted from measured depths of inter-reefal seafloor areas. We estimate seafloor depth using distance weighted k-nearest neighbour interpolation. At T = 0, for every reef site, , we identify the k closest non-reef sites, c, and their distances from the target site, D(c). We then estimate the seafloor depth at  ask will lead to smoother interpolation. In our backwards extrapolation, we use a range of k values:  to account for different possible seafloors and give our final error estimate based on the range of values produced across all extrapolations.The DEM of seafloor and reef morphology −1) . We use the symbol d for depth in year T. T = 0 corresponds to present day (2016) and thus d = Z. We then extrapolate backwards (equation (2.2)) assuming a static sea level as suggested by Late Holocene sea-level data for the region [g). This term uses established annual reef accretion rates g(d) and the depth d of each grid cell containing coral at year T. We assume that if water depth over a cell is greater than 0.5 m elevation (emergent) or greater than 4.0 m depth (below the euphotic depth), corals will not grow: g(d) = 0 for d > 4 m or d < 0.5 m:To calculate vertical reef growth and estimated error ΔRate, we used depth-variable accretion rates at 0.5 m intervals derived from cores . We then run the whole extrapolation B = 100 times to produce a range of different outputs and calculate the average and the 95% confidence interval of our measurements.To account for uncertainty in reef growth rates, we use a bootstrap procedure. First, we assume the amount of vertical accretion at every grid cell is independent of every other. For each bootstrap sample, we choose the accretion rate at depth L = 0.25 m yr−1) calculated from the average age difference between consecutive reef cores at the same horizontal distance on a transect, and the horizontal distance between these cores [p = L/s, where s is the side length of a grid cell, then p is the proportion by which the reef grid cell expands each year into a neighbouring non-reef grid cell (independent of adjacent cell depth). This is applied to all grid cells which do not yet contain reef but have neighbouring cells that do contain reef. For each of these (where R(T) = 0 initially), we accumulateδ is the Kronecker delta: δ = 1 if i = j; otherwise, it is zero. This means we add a fraction p, from neighbouring reef cells to the tally for its neighbouring non-reef cells. When R ≥ 1, the reef has covered the entire cell, and we stop the accumulation and allow the new reef cell to grow vertically at the designated depth-assigned accretion rate , as well as contribute to reef expansion into neighbouring cells.To model lateral reef growth, we use a lateral expansion rate (se cores . We assu(e)r) projected by Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCP) 4.5 (5.5 mm yr−1) and 8.5 (7.5 mm yr−1) climate change scenarios [r is subtracted from the heights of all grid cells each year :We then incorporated rates of SLR (cenarios . We replReef habitat extent was interpolated from modelled seafloor and reef morphology (forwards and backwards) at 100-year time-slices. Habitat maps were generated for each time period by allocating each grid cell a specific habitat type (H1–H7) based on the elevation/depth of that cell relative to LAT , and established habitat depth ranges observed in both living and palaeo-coral communities. Total habitat extent was tallied across the reef surface and the relative cover of habitats through time was determined by calculating the number of grid cells of each habitat compared to all reef grid cells. Estimates of coral cover, coral composition and reef rugosity associated with each habitat type are inferred from ecological datasets of living coral communities at PSRC .3.(a)To test the model reliability, the model was first run backwards from present day until all reef cells were removed. By shrinking the reefs backwards, the model indicates that the main reef complexes started growing approximately 1200–700 years before present day (yrs BP) . The mod(b)Lobophyllia, Goniopora, Galaxea) had initiated early framework accumulation that accreted slowly (2.4 mm yr−1) for several hundred years [−1) as they reached water depths between 1.5 and 3.5 m, and fast-growing coral taxa  became more abundant [−1) as coral communities shifted to lower abundance of slow-growing and exposure-tolerant taxa  [Core records suggest that by 500 yrs BP, reefs