This work reports engineered toehold RNA–based biosensors for COVID-19 diagnostics, with a simple color or luminescence readout that makes it easily deployable in both well-equipped labs as well as low resource settings. The continued resurgence of the COVID-19 pandemic with multiple variants underlines the need for diagnostics that are adaptable to the virus. We have developed toehold RNA–based sensors across the SARS-CoV-2 genome for direct and ultrasensitive detection of the virus and its prominent variants. Here, isothermal amplification of a fragment of SARS-CoV-2 RNA coupled with activation of our biosensors leads to a conformational switch in the sensor. This leads to translation of a reporter protein, for example, LacZ or nano-lantern that is easily detected using color/luminescence. By optimizing RNA amplification and biosensor design, we have generated a highly sensitive diagnostic assay that is capable of detecting as low as 100 copies of viral RNA with development of bright color. This is easily visualized by the human eye and quantifiable using spectrophotometry. Finally, this PHAsed NASBA-Translation Optical Method (PHANTOM) using our engineered RNA biosensors efficiently detects viral RNA in patient samples. This work presents a powerful and universally accessible strategy for detecting COVID-19 and variants. This strategy is adaptable to further viral evolution and brings RNA bioengineering center-stage. Betacoronavirus genus under the Coronaviridae family of viruses  people have been infected, and millions remain susceptible to this infection. The situation is compounded by the emergence of new variants of the virus, including potentially highly infectious strains. The COVID-19 disease is caused by a novel coronavirus SARS-CoV-2, belonging to the Preprint). This method is relatively faster but may generate false positives because of nonspecific amplification and primer interactions (Preprint) or used in an amplification-independent way via the use of more than one guide RNAs . We find that this assay is compatible with different modalities of detection wherein viral RNA is detected via luminescence, in a shorter period of time. By developing new biosensors, we offer PHANTOM , an ultrasensitive, highly accurate COVID-19 detection platform that does not require any sophisticated equipment and is usable even in a low resource setting.Toehold sensors are designed to specifically recognize and respond to a region of viral RNA . Each biosensor consists of a sensing region contiguous with a conserved stem-loop structure that contains a RBS and a translation start site followed by a reporter gene . In the 9-fold amplification , we performed in silico analyses of the SARS-CoV-2 RNA genome, specifically the strain MT012098.1 from India  Fig 1C)Fig 1C). Within the amplicons, using sliding windows of 36 nucleotides, we searched for contiguous single-stranded regions that would be accessible to the biosensor and would serve as the Trigger RNA . The comBased on these analyses we selected biosensors that (1) show high probability of forming the required sensor structure where, in the absence of viral RNA the “sensing region” and its complement are base-paired and (2) the sensing region and trigger are largely open to allow for base-pairing that allows opening of the switch. 19 potential biosensors with a diversity of scores for different parameters were taken further for in vitro studies (Tables S1 and S2 and 1to3 + reverse complement of trigger (blue) + conserved stem-loop (red) + first 11-nt of trigger (purple) + (N1) + linker (cyan) + reporter gene (lacZ/nano-lantern).Sequences for 19 biosensors and 19 triggers are shown. Each sensor sequence comprises 5′ − GTable S1 Scoring parameters for 19 biosensors and 19 triggers are shown.Table S2 To test which of the designed sensors respond to their respective Trigger RNAs , we used an in vitro transcription-translation (IVTT) coupled assay. In the absence of Trigger RNA the sensing region of the biosensor would base-pair with its complementary sequence, keeping the RBS and translation start codon inaccessible, hence keeping the sensor in its OFF state. Presence of the Trigger RNA would sequester the sensing region, thus exposing the RBS and start codon to enable translation of the downstream lacZ mRNA. This Trigger RNA-dependent production of lacZ protein is detected using colorimetry.We tested 19 predicted sensors for their ability to produce color in the presence of Trigger RNA . DNA cor576) in the absence of Trigger RNA, suggesting leaky expression of lacZ and potentially unstable “off” state RNA conformations for this subset of sensors (576) in the absence of Trigger RNA and an increase in absorbance in the presence of trigger RNA (on/off A576 ratio > 14.0). Four of these sensors  with a high fold-change in absorbance  were chosen for further analyses.We find that all of the tested sensors showed absorbance at 576 nm in the presence of Trigger RNA. However, 12 of these sensors also showed detectable absorbance  have been observed in naso-pharyngeal swabs of patients  RNAs spread across the SARS-CoV-2 genome and tested them for their ability to turn on Sensor 12. We find that our sensor is highly selective for its cognate target RNA and does not show any detectable response to the other regions of SARS-CoV-2 . We furt6 copies of RNA show discernible color (A576 > 1) even at ∼60 min, whereas 100 copies of RNA are detectable at 100 min . This implies that our detection strategy would work for these key variants. Of these variants, the δ variant (B.1.617.2) is not only highly contagious but has become the dominant variant in several populations , 57, 58.In this report, using computational methods, we have designed toehold RNA–based biosensors that are tuned to sense different fragments of the SARS-CoV-2 RNA, spread across the genome. Extensive in vitro screening and characterization led us to identify biosensors that turn on translation of the reporter lacZ, in response to SARS-CoV-2 RNA fragments. Taking one of these biosensors forward, we coupled isothermal NASBA based RNA amplification to the IVTT assay to achieve sensitivities in the range of 100 copies of viral RNA. Alternate luminescence based detection enabled a faster time response post-amplification, highlighting the modularity of our system. When applied to patient samples, our assay provides a clear response that discriminates viral-positive from negative samples. Collectively, we present here a diagnostic platform with a read-out that is quantifiable and correlates excellently with the gold standard RT-qPCR assays. Furthermore, this assay can be deployed in a low resource setting as the read-out is easily visualized by eye as well as through a simple cell phone camera, and the assay itself does not require any expensive equipment.specific and direct detection of any RNA sequence. A key challenge in this toehold design is to ensure that the sensor is truly “off” in the absence of target RNA and turns on only in response to the target. We found that both the computational analyses and in vitro screening were crucial to overcome this challenge. We initially chose sensors with diverse scores across bioinformatic parameters. Combining this with targeted in vitro screening, we were able to identify the sensors that show a suitable ON to OFF state response.A key advance of this work is to exploit the toehold concept for SARS-CoV-2 detection. The toehold switch concept itself is an elegant strategy for RNA detection that has been used successfully for viral infections and other pathologies , 28. A hThe toehold switch concept is highly modular, allowing different reporters and hence diverse read-outs for detection , 31, 32.Another significant step of optimization in our assay is during RNA amplification using NASBA. Previous reports have also highlighted the importance of primer design and optimization to achieve efficient NASBA , 52, 59.With a robust and sensitive assay in hand, we asked if the time taken for this assay could be optimized further. One of the key steps here is the NASBA amplification hence we tested to see if the time taken for NASBA could be decreased. We found that even at the limit of detection , we were able to complete the NASBA amplification step in ∼60 min . This, aFinally, our results with the human patients show that our assay can clearly distinguish viral-positive from negative samples. Indeed there is strong correlation between the assay response and Ct values obtained from the RT-qPCR test. The overall sensitivity in the attomolar range ensures detection of infection in most COVID-19–positive patients in a population. The GISAID database reports ∼2,418,000 high quality sequenced genomes  from different clades of the SARS-CoV-2 virus. The viral RNA fragment that turns on our sensor is located in the ORF1ab (Nsp13) region of the genome. This region is completely conserved in greater than 98.32% of deposited genomes of SARS-CoV-2. This indicates that our sensors would be capable of detecting nearly all of the currently sequenced strains of SARS-CoV-2. Most significantly, this region is invariant among 99.28% δ variant (B.1.617.2), 98.45% α (B.1.1.7) variant, and 99.62% and 99.41% in the γ (P.1) and the β (B.1.351) variants of SARS-CoV-2 respectively. Our data clearly show that the PHANTOM assay reliably detects the presence of the δ variant in patient samples. A feature of toehold-based biosensors is the multiple check-points for specificity in detection. One level of specificity comes from primers that amplify only a given region of the viral RNA and second comes from sequence-specific interactions between the viral RNA fragment and the biosensor. This is exemplified by our results wherein we observe very low false-positive rates. Detectable color is produced in positive patients (20 positive) but no color in negative patients (27 negative patients). Finally, our assay works well with the standard mode of nasopharyngeal sample collection. Combining this method of detection with other modes of sample collection such as saliva would be a significant advance going forward.In conclusion, our engineered biosensors along with the PHANTOM platform provide a powerful strategy for COVID-19 detection. This can not only mitigate uncertainties in global supply chains and counter a shortage of reagents but also serve different local conditions and contexts that may benefit from diverse testing strategies.MT012098.1) was downloaded from National Center for Biotechnology Information (NCBI) and used for analysis.To establish a pipeline for designing SARS-CoV-2–specific toehold sensors, we started by searching for primers that would anneal to the SARS-CoV-2 genomic RNA. An Indian strain of SARS-CoV-2 (accession code (1) First 