Controlled genes are expressed from a strong semisynthetic promoter repressed by the prokaryotic TetR, which also represses its own synthesis; with basal expression abolished by a second, 'zeroing' repressor. The autorepression loop lowers cell-to-cell variation while enabling precise adjustment of protein expression by a chemical inducer. WTC846 allelic strains in which the controller replaced the native promoters recapitulated known null phenotypes , exhibited novel overexpression phenotypes (IPL1), showed protein dosage-dependent growth rates and morphological phenotypes , and enabled cell cycle synchronization (CDC20). WTC846 defines an 'expression clamp' allowing protein dosage to be adjusted by the experimenter across the range of cellular protein abundances, with limited variation around the setpoint.Conditional expression of genes and observation of phenotype remain central to biological discovery. Current methods enable either on/off or imprecisely controlled graded gene expression. We developed a 'well-tempered' controller, WTC Moreover, since differences in global ability to express genes into proteins  lead to 846 (WTC846), that fulfils the criteria outlined above. This development had three main stages. We first engineered a powerful eukaryotic promoter that is repressed by the prokaryotic repressor TetR and induced by the chemical tetracycline and its analogue Anhydrotetracycline (aTc), to use as the promoter of the controlled gene. Next, we used instances of this promoter to construct a configuration of genetic elements that show low cell-to-cell variation in expression of the controlled gene, by creating an autorepression loop in which TetR repressed its own synthesis. Third and last, we abolished basal expression of the controlled gene in the absence of the inducer, by engineering a weakly expressed 'zeroing' repressor, a chimera between TetR and an active yeast repressor Tup1. With WTC846, adjusting the extracellular concentration of aTc can precisely set the expression level of the controlled gene in different growth media, over time and over cell cycle stage. The gene is then 'expression clamped' with low cell-to-cell variation at a certain protein dosage, which can range from undetectable to greater abundance than wild type. We showed that strains carrying WTC846 allelic forms of essential genes recapitulated known knockout phenotypes, and one demonstrated a novel overexpression phenotype. We constructed strains bearing WTC846 alleles of genes involved in size control, growth rate, and cell cycle state and showed that these allowed precise experimental control of these fundamental aspects of cell physiology. We expect that WTC846 alleles will find use in biological engineering and in discovery research, in assessment of phenotypes now incompletely penetrant due to cell-to-cell variation of the causative gene, in hypothesis directed cell biological research, and in genome-wide studies such as gene-by-gene epistasis screens.Here, we describe the development of a prokaryotic repressor-based transcriptional controller of gene expression, Well-tempered ControllerTDH3P, has three key Transcription Factor (TF) binding sites, one for Rap1 and two for Gcr1 (TDH3P repressed activity of this promoter (1tetO)   immediatte P5tet . We intete P5tet  of constU2 locus .2tetP], Y2564[3tetP], and Y2566[5tetP] with an otherwise-isogenic strain in which Citrine was expressed from native TDH3P (Y2683). This fluorescence signal measures Citrine expression, but also includes autofluorescent background from the yeast cells. We quantified this background by using the otherwise-isogenic parent strain Y70. Measured in this way, 2tetP had 76%, 3tetP 69%, and 5tetP 51% of TDH3P activity . By this measure, TetR repressed 2tetP by a factor of 12, 3tetP by a factor of 1.5, and 5tetP by a factor of 12  from these promoters to quantify their activity. We compared the strains Y2551 We therefore picked this cAR architecture for our controller.”The text in the Results section now reads:“Compared with cells bearing the SR architecture, otherwise-isogenic cells bearing the AR architecture showed increased basal expression (6.3 vs. 4.1fold over autofluorescence background). […] We therefore picked this cAR architecture for our controller.”We also made the following addition to the Discussion:846-driven expression is highest at low aTc levels, because control in this regime depends mostly on the higher variability Simple Repression by TetR-Tup1 expressed from PRNR2. […] This autorepressing circuitry operationally defines WTC846 as an ”expression clamp”, a device for adjusting and setting gene expression at desired levels and maintaining it with low cell-to-cell variation, and so allowing expressed protein dosage in individual cells to closely track the population average.”“Cell-cell variation in WTCWe also made the following addition to the legend in the Plasmids table (Supplementary Table 2), to make it clear that we have provided information and materials sufficient to make strains with the other architectures used in this study.846 strains carrying the cAR architecture without any further construction. […] Construction of SR strains would require deletion of the negative feedback-controlled TetR from (P2365/2370/2371/2372/2374), and construction of AR strains would require deletion of the constitutively expressed TetR-Tup1 from the same plasmids.”