comprising sediment-tolerant coral genera  = 2) . Model outputs then show that by 200 yrs BP, these patch reefs grew above the seafloor (+1.5 m) and expanded their spatial footprint  (Montipora and Acropora framework habitat (H3: 0.5–1.5 m depth), covering 24% of the total reef area, and deeper Turbinaria carpets habitat (H5: 2–3 m depth), covering 34% of total reef area at present day .Our model shows highly consistent spatial patterns with the above core-derived time points as reefs are grown from initiation to the present day. The model shows that early reef growth (at 500 yrs BP) begins with isolated patch reefs Turbinaria carpets habitat (H5: 2–3 m) to 10% of total reef area, and expansion of shallow-water Montipora and Acropora framework habitat  to 32% of total reef area by +300 years  and rapid vertical accretion (3.4 mm yr−1) . At these rates of reef growth, reef flat habitat (H1: >LAT) will form across 25% of reef surfaces (figures Goniastrea), encrusting corals , rubble (74 ± 12.3%) and macroalgae (2.5 ± 3.9%) will occur . These habitat reconfigurations will occur first on shore-attached reefs (e.g. PS at +100 years), followed by deeper shore-detached reefs (e.g. OPS–OPSD at +300–500 years).The model was parametrized with static sea-level conditions and run forwards from the present day up to +500 years to determine how quickly reefs would fill their vertical accommodation space and form reef flats . Models 00 years . Coral cat H5: 2– m to 10%(d)−1), a higher relative water level would mean that on shore-attached reefs (e.g. PS), habitat will shift from primarily Goniastrea reef flat habitat (H1) at present day to Montipora and Acropora framework habitat (H3) by +100 years . Based on observations of living coral communities, this would result in a transition to a high coral cover and reef complexity state . Conversely, on shore-detached reefs (OPS–OPSD), higher relative water levels will submerge reefs (figure 5b), and the spatial extent of Montipora and Acropora framework habitat (H3) will decline to 5% by +100 years (figure 5a). Low coral cover and rugosity habitats (H6 and H7) will expand to 51% of the total reef area , as deeper reef slope coral communities  move below the euphotic depth.Sea level will not remain static in the future and, therefore, we also ran the reef growth model forwards (+100 years from present day) under average global rates of projected SLR . Under t00 years a,b. Base−1), the models show exaggerated trends in habitat change (−1). The model predicts the loss of Goniastrea reef flat habitat (H1) at +100 years as present-day reef flats are submerged (a). At shore-attached (PS) reefs, these areas could become recolonized by Montipora and Acropora framework habitat . However, higher relative water levels (0.25 m higher than RCP4.5 at +100 years) on shore-detached reefs (OPS–OPSD) are predicted to restrict Montipora and Acropora framework habitat (H3) to only nodal topographic areas. As a result, the model shows major losses of present-day shallow-water habitat and reductions in coral cover (to between 0 and 20%) and reef structural complexity (R = 1–2) on upper reef surfaces by +100 years . Reef slope habitat (H6) will deepen to expand non-reef-building substrates (H7). Habitat maps also show the dominance of Turbinaria carpets (H3) as the main coral habitat on shore-detached reefs by +100 years (a), inhabiting deep and muddy waters (b), which comprise high coral cover , but low generic diversity of only very resilient coral taxa .Under the high emission scenario of RCP8.5 and higher rates of SLR  a high-level resistance to thermal stress events ,27, (ii)−1) will fill vertical accommodation space within the next 500 years under a static sea level  sea level. Past large-scale perturbations in benthic communities and reef accretion in response to relative sea-level change are widely documented. Sea-level fall on the GBR (1–1.3 m at approx. 2000 yrs BP) during the Late Holocene have previously caused the ‘turn-off’ of productive coral growth within shallow-water habitats, and a temporary hiatus of vertical reef growth as benthic communities shifted to rubble and macroalgal assemblages –35. Conv2 is stabilized to around 550 ppm by 2100 (RCP4.5 = 5.5 mm yr−1) could have some positive outcomes by increasing vertical accommodation space and re-establishing productive coral growth on shore-attached (e.g. PS) reef flats. However, central to whether transitions back to high coral cover states are possible will depend on the health of living coral populations to provide a viable recruitment source [Our models show that moderate rates of SLR under conditions where atmospheric COt source , and thet source , reef flt source , with ext source . Algal–ct source  that cant source .