6-nt GC count (including hybridized region for forward primers)(2) Last 6-nt AT count(3) Total % GC (including hybridized region for forward primers)(4) Single strandedness(5) Concentration of primer remaining in monomeric form(6) Single strandedness of the last six nucleotides(7) Contiguity lengthUsing a custom program we divided the genomic sequence (and its reverse complement) into all possible fragments of 20–24 nucleotides. Fragments which end with an Adenosine, do not have a continuous stretch of four or more of the same nucleotide, show a GC content between 40 and 60%, and have a melting temperature above 41°C were shortlisted and considered as primers. Fragments arising from the sense strand were considered as forward primers (P2 primer) and were prefixed with a T7 promoter sequence (AATTCTAATACGACTCACTATAGGGAGAAGG). Fragments from the reverse complement were considered as reverse primers (P1 primer). All primers were scored using Primer3 (v. 2.5.0)  and NUPAcomplexdefect function in the NUPACK package (Forward and reverse primers that are separated by 120–170 nucleotides (inclusive of primer length) are paired. The region amplified by each primer pair is considered as an amplicon. Single-strandedness of all resulting amplicons was estimated using the  package . For thi package , forwardpairs function in NUPACK, we calculated the pair probabilities for the whole amplicon. Using a custom code, we then extracted probabilities related to the Trigger regions. This Trigger single-strandedness (in the context of the whole amplicon) was considered for our analysis.Each amplicon from the previous step was split into continuous windows of 36 nucleotides each. Each 36-nt sequence is considered a Trigger RNA. Using the To construct the biosensors, the reverse complement of each Trigger sequence was appended to the 5′ end of the conserved stem-loop in the toehold design. The sequence of the conserved stem-loop was taken from Series B toehold sensors as described in Pardee et al (2016)  Thus, eaUsing NUPACK, the sensors were analyzed for single strandedness of the toehold region  and the probability of formation of the lower (variable) stem b–b* in . In addiFour parameters were considered while choosing sensors for in vitro screening. Trigger single-strandedness (>0.6), toehold single-strandedness (>0.6), probability of formation of the variable stem (>0.98), and the similarity to expected secondary structure (>0.75) were used as minimum criteria. From the list of sensors that passed these criteria, 25 sensors were chosen. Their trigger sequences were checked for potential similarity to the human genome (Accession code: 000001405.38) and transcriptome  using the megablast module of NCBI-BLAST, with default parameters . Only the triggers that did not have any hits were chosen for in vitro analysis (sensor parameters in Table S2). 19 sensors meeting these criteria were picked for screening.lacZ or nano-lantern gene in a standard Escherichia coli plasmid. Then, we purchased DNA oligos (listed in Table S3) containing the T7 promoter, sensor, RBS, the start codon, and the linker. A PCR was carried out to stitch this DNA oligo (T7 promoter to linker) with the linker-lacZ DNA, using relevant primers (Table S3). This results in a linear DNA template that was purified  and subsequently used as input to the IVTT assays.The complete DNA template for an RNA biosensor consists of the T7 promoter, sensor sequence with RBS and start codon, linker and the lacZ (or Nano-lantern) reporter gene. To construct this, we first cloned the linker  in frame with the List of oligos used in the study are shown.Table S3 2, 4 μl of 25 mM rNTPs , 2 μl (100 units) of T7 RNA polymerase enzyme , 2 μl (0.2 units) Yeast Inorganic Pyrophosphatase , 0.5 μl (20 units) RNaseOUT , and the remainder of the reaction volume was made up to 40 μl with nuclease free water and incubated at 37°C for 2 h. After this, the samples were treated with 2 μl (4 units) of DNAse I enzyme  at 37°C for 1 h and purified using the ZymoResearch RNA Clean and Concentrator RNA purification kit (Cat. no. R1015). The final RNA sample was eluted in nuclease free water for further use.The RNA templates for NASBA reactions and Trigger RNAs for cell free IVTT reactions were synthesized by in vitro transcription reactions. This was done in a 40-μl in vitro transcription reaction system. Each reaction contained 2.5 μg of the relevant DNA template, 4 μl of 10X T7 polymerase reaction buffer , 5.5 μl of 50 mM MgCl13–104 in each reaction tube. For IVTT reactions initiated with NASBA products, 2.5 μl of the NASBA reaction was used as input.Cell-free IVTT reactions  were prepared using the manufacturer’s protocol. In a total reaction volume of 10μl, 4 μl of solution A was added along with 3 μl of solution B, 0.25 μl (10 Units) of RNase Inhibitor , and 125 ng linear DNA template. Reactions were initiated with Trigger RNA or NASBA product wherever applicable and incubated at 37°C for 2 h. For IVTT reactions with Trigger RNA , 1 μl of2CO3 absorbance recorded at 576 nm. Absorbance (576 nm) was recorded for the samples (at a dilution of 1:10), using a cuvette of path length 1 mm, on a Spectrophotometer (Eppendorf Biospectrometer). Fold change in absorbance was calculated relative to the sensor “OFF” state.For reactions with ChlorophenolRed-β-D-galactopyranoside-CPRG , 0.75 μl of 12 mg/ml substrate was added from the start of the IVTT reaction and incubated at 37°C for 2 h. Samples were quenched with 2 μl of 2 M NaAlternately, we performed IVTT reactions in 384-well plates . Here, total IVTT reaction volume was proportionately reduced to 5 μl. Reactions were set up as described above and the plates were placed in a Varioskan Lux instrument (Thermo Fisher Scientific) set at 37°C. Absorbance was monitored at 576 nm, at 5 min intervals, for 150 min. The linear measurement range of the Varioskan Lux multiplate reader is 0–3 absorbance for a 384 well plate. The number of replicates for each experiment is indicated in the individual figure legends. Information on statistical tests carried out and statistical measures plotted are also indicated in the individual figure legends. All absorbance based plate reader experiments were first baseline corrected using a blank sample and each sample was normalized such that the lowest absorbance measurement was set to 0. All data were plotted using GraphPad Prism 8 and figures were made using Adobe Illustrator.Post IVTT reaction, the Corning 384 well clear bottom microplate was placed upside down on a white LED light source. An RGB image was acquired using a smart phone camera . Image was further processed and analyzed using Fiji/image J 1.52p . First, For luminescence assays with Coelenterazine H , 0.25 μl of 400 μM substrate was added from the start of the IVTT reaction. IVTT reactions were set up in 384-well plates as described above and monitored using the Varioskan Lux instrument (Thermo Fisher Scientific) set at 37°C. Intensity was measured in the “normal” luminescence mode without wavelength selection.13 copies) was incubated with 4 μl of 5X AMV RT Buffer , 1.6 μl of 50 mM MgCl2, 2 μl of 25 mM rNTPs , 2 μl of 10 mM dNTPs , 0.18 μl of 1 M DTT , 3 μl of 100% DMSO , 0.2 μl of 10 mg/ml BSA , and 0.5 μl each of 10 μM forward and reverse primers. The assembled reaction mix was initially incubated at 65°C for 5 min, followed by 50°C for 5 min, post which an enzyme mix containing 1 μl (50 Units) T7 RNA Polymerase , 1 μl (20 Units) AMV-RT  0.1 μl (0.2 Units) RNaseH , and 0.2 μl (12.5 Units) of RNaseOUT  was added and the reaction was incubated at 42°C for 2 h. Post-NASBA, the samples were either stored at −80°C or taken further for IVTT assays.For NASBA reactions, a relevant RNA template from SARS-CoV-2 genome was made using in vitro transcription as described above. In a reaction volume of 17.7 μl, RNA template (ranging from 10 to 10MT012098.1 accession code) was used at different starting copy numbers (10 to 1012). A sequence of GGGAGAAGG was appended to the 5′ to increase transcription efficiency. For the NASBA primer screening experiments (in MT012098.1 accession code) was used initially at 108 and subsequently at 104 copies. For testing the sensitivity of the best NASBA primer pair, a commercially available synthetic SARS-CoV-2 RNA  was used. Design and rationale behind the choice of primers is described in the Bioinformatics section above. For patient samples that were tested  that showed the fastest development of color and the highest signal to noise ratio was finally selected. NASBA Primer pairs for sensor 12 are listed in Table S4.A total of 28 primer sets were selected for sensor 12. Of these, 14 primer sets contained a forward primer (P2) with a minimal T7 promoter (TAATACGACTCACTATAGG), and are referred to as S01–S14 primer sets. The remaining 14 primer sets contained a longer T7 promoter with a purine stretch (AATTCTAATACGACTCACTATAGGGAGAAGG) as used in Deiman et al (2002) , and arehttps://github.com/ShyamsundarR/silicasense) and available upon reasonable request.The datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Custom software is deposited at Github (


E1, E2, and E3, are the dominant factors driving time to flower and reproductive period. However, functional genomics studies have identified additional loci which affect reproductive timing, many of which are less understood. A better characterization of these genes will enable fine-tuning of adaptation to various production environments. Two such genes, E1La and E1Lb, have been implicated in flowering by previous studies, but their effects have yet to be assessed under natural photoperiod regimes.Soybean is an economically important crop which flowers predominantly in response to photoperiod. Several major loci controlling the quantitative trait for reproductive timing have been identified, of which allelic combinations at three of these loci, E1La and E1Lb were identified and introgressed into lines harboring either E1 or its early flowering variant, e1-as. Lines were evaluated for days to flower and maturity in a Maturity Group (MG) III production environment. These results revealed that variation in E1La and E1Lb promoted earlier flowering and maturity, with stronger effects in e1-as background than in an E1 background. The geographic distribution of E1La alleles among wild and cultivated soybean revealed that natural variation in E1La likely contributed to northern expansion of wild soybean, while breeding programs in North America exploited e1-as to develop cultivars adapted to northern latitudes.Natural and induced variants of E1 paralogues, E1La and E1Lb, which promote flowering and maturity under natural photoperiods. These loci represent sources of genetic variation which have been under-utilized in North