The most abundant form of RNA editing in metazoa is the deamination of adenosines into inosines (A-to-I), catalyzed by ADAR enzymes. Inosines are read as guanosines by the translation machinery, and thus A-to-I may lead to protein recoding. The ability of ADARs to recode at the mRNA level makes them attractive therapeutic tools. Several approaches for Site-Directed RNA Editing (SDRE) are currently under development. A major challenge in this field is achieving high on-target editing efficiency, and thus it is of much interest to identify highly potent ADARs. To address this, we used the baker yeast Saccharomyces cerevisiae as an editing-naïve system. We exogenously expressed a range of heterologous ADARs and identified the hummingbird and primarily mallard-duck ADARs, which evolved at 40–42°C, as two exceptionally potent editors. ADARs bind to double-stranded RNA structures (dsRNAs), which in turn are temperature sensitive. Our results indicate that species evolved to live with higher core body temperatures have developed ADAR enzymes that target weaker dsRNA structures and would therefore be more effective than other ADARs. Further studies may use this approach to isolate additional ADARs with an editing profile of choice to meet specific requirements, thus broadening the applicability of SDRE. RNA editing alters genetic information at the RNA-level. The most common type of RNA editing is the conversion of adenosine (A) to guanosine (G), which can lead to protein diversification beyond the genomic DNA blueprint. A-to-G editing is catalyzed by members of the highly conserved ADAR protein family, which bind to specific double-stranded RNA (dsRNA) structures. The ability of ADARs to recode at the mRNA level makes them attractive therapeutic tools. The main challenge is to artificially create an editable dsRNA structure around a defined target and redirect ADAR activity to achieve high on-target editing efficiency. It is therefore of much interest to identify highly potent natural ADARs that could be used to induce high-level editing. Here we developed a novel approach to identify such potent enzymes. Using the baker’s yeast Saccharomyces cerevisiae as a neutral testing ground, we identified two exceptionally active editors: the hummingbird ADAR2, and the mallard-duck ADAR1. We provide evidence that these birds which evolved to live with higher core body temperatures have developed ADAR enzymes that target the temperature sensitive dsRNA structures and would therefore be more potent than other ADARs. Further studies may use this approach to pinpoint additional ADAR enzymes to further broaden their applicability. Over the last few years, RNA modifications have become a focus of attention in many research fields. RNA editing is unique among such modifications as it alters not only the cellular fate of RNA molecules but also the genomically-encoded sequence –4. The mA-to-I editing is mediated by members of the Adenosine Deaminase Acting on RNA (ADAR) enzyme family, which bind to specific double-stranded RNA (dsRNA) structures , 12. TheThis ability of ADARs to re-code makes them attractive therapeutic tools to correct genetic mutations and reprogram genetic information at the mRNA level. The prospects of utilizing CRISPR-Cas9 genome editing technology for therapeutics have attracted much attention in recent years. This approach offers a solution to chronic conditions by introducing a life-long permanent genetic modification –16. HoweSaccharomyces cerevisiae, an organism whose origin precedes the emergence ADARs and therefore does not possess an endogenous capacity for A-to-I RNA editing. We exogenously expressed different heterologous ADARs to reveal their distinct editing potential and identified two exceptionally active A-to-I editors: the hummingbird ADAR2, and primarily the mallard-duck ADAR1, whose core body temperature is 40–42°C. We provide evidence that these birds which evolved to live with higher core body temperatures have developed ADAR enzymes that target weaker dsRNA structures and would therefore be more potent than other ADARs .Several approaches for Site-Directed RNA Editing (SDRE), utilizing ADAR’s catalytic activity for RNA engineering, are currently under development. The simplest strategies use only an RNA oligo to create the dsRNA structure that would guide native ADARs to edit the target adenosine of choice –26. HoweYeast cells do not encode the ADAR proteins, and thus are not capable of A-to-I RNA editing and are not adapted to it. Accordingly, imposing editing on these cells by introduction of exogenous ADAR enzymes results in impairment of their growth rate . We thusADARs contain dsRNA Binding Domains (RBDs) that recognize RNA secondary structures surrounding the target adenosine. The stability of these structures is temperature-dependent , 34, andHomo sapiens), whose ADARs are commonly used in base editing research We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Heather HundleyGuest EditorPLOS GeneticsQuanjiang JiSection EditorPLOS GeneticsOverall, all three reviewers found the study of interest, that the manuscript was well-written and the experiments properly controlled. However, all three reviewers also noted that the data provided do not support the overall hypothesis. The two major points of concern in this regard are that the authors have not clearly demonstrated the dsRNA structures edited by the ADAR enzymes differ in stability and the differences in catalytic activity are due specifically to the changes in editing efficiency vs dsRNA binding or evolutionary fitness.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this work, the authors used a yeast system to explore the efficiency of editing by ADAR enzymes taken from different organisms. They used yeast because it doesn’t contain ADAR enzymes and editing, making it a clean background for finding A-to-I changes. Testing several organisms, they found 3 to be very efficient in this setup. The most potent editor was from mallard-duck, which seems to be functional even in elevated temperatures. The two others that were tested seemed to be less efficient when the temperature rises, probably because that dsRNAs are less stable in elevated temperatures. They concluded that probably because mallard-duck natural temperatures are relatively high, ADARs in this organism evolved to bind weaker dsRNA structures.The paper is well written and its approach and findings are interesting and worth publishing.Although the findings are strong, the main weakness of the paper is that it is very speculative in concern of the dsRNA structures. Please see below the specific comments.Specific comments:1. It is unclear to me why in Figure 1D the authors focused on owADAR2 and not OWADAR1, which seems to be more imperative. Figure 1D should be better explained and why the authors chose to focus on these three proteins.2. In which regions of the yeast genome editing occur? As yeast does not contain many introns and ORFs tend not to have dsRNA structures. It will be interesting to compare editing regions in the original organism to the new sites in yeast.3. The hypothesis is that dsRNA structures are less stable at high temperatures and therefore are less edited. Specifically, in hADAR2 and hbADAR2. I would like to see specific examples. Concentrating on editing sites in more stable dsRNA structure, these sites should still be edited at high temperatures while less stable dsRNA should lose their editing. Showing the RNA folding experimentally would be great but this can be done bioinformatically by estimating the dG.4. The last point is also important because of the difference in the preference of the editing enzymes, for example, hADR1 and hADAR2. It is possible that one enzyme  edits every site in a dsRNA (or even ssRNA) because of preference and not because of high temperatures and the others target specific structures that could change in variable temperatures.5. I would like to see statistics  for Figure 5, specifically 5B. These are biological replicas and it shouldn’t be a problem.Reviewer #2: In this manuscript, the authors applied the baker yeast as a system to evaluate the editing potential of ADARs from five species  that inhabit different environmental niches. They identified hummingbird and mallard-duck ADARs, which evolved at 40-42 degrees, as two exceptionally potent editors. They proposed that ADAR enzymes that target weaker dsRNA structures might be more effective than other ADARs and be used for Site-Directed RNA Editing.Overall, this study proposes a novel system to identify better ADARs for RNA base editing. The major limitation of the current study is that the authors provide no direct evidence to support their hypothesis. Therefore, more experiments are needed to support their claims.Major points1. The authors propose that hummingbird and mallard-duck ADARs may target weaker dsRNA structures and therefore be more potent than other ADARs. Thus, the authors may compare the secondary structures surrounding the editing sites targeted by ADARs from the five species to confirm this claim.2. For RNA base editing using exogenously expressed ADARs, we typically fused the ADAR’s catalytic domain with a λN peptide, a SNAP-tag, or a Cas protein (dCas13), and a guide RNA was designed to recruit the enzyme to the specific site. To achieve high on-target editing efficiency, we need a catalytic domain with high editing efficiency. In this study, it is unclear whether the high editing efficiency of hummingbird and mallard-duck ADARs is due to the less stringent structural requirement of their dsRNA binding domains or the highly active catalytic domains. Thus, the authors need to show that the catalytic domain of hummingbird or mallard-duck ADAR has high editing efficiency. e.g. a comparison of the editing efficiency between dCas13-human ADAR catalytic domain and dCas13- mallard-duck catalytic domain is essential for this study.3. Since RNA base editing is mainly applied to mammalian systems, particularly in humans, the authors may express hummingbird and mallard-duck ADARs in human cells and examine their editing profiles/efficiencies.Minor points1. The editing sites may be better characterized. e.g. the editing level distribution and the genic location of the editing sites can be shown.2. Figure 1A is somehow misleading. It seems that the authors inserted 5 genes into one plasmid.Reviewer #3: As the authors note, use of ADAR RNA editing enzymes to alter mRNA sequences for therapeutic means is an active area of research, and optimizing editing efficiency at specific adenosines is a key goal in these endeavors. Since ADARs target dsRNA, which is less stable at higher temperatures, the authors propose that animals with higher core temperatures, or that live at higher temperatures, might have ADARs that target less stable dsRNA structures.Prior studies by the Beal lab showed that increased ADAR editing in S. cerevisiae impairs cell growth, so in a clever use of this information, the authors express ADARs from endothermic vertebrates with different core body temperatures, or an ectothermic invertebrate that inhabits environments with different temperatures, in yeast, and use cell growth to screen for ADARs with different editing activities. Endogenous editing levels in the various yeast strains were determined with multiple assays that the authors have substantial expertise in from prior research. Indeed ADAR enzymes that cause the highest levels of editing  ADAR1, hummingbird (hb) ADAR2, orca whale (ow) ADAR2) are those that cause the most severe growth defects, thus