−1) could raise water levels by at least 0.5 m by 2100 to amplify the above trends, with negative impacts on coral habitat availability  were considered turbid [Although our study focuses on the PSRC , other nearby reefs on the GBR show similar spatial distributions in habitat type and coral community structure . Furtherd turbid . This hi5.Previous attempts to predict future reef growth trajectories have often been hindered by spatially limited core records, and distinct ecological divergence between the past and present reef communities that drive carbonate productivity ,10. The


One strategy being pursued to clear latently infected cells that persist in people living with HIV (PLWH) on antiretroviral therapy (ART) is to activate latent HIV infection with a latency reversing agent (LRA). Surrogate markers that accurately measure virus production following an LRA are needed.ex vivo with different LRAs, including histone deacetylase inhibitors (HDACi). MS and plasma HIV RNA were also quantified from PLWH on ART (n-11) who received the HDACi panobinostat.We quantified cell-associated unspliced (US), multiply spliced (MS) and supernatant (SN) HIV RNA by qPCR from total and resting CD4+ T cells isolated from seven PLWH on ART before and after treatment ex vivo stimulation with LRAs, we identified a strong correlation between the fold change increase in SN and MS RNA, but not the fold change increase in SN and US RNA. In PLWH on ART who received panobinostat, MS RNA was significantly higher in samples with detectable compared to non0detectable plasma HIV RNA.In total and resting CD4+ T cells from PLWH on ART, detection of US RNA was common while detection of MS RNA was infrequent. Primers used to detect MS RNA, in contrast to US RNA, bound sites of the viral genome that are commonly mutated or deleted in PLWH on ART. Following Following administration of an LRA, quantification of MS RNA is more likely to reflect an increase in virion production and is therefore a better indicator of meaningful latency reversal.NHMRC, NIH DARE collaboratory. It is also unknown which marker of HIV persistence best predicts viral protein expression and subsequent immune recognition following a clinical intervention. To date in clinical trials of LRAs, a common primary endpoint for efficacy has been unspliced (US) HIV RNA. Although clinical trials of LRAs in PLWH on ART have shown increases in US RNA in vivo, they have not shown any decrease in the frequency of infected cells and have not shown consistent increases in plasma HIV RNA, suggesting that US RNA may not be the best biomarker for latency reversal. Here we sought to determine if multiply spliced (MS) HIV RNA could serve as a biomarker for efficient latency reversal, both ex vivo and in vivo.In people living with HIV (PLWH) on antiretroviral therapy (ART), HIV persists as a long-lived latent form. One strategy being developed to eliminate infected cells that persist on ART is to activate latent virus to produce viral proteins or virions. There are currently no biomarkers or determinants of effective HIV latency reversal following ex vivo with a range of LRAs, the fold change increase in MS RNA correlated with the fold change increase in supernatant (SN) HIV RNA, whereas a similar relationship between US RNA and SN HIV RNA was not detected. In a prospective clinical trial of the LRA panobinostat in PLWH on ART, we found that higher levels of MS RNA were associated with detection of plasma HIV RNA. Finally we showed that detection of MS RNA was not only a measure of increased transcription, but reflected the frequency of intact virus, given the location of primers used to detect MS RNA bind in sites of the viral genome that are commonly mutated or deleted in PLWH on ART.We showed that in PLWH on ART in the absence of any intervention, MS RNA was rarely detected whereas US RNA was always detected. Following stimulation of both resting and total CD4+ T cells from PLWH on ART Following administration of an LRA, quantification of MS RNA is more likely to reflect an increase in virion production and is therefore a better indicator of meaningful latency reversal.Alt-text: Unlabelled box1ex vivo or in vivo and more importantly, which marker of HIV persistence best predicts viral protein expression and immune recognition following an intervention.Long-lived and proliferating latently infected CD4+ T cells represent the main barrier to a cure in people living with HIV (PLWH) on antiretroviral therapy (ART) After HIV integration, multiple steps are required to produce infectious virions. Initiation of viral transcription from the integrated provirus is followed by efficient m7G-capping and processive elongation in vivo, a larger fold increase in US RNA post-vorinostat was associated with a higher basal level of US RNA ex vivo or in vivo. However, a change in US RNA may not result in successful virus production given that the process of HIV transcription initiation, capping, elongation, splicing and termination occurs inefficiently in latently infected CD4+ T cells in the blood Traditionally, latently infected cells have been described as being transcriptionally silent. However, it has been known for over two decades that US RNA is nearly always detected in CD4+ T cells collected from blood from PLWH on ART Evaluation of LRAs that modulate transcription in clinical trials of PLWH on ART have demonstrated an increase in the initiation of HIV transcription from an integrated provirus, measured as an increase in cell-associated US HIV RNA, but varying efficacy in increasing plasma HIV RNA in vivo, and which LRAs are able to induce production of HIV proteins and virions from latently infected cells.Toll like receptor agonists can also reverse HIV latency with varying efficacy in human and non-human primate studies. The toll like receptor (TLR)-9 agonist induced an increase in plasma HIV RNA but with no change in US RNA ,30. In aIn this study, we measured US, MS and supernatant (SN) HIV RNA from both total and resting CD4+ T cells isolated from blood from PLWH on ART following stimulation with different LRAs. Using a database of published HIV proviral sequences derived from CD4+ T cells from PLWH on ART 22.1NCT01680094 or as previously described The use of blood samples from HIV negative donors for this study was approved by the Human Research and Ethics Committees from the Alfred Hospital (HREC156/11), Monash University (CF11/1888) and the University of Melbourne (1443071). Adult donors were recruited by the Red Cross Blood Transfusion Service and all provided written informed consent for the use of their blood products for the research. The use of blood samples from adult PLWH was approved by the Alfred Hospital (HREC214/15) for the study entitled Large volume peripheral blood mononuclear cells (PBMC) collection by leukapheresis to define HIV persistence in HIV-infected adults. Adult PLWH were recruited with viral loads <50 copies per mL for at least 2 years. All participants provided informed consent and the protocol was approved by the local Institutional Review Board. For the prospective panobinostat clinical trial, adult PLWH were enrolled with viral loads <50 copies per mL for at least 2 years. Additional details can be found at ClinicalTrials.gov number 2.2NCT01680094).PLWH on ART with a viral load <20 copies/ml for ≥3 years were eligible for enrolment. Peripheral blood mononuclear cells (PBMCs) were collected by leukapheresis  and stored in liquid nitrogen. In a separate study, PLWH on ART were enrolled in a prospective single arm study of intermittent panobinostat 20 mg three times a week, every other week for 8 weeks . Cells were cultured at 5 million cells per well in a 24-well plate in RPMI-1640  supplemented with 10% FBS (Interpath) and pen/strep/glut . Cells were stimulated with DMSO , vorinostat , romidepsin , panobinostat , JQ1 , or PMA+PHA , all in the presence of raltegravir  and IL-2  for 3 days. For the panobinostat study, frozen PBMCs were thawed and total CD4+ T cells were isolated via negative selection using the CD4+ T Cell Isolation Kit, Human .PBMCs were thawed and either total or resting CD42.4ex vivo HIV RNA data, HIV RNA was normalized per RNA input, measured by NanoDrop (ThermoFisher), and presented as copies per 500 ng RNA. For the prospective panobinostat clinical trial, HIV RNA was normalized to the 18s housekeeping gene using the 18s Taqman gene Expression Assay  and HIV RNA is presented as copies per 10^6 18s copies, as previously described.After 3 days, cells were harvested in Trizol . Cell-associated RNA was extracted following the manufacturer's protocol and the RNA was then DNase-treated with RQ1 DNase . 