American breeding programs to control reproductive timing, and which can be valuable additions to a breeder’s molecular toolbox.This research identified novel alleles of the The online version contains supplementary material available at 10.1186/s12870-021-03197-x. Glycine max (L.) Merr.] is the world’s most economically important oilseed crop and was domesticated from its wild progenitor (Glycine soja [Sieb. & Zucc.]) more than 5000 years ago . A . A E1La E1 and E1La are candidates for driving northern expansion of wild soybeans; we hypothesized that the geographic location of e1-as and e1la:K82E alleles in G. soja and G. max accessions would illuminate their origin and distribution. For a set of 92 G. soja Plant Introduction (PI) accessions from the Germplasm Resources Information Network (GRIN) categorized as Maturity Group II and earlier, we directly determined the allele status of E1 and E1La by Sanger sequencing; in addition, the alleles of E1 and E1La were assigned from re-sequencing data for the subset of 56 G. soja accessions from Zhou et al., 2015 for which latitude information could be obtained  [e1-as (ss715593865 – GM06:20916554) and e1la:K82E (ss715587601 – GM04:37750626) were used as proxy markers to assess geographic distribution among North American cultivars. The full list of all North American accessions available from the GRIN was filtered to contain only cultivars which had homozygous allele calls for the E1 and E1La proxy SNPs, and for which latitude and longitude information could be obtained. After filtering, the final accession list contained a total of 594 cultivars  and E1/E1La-containing (37.8°N) cultivars  , 27. TheE1La may be present in soybean germplasm for which SoySNP50k genotype information was not available, we genotyped E1La from among a set of 26 natto and 19 tofu lines from the North Dakota State University breeding program. Interestingly, 24 of the 26 natto lines contained the e1la:K82E allele, however, all of the tofu germplasm possessed the Reference allele of E1La  on chromosome 04 in the region around position 28,293,933 to 28,294,806, and ten G. soja accessions contained a haplotype that included a nonsynonymous A/G variant at position 28,294,378. The ten G. soja accessions also contained a synonymous variant (G/T) at Gm04:28,294,356 that was present in an additional 14 G. soja accessions. The soybean allele catalog  was used to assess the distribution of alleles of E1La and E1Lb in our curated data set of 775 whole genome re-sequenced soybean accessions [E1La, was used to obtain the orthologous sequences from 23 different legume species with the highest percent identity  at 20, 25, 30, and 35 Gy doses at the McClellan Nuclear Radiation Center . To determine the copy number variation (CNV) events induced in the mutagenized population, select mutants were analyzed by comparative genomic hybridzation (CGH) using a Roche NimbleGen 696,139-feature soybean CGH microarray following previously published protocol , 29. The1Lb gene .G. soja accessions were obtained from the GRIN, and a subset of 419 of the accessions phenotyped for maturity group II or earlier was selected for characterization. DNA was isolated from ground seed tissue using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions. Samples were first evaluated for E1 or e1-as alleles using our established SimpleProbe assay [e1-as, and 244 accessions were E1. Subsequently, 84 E1 and 20 e1-as accessions were evaluated for their E1La sequences. Sanger sequencing at the University of Missouri DNA Core Facility of 642 bp E1La PCR amplicons from G. soja accessions utilized PCR primers E1La-F1: 5′- AAACACTCAAAGCCCGATCA-3′ and E1La-R2: 5′- GATTTGAAAGTAGAATAAAGCTAACACAG-3′ as described previously, except amplicons were isolated by ethanol precipitation prior to sequencing with the primer E1La-F1 [As part of a separate project, seeds from the entire set of be assay , and of  E1La-F1 .E1La or e1la:K82E alleles. Reactions were carried out in 20 μl containing template, primers, 0.2 μM final concentration of SimpleProbe, buffer , 5% DMSO, 200 μM dNTPs, and 0.2X Titanium Taq polymerase . The e1la:K82E antisense probe was 5′-Fluorescein-SPC-GGCAAAATTTGCTCCTTCACCAAATC-Phosphate-3′. Originally, the primers E1La-F1 and E1La-R2 were used in the assay, but difficulties amplifying from FTA card samples led to the replacement with nE1La-F1 (5′-GGGAGTTTCAACAACACTGAAGC-3′) and nE1La-R2 (5′-GGTGTCCATGTCCCAAACTCTAAC-3′) that targeted a 233 bp product. In both cases, the forward primers (E1La-F1 or nE1La-F1) were used at 5 μM final concentration and the reverse primers (E1La-R2 or nE1La-R2) were used at 2 μM final concentration in the PCR. Genotyping reactions were performed using a Lightcycler 480 II real time PCR instrument (Roche), using the following PCR parameters: 95 °C for 5 min followed by 40 cycles of 95 °C for 20 s, 60 °C for 20 s, 72 °C for 20 s, and then a melting curve from 50 °C to 70 °C.Plant samples for genotyping were tagged and leaf presses were prepared on FTA cards as described . A new SE1Lb and e1lb:Del alleles. Because the e1lb:Del allele is a deletion of the entire E1Lb gene, this assay was unable to distinguish between the REF E1Lb allele and lines that were heterozygous for E1Lb. Reactions were carried out in 20 μl containing template, primers, 0.063 μM final concentration of EvaGreen Fluorescent Dye , buffer , 5% DMSO, 200 μM dNTPs, and 0.2X Titanium Taq polymerase . Genotyping reactions were performed using a Lightcycler 480 II real time PCR instrument (Roche), using the following PCR parameters: 95 °C for 3 min followed by 35 cycles of 95 °C for 20 s, 60 °C for 20 s, 72 °C for 20 s, and then a melting curve from 72 °C to 87 °C. Primers for E1Lb were: E1Lb-F1 (5′-GTGTAAACACTCAAAGTCCTT-3′), E1Lb-R (5′-CTCCTCTTCATTTTTGTTGCTGC-3′), 3Ad1 (5′-TTGCATCACCATGGTCATCAT-3′), 3Aix (5′-AGCTATTATCTAGCATTAACCTCA-3′).A Tm-shift assay , based oE1/e1-as and E2/e2 as previously described [E3 from e3-tr alleles [Simple probe assays were utilized for genotyping escribed ; a gel-b alleles .Tof11/tof11–1 and Tof12/tof12–1 allele genotyping assays utilized a GC tail and a nonspecific DNA-binding dye, and produced distinct melting temperatures for different alleles [Tof11 were DTF1tailf1: 5′-GCAACACCTTGACAATCAGAAT-3′, DTF1tailW82r2(5′- gcgggcagggcggcAGCCACATTGCCATTTCTA-3′), and DTF1tailGsr2 (5′- gcgggcAGCCACATTGCCATTTTCAA-3′). Primers for Tof12 were DTf2TailW82 (5′-gcgggcagggcggcCATAAAGCTGCAGTAGATACCT-3′), DTf2TailGs (5′-gcgggcCATAAAGCTGCAGTAGATTCCC-3′), and DTf2TailR1 (5′-GCATTTGATGATACACATTGCG-3′).The  alleles . Genotyp alleles , and priG. soja accessions PI52226 and PI547831 containing e1la:K82E alleles were obtained from the GRIN. A line from fast neutron mutagenesis of Williams 82 was the source of the W82 FN e1lb:Del alleles. Jake is a MG V determinate cultivar provided with permission by the developer [e3 line was an experimental seed composition line verified to contain e3-tr alleles developed by the authors [Seeds of eveloper , Williameveloper , Deuel ieveloper , Candor  authors . The gen1 seeds were produced at the South Farm Research Center near Columbia, Missouri during the summer field season followed by a cycle of self-pollination that produced F2 seeds in the winter nursery near Upala, Costa Rica. During the second cyle in the winter nursery, the F2 plants were sampled and underwent genotypic selection prior to single plant harvest of F2:3 seeds. To isolate the e1la:K82E alleles out of the G. soja genetic background, breeding efforts with PI547831 and PI522226 with G. max parents were directed at selecting for e1la:K82E alleles and the desired E1 alleles leading to the parent line KB16-2B #666, which was still segregating at E1 and the parent line KB16-W2F3, which was fixed for e1-as  and 5, 6, and 7 (e1-as), respectively, as well as for phenotypic analysis as parts of the population 1 and 2 experimentally tested lines  was identified from a set of fast neutron mutagenized Williams 82 lines, and lines with E1 or e1-as alleles along with e1lb:Del alleles were selected by gentoype from two populations. Population 3 lines were selected for e1lb:Del/E1 and population 4 lines were selected for e1lb:Del/e1-as and  on May 15th, 2018 at the South Farm Research Center in Columbia, Missouri. The following year, F4 lines fixed for e1la:K82E and e1lb:Del, along with E1La and E1Lb reference control lines, were planted in 5′ plots  on May 31st, 2019 in Columbia, Missouri. In both years, lines were grown in a randomized complete block design with three replicates per line and were scored for flowering time (R1) and maturity (R8) as a function of days after planting (DAP). In 2018, the R1 dates for each plant were averaged to get the mean R1 date for each plot. In 2019, plots were marked as R1 once flowers were observed on at least three plants in the plot. In both years, plots were marked as R8 once 95% of pods on the main stem were mature. First frost occurred on October 16th in 2018 (day 152) and on October 12th in 2019 (day 134). For the statistical analysis, any plot that did not mature by this time was given an R8 score of the day of the first frost. Means comparisons were conducted using an ANOVA in R, and significance groups were obtained using a Fisher’s LSD test with false discovery rate (FDR) correction (P = 0.05). For the 20 experiment, 50-seed plots of F3:4 lines along with controls were planted in random order in 5′ plots  on June 1st, 2020 in Columbia, Missouri. Genotypes were replicated, but lines were not. Plots were marked as R1 once flowers were observed on at least three plants in the plot, and maturity was estimated for 95% mature pods on the main stem, or maturity was forecasted 2–3 days in advance. The first frost occurred on October 16th in 2020 (day 137).Two field experiments were conducted, one that included selections from populations 2–5 in 2018 and 2019 (18/19) and one that included selections from populations 6–10 in 2020 (20) Table . For theE1 and E1La genotypes for 56 re-sequenced Glycine soja accessions were obtained from Zhou et al., 2015 analyzed in SNPViz [E1 and E1La genotypes for 92 additional MG II or earlier Glycine soja accessions were obtained from Sanger sequencing. As North American cultivars derived from early maturity groups were underrepresented in our resequencing panel, cultivars with SoySNP50k data were instead pulled from the GRIN. The E1 and E1La genotypes for each cultivar were estimated using a proxy SNP from the SoySNP50k array. E1 and E1La genotypes were first determined for a set of 775 resequenced accessions [e1-as and e1la:K82E causal mutations and all of the SoySNP50k variants within 1 Mbp of the causal mutations on chromosomes 06 and 04, respectively, was calculated using a parameter called“combined pessimistic