validating the assay. For the most part these experiments are convincing and well-controlled, and obvious caveats like possible effects from altered protein levels are ruled out.The authors next focus on mdADAR1 and hbADAR2, the enzymes that produce the greatest editing levels in yeast, and using human ADAR2 (hADAR2) as control, they perform the same experiments in yeast grown at different temperatures . As I understand it, the stated goal is to test the idea that mdADAR1 and hbADAR2 have higher editing levels because they can edit dsRNA that is less stable, and thus should have higher levels of editing than hADAR2 at higher temperatures. Although the authors see that temperature has a big effect on editing levels in yeast expressing hADAR2, there is little effect of temperature on editing when hbADAR2 or mdADAR1 are expressed, although the authors note in the Figure 5 legend:“Res-scanner detection of editing sites (Methods) demonstrates a clear negative correlation between the growth temperature and editing activity for hADAR2 and hbADAR2.”The data shown in Figure 5 do not support the above statement. Evaluating the data shown in Figure 5 with a student’s t-test is warranted, and may indeed support this statement, at least for hADAR2. That said, an effect of temperature on hADAR2 editing levels, without an effect on those from hbADAR2 or mdADAR1 might be expected if the authors’ hypothesis that the latter enzymes can edit dsRNA that is less stable is true. However, additional data would be necessary to support this hypothesis.In summary, I am convinced that the authors have identified at least two ADARs that appear to be highly active, and thus possibly useful for improving therapeutic uses of ADARs, but the observations provide little support for the idea that the differential effects are because these ADARs are editing less stable structures. In this regard unless additional data are provided, the authors should tone-down statements about this, including the sentence in the Introduction that reads: “We provide evidence that these birds which evolved to live with higher core body temperatures have developed ADAR enzymes that target weaker dsRNA structures, and would therefore be more potent than other ADARs .”Importantly, the authors already have data that could be mined to test their hypothesis. For example, the authors have mapped a huge number of editing sites, and if their hypothesis is correct, the editing sites unique to mdADAR1 shown in Supplementary Figure 1 would be predicted to occur in less stable structures. There are several algorithms that could make a start in determining whether these sites occur in less stable structures.In truth, from the data shown, it is impossible to deconvolute changes in editing levels from changes that might occur because certain enzymes have evolved to have a specific optimal temperature. One way to specifically address this would be to choose one temperature and perform in vitro experiments with the various enzymes to compare their ability to edit several different dsRNAs designed to have different thermodynamic stabilities.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , and click on the Fetch/Validate link next to the ORCID field.  This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.In the meantime, please log into Editorial Manager at If you have a press-related query, or would like to know about making your underlying data available , please see the end of this email. If your institution or institutions have a press office, please notify them about your upcoming article at this point, to enable them to help maximise its impact. Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Heather HundleyGuest EditorPLOS GeneticsQuanjiang JiSection EditorPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):We thank the authors for addressing the reviewers' initial concerns and apologize for the delays in re-review. In particular, the addition of bioinformatic data regarding the thermodynamic stability of the secondary structures around the editing sites provided additional confidence in the overall conclusions of the manuscript.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: I went through the revised manuscript, and I am satisfied that the authors answered all of the concerns raised by all reviewers. Notably, the addition of figure 3c, which shows the thermodynamic stability, strengthens the paper’s conclusion. I recommend acceptance of the manuscript.Reviewer #2: I am satisfied with the revision and support the publication of this study.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  1 Mar 2023PGENETICS-D-22-00838R1 Identification of exceptionally potent adenosine deaminases RNA editors from high body temperature organisms Dear Dr Ben-Aroya, We are pleased to inform you that your manuscript entitled "Identification of exceptionally potent adenosine deaminases RNA editors from high body temperature organisms" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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RNA molecules play a key role in countless biochemicalprocesses.RNA interactions, which are of highly diverse nature, are determinedby the fact that RNA is a highly negatively charged polyelectrolyte,which leads to intimate interactions with an ion atmosphere. AlthoughRNA molecules are formally single-stranded, canonical (Watson–Crick)duplexes are key components of folded RNAs. A double-stranded (ds)RNA is also important for the design of RNA-based nanostructures andassemblies. Despite the fact that the description of canonical dsRNAis considered the least problematic part of RNA modeling, the imperfectshape and flexibility of dsRNA can lead to imbalances in the simulationsof larger RNAs and RNA-containing assemblies. We present a comprehensiveset of molecular dynamics (MD) simulations of four canonical A-RNAduplexes. Our focus was directed toward the characterization of theinfluence of varying ion concentrations and of the size of the solvationbox. We compared several water models and four RNA force fields. Thesimulations showed that the A-RNA shape was most sensitive to theRNA force field, with some force fields leading to a reduced inclinationof the A-RNA duplexes. The ions and water models played a minor role.The effect of the box size was negligible, and even boxes with a smallfraction of the bulk solvent outside the RNA hydration sphere weresufficient for the simulation of the dsRNA. Charge neutralization plays a crucial role in the processof folding nucleic acids by allowing tertiary contacts of negativelycharged backbones in space. The negative charge of the polyanionicbackbone of nucleic acids is shielded by a sufficient number of metalions in the condensation layer around the DNA and RNA, and its functionis to neutralize the backbone’s negative charge. Counterionscan interact either directly with the RNA structure (referred to aschelated ions)8 or through a shell of coordinatedwater molecules (referred to as diffuse ions or “ion atmosphere”).9 The mobility of chelated ions is low becausethey are usually tightly bound to RNA. Tightly bound ions, with twoor more first shell contacts to RNA, are the least abundant but, insome cases, make an important contribution to specific local or evenglobal conformations. The tightly bound ions are precisely localizeddue to the discrete interactions with the RNA and cannot be easilyreplaced by other ions from the solvent.11Ions are an integral part of the biomoleculeenvironment and playan important structural and functional role that has yet to be fullyunderstood.8 These ions tendto remain fully hydrated and do not occupy any particular structurallywell-defined binding sites, which makes their structural characterizationdifficult by most biophysical techniques. Their high mobility enablesmeasurement of the distributions but not the positions of individualions. An experimental approach that has been successfully used tostudy the diffuse ions is ion counting (IC). This method quantifiesthe number of thermodynamically accumulated cations and thermodynamicallyexcluded anions around a negatively charged nucleic acid. Experimentalmethods based on IC include buffer equilibration atomic emission spectroscopy,17 anomalous small-angle X-ray scattering,21 and titration with fluorescentdyes.22 Ion condensation around nucleicacids can be also studied following theoretical computational approaches.A traditional method for describing ionic solutions is the Poisson–Boltzmanntheory, which has been successfully used to predict charge distributionand interactions between the charged biomolecules.24 This method is a starting point for many theoretical approachesas it is based on a simple and analytically tractable model that canbe easily extended and modified. Although the theory has limitationswhen the ion correlation becomes significant,25 it provides meaningful results that are in good agreement with manyexperiments.26 Another theoretical toolfor treating the distribution of counterions around highly chargedpolyelectrolytes is Manning’s counterion condensation theory.31 This theory suggests that the fixed charges on the rod are neutralizedby 0.7–0.8 monovalent cations being condensed onto them, whilethe non-condensed counterions are distributed according to the Poisson–Boltzmanntheory. This approach is useful for rod-like molecules such as DNAbut not applicable to non-rod-like biomolecules such as folded RNA.Ion condensation around folded polyelectrolytes can be predicted bynon-linear Poisson–Boltzmann equations.32 Unfortunately,this method does not provide information about the exact distributionof ions around nucleic acids. Atomic details of the ionic interactionsof nucleic acids can be obtained adopting integral equation-basedmolecular solvation methods such as 3D RISM35 or by moleculardynamics calculations.54In contrast, diffuse ions are an important factor in the folding,stabilization, and interactions of the RNA tertiary structures dueto their vastly larger populations. They act as a charge-neutralizinglayer around the RNA.in vivo conditions by molecular dynamics(MD) simulations. Although a large variety of ions interact directlywith RNA in various biophysical experiments, the most biologicallyrelevant ions are K+, Na+, Mg2+,and Cl–. This reduces the set of biologically relevantions for MDs of RNA. Due to the challenges arising from the forcefield and sampling when describing divalent ions,55 MD simulations of nucleic acids are mostly performed solelyin the presence of monovalent ions. K+ is the major cationfound in the cell (cK+ = 0.15 M), whileNa+ dominates in extracellular fluids. However, a limitednumber of studies compared the effects of Na+ versus K+ cations on the RNA structure. Some of them reported thatsimulations with net-neutralizing Na+ ions and under 0.2M excess salt conditions seem to be equivalent in all aspects.58 Other simulations showed that 0.4 M and more excess salt causeda modest sequence-dependent compaction of canonical A-RNA double helices.60 On the other hand, several studies that tested different conditionssuch as Na+ vs K+ and net neutral vs excess-saltsimulations found that the simulations are insensitive to the typeand concentration of the monovalent ions.64 More likely, the simulation results showed that the systems aremore sensitive to the explicit water model.65 Nevertheless, it is possible that the (in)sensitivityto the type of ion is to some extent dependent on the simulated systems.In MD simulations of nucleic acids, anions are generally consideredto have a minimal effect on the results, but they