500 ng of RNA was reverse transcribed into cDNA using random hexamers , oligo(d)T , and Superscript III reverse transcriptase . 125 ng of cDNA per condition was then used to perform a semi-nested real time quantitative (q) PCR assay for US RNA as previously described ,37. MS R2.5After 3 days, culture supernatant was harvested by centrifugation at 1000 x g for 10 min to remove cells and debris. 1 mL of supernatant from each condition was then run on the COBAS® AmpliPrep/COBAS® TaqMan HIV test kit v2.0 for the quantification of HIV RNA (Roche Diagnostics). Culture supernatants assessed using this method have previously been shown to be free of contaminating HIV DNA 2.6CCR5 gene, as previously described Total cellular DNA was extracted using the Qiagen AllPrep kit . Total HIV DNA was quantified as described previously 2.7Defective proviral sequences were obtained using the HIV Proviral Sequence Database 2.8Total and resting CD4+ T cells were isolated as described previously from buffy backs from three HIV-uninfected individuals from the Red Cross blood transfusion service. CD4+ T cells were plated at 500,000 cells per well in a 96-well plate and stimulated with LRAs for 1, 3 and 6 days as described above. Following stimulation, cells were stained with live/dead cell viability dye  for 15 min at room temperature followed by surface staining for CD3-BV711  and CD4-APC  on ice for 30 min. Cells were then intracellularly stained for Ki-67-PE  using the BD Cytofix/Cytoperm Fixation/Permeabilization Solution Kit  according to the manufacturer's protocol. Flow cytometry was assessed on an LSRFortessa (BD Biosciences) and data were analyzed using FlowJo v.10.2.9https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1307&context=pare As there are no principal objections to using a t-test with Ns as small as 2, we preferred using t-tests because Wilcoxon signed-rank tests are unable to incorporate quantitative information. In addition, the logarithmic transformation that we have used has largely resulted in approximate normality for these measures in separate larger studies. While outliers can disrupt t-tests, we did not appear to have significant outliers in our data sets. Pearson correlations with 95% confidence intervals were calculated to compare baseline RNA to day 3 RNA . Multiple observations from the same donor (six different conditions) are not independent observations, so we modified analyses for correlations involving multiple observations per donor. We first obtained residuals from a model of each RNA measurement that controlled for donor. We then examined Spearman correlations among the models’ residuals for the pairs of RNA measurements of interest   . At baseline, HIV DNA and US RNA were significantly higher in total versus resting CD4+ T cells  a. MS RNAt test]) a. These Given that in PLWH on ART ~95% of proviruses in circulating CD4+ T cells are defective https://psd.cancer.gov/; We analysed 993 defective proviral sequences obtained via near full-length sequencing (nFLS) of CD4+ T cells from PLWH on ART  and resting CD4+ T cells . The highest increases in US RNA for total CD4+ T cells were seen following treatment with romidepsin and JQ1 . With continuous exposure to romidepsin and panobinostat for 6 days, we found substantial cell death . Therefore, we chose to focus our studies on day 3 post-treatment with LRAs only. After 3 days post-treatment with LRAs, cells were harvested for US and MS RNA and supernatant was collected for HIV RNA quantification . There wp = 0.0216)) and PMA+PHA in total  and resting  CD4+ T cells. . Consistent with these findings, romidepsin, panobinostat, and PMA+PHA induced significant increases in SN RNA in both total  and resting  CD4+ T cells  and resting  CD4+ T cells  CD4+ T cells and resting  CD4+ T cells  occurred 3.3p = 0.0008 and <0.0001 respectively, [Spearman correlation], p = 0.2600 and p = 0.1000, respectively, [Spearman correlation], We next looked at correlations between US, MS, and SN HIV RNA following stimulation of either total or resting CD4+ T cells with a panel of LRAs and accounted for the possible lack of independence among the multiple observations from the same donor (see methods). We observed a statistically significant correlation between MS and SN RNA for both total and resting CD4+ T cells  with the exception of treatment with vorinostat and JQ1 in