accuracy.” Accuracy for each SNP is calculated as the percentage of the 775 resequenced accessions with either the REF or ALT haplotype combinations between SNP and causal mutation.n SNPViz . E1 and cessions , 25. StrE1 and E1La genotypes for all North American cultivars pulled from the GRIN . To avoid overplotting on map figures, genotype groups containing more than 200 cultivars were randomly sampled. Latitude and longitude coordinates for plotting were obtained directly from the GRIN, where available. For accessions where coordinates were not available in GRIN, Google geocoding was used to obtain latitude and longitude coordinates for the state/province of origination. A small amount of “randomness” was introduced to plotting coordinates to prevent complete overlap of accessions originating from the same state/province. Maps were generated using ggplot2 in R using the Natural Earth package. Means comparisons for boxplots were conducted using an ANOVA in R, and significance groups were obtained using a Fisher’s LSD test with false discovery rate (FDR) correction (P = 0.05).This “combined pessimistic accuracy” equation is modified from Metz (1978) to capture combined sensitivity, specificity, and missing data . The sine1la:K82E alleles by country of origin, country of origin assignments for the 476 G. max and 499 G. soja accessions containing the e1la:K82E SoySNP50k Proxy SNP (ss715587601 – GM04:37750626) were obtained from the GRIN. Accessions missing country of origin information in the GRIN were assigned a value of “Unknown” for plotting. Histograms were generated in R v4.0.2 using the ggplot2 package, v3.3.2.For the frequency distribution of imputed E1/e1-as and E1La/e1la:K82E were conducted as described above with the DNA samples and controls.DNA was prepared as previously described from a single dry seed from 19 tofu and 26 natto experimental lines from the North Dakota State University soybean breeding program . SimplePAdditional file 1: Supplemental Figure 1.Breeding schemes to transfer e1la:K82E alleles from G. soja PI 547831 into G. max background. A. Parents and experimental lines used to develop the experimental parent line KB16-2B #666. B. Lines used in development of the KB17–2 population. C. Lines used in the development of KB17–1 population. Supplemental Figure 2.Breeding scheme to transfer e1la:K82E alleles from G. soja PI 522226 into G. max background. Supplemental Figure 3.Frequency of GRIN-derived Glycine max (top panel) and Glycine soja (bottom panel) accessions containing the e1la:K82E proxy SNP by country of origin. Country of origin assignments for each accession were obtained from the GRIN. Accessions lacking country of origin information were assigned a value of “Unknown”. Supplemental Table 1. Flowering time and maturity genes affecting soybean photoperiod response. Supplemental Table 2. NCBI Blastp results for Legume orthologues of E1La.Supplemental Table 3 List of 49 predicted genes deleted as a result of Fast Neutron-induced lesion. Supplemental Table 4. Maturity gene alleles for controls and test lines. Supplemental Table 5. Origin information for geographic assessment of G. soja accessions. Supplemental Table 6. Origin information for geographic assessment of North American cultivars. Supplemental Table 7.E1 and E1La genotype status of North Dakota tofu breeding lines. Supplemental Table 8.E1 and E1La genotype status of North Dakota natto breeding lines.


Viral RNA sensing triggers innate antiviral responses in humans by stimulating signaling pathways that include crucial antiviral genes such as interferon. RNA viruses have evolved strategies to inhibit or escape these mechanisms. Coronaviruses use multiple enzymes to synthesize, modify, and process their genomic RNA and sub-genomic RNAs. These include Nsp15 and Nsp16, whose respective roles in RNA capping and dsRNA degradation play a crucial role in coronavirus escape from immune surveillance. Evolutionary studies on coronaviruses demonstrate that genome expansion in Nidoviruses was promoted by the emergence of Nsp14-ExoN activity and led to the acquisition of Nsp15- and Nsp16-RNA-processing activities. In this review, we discuss the main RNA-sensing mechanisms in humans as well as recent structural, functional, and evolutionary insights into coronavirus Nsp15 and Nsp16 with a view to potential antiviral strategies. Viral infections are classified among the most devastating infectious phenomena, and in certain cases are responsible for the generation of severe pathogenic phenotypes that characterize well-known human diseases ,2. The rIn prokaryotes, most viruses harbor double-stranded (ds) DNA as genome, while single-stranded (ss) DNA viruses are significantly fewer and there is only a limited presence of RNA viruses. On the contrary, viruses with RNA genomes dominate the eukaryotic virome. The International Committee on Taxonomy of Viruses (ICTV) reports 158 RNA virus species compared to 91 DNA virus species that infect humans . PositivThe members of (+)ssRNA viruses are characterized by small genomes that rarely exceed 30 kb in size and evolve fast due to the relatively high mutation rate during their replication process and the short generation times ,9,10. DeDespite the evolutionary divergence within the group of (+)ssRNA viruses, its members share common features in their replication processes within the host cell. Their replication takes place at the cytoplasmic membranes of the infected cells and is characterized by regulated coordination of RNA synthesis that is mediated by the viral replication complex. Besides the RNA-dependent RNA polymerase (RdRp), this protein complex consists of a core of viral enzymes with protease and RNA helicase activities that are common among most (+)ssRNA viruses. The positive-sense single-stranded RNA genome can be directly translated into viral proteins upon release into the cytoplasm of a host cell. After completion of this translation process, viral replication proteins recruit the viral (+)RNA. Intracellular membranes play an essential role in this step as they can form anchor sites for the assembly of viral RNA replication complexes (VRCs). Moreover, host membranes and proteins may protect and concentrate viral RNAs, and also isolate the replication intermediates from templates during the replication steps . In the Recognition of virus-derived nucleic acids is among the most important processes of the host cell defense. During the replication of positive single-stranded RNA viruses (+ssRNA), RNA-replication intermediates such as double-stranded RNA molecules (dsRNA) of more than 30-bp length accumulate. Detection of dsRNAs by specific host protein sensors such as RIG-I-like receptors (RLRs), the protein kinase R (PKR), and oligoadenylate synthetases (OASes) represent some of the central mechanisms of interferon (IFN) pathway activation in infected cells . IFIH1 gene in humans, belongs to the same family of cytoplasmic RNA sensors. One of the best-studied RLRs with binding activity of non-self dsRNA is the RIG-I (retinoic acid-inducible gene-I), a DExD/H box RNA helicase important for the activation of transcription factors such as IRF3 and NF-κB . RIG-I iUpon dsRNA binding, these proteins activate the mitochondrial membrane localized adaptor MAVS which in turn, recruits multiple factors  that promote the formation of a MAVS signaling complex. The latter induces the phosphorylation and nuclear translocation of interferon regulatory factor 3 (IRF3) and interferon regulatory factor 7 (IRF7) as well as the activation of NF-κB . This MAPKR is a dsRNA-dependent protein kinase and is transcriptionally upregulated by interferon. This protein exists in an auto-repressed monomeric state and dsRNA binding activates its dimerization and its catalytic activity . PhosphoThe oligoadenylate synthetase family consists of interferon-induced enzymes (OASes) that upon binding to virally produced dsRNA synthesize 2′−5′ phosphodiester–linked oligoadenylates (2–5 An) which in turn activate the endoribonuclease RNase L within infected cells . ActivatEukaryotic cells have evolved complex networks of specialized response systems to numerous extracellular stimuli, leading to the formation of transcriptional complexes across the appropriate regions of the genome. Virus-inducible gene expression is regulated by virus-activated transcription factors , cis-regulatory elements of the genome such as enhancers, and local chromatin structure ,28. The Phosphorylated STAT1 and STAT2 form a heterodimer, which binds IRF9 to form the trimeric complex called IFN-stimulated gene factor 3 (ISGF3) . The nucCoronaviruses (CoVs) are RNA viruses  characterized by significantly long positive-sense single-stranded RNA genome (26–32 kb). Their genome can be translated by different open reading frames (ORFs) and can also be used as a template for replication and transcription. Negative-sense RNA intermediate is generated to serve as the templates for the synthesis of positive-sense genomic RNA (gRNA) as well as numerous subgenomic RNAs (sgRNAs). Upon the release of genomic RNA into the host cell cytoplasm, translation begins in ORF1a and continues in ORF1b after a −1 ribosome frameshift that occurs immediately upstream of the ORF1a stop codon, producing two polypeptides, pp1a and pp1ab. Proteolytic cleavage of these two polyproteins by virally encoded proteases produces fifteen or sixteen non-structural proteins (Nsps) that are involved in genome replication. Most of the remaining one-third of the genome encodes four structural proteins: the transmembrane (M) glycoprotein, the spike (S) glycoprotein, the envelope (E) protein, and the nucleocapsid (N) protein. The expression of the structural proteins is mainly regulated at the level of transcription via the synthesis of a nested set of subgenomic RNAs (sgRNAs).CoVs are classified into four genera, Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus infecting a plethora of hosts ranging from birds to mammals ,40. The More recently, MERS-CoV , SARS-CoV  and SARS-CoV-2  represent emerging Beta-CoVs of mammal origin that are associated with respiratory illness that may lead to severe pneumonia with massive virus replication and inflammation and acute respiratory distress syndrome (ARDS) . AlthougInnate immune response is important for: (a) Suppressing the early virus replication phase and consequently reducing the virus load; (b) promoting the antigen presentation and natural killer cell functions; and (c) activating the adaptive immune system. The latter includes the induction of antigen-specific T- and B-cell responses and the development of immunological memory . TherefoImportantly, two major arms of the antiviral gene expression program are shaped within the early virus infection phase. The first one