are widely usedto compensate for the positive charge in the excess salt condition.The ionic conditions in cells should be taken into accountwhenmodeling Table S1 in the Supporting Information). The RNA A-form helical shape is dominantlyaffected by the applied RNA force field. Some of the tested forcefields appear to underestimate the inclination of the dsRNA, althoughthe experiments do not provide an exact quantitative benchmark datain this respect. The inclination values reported by crystallographicdata might be affected by crystal packing and crystalline conditions,while the solution experiments provide only indirect evidence aboutthe inclination, with the refinement procedure possibly biasing theparticular reference value. Nevertheless, the ability of a force fieldto provide a sufficient inclination value of the dsRNA seems to bea specific issue that needs to be considered when parametrizing forcefields since a significant reduction in the inclination value maylead to undesirable results of certain force-field modifications.The A-RNA shape is only moderately modulated by the ionic parameters,concentrations, and water model. Among the various tested ionic parameters,the Joung–Cheatham parameters associated with the SPC/E watermodel have a stronger tendency than the other tested ions to drivethe RNA duplexes into more compact helical structures. Although wehave studied only a limited set of sequences, we confirm that theshape of the canonical A-RNA was affected by the RNA sequence. Whatremains unexplored and should be addressed by further research ishow well the simulations can determine the sequence effect on A-RNAproperties that can be characterized by experimental tools.In this study, we used all-atom explicit solvent MD simulationsand investigated the effect of monovalent counterions and size ofthe solvent box on the shape of the canonical A-RNA duplexes. We focusedon several basic objectives: (i) the effect of the number of ionson the A-form RNA, (ii) the description of the distribution of theions around the RNA, (iii) the effect of the size of the used simulationbox on the RNA structure, (iv) the effect of the used ions and theion concentration on the RNA molecule, and finally (v) the effectof the used solute (RNA) force field  all NH–N hydrogenbond (H-bond) interactions were supported by 1.0 kcal/mol and (ii)all OH–nbO H-bond interactions were weakened by −0.5kcal/mol, for more information see Ref .The 2GBH structure contained eight base pairs with sequence r(GGGCUAAU).The fourth system containing alternating CG base pairs was initiallybuilt using Nucleic Acid Builder of AmberTools79 with sequence r(CG)5. We note that the simulationsof the canonical dsRNAs initiated from the X-ray or NMR measurementsand the built-up structures were equivalent .The starting structures of the three RNA double helices weretaken from 1RNA and 1QC01RNA,84 Na+ ,84 and Cl– .84 For the description of the simulation set-up, see the Supporting Information. All simulations wererun with a 2 fs integration step on the 1 μs time scale, whichproved to give sufficient convergence for the A-RNA duplexes.59All RNA systems were immersed into a solvationbox of TIP3P,5. The CG-tract was used on the basis of our previous workswhere we showed that it was the most sensitive one to the choice ofthe force fields and the ionic strength.59 The three different sizes of the simulation box were prepared usingvariable minimal distance between the solute and the edge of the periodicbox. The distance between the outmost nucleic atom and the closestsimulation box face varied from 10 Å  to 15.5 Å (denoted as M) to 42 Å (denoted as L). Further,we changed the concentration of the ions in the solvation boxes .The smallest boxes denoted as S were simulated with counterions varyingfrom 0 to 18 .For simulations with the uncompensated charge , a uniform charge density was usedto neutralize the simulation box. In addition, the simulation usingsmall boxes was run five times under net-neutral conditions in orderto check convergence. The box denoted as M was simulated using varyingionic concentrations ranging from 0.15 to 7 M KCl  and finally the boxescalled L were used with three different concentrations: L1 contained18 K+ ions , L2 involved 120 K+ and 102 Cl– ions , and L3 included 933 K+ and 915 Cl– ions. The schematic view of all simulations used in this study togetherwith the information about simulated system, ion type, used watermodel, and size of solvate box is shown in As a model system for testing the box sizeand ionic concentrations, a built-up A-RNA helix was chosen, consistingof 10 base pairs with a repetitive CG sequence – r(CG)+ or K+ conditions or under 0.15M NaCl or KCl excess salt.To test the influence of different types of ions, we performed aseries of simulations of the CG duplex and the 1RNA duplex with differentexplicit water models. The simulations were run under net-neutral(0.15 M) Na85 (hereafter abbreviatedas ROC ff) with 0.15 M KCl in combination with TIP3Pwater model ,84 (2)a set of simulations of 1RNA, 1QC0, and 2GBH using the Chen–Garcia force field86 with 0.15 M KCl and the TIP3P water model , and (3) a set of simulationsof 1RNA, 1QC0, and 2GBH using force field published by Shaw and co-workers89 (hereafter abbreviated as DESRES ff) with 0.15 M KCl and the TIP4P-D water model .91 It should be noted that the aim of our work was not to provide afull test of all available force fields. Our objective was to obtaina basic insight into the sensitivity of the dsRNA simulations of theRNA force field, rather than to perform an extended test of RNA forcefields. The present data can be easily used as a benchmark for anyother force field versions.The OL3 force fieldis currently the most widely used and tested RNA force field. We alsoperformed some additional simulations as follows: (1) A set of simulationsof all the abovementioned duplexes using a force field developed byAytenfisu et al.cpptraj module (AMBER tool).92 Prior to the analysis of the density maps, each system was centered,imaged, and RMS fitted, and finally the density of the ions was calculatedusing cubic grids distributed over the entire box volume. The outputfile was visualized using the VMD93 program(http://www.ks.uiuc.edu/Research/vmd/). The 2D ion concentration maps around the RNA were calculated fromthe counterion density maps by being projected onto the x–y plane through the central part of the CG duplex in the z axis . It should be noted that these concentration maps may havebeen biased by the selection of the part of the duplex over whichthe x–y projection was made. Further, the cpptraj module was used for monitoring the distances betweenthe atoms, end-to-end distances, dihedral backbone angles, sugar pucker,and helical parameters. Because of the higher flexibility of the nucleotidesat the end of the helices, the helical parameter analysis omittedtwo terminal base pairs at both ends of the studied structures. Thefrequency of the base pair opening (base pair fraying) of the terminalresidues among different simulation settings was mapped separately.The base pair fraying at the helix termini was monitored using anin-house program for the identification and classification of thetype of the base pair family. The program automatically identifiesand classifies all types of RNA base pairs formed by various combinationsof the three edges. The concentration radial distribution functionwas calculated from minimal RNA-ion distances from all surface atomswith 0.1 Å binning by an in-house program using non-imaged trajectories.Both in-house programs are part of the Supporting Information. Moreover, we analyzed the nuclear Overhauser effectintensities for the 2GBH duplex. We report the χNOE2values as thedifference between the calculated and the experimental NOEs, assumingGaussian experimental errors, i.e.,N is the total numberof analyzed experimental NOEs78, xi, EXPerrorNOE is the higher from the reported  values, and xi, CALCNOE are averages over the M samples obtainedfrom MD simulations, i.e., NOE2 value, the betteragreement between the simulation ensemble and the experimental data.The global behavior of each systemand the density maps of counterions were analyzed with the 64 In contrast to end-to-end distance,which may also be used as a measure of the A-RNA helix compactness,the inclination is not sensitive to the terminal base pair frayingand therefore provides a more accurate insight into the A-RNA helixcompactness (see Table S3). Some otherhelical parameters and end-to-end distances are documented in the Supporting Information. We note that the inclinationis mathematically directly coupled to the base pair roll through ahelical twist, so that the inclination and the base pair roll mutuallycorrespond with the helical parameters that are defined in the globaland local coordination frames, respectively. A-RNA inclination verycorrelates with helical rise.61We report a large number ofstandard MD simulations using fourA-RNA duplexes with a total simulation time of over 250 μs.To simplify the presentation, we focused our analyses on the compactnessof the A-RNA structure, which is best (and dominantly) described bythe inclination of the base pairs with respect to the helix axis.94 who suggested that 5–10 simulations are sufficientto assess the variance in the data. The TIP3P, TIP4P-D, and OPC watermodels showed similar inclination values .In order to obtain an insight into how ions werearranged aroundthe A-RNA, we calculated radial concentration profiles of ions and2D ion concentration maps of the central part of the CG duplex. Inall cases, the cation RDFs were characterized by two well-structuredpeaks indicating the formation of two ionic shells around the A-RNAwith counterions accumulated within ∼8 Å of the RNA surface.These two peaks were attributed to specific ion-binding sites andstructural features of the A-RNA. The first major peak correlatedwith the ions bound to the major and minor grooves. The second (minor)peak corresponded to the binding of ions to the backbone oxygens.The comparison of the radial concentration profiles for differentwater models see 1 showed + concentration). Our previousstudies64 have shownthat the choice of an explicit water model has a certain effect onthe helical parameters of the canonical A-form RNA. However, thiseffect has always been studied with counterions, which may mask thenet effect of the water model.Detailed observations obtained from RDF analyses spurred furtherinvestigation into the effect of the number of counterions aroundthe nucleic acid structure. The idea was motivated by the search forthe minimum number of counterions that are necessary to stabilizethe nucleic acid structures. Therefore, we carried out a series ofsimulations of the CG duplex using small boxes (S-box) with a variablenumber of ions ranging from 0 to 18  and the SPC/E model appeared to have a larger inclinationcompared to the other three water models. The distinct behavior ofthe SPC/E water became fully visible from ∼1/3 of net neutralions present in the system  led to a significantincrease in the A-RNA inclination, while the addition of another sixK+ ions  led to an essentially saturated inclination value . The average number ofions changed with nucleotides and was symmetric for the two strands.This symmetry was given by the formation of bridges between the GC/CGbase pair steps. In addition, we found that the ions mainly boundto the major grooves and to the oxygens of the phosphates, with onlya small fraction of ions bound in the minor groove . In summary, simulations with the SPC/E water model