total CD4+ T cells .We also wanted to know if baseline US or MS RNA was a predictive measure of US or MS RNA production post-treatment with different LRAs. We found strong and highly consistent correlations between US RNA at baseline and at day 3 post-treatment with each LRA for both total and resting CD4+ T cells from PLWH on ART . Conversely, we did not find statistically significant correlations between MS RNA at baseline and at day 3 post-treatment with each LRA for both total and resting CD4+ T cells from PLWH on ART  To determine if a change in MS RNA and plasma virus following an LRA was also observed p = 0.0340, [Chi squared test for trend], p > 0.05, [Fisher's exact test], p = 0.0012, [Mann–Whitney test], in vivo, similar to what we previously described with US RNA and plasma HIV RNA At baseline, US RNA was detected in blood from all participants, whereas only 27.3% of participants had detectable MS or plasma HIV RNA a. Follow4In this study, we showed that there was a high level of constitutive expression of US RNA in both total and resting CD4+ T cells in PLWH on long-term ART. The level of baseline MS RNA was low to undetectable in both total and resting CD4+ T cells. The primers used to detect MS RNA bind to regions of the virus which are commonly defective and therefore detection of MS RNA is not only a reflection of viral transcription but possibly also the frequency of intact virus. Following stimulation of CD4+ T cells collected from PLWH on ART, we found that all LRAs led to an increase in US RNA except for vorinostat, but only PMA+PHA and to a lesser extent romidepsin and panobinostat led to increases in MS RNA . The chain vivo in PLWH on ART prior to any clinical intervention. It has recently been shown that despite consistent detection of US RNA in vivo or ex vivo on a population level in the absence of any intervention, >90% of CD4+ T cells are transcriptionally silent on the single cell level [Latently infected cells have classically been described as containing integrated HIV DNA while being transcriptionally silent and therefore remaining invisible to the host's immune system ll level ,51, consll level ,44,46,50cis-acting sequences within the HIV genome that must be intact Based on our primer sequence analysis, we found that the primers used to detect US RNA would detect close to 40% of defective proviruses, while primers to detect MS RNA would only detect 8% of defective proviruses. Given that the nFLS database is derived from DNA sequencing and not RNA transcript sequencing and that we could not assess the binding of the first forward primer for US RNA that binds to the 5’LTR, this is likely an overestimation given that the generation of MS RNA requires ex vivo, but production of MS RNA and SN RNA, which is more relevant for ultimate clearance of infected cells, was only observed following stimulation with PMA+PHA and some HDACi. Therefore, measurement of MS RNA and either SN RNA (in vitro) or plasma HIV RNA (in vivo) are more relevant markers of latency reversal.We found a positive relationship between constitutive expression of US RNA and the increase in US RNA following stimulation. This was consistent with our previous observations in a clinical trial of vorinostat in PLWH on ART. We previously described that following vorinostat treatment, the fold change in US RNA was correlated with baseline US RNA We found less efficient generation and accumulation of MS RNA and virion production with all LRAs compared to PMA+PHA. In contrast to other reports in vivo using results from a clinical trial of the LRA panobinostat. We also showed that the likelihood of a defective virus producing US RNA compared to MS RNA is far greater, based on a published large sequence database of defective viruses. It is important to note that in vitro defective proviruses can still produce spliced RNA transcripts and proteins [ex vivo screening of LRAs.Our findings are consistent with a previous report which investigated the effects of stimulation of total CD4+ T cells with full T cell activation using plate-coated anti-CD3/CD28 antibodies proteins ,56 whichex vivo stimulation with LRAs [We did not assess the contribution of read-through transcripts to the pool of US HIV RNA. As HIV tends to integrate into actively transcribing genes, HIV sequences can be generated by transcription from a host promoter and contain LTR-gag sequences that would appear to be LTR-driven US HIV RNA by qPCR. It