is distinguished by the upregulation of type I and III IFNs followed by the activation of ISGs ,53 and tEarlier studies on MERS, SARS-CoV, and Mouse Hepatitis Virus (MHV) have demonstrated that both structural and non-structural proteins of these viruses impair IFN responses in multiple ways. For instance, in both SARS-CoV and MHV, Nsp1 inhibits STAT1 phosphorylation and thereby disrupts IFN signaling ,59. SimiBesides the function of specific viral proteins as interferon antagonists, coronaviruses have evolved several strategies to evade innate immunity. First, coronaviruses replicate in the interior of double-membrane vesicles which prevents recognition of dsRNA replication intermediates by host proteins that sense viral RNA structures ,69,70. TLike many RNA viruses, coronaviruses protect the 5′ end of their RNA genome and subgenomic RNAs from degradation and evade recognition from the host RNA sensor proteins of the innate immune system by a cap structure generated during replication. More specifically, the viral cap structure at the 5′ end of the RNA molecule consists of a N-methylated guanosine triphosphate and a C2′-O-methyl-ribosyladenine. This structure resembles the cap of eukaryotic mRNA. Viral proteins Nsp14 and Nsp16 catalyze the methylation of the cap on the guanine of the GTP and the C2′ hydroxyl group of the following nucleotide, respectively. Both Nsp14 and Nsp16 are S-adenosylmethionine (SAM)-dependent methyltransferases (MTases). Since host sensor proteins of viral RNA recognize the 5′ cap in order to distinguish the host mRNA from viral RNA, this structure protects coronavirus RNA from recognition by Mda5 and thus prevents Mda5-driven interferon upregulation in virus-infected cells . The impAnother conserved molecular mechanism associated with innate immune evasion in coronaviruses includes the degradation of dsRNA intermediates by viral proteins with processing activity. Coronavirus encoded Nsp15 protein, a uridine-specific endoribonuclease conserved across coronaviruses is an integral component of the coronaviral replicase-transcriptase complex (RTC) that processes viral RNA to evade detection by host defense systems. This protein is considered a member of the nidoviral EndoU (NendU) family. It is well accepted that Nsp15 uridylate-specific nucleolytic activity on single-stranded and dsRNA limits the formation of dsRNA intermediates and thus inhibits the ability of specific cytoplasmic viral RNA sensors to activate the IFN-I response of innate immunity to infection ,76. In tThe small size of RNA virus genomes has been associated with error-prone replication that lacks proofreading and leads to high mutation rates. Genome expansion in RNA viruses has been identified in order Nidovirales,  a large group of positive single-stranded RNA viruses that includes those with the largest genomes known to date (larger than 20 kb) that mainly belong to Coronaviridae and Roniviridae families. Those nidoviruses uniquely encode ExoN, an exoribonuclease with RNA 3′ end mismatch excision activity that enhances their replication fidelity and resides in Nsp14 in case of coronaviruses . It is wRNA viruses employ diverse methods to add a cap structure or to mimic this structure in the 5′ end of their RNAs in order to protect them from recognition by cytoplasmic sensors. For instance, rhinoviruses that are members of the picornavirus family and harbor a positive single-stranded RNA (+ssRNA) genome, encode a cap mimicking peptide (VPg) to protect the 5′end of their RNA . On the On the other hand, the NendoU domains that exhibit endoribonuclease activity toward dsRNA intermediates have been identified only in Nidovirales and no homologs have been found in any other RNA viruses. Therefore, NendoU is considered as genetic marker for this order of viruses that discriminates nidoviruses from all other RNA virus families . These rIn most eukaryotic and viral mRNAs the RNA cap is made of an N7-methylated guanine nucleotide connected through a 5′-5′ triphosphate bridge to the first transcribed nucleotide, generally an adenine. This cap structure formation is typically processed in three sequential steps: (1) An RNA triphosphatase (RTPase) hydrolyses the 5′ γ-phosphate of RNA to generate a 5′ diphosphate RNA end; (2) a guanylyltransferase (GTase), transfers a guanine monophosphate nucleoside (GMP) to the 5′-diphosphate mRNA; and (3) an S-adenosylmethionine (SAM)-dependent (N7-guanine)-methyltransferase (N7MTase) methylates the cap onto the N7-guanine, releasing S-adenosylhomocysteine (SAH). This first methylation in N7, forms a cap-0 structure and is required for the subsequent methylation at the 2′-OH position of the following nucleotide (cap-1 structure) by a SAM-dependent (nucleoside-2′-O-)-methyltransferase (2′-O-MTase). SAM-dependent MTases are present in all life forms, catalyzing the transfer of the SAM methyl group to a wide spectrum of methyl acceptors. Studies of viral MTases involved in RNA capping show low levels of sequence identity and structural similarity. In coronaviruses, the carboxy-terminal part of Nsp14 contains the N7-MTase activity that is required for the addition of a methyl group to the cap guanosine at the N7 position (m7G). The second methylation step at the O-2′ position is associated with the Nsp16 protein (2O-MTase), which requires a cofactor, nsp10, for its proper activity.CoV Nsp14 is a 60-kDa protein that participates in the formation of the replication–transcription complex. It is considered as a bifunctional enzyme that harbors both 3′-5′exoribonuclease (ExoN) and N7-MTase activities,91. ExoNIn 2003, the CoV 2′-O-MTase domain was associated to Nsp16 protein. All RNAThe crystal structure of SARS-CoV and SARS-CoV-2 RNA Cap 2′-O-methyltransferase Nsp16/Nsp10 complex, revealed that Nsp16 from both coronaviruses adopt the canonical O-MTase fold. Moreover, the comparative analysis of their structures showed a high degree of overall fold similarity and a high conservation of residues that form the SAM-binding site ,102.2+ ions for SARS-CoV-2 Nsp15 catalysis is conflicting with the proposed RNase A-like reaction mechanism since RNaseA catalysis is metal independent. NendoU endoribonuclease activity has been functionally assigned to Nsp15 protein among different human  and animal coronaviruses (MHV) ,104,105.As emphasized above, Nsp15 and Nsp16 activity are important for SARS-CoV-2 innate immune escape during the viral cycle. Thus, the development of molecules capable of inhibiting these proteins might open new treatment avenues to restore viral RNA recognition and stimulate the host antiviral response against SARS-CoV-2. Recent studies have shown that Nsp16 2′ O-MTase activity can be suppressed by conventional SAM antagonists. For instance, sinefungin (SFG)—a SAM analog—efficiently binds the SAM-binding site of SARS-CoV-2 Nsp16, inhibiting its activity  Figure A,B. CrysThe analysis of Nsp16:Nsp10 crystallographic data for MERS, SARS-CoV, and SARS-CoV-2 have indicated that RNA guanosine cap binds to a region adjacent to the SAM-binding pocket, placing its ribose ring in close proximity to the amino group of SAM ,101,115.Nsp15 protein is an attractive target in the field of drug design as it is exclusively present in nidoviruses with no close human homologs identified. Since structural studies indicate a high degree of similarity between the catalytic sites of CoV Nsp15 and RNase A, and a common catalytic mechanism, small molecule inhibitors of RNase A were among the first drugs tested for their ability to inhibit Nsp15 activity. In this direction, Benzopurpurin B and Congo red were shown to display the higher inhibition on Nsp15 activity. In addition, these drugs reduced the infectivity of the SARS-CoV in Vero cells . Based oUsing the available crystallographic data for NSP15 from MERS, SARS-CoV, and SARS-CoV-2, several groups have employed virtual screening, molecular docking, and molecular dynamic simulation techniques to identify putative antivirals against Nsp15 ,124. NspRNA viral recognition by cytosolic RNA sensors can stimulate the type I and III IFNs production that in turn, establish the cellular antiviral response and activate the adaptive immunity. The co-evolution of RNA viruses with their host has led to the emergence of viral evasion mechanisms of intracellular RNA sensing. Upon early phases of coronavirus infection, IFN response appears to be reduced and delayed indicating an innate immune escape. At the molecular level, the cap structure formation to the 5′ end of the viral genome and subgenomic transcripts and the degradation of dsRNA intermediates that are produced during replication is considered to hide coronaviruses from host sensor proteins that are able to stimulate the IFN signaling pathway. In the current review, the specific function of coronavirus-encoded enzymes Nsp15 and Nsp16 on dsRNA degradation and viral cap methylation respectively, have been discussed. These RNA-processing proteins and the associated innate immune escape mechanisms have evolved in nidovirales after the acquisition of the proofreading exoribonuclease (ExoN) activity and the expansion of their genomes. This suggests that the specific innate immune escape profile of coronaviruses has been developed in close association with complex evolutionary processes related to gain of Nsp14 proofreading activity, and genome expansion events. Progress in the field of coronavirus genome replication biology will allow us to understand how evolution can lead to the acquisition of new RNA enzymatic activities, which in turn provides the capability to modulate the innate immune response. It is characteristic that Nsp15 is exclusively present only in nidovirales evolutionary clade. Since coronaviruses use host proteins as part of their replication processes, it has also become clear that Nsp15 and Nsp16 protein evolution and function were affected by virus–host interactions. For instance, it has been proposed that NSP15 degrades the excess amount of dsRNA that escapes from replication-transcription complexes in the inner core of cytoplasmic vesicles that are formed by host membranes and proteins. Since the innate immune escape mechanisms are based on complex virus–host interactions, it is possible that Nsp15 and Nsp16 RNA-processing enzymes have evolved to synergize with other viral Nsp proteins that function as IFN antagonists to counteract anti-viral immune responses.Numerous studies have highlighted the importance of bypassing or modulating innate immune defense for coronavirus replication. A key challenge is to translate this knowledge into useful applications for the development of new antivirals. In this direction, the pharmacological inhibition of Nsp15 and Nsp16 proteins may potently induce antiviral responses for long-lasting immunity.