showeda higher number (populations) of bound ions in all binding sites thanthe simulations with the other tested models.The different behavior of the SPC/E water was alsoevident whenwe compared the behavior of the ions around (i) the C and G nucleotides,(ii) the atoms in the major and minor grooves, and (iii) the phosphateatoms see S1. The aHowever, the data above do not show whether theeffect was dueto the water model or the ion parametrization. To find out why thesimulations using the SPC/E water model behaved differently, we preparedCG duplex simulations by swapping the ion parameters for the TIP3Pand SPC/E water models. These simulations showed that when the ionparameters were used for the TIP3P water in the simulations with theSPC/E water model, there was a significant decrease in the inclinationvalue (from 14.76° to 13.69°). Conversely, when the ionparameters were used for the SPC/E water model in the simulationswith TIP3P, there was a slight increase in the inclination value (from13.67° to 14.17°). We also analyzed the number of ions inthe first and second solvation spheres. The ion exchange in the TIP3Pwater model led to an increase in the number of ions in both solvationspheres (from 6 to 8 for the first layer and from 11 to 12 for thesecond layer) and, conversely, in the SPC/E water model, the ion exchangeled to a decrease in the number of ions (from 8 to 7 for the firstlayer and from 14 to 13 for the second layer). Therefore, the differentbehavior of the SPC/E water model simulations can be mainly explainedby the parametrization of the ions, which affected the binding ofthe ions into the first and second solvation layer.In conclusion,our new data indicate that the sensitivity of theA-RNA compactness to the water model is a more complex phenomenonthan previously thought. The effect apparently reflects an intricateinterplay between the water model, the specific ion parametrization,and obviously also the non-bonded terms of the RNA force field, meaningthat it is not determined merely by the water model.+, we simulated an M-box with varying ion concentrationsfrom 0.15 M K+ to 7.0 M KCl and an L-box with concentrationsfrom 0.02 M K+ to 1.2 M KCl (for more information see Methods).The lowest concentration always corresponded to the net-neutral conditions.We further analyzed the effect of the box size and ion concentration.Besides the net-neutral S-box simulation with a concentration of 0.3M KThe results of the net-neutral simulations showed that an increasein the size of the simulation box did not lead to a dramatic changein the inclination see 7. The si+ to KCl led toa certain increase in the inclination value. This increase was insome cases more than 1° and was evident in both the M-box andL-box (A change in the ionic condition from KndL-box 7. ForsiTable S6).Asmentioned above, the inclination values for the net-neutralsimulations did not change dramatically. However, this was not thecase for the number of ions in the first and second solvation spheres.The largest number of ions was observed in the net-neutral S-box simulations,and the number of ions decreased when the box size increased . As theconcentration increased, the ion concentration in the immediate vicinityof the RNA molecule increased and the individual peaks became morestructured. This was also seen in the number of cations in the secondsolvation layer. At lower concentrations, the second solvation layerwas not well defined (Table S6). Indeed, these findingswere not unexpected. However, a closer comparison of the radial distributionfunction showed that at lower concentrations (up to 1.0 M), the radialdistribution function of Cl– was not well structured.For concentrations of 1.0 M and above, this function contained threepeaks, which became more clearly defined as the concentration increased.These peaks showed a high degree of structuring for simulations below5.0 and 7.0 M KCl. The positions of the chloride distribution maximawere correlated with the distribution of the potassium ions. The firstand second peaks for anions corresponded to the interactions withcounterions in the first solvation shell. Further, the first peakalso corresponded to the anions directly bound to the electropositiveedges of the nucleotides.Regardless of the ion concentrations, the behaviorof the radialdistribution function showed the same trends for the different watermodels on the A-RNA shape, we performed a series of simulations of the experimentallydetermined structure (1RNA), which was essentially the AU-tract A-RNAhelix and CG duplex. Moreover, the 1RNA simulations in the presenceof K+ were also used for the comparison with the GC-tractduplex data.In the previous sections,we focused on K+ ions (Tables S7–S11). In general, the SPC/Ewater model yielded the largest inclination value, while the changefrom the net-neutral to the excess salt environment also led to amore compact structure, i.e., an increase in the inclination. Forall water models, except the TIP4P-D, there was an increase in theinclination when Na+ was changed to K+  Na+ and K+ parameters could resultin different Na+ vs K+ behavior.The AU-tract simulations showed the same trendsas the CG-tract simulations with the K+ ions 8. The rao K+ see 8. This iTables S12–14). For comparison, we used the 1QC0 and1RNA duplexes using 0.15 M KCl. Specifically, we used the followingforce fields: the version by Aytenfisu et al.85 (ROC ff) in combination with the TIP3P water model,the Chen–Garcia force field86 withthe TIP3P water model, and the DESRES89 force field (DESRES ff) in combination with theTIP4P-D water model. It should be noted that the force fields weretested with ion parameters as specified in the Methods section. Assome force fields required water/ion models, the comparison resultsmay be affected by different water models since the experiment/testwas not possible to conduct under a uniform set of conditions.As a final set of calculations,we compared the A-RNA inclination predicted by the OL3 force field(OPC water model) with three different RNA parameters proposed overthe last decade . Bothforce fields described these two canonical A-RNAs very well. Thisresult is in agreement with the original work89 and is consistent with the fact that the DESRES ff was tuned to give good performance for the A-form RNA (both duplexand single strand). The DESRES ff is known to outperformthe OL3 force field (unless the latter is combined with the gHBfix)for RNA tetranucleotides. However, previous research showed that theDESRES ff had major problems describing some foldedRNAs,75 which was not apparent in the originalpublication. In fact, simulations of the kink-turn RNA motif and theribosomal L1-stalk RNA segment using DESRES ff ledto complete rearrangement or disruption of the experimental structures.75 The OL3 force field dealt with the descriptionof these systems without significant problems.97 The remaining two force fields, i.e., Chen–Garcia ff and ROC ff appeared to underestimatethe inclination value, meaning that they may underrate the compactnessof dsRNA (see Tables S12 and S13). Theunderestimation of the inclination value for the ROC ff was already apparent in the original paper,85 where the [U(AU)6A]2 duplex was studied. Wealso note that the ROC ffs may show an increasedtendency for duplex end fraying (see Tables S12 and S13). However, a converged analysis of fraying would requirea significantly longer simulation time scale, so we only present thedata in the Supporting Information. Thecomparison of the inclination between the OL3 force field and thepreceding AMBER ff99bsc0 version can be found in ref .Although the crystal structures provided directinformation aboutthe inclination and the A-RNA duplex compactness via the observedelectron densities, the RNA structure might have been affected bythe crystalline conditions. Besides the crystal packing, also thelocal ionic and water environment around the RNA molecules did notfully resemble the solvent conditions. To complement our force fieldtest, we performed simulations of the solution NMR structure of the2GBH duplex and compared them with the predicted and experimentallyobserved NOEs and with the inclination values obtained from the simulationsand the refined NMR structure. We observed that the agreement withexperimental NOEs in the simulations did not implicate good congruencewith the inclination value observed in the refined NMR structure.This might indicate that the inclination as observed in the refinedNMR structures might have also been affected by the refinement protocoland therefore cannot be used for direct comparison of the force fieldperformance to investigate the effect of the solvation and co-solvation conditionswith respect to not only the type of ions but also the water model,ionic concentration, box size, and choice of the force field on thecompactness of the canonical A-RNA double helix as the most abundantRNA motif.The helical structure of A-form RNA duplexes wasdominantly influencedby the solute force field, when moderately modulated by ionic parametersand concentration, and by the water model. On the other hand, thestructure of A-form helices was rather invariant to the box size,even when the solvation box contained only negligible part of thesolvent bulk outside the hydration spheres around the RNA. While simulationswith small boxes have been considered for a long time to provide insufficientdescription of realistic ionic conditions, our data show that thissimulation setting can be satisfactory as well. Among the tested soluteforce fields, the OL3 and DESRES described correctly the A-form duplex,whereas the ROC and Chen–Garcia force fields had a tendencyto underestimate the inclination and the roll helical parameters,which affected the shape of the duplex. For this comparison, we usedthe experimental inclination value obtained under crystalline conditions,which might have been affected by crystal packing. Nonetheless, thecomparison with solvent experiments was not straightforward as theydid not provide direct information about the inclination and the A-formduplex compactness.It has been suggested in the past that theeffect of the watermodel on the A-RNA compactness is due to a specific hydration patternin the A-RNA minor groove. The present simulations suggest a morecomplex picture, showing that the compactness of the A-RNA duplexeswas influenced by the interplay between the water model, ion parametersand conditions, and the non-bonded terms of the RNA force field. Thus,while the water models affected the compactness of the A-RNA duplex,the differences between the different water models were far from beinguniform.