has previously been shown that readthrough transcripts are detected in PLWH on ART and can be increased following ith LRAs ,57,58. TThroughout this study, we performed parallel analyses of total and resting CD4+ T cells to evaluate the response to LRAs. While HIV latency was first described in resting CD4+ T cells, it has since been well described that HIV can persist on ART in many T cell subsets, including activated and proliferating CD4+ T cells ,59, whicex vivo and in vivo  best correlates with changes in MS RNA. Even though defective virus can indeed produce viral proteins In conclusion, there is a clinical need for high throughput assays to determine whether an LRA can effectively reverse HIV latency by activating intact replication-competent virus. To date, most clinical trials of latency reversing agents have relied on demonstrating an increase in US RNA ,27,60,61Conceptualization, JMZ, GK, DFJP, and SRL. Methodology, JMZ, GK, WZ and JLA. Investigation, JMZ, GK, WZ, AR, MJG, RDP, MR, AD, MG, JLA. Resources, SGD, JM, TAR. Laboratory experimental analyses, JMZ, MJG, MR, SRL; Statistical analyses JMZ, PB and SRL. Writing - original draft, JMZ, GK and SRL. Writing – Review & Editing, All authors read and approved the final draft and have had access to the raw data. JMZ and SRL can verify the accuracy of all raw data in this study. Funding acquisition, SRL, DFJP.SRL's institution has received funding from the National Health and Medical Research Council (NHMRC) of Australia, National Institutes for Health, American Foundation for AIDS Research; Merck, ViiV and Gilead for investigator-initiated research; Merck, ViiV and Gilead for educational activities. She is on the advisory board of Abivax and Innivirax.


With the approval and distribution of demonstrably safe COVID-19 vaccines bearing exceptionally high efficacy profiles, it may be tempting to envision a return to “normal” in the coming months. However, if there is one lesson to be learned from the ongoing pandemic, it is that, in a world of evolving zoonotic viruses, we must be better prepared for the next deadly outbreak. While the acute nature of the COVID-19 pandemic demanded a highly specific approach, it is advisable to consider the breadth of seemingly endless possibilities in our approach to managing the next inevitable occurrence of an outbreak. Though there is little chance of discovering a “magic pill” to combat all future pathogens, the highly conserved nature of non-surface viral proteins exposes an “Achilles’ heel” in the structural genome of viral pathogens. Herein, we consider the potential of targeting such proteins to develop broad-spectrum therapeutics for the future. To illustrate this point, we outline the therapeutic potential of targeting the nonstructural protein 16 methyltransferase, which is conserved across most coronaviruses. The novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates in the host cytoplasm, underlying its dependence upon an autonomous mRNA capping mechanism cap-1/m7GpppNm) /m7GpppNm,4. ThereThe Middle East respiratory syndrome coronavirus (MERS-CoV) NSP16 is an S-adenosyl-L-methionine (SAM)-dependent 2-O-methyltransferase (2-O-MTase) that methylates the ribose 2-OH of the first transcribed nucleotide (N1) of viral RNA cap structures. This 2-O-MTase activity is regulated by NSP10. The 2-O methylation prevents virus detection by innate cell immunity mechanisms and viral translation inhibition by the interferon-stimulated IFIT-1 protein. The mechanism for binding the RNA-substrate, SAM, capping the RNA substrate and releasing SAH is illustrated in Aouadi et al. 2017 . The NSPThe process of developing a broad-spectrum coronavirus methyltransferase inhibitor can be further facilitated by screening commercially available molecules from existing databases for virtual screening and molecular docking ,7,8. An The greater value in targeting such an enzyme is that it is highly conserved across a range of viruses. As a result, in addition to developing a therapeutic that will target the coronavirus associated with the current outbreak, one would also be developing a broad-spectrum therapeutic that will be useful against known coronaviruses, future coronaviruses, and perhaps other groups of viral pathogens altogether. This “broad-spectrum” approach, when more broadly applied across a range of viral threats, represents a more efficient and comprehensive means of proactively managing future pandemics.