Post-synthesis modification of biomolecules is an efficient way of regulating and optimizing their functions. The human epitranscriptome includes a variety of more than 100 modifications known to exist in all RNA subtypes. Modifications of non-coding RNAs are particularly interesting since they can directly affect their structure, stability, interaction and function. Indeed, non-coding RNAs such as tRNA and rRNA are the most modified RNA species in eukaryotic cells. In the last 20 years, new functions of non-coding RNAs have been discovered and their involvement in human disease, including cancer, became clear. In this review, we will present the evidence connecting modifications of different non-coding RNA subtypes and their role in cancer. More than 100 chemical modifications of RNA molecules have been discovered during the last several decades. These modifications are found in both prokaryotic and eukaryotic cells and have been observed on all RNA subtypes [In the last two decades, the advent of next generation sequencing has allowed the development of new detection methods for RNA modifications. At the same time, the discovery of new RNA species and novel regulatory mechanisms mediated by RNA sparked renewed interest in RNA biology. In particular, the roles of newly identified non-coding RNAs (ncRNAs) such as micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) highlighted the scope of RNA biology both in physiology and disease.6A) [5C) [6A-modified RNAs were enriched through RNA immunoprecipitation (RIP) with a specific antibody [5C-carrying RNAs were firstly enriched through m5C-specific RIP and then subjected to bisulphite treatment [5C at base resolution. The enzymes responsible for these modifications, the METTL3/METTL14 complex for m6A [5C [6A was shown to promote degradation of cellular mRNAs [5C was shown to increase the stability of transfer (t)RNAs [VAULT ncRNAs (vtRNA) [6A erasers and the discovery of the YTH protein family as specific m6A readers [1A) [7G) [These conditions paved the way for the development of the new field of epitranscriptomics. Several modifications were mapped to the transcriptome and new functions of RNA modifications were described. In particular, two primary RNA methylations, N-6-methyladenosine (m6A) , 3 and 56A) [5C) , were maantibody , 3 beforreatment  in orderar mRNAs , 8, whil (t)RNAs , 10 and  (vtRNA) . Finally (vtRNA)  and ALKB (vtRNA)  as m6A e readers , N1-methers [1A) , 17 and 1A) [7G)  were map7G or Ψ [Depending on their position within nucleotides, RNA modifications can affect the pairing of RNA bases, therefore impacting the secondary structures and physiological functions of RNAs. For example, the deamination of adenosines to inosines (A-to-I), mediated by ADAR1 and ADAR2, converts base pairing from A-T to I-G . RNA edi7G or Ψ .The general functions of RNA modifications were thoroughly reviewed by Roundtree and colleagues . RecentlWhile mRNA is an intermediate of gene expression, ncRNAs are effector molecules and their functions mostly rely on a correct structural folding and activity. Since modifications can directly affect RNA stability and structure, they might be particularly important for proper ncRNAs functioning.Furthermore, ncRNA covalent marks are in many cases needed to fine-tune RNA function and are often dispensable for normal physiology, but their mis-regulation can promote tumour growth. Consequently, the enzymes responsible for such marks represent ideal candidates to specifically target cancer cells. In this review, we will focus on the modifications occurring on the different types of ncRNAs and discuss their role in cancer.28S, 18S, 5S and 5.8S [5S is transcribed by RNA polymerase III (POL3). In the nucleolus, rRNAs are processed co-transcriptionally by a large number of protein complexes [Ribosomes are molecular complexes composed of 80 protein subunits and 4 different ribosomal RNAs (rRNAs), and 5.8S . Three oomplexes . rRNA moomplexes . They caomplexes . rRNA moomplexes .c-MYC and VEGFA [The most abundant modification on rRNAs is the 2’-O-methylation of ribose residues, mediated by a complex including C/D box snoRNAs  and the nd VEGFA . Importand VEGFA , 32 . This modification is present in over 100 sites throughout all rRNA subunits . Ψ depos and p53   (Secondary hyper-modification of Ψ1248 within m1acp3Ψ) , which im1acp3Ψ) . The mol6A and m5C. The former is present on one residue in 28S (A4220) [18S (A1832) [28S rRNA  [Additionally, there are less abundant modifications found in rRNA, such as m (A4220)  and one  (A1832) , while t6A on 18S is catalysed by METTL5 [28S RNA, it is catalysed by ZCCH4 [6A on 28S [my METTL5 , whilst by ZCCH4   are necessary for accurate translation (5U) [5U is converted into 5-methoxycarbonylmethyluridine (mcm5U) by ALKBH8[5U into 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) [LysUUUtRNA, GluUUCtRNA and GlnUUGtRNA. Despite being catalysed in a tightly controlled manner and being necessary for accurate transcription, depletion of the enzymes responsible for U34 modifications is not generally lethal in yeast or normal human cells [Modifications of the U34 wobble position of a subset of tRNAs (nslation . Modificion (5U)   . This laan cells . StrikinThe enzymatic subunits of the elongator complex ELP1 and ELP3 and the thiolase CTU2 are overexpressed in human melanoma, particularly in melanomas carrying the BRAFV600E mutation  Table 1Table 1. ELP3, CTU1 and CTU2 are also overexpressed in breast cancer , where tELP3 is also overexpressed in colorectal cancer and it is required for tumour initiation in a WNT-driven colorectal cancer mouse model  Table 1Table 1. 5C is found on a subgroup of tRNAs at several positions and is mediated by NSUN2 [mby NSUN2 , DNMT2 [by NSUN2  and NSUNby NSUN2   occurs at different positions in several tRNAs and its deposition is catalysed by two tRNA-specific enzymes, METTL2B and METTL6 [3C insertion in mammalian tRNAs, it was recently demonstrated that m3C is a pre-requisite for C-to-U deamination in protozoans [3C at C32 in specific tRNASer isoacceptors. Depletion of METTL6 in hepatocellular cancer cells (HCCs) affects translation of mRNAs related to cell proliferation and growth [Finally, 3-methylcytidine  3' overhang, including most ed cells . Howevered cells .Overall, poly- and mono-uridylation of miRNA precursors represents a common mechanism to regulate miRNA levels and can contribute to human tumorigenic processes.6A methylation, deposited by METTL3 on miRNA precursors. METTL3/METTL14-dependent m6A methylation of primary miRNAs improves the recognition and binding of pri-miRNAs to DGCR8, thereby enhancing miRNA processing and maturation [miRNAs can also undergo mturation  , where it prevents cancer progression . Human lsurvival   . In part binding  .Overall, miRNA covalent modifications may represent a post-transcriptional phenomenon to establish and finely modulate a wide range of cellular programmes in different cell types. Thus, a thorough characterization of miRNA modifications and their related catalytic machinery may help to dissect the molecular basis of cancer. Furthermore, the possibility that miRNAs could be specifically modified in selected human malignancies could be leveraged to set up more robust cancer biomarkers.6A, m5C, m1A, A-to-I editing and Ψ [Long ncRNAs are heterogeneous RNA transcripts longer than 200 nucleotides that are not translated into protein. They include long-intergenic RNAs (lincRNAs), antisense transcripts to mRNAs, enhancer RNAs (eRNAs) and RNAs deriving from transcription of transposable elements . LncRNAsng and Ψ . Recentlng and Ψ .6A is the most characterized modification on lncRNAs (XIST), the Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) and the HOX Transcript Antisense RNA (HOTAIR) [6A modification of XIST, mediated by METTL3/METTL14, induces its degradation, as expected from the known effects of this modification on mRNA. Loss of METTL14 in human colorectal cancers correlates with high levels of XIST and poor patient survival [m lncRNAs , first i(HOTAIR) . A recensurvival   and its expression is altered in several cancer types, further investigations may reveal the role of m6A modification of MALAT1 in cancer cells. Similarly, many m6A sites were identified on HOTAIR transcripts [6A sites was shown to regulate HOTAIR recruitment to chromatin and promote breast cancer cells proliferation [Beyond the divergent regulation of lncRNAs stability, mpartners , 114. Ginscripts . Importaferation .6A on lncRNA is highly heterogeneous and cancer type dependent. Despite showing insight into the role of m6A in lncRNAs, one major problem with these studies is that they generally fail to address whether the effect of m6A modification of lncRNA is indeed responsible for the observed cancer phenotypes. Considering that m6A can affect the stability and translation of thousands of coding and non-coding transcripts, further studies are required to determine the specific effect of lncRNA modifications.Similar to other systems, both the biological role and molecular mechanisms mediated by m5C sites on many lncRNAs [5C is highly abundant on NMR lncRNA , supporting its overexpression and promoting tumour progression [5C modification of NMR promote its stability. In turn, NMR upregulates the transcription of pro-metastatic factors such as MMP3 and MMP10.To date, different transcriptome-wide studies have mapped novel m lncRNAs   possesses highly conserved Ψ residues within a region essential for telomerase activity and TERT binding [TERC, DKC1 and high levels of Ψ on telomerase RNA correlate with poor prognosis and malignant progression of lung [The telomerase RNA component  enhanced its ability to bind and suppress the PRUNE2 pre-mRNA, thus promoting cancer cell proliferation, adhesion and migration [ADAR1-mediated A-to-I editing is widespread on hy cells , 125  comprise a large class of ncRNAs originating by backsplicing events, in which a downstream splice donor site is covalently linked to an upstream splice acceptor site . Most ci6A modification to thousands of human circRNA transcripts [6A recruits the translational initiation factor eIF4G2 to the start codon of exons contained in circRNAs and, in turn, promotes their cap-independent translation [6A-modified circRNAs showed distinct expression profiles comparing embryonic and cancer cells, suggesting a specific tumorigenic pattern [6A methylation is observed on a number of circRNAs originating from unmodified coding transcripts [6A deposition on circRNAs. Furthermore, m6A is required to direct the backsplicing reaction, as demonstrated for circZNF609 in rhabdomyosarcoma tumours, providing a link between m6A deposition and circRNA biogenesis [circZNF609 is also translated in a cap-independent manner and its expression correlates with the