Humans can make abstract choices independent of motor actions. However, in laboratory tasks, choices are typically reported with an associated action. Consequentially, knowledge about the neural representation of abstract choices is sparse, and choices are often thought to evolve as motor intentions. Here, we show that in the human brain, perceptual choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. We measured MEG signals while participants made choices with known or unknown motor response mapping. Using multivariate decoding, we quantified stimulus, perceptual choice, and motor response information with distinct cortical distributions. Choice representations were invariant to whether the response mapping was known during stimulus presentation, and they occupied a distinct representational space from motor signals. As expected from an internal decision variable, they were informed by the stimuli, and their strength predicted decision confidence and accuracy. Our results demonstrate abstract neural choice signals that generalize to action-linked decisions, suggesting a general role of an abstract choice stage in human decision-making. Humans can make abstract choices independent of motor actions, but in lab tasks, choices are typically reported with an associated action. This study shows that the human brain encodes perceptual choices independently of the specific motor actions used to implement them, even if such abstraction is not required by the task context. Sensory decisions are often linked to an appropriate motor action. This has led to a framework of choices emerging as action intentions , supportSince most studies use a fixed mapping of perceptual choices (in the following referred to as “choices”) to motor responses, the role of abstraction in sensorimotor decision-making remains elusive. A few notable exceptions, using behavioral tasks with a variable mapping of choices to motor responses, have identified neural representations of abstract choices ,9–17. HoIt is therefore unclear whether, and under which conditions, the same neural representations underlying abstract choice in an action-independent context are also present during choices that are linked to actions. If this were not the case, it would suggest that abstract processing is readily bypassed or attenuated when the choice context does not require it. Furthermore, the spatiotemporal dynamics of abstract choice signals are unknown, and it remains unclear whether abstract choice signals constitute an internal decision variable that tracks accumulated evidence. Consequentially, the demonstration of a context-independent, abstract decision variable would be important to confirm predictions of abstraction as an essential stage in perceptual decision-making.To address this, we investigated human brain activity underlying flexible sensorimotor choices using magnetoencephalography (MEG). The task design and a multivariate analysis framework allowed us to pinpoint abstract neural choice signals in an action-linked as well as in an action-independent context. MEG activity was predictive of participants’ perceptual choices independently of both sensory input and motor behavior. Crucially, a novel metric for the assessment of cross-decoding results enabled us to conclude that abstract choice representations were not only present in both contexts, but indistinguishable between them. Furthermore, choice signals dynamically evolved along the sensorimotor hierarchy and predicted both decision confidence and accuracy, thus exhibiting a hallmark property of an internal decision variable. Our results cast doubt on a purely action-based framework and suggest a general role for abstraction in sensorimotor decision-making.t25 = 1.27, P = 0.21, two-tailed t test) nor criterion  were different between tasks, and neither choice was preferentially associated with a particular motor response . Furthermore, participants’ performance was not significantly different between coherent and incoherent stimuli . In both task conditions, responses had to be withheld until the fixation point disappeared, and while reaction times  were higher in the post- than the pre-condition  = 7.77, P = 0.0056), in noise than in signal trials , and in incorrect than in correct trials , they were not significantly different between choices  or responses  .We recorded MEG in 33 human participants, while they performed a sensorimotor decision-making task . In eachFor each task condition separately, we quantified neural information about the stimulus, response, choice–response mapping, and choice using a multivariate analysis approach . Even though participants’ choices were related to the presented stimuli and behavioral responses, our analysis framework ensured that choice information could not be explained by neural variability due to either stimuli or responses. Thus, choice information was stimulus and response independent. In both conditions, choices could be predicted before stimulus onset , indicating that they were partly based on purely internal priors.Crucially, we also found information about the perceptual choice , rising during stimulus presentation and remaining present until the end of the trial. Choice information could not be explained by eye movements . Furthermore, the magnitude of cross-information was similar to the magnitude of choice information in the “pre”- and “post”-conditions. To quantify this, we derived an estimate of the expected cross-information under the assumption of identical representations in both conditions, i.e., representations relying on the same multivariate pattern and differing only in signal-to-noise ratio between conditions . Thus, abstract choice representations were not only present but were also shared between an action-linked and an action-independent choice context.Cross-decoding of choices was positive throughout the trial . We found similar results when repeating the searchlight analysis independently for pre- and post-condition trials and extracting correlation values for the early stimulus-related and the later response-related cluster. Despite the reduced number of trials, 2 out of 4 correlation values were significant, and all 4 had the same directionality as in the pooled data .We found that the distribution of choice information changed dynamically over the course of the trial, rising first in occipital areas, before spreading throughout the brain. After the go cue, choice information remained strongest in parietal cortex and central motor areas , bottom.The spatial overlap between choice, stimulus, and response information raised the question whether there were shared representations between stimulus and choice during evidence accumulation and between choice and response during motor execution, respectively. We used cross-temporal and cross-variable decoding to test this and evaluated both the temporal dynamics of representations and the relationships between stimulus, choice, and response representations .First, we focused on the temporal dynamics of representations. By using data from one time point for training, and from another time point for testing, cross-temporal decoding can reveal time periods of relative stability . FurtherP = 0.11 and P = 0.22), and cross-decoding was significantly lower than expected for identical representations (P < 0.0001).How did the neural representations of different variables relate to each other? The multivariate patterns that encode any 2 variables are either orthogonal, indicating nonoverlapping underlying population subspaces, collinear, indicating indistinguishable circuits underlying both representations, or somewhere in between . After selection of a motor response, choices may still have been represented as a modulation of the motor signal, e.g., leading to a relative strengthening of the activity pattern associated with the upcoming motor response for “yes”-choices compared to “no”-choices. We thus assessed the magnitude of response information, separately for each choice. However, we found no difference between both conditions , indicating that even during response execution, choices were not represented as a modulation of neural motor activity. . As expected, there was no significant stimulus predictability before stimulus onset, even though there was a small amount of choice information  and in trials with signal than in those with noise stimuli . In addition, and critically, they reported high confidence more often in correct trials than in incorrect trials . We quantified this relationship using the meta-d′ measure of metacognitive sensitivity [t18 = 7.4, P = 7.5 × 10−7, two-tailed t test), correlated with d′ , but tended to be smaller than d′ . This showed that participants veridically reported their confidence and suggested that their confidence judgements were largely, but not perfectly based on the same sensory evidence as their choices [t18 = 1.2, P = 0.24) nor meta-d′  were significantly different between conditions.First, we behaviorally assessed the relationship between participants’ choices and confidence ratings. Participants were more confident in yes- than in no-choices P = 0.034 choices –36. ThesP < 0.01, cluster permutation statistics, one-tailed, P < 0.01, cluster permutation statistics, one-tailed, P > 0.05 for all time points, two-tailed, P > 0.05 for all time points, two-tailed, Secondly, we asked whether confidence was represented in the neural data. We thus repeated our decoding analysis, now adding decision confidence as an additional variable. There was significant neural information about confidence, starting around stimulus presentation and continually rising until the end of the trial . Importantly, participants’ accuracy and confidence were assessed as separate factors. Thus, the relationship between choice and confidence could not simply be explained by accuracy or vice versa. We additionally performed this analysis separately for the pre-cue and post-cue task conditions, after excluding the factor of response from our model in order to retain a sufficient number of trials per condition. There was a similar pattern in both tasks . In contrast, there was no clear relationship between choice confidence or accuracy and the strength of stimulus or motor representations . In contrast, this ordering does not fit well with a timing confound arising from a potential asymmetry between yes- and no-choices.Finally, we investigated the relative placement of correct and incorrect trials of both choices on the neural choice axis. As the stimulus design used was inherently asymmetric , a similar asymmetry may be expected for the neural representations of yes- and no-choices, opening the door for potential, choice-unrelated confounds. For example, the timing of choice commitment may be different for yes- and no-choices, differentially affecting the neural signal. While our fixed-time design did not provide access to commitment times, there is one previous study investigating reaction times in a similar forced-response detection task. In that study, the authors found responses to be slower for no- than for yes-choices, and highly similar between correct and incorrect no-choices . This isIn sum, our behavioral results pointed to the existence of an internal decision variable, which informed both choices and confidence ratings. Furthermore, the strength of the choice-predictive neural signal varied with confidence and accuracy, precisely following a pattern predicted from signal detection theory. Thus, neural choice information measured in MEG did not only predict abstract perceptual choices but appeared to reflect the underlying internal decision variable.Studies of the neural basis of sensorimotor decision-making have often neglected abstract, motor-independent choices . This isBy combining noninvasive MEG in humans with an advanced multivariate analysis framework, we robustly read out abstract choice contents from whole-brain neural activity. In accordance with current theories ,40, abstAbstract choices were represented in brain activity not only when decisions had to be made abstractly but also when the sensorimotor mapping was known in advance. Importantly, our cross-decoding analysis showed that choice representations in both task contexts were indistinguishable from one another. While the fundamental limits of MEG spatial resolution and sensitivity prevent the conclusion that the underlying circuit representations are identical, this striking similarity requires any potentially remaining differences between conditions to be small and of a type