proliferative status of cells, pointing out its potential role in sustaining rhabdomyosarcoma cell growth, where proliferation predominates over differentiation [Recently, two independent studies mapped mnscripts , 131 (Fi pattern . Interesnscripts . These rogenesis . circZNFntiation .6A-modified circRNAs was discovered in colorectal cancer, where the overexpression of circNSUN2 was identified in tumour tissues and serum samples from colorectal carcinoma patients with liver metastasis and predicts poor patient survival [6A modification of circNSUN2 modulates its export from the nucleus to the cytoplasm, enhancing the stability of HMGA2 mRNA by forming a circNSUN2/IGF2BP2/HMGA2 RNA–protein ternary complex [6A modification marks and signals ‘self’ circRNAs, whereas foreign and exogenous circRNAs are unmodified, triggering both innate and adaptive immune responses [6A modification as a strategy to trigger immune response to cancer cells.A different function of msurvival . In this complex . Finallyesponses . This evThe investigation of circRNAs epitranscriptomics is still at a very early stage: given the nature of circRNAs, most of the common detection methods are unable to detect their modifications and therefore dedicated epitranscriptomic studies will be required.The landscape of ncRNA in eukaryotic cells encompasses a great variety of subtypes and functions. ncRNAs are involved in all processes in living cells, from the most fundamental mechanisms of protein translation to the fine-tuning of gene expression and response to signalling and environmental cues. Therefore, it is not surprising that ncRNAs are implicated in the process of cell transformation and cancer progression on multiple levels.RNA modifications add another layer of complexity to the non-coding transcriptome. They can directly influence the three-dimensional structure of RNA , affect miR-455 [miR-589-3p [Modification of miRNAs can affect multiple aspects of their function. Firstly, their complex post-transcriptional processing is prone to be tightly controlled and several modifications act at this level. Besides processing, modifications of miRNAs can influence their stability and impact mRNA targeting. In this way, alteration of miRNA modifications can have a simultaneous effect on the expression of a large number of coding transcripts. The case of A-to-I editing is peculiar since multiple reports show that editing of miR-455  and miR-R-589-3p  can specper se is not generally well understood. Further studies will be required to elucidate the functional effects of modifications on lncRNAs.Finally, lncRNAs are also subject to extensive modifications, but the functions of such modifications are still largely unknown. Importantly, lncRNAs mechanism of action Altogether the reported studies strongly support the central role of ncRNA epigenetics in cancer. RNA modifiers, being catalytically active enzymes, are ideal candidates as drug targets. Thus, the development of epitranscriptomic therapeutics will provide new strategies to modulate ncRNAs involved in cancer.Chemical modifications of non-coding RNA are abundant and heterogeneous.Modifications of transfer RNA and ribosomal RNA have specific roles in cancer cells.Modifications of microRNA control gene expression programs in cancer.Epigenetic regulation of long non-coding RNA directly controls their functions.Enzymes responsible for non-coding RNA modifications show great therapeutic potential in cancer.


Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants’ sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making.NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making. Many decisions need to be made on the basis of noisy, incomplete, or ambiguous decision-relevant information. An extensive body of research on perceptual decisions under uncertainty has converged on the idea that evidence about the state of the sensory environment is continuously accumulated across time , 2. In tReal-life decisions are not isolated events but embedded in a sequence of judgments based on continuous streams of information, raising the question of whether and how successive decisions interact. Even in elementary perceptual decisions, postdecisional neural signals reflecting the previous choice have been identified in several regions of the monkey , 19 and Here, we studied the relationship between the nonselective and selective changes in sensitivity following a choice and assessed their impact on temporal evidence weighting profiles. To this end, we reanalyzed the data sets from both these previous studies , 42. For1) report a binary choice about the direction of dots in the first interval (clockwise (CW) or counterclockwise (CCW) with respect to the reference; two-third proportion of all trials) by pressing left/right mouse buttons; or 2) make a choice-independent button press  by pressing the central mouse wheel. This intermittent response allowed us to investigate if participants showed different sensitivity to the second stimulus depending on whether they reported a binary choice  or made a choice-independent motor response . The position of the reference line for each participant was constrained to be within the top half or bottom half of the stimulus annulus  to ensure a fixed choice (CW/CCW)–response (left/right buttons) mapping. The delay between the first and second stimuli was fixed (2 s), regardless of the reaction time of the subject. Participants gave their estimation response by dragging a red line around the circle using the mouse, starting from the reference line, and clicking the mouse at the end point. Half of all choice trials ended with an auditory feedback about the correctness of the binary choice to motivate participants to take the binary choice component seriously. An animation of the task structure is shown in the supplemental video file . Feedback for the estimation response was provided at the end of each block  as the mean deviation of the estimation report from the physical stimulus direction. The coherence of the stimuli was fixed at a predetermined level for each subject, and the direction of coherent dots in the two intervals was sampled independently from five possible values . In all, 23 possible combinations of directions were used in the experiment (excluding the two most obviously conflicting directions: −20°/20° and 20°/−20°). A total of 90 trials for each combination of stimulus directions in the first and second intervals  were presented to the subjects. Subjects were not explicitly instructed about the distribution of stimuli. However, it is possible that subjects may have learned during the course of the task that the extremely inconsistent stimuli in the two intervals  are unlikely in the task. However, such knowledge does not affect the sensitivity measures we computed since the stimulus sequences across both Choice and No-Choice conditions were counterbalanced for every subject .The University of Amsterdam ethics review board approved the study (reference number 2014-BC-3517). All participants gave their written informed consent. Participants were presented with two random dot motion stimuli in succession and were asked to estimate the average motion direction across the two intervals in each trial A. A whitA and B.In all the analyses that follow, we used trials where participants made an estimation judgment . We excluded trials in which participants did not comply with the instructions, i.e., when they pressed the mouse wheel on Choice trials or a choice key on No-Choice trials, trials in which the binary choice response time was below 200 ms, and trials where estimations were outliers. Outliers were defined as those trials whose estimations fall beyond 1.5 times the interquartile range above the upper-quartile or below the lower-quartile of estimations. Together, these excluded trials corresponded to ∼7% of the total trials across participants. The distributions of trials used for analysis are shown in Supplemental Fig. S1, We analyzed data from the same set of participants as in our earlier report . Please We reanalyzed data from the numerical integration task in Ref. The sequence of eight numbers in each interval was generated from one of the four predefined triangular skewed-density distributions with means of 40, 46, 54, or 60 . The numsubject 21) failed to do the task . Please see the earlier reports . The eye tracker was calibrated before each block. Blinks detected by the EyeLink software were linearly interpolated from −150 ms to 150 ms around the detected velocity change. All further data analysis was done using FieldTrip , 48, 51  studies , 48.Within each experimental condition , we averaged the corrected pupil responses across trials, time-locked to the onset of the first evidence sequence and spanning up to the onset of the next trial, or a maximum duration of 6 s.ϕ1 was the stimulus direction  in the first interval, α was the slope of the psychometric function , and δ was the horizontal shift of the psychometric function . The free parameters α and δ were estimated using maximum likelihood estimation  to the stimulus direction  in the first interval, as follows:timation . In the We used a statistical modeling approach to estimate the relative contribution of the evidence  conveyed by successive dot motion stimuli in each interval  to participants’ trial-by-trial estimation reports. The noisy sensory  evidence was described by:i ∈  denotes the interval, iϕ is the physical stimulus direction , N  was zero mean Gaussian noise with standard deviation σ (= 1/α), δ and α were each observer’s individual overall bias and sensitivity parameters respectively taken from the psychometric function fit to the binary choice data (cy (ncy) was the vector of estimations across all Choice (No-Choice) trials, w1c (w1nc) and w2c (w2nc) were the weights for the noisy evidence X1c (X1nc) and X2c (X2nc) encoded in intervals 1 and 2 in Choice (No-Choice) trials, respectively. N  was zero-mean Gaussian estimation noise with variance ξ2 that captured additional noise in the estimations, separately for Choice (cξ), and No-Choice (ncξ) trials, over and above the sensory noise corrupting the binary choice.We modeled a global, choice-related change in sensitivity to evidence following an overt choice, by allowing the weights to vary separately in Choice trials and No-Choice trials. The estimations were modeled by: w1cc (w2cc) and w1ic (w2ic) were the weights, ccξ and icξ were the estimation noise parameters for Consistent and Inconsistent trials, respectively, 2ϕ was the vector of physical stimulus directions  from the second interval, and D was the vector of intermediate binary choice . We excluded the subset of trials where consistency is not defined in ϕ2 = 0° in Perceptual task, and 50 in the Numerical task. Confirmation bias