that MEG is blind to.Our finding of abstract choice representations generalizing between contexts in which actions can be planned and those in which they cannot is in line with behavioral evidence suggesting analogous mechanisms underlying decision-making in action-linked and action-independent contexts . MoreoveThis—as well as any other—decision-making study lives off the fact that sometimes participants make different choices for identical stimuli. How does this variability arise? In principle, 2 broad, and not mutually exclusive, classes of explanations exist. First, it could be bottom-up driven, with sensory noise having a causal effect on choices. This sensory noise could be internal, arising from the inherent variability in neural responses for identical stimuli, or due to uncontrolled external variability, such as small differences in the stimulus itself. Second, it could be top-down driven, with internal factors such as expectations, biases, or beliefs or simply nonsensory noise pushing choices one or the other way. Several of our results consistently suggest that the demonstrated choice signals are positioned at an intermediate stage between these extremes. First, if the choice signals directly reflected sensory noise, we would expect this noise to inhabit the same neural subspace as the stimulus signals themselves—in other words, there should be strong cross-information between stimulus and choice. In contrast to this, our results are better compatible with choice signals reflecting integrated sensory noise represented distinctly. For example, one may expect to find instantaneous sensory noise represented in the middle temporal visual area (MT), but integrated sensory information, and therefore integrated noise as well, represented in area LIP. Notably, such an integration stage would still be expected to be modulated by the stimulus, which may lead to stimulus-choice cross-information, but only subtly so. In our data, this effect did not result in significant cross-information but was nonetheless apparent in the hypothesis-driven finding of stronger choice signals in correct than in error trials. Second, we found that signed choice information predicted the stimulus—despite near-orthogonality of the representations of both variables. This suggests that choice information was indeed reflective of a stage separate from but influenced by the early sensory representation. This prediction increased during stimulus presentation and then remained stable, similar to the choice information time course itself, and consistent with the time course expected from temporal integration. In contrast, choice signals at an instantaneous, early sensory nonintegration stage would also predict the stimulus, but predictions should be at the same level throughout the stimulus presentation interval, and subsequently taper off. Third, we found small amounts of choice information before stimulus presentation. As these could not have arisen due to the stimulus, they must reflect intraneous factors. In conclusion, the most parsimonious explanation for our data is an intermediate choice stage that reflects both accumulated sensory evidence and top-down contributions, akin to an internal decision variable. Importantly, these considerations allow us inferences about the nature of the measured signal, regardless of its exact anatomical origin. While reflecting an intermediate, abstract stage, the choice signal may well be fed back or forward to sensory or motor populations ,25,49, rThe stimuli used in the present study, and therefore the corresponding choices, were inherently asymmetric. One may ask if this asymmetry could underlie the decodablity of choices: An unobserved, confounding variable correlated with choice may result in the seeming readout of choice information. To address this, we ruled out potential confounds associated with stimulus asymmetry. First, our finding of a significant difference of the choice signal between correct and incorrect no-choices is incompatible with a timing-related confound due to this asymmetry . Second,The cortical distribution of abstract choice signals may be modulated by response modality. Recent work using fMRI suggested that, for vibrotactile comparisons, abstract choice representations are present in nonoverlapping, modality-specific cortical areas ,50. On tPerceptual decisions involve a complex interaction of feedforward and feedback processes throughout the brain ,49,54. HA growing body of evidence has related the formation of action-linked sensorimotor decisions to activity in motor and premotor areas ,26,42,54We conclude that an abstract choice stage may be universally present in human perceptual decision-making, enabling the evaluation of motor-independent choice options even during action-linked decisions.Participants provided written informed consent prior to the start of the experiment. The study was conducted in accordance with the Declaration of Helsinki and was approved by the ethical committee of the Medical Faculty and University Hospital of the University of Tübingen .A total of 33 healthy, right-handed human volunteers  participated in this study and received monetary reward. All participants had normal or corrected-to-normal vision.Participants performed a flexible sensorimotor decision-making task. In each trial, they had to decide whether a random dot kinematogram contained coherent downwards motion or not and reported their choice with a left- or right-hand button press. Crucially, the mapping between response hand and choice varied on a trial-by-trial basis. Moreover, the mapping was revealed either before (pre-condition) or after (post-condition) the stimulus. Additionally, an irrelevant cue was presented after (pre-condition) or before (post-condition) the stimulus.Participants started a trial by acquiring fixation on a fixation spot. After a fixation period, the first cue appeared for 250 ms, followed by a delay of 1,000 ms, the presentation of the random dot stimulus for 2,000 ms, another 1,000-ms delay, and the second cue for 250 ms. A third 1,000-ms delay was followed by a 33-ms dimming of the fixation spot, which served as the go-cue for the participant’s response. The response consisted in a button press using the left or right index finger, according to the choice and the choice–response mapping. Participants chose one of 2 buttons on either side to indicate whether they were confident in their choice or not. Participants received a 100-ms visual feedback , 250 ms after their response.The random dot stimuli consisted of 1,500 white dots with a diameter of 0.12 degrees, presented in an 8.5 degree diameter circular aperture on a black background. Dots moved at a speed of 10 degrees per second. For each participant, we used only 2 stimuli, each presented in half of the trials: First, a target stimulus, in which, on each frame, a fraction of dots moved coherently downwards, whereas the rest moved in random directions. Second, a noise stimulus, in which all dots moved in random directions. In a separate session before the MEG recordings, the motion coherence of target stimuli was titrated to each participant’s individual perceptual threshold using a staircase procedure with 280 trials. Motion coherence was adaptively lowered by one level after each correct choice and increased by 2 levels after each incorrect choice. To determine the coherence threshold, a Weibull function was fit to the resulting data, excluding the first 50 trials. Choice–response cues and irrelevant cues all had the same luminance and size (0.85 degree diameter).Each participant took part in 2 recording runs of one of 2 task versions, which differed in the details of the choice–response cue as well as the confidence report. Participants 1 to 20 performed version A: Here, the choice–response cue consisted of a centrally presented red or green square , whereas the irrelevant cue was a blue square. The outer button always indicated a confident, the inner one an unconfident choice. In this version, the fixation baseline at the beginning of each trial lasted 1,500 ms. Each recording run consisted of 400 randomly ordered trials, of which 120 were pre-cue trials, 120 post-cue trials, and 160 belonged to one of 2 control conditions not reported here. Participants 21 to 33 performed version B: Here, the choice–response cue consisted of 2 vertical rectangles  forming a square, whereas the irrelevant cue consisted of 2 horizontal rectangles . The confidence mapping (inner or outer button for confident/unconfident responses) was changed in each recording run. Here, the fixation baseline was 1,000 ms. Each run consisted of 400 randomly ordered trials, 200 of which were pre-cue and 200 post-cue trials. The changes in version B were designed to minimize sensory and motor confounds in a separate analysis of task- and confidence-related effects (not reported here). The data from version A were previously used in another publication .t test to test whether accuracy was different between task conditions. To make sure participants did not systematically associate one of the motor responses with one of the choices, we computed the percentage of “right” button presses for “yes” and “no” choices separately and compared both against 50% using two-tailed t tests.To ensure that participants were performing both task conditions well, we computed overall accuracy as the percentage of correct trials. We used a two-tailed paired We recorded MEG  with 275 channels at a sampling rate of 2,343.75 Hz in a magnetically shielded chamber. Participants sat upright in a dark room, while stimuli were projected onto a screen at a viewing distance of 55 cm using an LCD projector  at 60 Hz refresh rate. Stimuli were constructed offline and presented using the Presentation software . To ensure continuous fixation, we recorded eye movements using an Eyelink 1000 system .We used time-domain data for the decoding analyses. Thus, we low-pass filtered MEG and eye-tracking data at 10 Hz  and down-sampled to 20 Hz to maximize SNR by reducing the impact of high-frequency noise, to focus our analysis on slow cortical potentials that may be linked to the gradual build-up of a decision variable, and to avoid the necessity for any effects to be precisely temporally aligned across trials. Trials containing eye blinks were rejected. We chose not to apply a high-pass filter in order to avoid filter artefacts . At the For source reconstruction based on each participant’s individual anatomy, we recorded structural T1-weighted MRIs (echo time (TE) = 2.18 ms, repetition time (TR) = 2.3 ms, longitudinal relaxation time (T1) = 1.1 ms, flip angle = 9°, 192 slices, voxel size 1 × 1 × 1 mm3) with a Siemens 3T Tim Trio scanner and a 32 channel Head Coil. We generated single-shell head models  and estiThe experimental design resulted in a number of variables of which each trial instantiated a combination. For each trial, we defined the task (pre- or post-cue), stimulus (target or noise), response (left- or right-hand button press), mapping (target = left or target = right), choice (yes/target or no/noise), accuracy (correct or incorrect), and confidence (high or low). Not all of these variables were independent of each other: For a given stimulus and choice, accuracy is fixed; and for a given choice and mapping, response is fixed. Thus, 5 independent variables giving rise to 32 conditions remained . While tDue to the variability in behavioral responses, as well as the rejection of trials containing eye blink artefacts, we did not retain the same amount of trials from each condition for