is quantified as the difference in the weights w2cc and w2ic.Using data from the sensory and numerical tasks, we showed in our previous work that people exhibited confirmation bias by overweighting evidence in the second interval that was consistent with the intermittent binary choice . To thisThe models described in Eq. 2) representation of the stimulus in interval 1 (wnc1Xnc1∼N(wnc1(ϕ1 + δ), wnc12σ2)), the weighted noisy representation of the stimulus in interval 2 (wnc2Xnc2∼N(wnc2(ϕ2 + δ), wnc22σ2)), and estimation noise ). The estimation report, being the sum of three Gaussian variables, is a Gaussian variable itself with the following density function (or estimation distribution): Importantly, we obtained the estimation distributions of the models numerically, which allowed us to estimate maximum likelihood parameters accurately without relying on stochastic stimulations. To illustrate our numerical approach, we next describe how the estimation distribution is calculated in the global gain model (Eq. 3.1). In contrast to a No-Choice trial, however, the weighted noisy representation of the stimulus in interval 1 is conditioned upon the categorical choice that was given at the end of that interval. Specifically, conditioning upon the choice means that the density of the N(wc1(ϕ1 + δ), wc12σ2) probability function is set to zero for all positive (negative) stimulus values, if a negative (positive) categorical choice was made (positive refers to the side on the right of the reference and negative to the side on the left). This density truncation captures the fact that the noisy instantiation of the interval 1 stimulus gives rise (and thus cannot be incongruent) to the categorical choice. Due to the truncation, the weighted noisy representation of the interval 1 stimulus is a non-Gaussian variable and, accordingly, the estimation report in Choice trials is also a non-Gaussian variable. To derive the estimation distribution, we thus numerically convolved the interval 1 non-Gaussian density function with the other two Gaussian density functions . A similar convolution approach was used when deriving the estimation distribution of the selective gain model  (We assessed the impact of an overt choice on the evidence in ic (ROC) , similaric (ROC) . The ROCinterval 1 (ϕ1). For each ϕ1, we ran the ROC analysis on all pairs of estimation distributions, separated by 10° of directional evidence in interval 2 (ϕ2): −20° versus −10°, −10° versus 0°, 0° versus 10°, and 10° versus 20°. This gave us four ROC indices per ϕ1, one index for every pair of distributions compared. We then computed a weighted average ROC index for each ϕ1, weighting the individual ROC indices by the number of trials that went into the ROC analysis. This approach ensured that the resulting ROC indices are robust to changes in ϕ1. These indices are averaged to obtain one single ROC index per subject for each condition.For the perceptual task, in each condition (Choice and No-Choice), we first sorted trials based on the directional evidence in interval 1 and interval 2 were binarized (mean >50 or mean <50) resulting in two bins for interval 1 and interval 2, respectively. We obtained qualitatively similar results using four bins to compute ROC indices. These binarized values were treated equivalent to ϕ1 and ϕ2 in the perceptual task above.ROC indices for the numerical task were computed similar to the above procedure with the following exceptions: mean evidence in Eq. 3.1) in individual participants in both the tasks. To this end, we first calculated the noisy representations X1 and X2 corresponding to intervals 1 and 2, using Eq. 2 above. We ensured that the sign of representations in X1 matches the binary choices of subjects (by randomly sampling the representations until a sample with the correct sign as that of the subjects’ choice was obtained), and then combined the representations from both intervals with the corresponding parameters for each individual subject using We simulated estimations for Choice trials using the trial distributions and best fitting parameters of the Global Gain model  with interval and condition  as factors to test for group-level significance of the interaction between condition and interval for both the tasks A. We usehttps://doi.org/10.6084/m9.figshare.7048430 .On a subset of trials , participants were also asked to report an intermediate choice after the first stimulus: a fine direction discrimination judgment relative to a visually presented reference line  or comparison of the numerical mean with 50 . On the remaining set of trials , participants were asked to press a button for continuing the trial, without judging the first evidence stream. The cue informing participants whether to report the discrimination judgment or to press a choice-independent button press came after the first stimulus interval. This design enabled us to quantify the degree to which evidence in each interval contributed to the final estimation and whether this depended on the overt report of a categorical choice .The mean accuracy of the intermediate choice was 81% ± 10.8% (mean ± SD) for the perceptual task and 67% ± 6.7% for the numerical task. Estimation responses in both tasks increased with mean directional evidence across the two intervals C and D amaterials and methods). Both approaches quantified the sensitivity of the final estimation judgments for evidence in each interval. Across participants, model weights for the second evidence stream were significantly smaller in Choice trials compared with No-Choice trials . These results indicate the distribution of a limited cognitive resource across both intervals: The intermittent choice after the initial evidence boosted sensitivity to that early evidence, at the cost of reducing sensitivity to subsequent evidence.The interaction yielded a marked change in the temporal evidence weighting profiles across both intervals: a flip from recency in the No-Choice condition to primacy in the Choice condition B. This fntervals D. No sucinterval 2 between the Choice trials and the No-Choice trials, and the selective gain modulation as the difference in weights of interval 2 between trials with choice-consistent and choice-inconsistent evidence , reflecting the pupil response to retinal illumination . Nonluminance-mediated dilations of the pupil are a marker of the responses of brainstem arousal systems , 62–64. mination . The inimination A and B. mination , 48, 66.middle), an effect that can neither be explained by the motor responses which occurred in both conditions, nor by the longer response times in the Choice condition [and the associated longer accumulation of central inputs in the peripheral pupil apparatus (P = 0.0112). Thus, the stronger pupil response during Choice than No-Choice trials likely reflected the intermittent decision . Due to the delay of the pupil response relative to the central arousal response . In sum, pupil responses in different phases of the trial reflected the cognitive factors that were associated with the dynamic changes in evidence weighting: the need to report an initial judgment of the first evidence stream , but not the No-Choice condition (right). The difference in pupil response amplitude between Choice and No-Choice conditions did not predict the corresponding difference between second-interval weights in these two conditions . We did not find an across-subjects correlation between mean pupil response amplitudes during the second interval on Consistent or Inconsistent trials and the corresponding weights for the second evidence , the pupil results should be regarded as a starting point for future investigations into the role of phasic arousal in the weighting phenomena studied here.Another possibility, mutually nonexclusive with the above, is that transient neuromodulatory inputs to the decision circuits from brainstem arousal systems play a role in the overall sensitivity reduction to postdecisional evidence. The intermittent choices were always prompted after the first interval by the experimenter and under uncertainty. This is a setting in which the cortical decision circuits might not yet have reached a stable decision state on individual trials. In such a setting, the release of neuromodulators in the cortex induced by the choice may push decision circuits into an attractor state . Note thAlthough our task design allows for assessing so far understudied aspects of decision-making, it also has limitations that open up alternative possibilities for explaining our findings. First, in all the No-Choice trials, the intermittent button press was followed by additional evidence in the second interval, whereas a proportion of Choice trials  ended with feedback about the categorical judgment without additional evidence. Consequently, participants may have expected a second stimulus on the No-Choice, but not the Choice trials, which may have affected (improved) the sensitivity for the upcoming stimulus –78. SecoTo conclude, we have shown that intermittent choices on protracted streams of decision evidence have versatile and coupled effects on evidence accumulation, which lead to a reweighting of evidence sensitivity over time as well as to confirmation bias. These insights open the door for connecting laboratory studies of decision-making to realistic settings requiring protracted evaluation of time-varying evidence in multiple successive decisions.This research was supported by the German Academic Exchange Service (DAAD) (to A. E. Urai), a European Research Council Starting Grant under the European Union’s Horizon 2020 research and innovation program (Grant 802905) (to K. Tsetsos), and the following grants from the German Research Foundation (DFG): DO 1240/2-1, DO 1240/2-2, DO 1240/3-1, and DO 1240/4-1 . We acknowledge computing resources provided by Dutch Research Council (NWO) Physical Sciences.No conflicts of interest, financial or otherwise, are declared by the authors.M.U. and T.H.D. conceived and designed research; A.E.U. performed experiments; B.C.T. and A.E.U. analyzed data; B.C.T., A.E.U., Z.Z.B., N.B., and K.T. interpreted results of experiments; B.C.T. prepared figures; B.C.T. and T.H.D. drafted manuscript; B.C.T., A.E.U., Z.Z.B., N.B., K.T., M.U., and T.H.D. edited and revised manuscript; B.C.T., A.E.U., Z.Z.B., N.B., K.T., M.U., and T.H.D. approved final version of manuscript; T.H.D. acquired funding for the research.https://doi.org/10.6084/m9.figshare.7048430, https://datadryad.org/resource/doi:10.5061/dryad.40f6v, https://doi.org/10.6084/m9.figshare.14039294, and https://github.com/BharathTalluri/choice-commitment-bias. These materials are not a part of this manuscript and have not undergone peer review by the American Physiological Society (APS). 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