all participants. However, to accurately estimate neural information, we needed to ensure that, first, there were trials of each condition, and second, the total number of trials was large enough in comparison to the dimensionality of the data to enable an unbiased estimate . Specifitest is the design matrix indicating the unique condition of each trial in the test set, Ctrain is the contrast vector the model is trained on, Ctest the test contrast vector and Σ−1 the inverted noise covariance matrix. Btrain and Btest contained the regression parameters of a multivariate general linear modeltrain and Ytest are the training and test data sets. The inverted noise covariance matrix Σ−1 was estimated using data from a baseline time point (−0.5 s with respect to the onset of the first cue):We used cross-validated MANOVA ,21 to esBecause the design matrix and contrast vector include all unique conditions, i.e., all combinations of variable levels , cvMANOVWhile cvMANOVA technically constitutes an encoding framework—modelling data variability due to experimental variables—it shares many similarities with commonly used multivariate decoding methods . Notablytrain and Ctest to only contain the conditions to be trained or tested on, respectively. We applied this to estimate neural information within and across the 2 task conditions (pre and post), as well as the 2 confidence levels, and the 2 choices. Additionally, we also used a model trained on all trials and tested it separately on correct and incorrect trials. To estimate whether information was shared between time points, we computed the pattern distinctness when using regression parameters Btrain from one time point, and Btest from another. We repeated this for every pair of time points. In order to assess whether 2 variables shared a common representational space, we used cross-variable decoding. We implemented this by using a training contrast Ctrain differentiating between the levels of one variable, and a test contrast Ctest differentiating between the levels of another. Before further processing, all decoding time courses were smoothed using a Hanning window . Time–time generalization matrices were smoothed using a 2D, 100 ms Hanning window.To achieve cross-condition decoding, we constructed contrast vectors CWe reconstructed low-dimensional geometric representations of neural activity in multiple conditions using the decoding results. Decoding and cross-decoding values between multiple variables define the distances and angles of condition difference vectors. We used these to plot subsets of conditions in 2D spaces defined by the axes spanned by 2 variables of interest. For example, in We repeated our main analysis in a searchlight fashion, in order to estimate the spatiotemporal distribution of neural information throughout the trial. For each of the 457 sources, we used cvMANOVA on that source as well as its immediate neighbors, including all 3 dipole directions. In order to maintain comparability between sources, we normalized the resulting pattern distinctness values by the square root of the size of the searchlight ,21. Afte1 and D2 denote the pattern distinctness in the 2 contexts. The cross-decoding D12 between both contexts would be expected to approach E12 for identical representations. Any cross-decoding values smaller than E12 indicate that the representations are not fully overlapping.The maximal amount of shared information between 2 contexts depends on the amount of information available in each individual context. Thus, in order to assess whether 2 representations are different, the strength of both representations has to be taken into account and compared with the strength of the shared representation. We thus estimated the expected cross-decodingTo test whether neural choice representations were informed by the stimulus, we projected each trial’s neural data onto the multivariate axis spanned by yes and no choices as defined by the cvMANOVA model. We then computed the sign of these single-trial estimates to assess whether it corresponded to the stimulus class.2+y2). We then applied the same decoding analysis using cvMANOVA, using these 4 channels. We split the 26 participants into the 13 with the highest and lowest choice information in their eye signals, respectively. This revealed that in a subset of participants, eye signals were predictive of choice. To test whether this could plausibly explain the neural choice information, we compared the choice decoding time courses in both splits. As neural choice decoding was, if anything, weaker in those participants with higher choice decoding from the eye signals, the neural decoding was unlikely to be explained by eye movements , we randomly multiplied the information time-course of each participant 10,000 times with either 1 or −1. In each random permutation, we recomputed information clusters and determined the cluster mass of the strongest cluster. Each original cluster was assigned a p-value by comparing its size to the distribution of sizes of the random permutation’s strongest clusters. The same procedure was used for cross-decoding analyses, however, using two-tailed t tests as true cross-decoding can also be negative. We also tested differences in information using this strategy, namely, between response information during “yes” and “no” choices  Task conditions and behavioral responses. Each combination of the binary task variables constituted one of 32 separate conditions. Because behavioral variables were partially correlated among each other and with experimentally controlled variables , the number of trials varied across the 32 unique conditions. The rows with variables below the histogram correspond to the contrast vectors employed in the cross-validated MANOVA. (B) We performed multivariate pattern analysis using cross-validated MANOVA to estimate the difference D between MEG source level patterns associated with the 2 levels of each task variable. Importantly, cross-validated MANOVA allowed to independently quantify information about each variable without confounding of other, potentially correlated variables. Each dot represents MEG activity during one of the 32 conditions. For example, choice information is computed as the contrast between all conditions containing “yes” trials and those containing “no” trials (middle), stimulus information as the contrast between conditions containing “signal” and “noise” trials (right). For our main analyses, this procedure was applied to the action-linked (“pre”) and action-independent (“post”) contexts separately. Using a cross-decoding framework, the angle between Dpre and Dpost enabled us to assess the degree of similarity between representations of any given variable during action-linked and action-independent contexts.(TIFF)Click here for additional data file.S2 FigP < 0.05). Colored lines and shaded regions indicate the mean +/− SEM across participants.Beta lateralization predictive of each trial’s response hand was computed for pre- and post-conditions in an individually localized source in motor cortex for each participant. Horizontal bars indicate significant clusters Click here for additional data file.S3 Fig(A) Choice information contained in eye traces , split into the 13 participants with the highest decoding values, and the 13 participants with the lowest. Eye traces were thus predictive of choice in a subset of participants. (B) Choice information contained in MEG data, split into the same groups as in (A). Participants in whom the eye traces were predictive of choice did not show stronger decoding of choice from MEG data . This indicates that choice information in MEG was not driven by eye movements or pupil size. (C) There was no positive across-subject relationship between the amount of choice information contained in eye traces and the amount of choice information contained in MEG data. Colored lines and shaded regions indicate the mean +/− SEM of information across participants.(TIFF)Click here for additional data file.S4 FigP < 0.01, N = 23). (B) There was significant information about the previous motor response throughout the trial, but information about the current choice and response remained significant when including previous response as a variable. Horizontal lines indicate clusters of significant information . Colored lines and shaded regions indicate the mean +/− SEM of information across participants.(A) There was no significant information about the previous choice, and information about the current choice remained significant when including previous choice as a variable. Horizontal lines indicate clusters of significant information Click here for additional data file.S5 FigP < 0.05, two-tailed t tests, N = 19).Time-averaged stimulus information (1.25 to 3.5 s) and response information (3.25 to 6.5 s) in correct and error, and high- and low-confidence trials. The model was trained on both correct and error trials, but trials were split by accuracy for testing. Stars denote significant differences ((TIFF)Click here for additional data file.S6 FigA) Neural information in the pre- (darker colors) and post-conditions (brighter colors), as well as empirical (grey) and expected cross-information. All conventions as in B) Time-resolved stimulus (top), response (middle), and choice (bottom) information in 4 groups of sources, as in C) Correlation of the cortical distribution of choice information with the distribution of peak stimulus information (red) and peak response information (yellow), as in We replicated the main results of Figs (TIFF)Click here for additional data file.S7 FigA) Neural information in the pre- (darker colors) and post-conditions (brighter colors), as well as empirical (grey) and expected cross-information in task version A. All conventions as in B) Neural information in task version B.We replicated the main results of (TIFF)Click here for additional data file.


Viral metagenomics is increasingly applied in viral detection and virome characterization. Different extraction and enrichment techniques may be adopted, however, reports on their effective influence on viral recovery is often conflicting. Using a three step enrichment steps, the effect of three extraction kits and the influence of DNase treatment with or without rRNA removal for respiratory RNA virus recovery from nasopharyngeal swab samples was evaluated. The viral cocktail containing six different RNA viruses pooled in equal volume were subjected to the different extraction and enrichment methods, sequenced using the Illumina MiSeq, and analysed using Genome Detective. The PureLink® Viral RNA/DNA Mini Kit (PureLink) was highly efficient with better recovery of all the viral agents in the cocktail. The use of rRNA treatment resulted in increased viral recovery with PureLink and QIAamp® Viral RNA Mini kit, while having comparable recovery rate as DNase only with the QIAamp® MinElute Virus Spin Kit. The observed low reads and genome coverage of some of the viruses could be attributed to their low abundance. Depending on sample matrix, extraction choice and enrichment strategy may influence recovery of respiratory RNA virus in metagenomics studies, therefore individual evaluation and adoption may be necessary for a robust result. •Choice of RNA extraction method impacts the recovery of respiratory RNA viruses in metagenomics next-generation sequencing.•The use of rRNA treatment significantly improves viral recovery and genome coverage of respiratory RNA viruses.•Low abundance viruses may negatively impact metagenomics result.•Depending on sample matrix and viral target, different extraction and enrichment strategy may need to be adopted. The technique circumvents the limitation of conventional and real-time PCR in a manner that enables the detection of known and emerging pathogens . VariousDespite the revolutionary potential of mNGS in clinical virology, biological samples are ladened with high background genetic material from host and bacteria which may significantly hamper the detection of viral pathogens . In thisThe goal of the study is to optimize mNGS for application in RNA respiratory virus diagnostics and discovery. Therefore, this study is structured on respiratory RNA virus recovery, majority of which are responsible for relevant (re)emerging viral threats with pandemic potential. Using clinical samples [nasopharyngeal swabs (NPS) positive for different RNA viruses in viral transport media , and the impact of DNase treatment only versus DNase treatment and ribosomal RNA (rRNA) removal on viral recovery in a mNGS workflow. The detection of targeted viruses and the percentage of pathogen genome coverage obtained were used as the measure of performance.22.1A biological reagent, containing six viruses belonging to different RNA viral families were assembled from clinical specimens [NPS in VTM) which tested positive for the different viruses during routine diagnostic testing . These k2.2A viral cocktail was prepared using 1000 µL of the VTM  of each viral agent. A 300 µL from the viral cocktail was used to confirm the presence of each viruses using the QIAstat-Dx-Respiratory SARS-CoV-2 Panel  as per the manufacturer’s information. The Panel is a 40-cycle automated multiplex real-time PCR (mRT-PCR) test that qualitatively detects and identifies multiple respiratory viral and bacterial nucleic acids (as shown in supplementary table S1) in VTM. The QIAstat-Dx Respiratory SARS-CoV-2 Panel simultaneously detected and generated the Ct values and amplification curves of each of the virus in the cocktail.2.3−1 RNAse One , and 0.1 U µL−1 TURBO DNAse , and thereafter incubated at 37 °C for 90 min.Based upon review of previous enrichment methods , To enri2.4Three commercially available kits including QIAamp Viral RNA Mini Kit (QIAamp Mini) , QIAamp MinElute Virus Spin Kit (QIAamp Spin), and PureLink Viral RNA/DNA Mini Kit (PureLink)  were evaluated for simultaneous isolation of viral RNA. Major characteristics of extraction kits are shown in Prior to extraction, samples were thawed and homogenized by vortexing. Nucleic acids were extracted as per the manufacturer’s instructions, beside the use of carrier RNA and eluted in 50 µL of the AVE buffer or RNase-free water. A no-template negative control (NTC) consisting of RNase free water was implemented per kit to evaluate for cross/kitome contamination during the process.2.5To assess reproducibility, workflow was performed in duplicates for each extraction kits (Two DNase only and two DNase and rRNA treatment per kits). Sequencing was also performed in two different runs.2.6The extracted RNA yield was quantified using the Qubit assay kit on the Qubit® 3.0 Flurometer  as per manufacturer’s instructions. The Qubit® RNA HS Assay Kit  is designed to be accurate for RNA sample concentrations between 250 pg/µL and 100 ng/µL.2.72.7.1DNAse treatment was performed on n = 12 extracted RNA (four for each kit). The DNase treatment was performed in a 50 µL reaction using the TURBO DNA-free™ Kit  as per manufacturer’s instructions. DNase treated samples were purified using the RNeasy Plus Mini Kit , as per the manufacturer’s instructions.2.7.1.1Additional rRNA removal step was performed on n = 6 DNase treated RNA (two for each kit). The rRNA removal was performed using the NEBNext® rRNA Depletion Kit (Human/Mouse/Rat)  as per the manufacturer’s instructions. Each viral extract was then subjected to Whole Transcriptome Amplification (WTA) using the QIAseq FX Single Cell RNA Library Kit  as per manufacturer’s instructions to generate the cDNA. Amplified cDNA was quantified using the Qubit® DNA HS Assay Kit  with the Qubit® 3.0 Fluorometer.2.8The libraries were prepared using the QIAseq FX Single Cell RNA Library Kit  as per the manufacturer’s instructions. The quality assessment of the libraries after preparation was performed on the Agilent 2100 bioanalyzer . The average fragment size obtained was approximately 450 bp for all the samples. The obtained library size was optimal for the desired read length of 250 bp paired end. The concentration of the libraries after preparation was 4 nM and a final concentration of 8pM was loaded into a V3 MiSeq reagent cartridge . Sequencing was performed in two independent runs using the Illumina MiSeq platform to generate a paired end read of 2 × 250 bp.2.9https://www.genomedetective.com/), an automated web system for virus identification from high-throughput sequencing data  of more than 30 for 80% samples, with a cluster density of 1014 K/mm2 and 98.1% of clusters passing filter. While the second sequence run generated a Q-score of more than 30 for 76% samples with a cluster density of 1214 K/mm2 and 96.5% clusters passing filter rate. The corresponding total reads and viral reads per extraction kit and enrichment methods are shown in 3.4With total reads of 71496, PureLink detected all the six targeted viruses. The highest genome coverages were for SARS-CoV-2 (100 %) and HRV A (94.4 %), while the lowest was for IVA (6 %) . The QIA3.53.5.1The use of DNase only with the PureLink resulted in detection of two of the six targeted viruses , a lower3.5.2The use of DNase only with the QIAamp Mini yielded the detection of two of six viral targets in both independent runs. However, the subsequent use of rRNA treatment on the already DNase treated samples resulted in detection of five of six viral targets. It also significantly increased the viral reads and genome coverage of HRV A and SARS-CoV-2 .3.5.3The independent use of DNase only or combined with rRNA did not significantly impact the viral recovery of QIAamp Spin. As shown in 3.6To assess the method repeatability, two independent sequence runs were performed. The first run included one DNase treated sample only and one DNase and rRNA treated sample per kit resulting in 6 samples per run. As shown in 3.7A correlation was observed with Ct value and viral detection rate across all extraction kits regardless of enrichment methods. As shown in 3.8As shown in 4The impact of choice of nucleic material extraction method in extraction efficiency has been widely reported with varying, sometimes debatable/conflicting conclusions on which is really the best . For diaRegarding the impact of extraction choice on viral recovery, Klenner and colleagues evaluated the mNGS viral recovery of four manual extraction kits and concluded that the choice of extraction kit does not significantly impact the yield of viral reads obtained by NGS . HoweverWhile PureLink performed better than QIAamp Mini and QIAamp Spin, the noted lower genome coverage and few reads numbers for HPIV-3, IVA, and HMPV could be attributed to the high Ct values of these viruses which is correlative of low viral abundance. This is also suggestive of why some of the viruses at lower abundance were not recovered by the other kits evaluated. This phenomenon is not surprising. A similar study by The use of rRNA after DNase treatment to get rid of the non-pathogen RNA is currently being bypassed by different studies. Many of the reason revolving around additional complexity and cost to the mNGS workflow . Some stAdditionally, this study further exhibited the reproducibility and reliability of the adopted workflow by setting up a duplicate run within one month of the first run. As noted, the viral reads in the duplicate sample one for both DNase only and DNase and rRNA treated samples of PureLink and QIAamp Mini exhibited a degree of consistency. However, the second replicates of QIAamp Spin for viruses at low abundance were not detected regardless of the enrichment method. This phenomenon could suggestively be due to freeze thawing which could have impacted the sensitivity of the kit for the already low abundance pathogens. Furthermore, regarding kitome/cross contamination, none of the targeted viruses nor other known or unknown viruses were detected in the NFW included in the workflow for the three extraction kits. The detection of bacteriophages at varying degree is however not unique to this study as the observation is consistent with a previously reported study , and mayThe method established in this study builds upon earlier mNGS techniques in the detection of RNA respiratory viruses, including those present at low abundance, and can play an important role in surveillance, molecular epidemiology, and diagnostics of respiratory RNA viruses. The use of different viruses with different characteristics (including varying genome size) suggests that the methodology established may be versatile in detection of other RNA viruses from NPS samples not included in this study. Additionally, the use of commercially available reagents and non-complex library preparation procedure suggest this method could be easily adapted in a variety of laboratory settings. Taken together, the approaches described here offer an efficient method to harness the power of mNGS in routine laboratory and clinical RNA respiratory virus detection and discovery.5The results in this study suggest that mNGS-based detection of respiratory RNA viruses from clinical nasopharyngeal swab samples may be impacted by nucleic acid extraction choice and the associated enrichment techniques adopted. While the complexity of workflow and cost around the use of rRNA treatment is debatable, this study shows that the adoption of rRNA treatment could result in significantly increased viral detection and genome coverage, especially when used with the PureLink. While the QIAamp Spin exhibited the least performance especially with viruses present at low abundance in the cocktail, its viral recovery rate with relatively good genome coverage on samples at high abundance even without the use of rRNA treatment could be explored. Noteworthily, numerous factors may influence the performance of extraction kits and enrichment methods. These factors includes sample matrix , pathogen of interest/abundance and budget. Therefore, individual assessment to determine the best workflow based on these stated factors may be imperative.The study was conducted in accordance with the Declaration of Helsinki and approved by the Health Science Research Ethics Committee (HSREC) of the University of the Free State, South Africa .Bill and Melinda Gate Foundation, grant number BMGF-OPP1180423_2017, the 10.13039/501100001321National Research Foundation (NRF), South Africa, grant number 120814, and 10.13039/501100001323Poliomyelitis Research Foundation (PRF), South Africa, grant number 19/16.This research was funded by the Project conceptualization was performed by Martin M Nyaga, Sabiu Saheed, and Ayodeji E Ogunbayo. Supervision and administration were done by Martin M Nyaga and Sabiu Saheed. Laboratory investigation was performed Ayodeji E Ogunbayo. Data analysis and presentation was performed by Ayodeji E Ogunbayo. Original draft was written by Ayodeji E Ogunbayo. Writing review and editing was performed by all co-authors. Funding was acquired by Martin M Nyaga.The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Also, we do not have any association whatever with any of the manufacturers of the kits. The results here, are purely based on finding for this study and may reflect otherwise in other experimental set-ups.