“Plasmids used in this study. * indicates plasmids available through Addgene. These plasmids are sufficient to allow construction of WTC846 system using aTc is a relatively slow process, at least judged by the Citrine read-out , taking place over >360 minutes (>3 cell divisions in rich medium). The authors should discuss why this is so, with reference to known transcriptional and/or translational kinetics in this complex feedback system.3. Induction with the WTCWe thank the reviewer for directing us to address induction or induction kinetics. We had not done so previously, partly because the story is complicated, and partly because what we knew was probative, not dispositive.846 strains, induction, judged by Citrine fluorescence signal  is detected within 30 minutes after aTc addition, and the release after Cdc20 depletion  occurs within 35 minutes after aTc addition. Given that induction is rather fast, why do WTC846 strains require 6 hours to reach steady state? Expression of the controlled gene is synchronized to expression of the autorepressing TetR. And yet, we know that the long time to steady state is not a consequence of the autorepressing feedback, as we observe that Simple Repression (SR) configurations also require a similar length of time (6 hours) to reach steady state . This time to steady-state derepressed expression is also comparable to that for a previously described TetR based system, which depends on galactose for activation, which required around 400 minutes to reach steady state (doi: 10.1038/nature01546). This time to steady state is still short compared to activation-based systems like the β-estradiol dependent system previously constructed in our lab (doi: 10.1093/nar/gku616).We need to distinguish between speed of induction and time to steady state. Reviewer notes that it takes >3 cell divisions to reach steady state after induction. In WTCOur explanation for the long time to equilibrium is that steady state concentration of the controlled protein (Citrine) is reached when degradation of existing protein and its dilution by partition into daughter cells, balances new synthesis. Therefore, the time to steady state will depend on the stability of the controlled protein. If the half-life is longer than doubling time, steady state will be reached in 6-7 hours. If the protein has a half-life shorter than doubling time, steady state will be reached faster. Citrine is long lived and is lost mainly due to dilution by cell division . Estimates vary as to the stability of the yeast proteome. A recent meta-analysis of 4 different datasets measuring half-lives suggests most proteins (between 50% and 85%) are just as stable and are lost mainly due to dilution by cell division (http://dx.doi.org/10.2139/ssrn.3155916).Equation 1 is a simple ODE model where a protein is produced at constant rate a and lost with a lumped linear rate (dilution + degradation) d. The analytical solution (Equation 2) of this model shows that the only constant that affects the time variable is the degradation + dilution rate. Dependence on this variable is also evident in the simulations we provide in Appendix 4—figure 1, based on this model. The smaller d is, , the longer it takes to reach steady state. On the other hand, changes in the production rate a have no effect on time to steady state, although both rates affect the maximum level as one would expect.th subsection of results:In order to make the distinction between induction and steady state kinetics clearer, we made the following changes in the first paragraph of the 4Old text read:“After induction, signal appeared within 30 minutes and reached steady state within 7 hours.”New text reads:“After induction, signal appeared within 30 minutes. Time to reach steady state, which will be shorter for proteins that degrade more quickly (see Appendix 4), was 7 hours for the stable protein Citrine.”We also added the simulations mentioned above as Appendix 4—figure 1, and we refer to it as seen below:846-controlled Citrine takes around 7 hours to reach steady state concentration. […] Therefore most WTC846-controlled endogenous proteins will likely have a time to steady state around 7 hours, except those with a shorter half-life which will exhibit a shorter time to steady state.“Time to steady state depends on the stability of the controlled protein. In the experiments in Figure 4, WTCth paragraph of the Discussion:We also made the following addition to the 7846 are rapid, as both Citrine and“Both induction and shutoff with WTCCdc20 expression occur within 30 minutes of induction, and shutoff of Citrine expression is observed within 60 minutes. […] Those proteins with shorter halflives will reach steady state faster.”4. They should also discuss the potential implications of this for screens based upon plate measurements of single-colony growth rates or growth of robotically "pinned" cell cultures. They show a limited number of growth ("spot") assays on solid media that would appear to indicate abrupt cut-offs for colony formation, though the data show only one time point in the growth assay and might obscure growth rate differences.846, were not meant to measure growth rates but only the ability of single cells to divide and form colonies (a measure of cell viability) at a terminal time point.First, viability experiments. Our plate-based assays using WTCrd paragraph of the last Results section now reads:The revised results text in the 3“We spotted serial dilutions of cultures of the final seven strains on YPD, YPE, SD, S Glycerol and SD Proline plates, with and without inducer, and assessed the strains' ability to grow into visible colonies at a single time point, at which cells of the parent strain formed colonies in all serially diluted spots.”846 used in plate based and robotically pinned growth dependent screens. We know there are differences in time to approach steady state expression for different strains expressing different proteins from WTC846 – or by any other inducible system. These differences will lead to different effects on growth rate for the first doublings on solid medium or liquid culture. This fact admits to an obvious workaround, which is to suggest that experimenters pre-induce the strains before plating cells or pinning them robotically into wells on a dish.But this is not what the reviewer asks. We would like to see WTC846 for pinned growth rate experiments. The text now reads:In response, we added wording to the last paragraph of the Discussion section about the suitability of WTC846 controlled genes define a new type of conditional allele, one that allows precise control of gene dosage.[…] WTC846- alleles may find use in engineering applications, for example in directed evolution of higher affinity antibodies expressed in yeast surface display, where precise ability to lower surface concentration should aid selection of….”“Taken together, our results show that WTC5. The authors should examine shut-off kinetics, ideally by as direct a measure of RNA Pol II initiation as possible. This could be of interest in exploring the function of essential genes, for example.846, elimination of protein from the cell (which is what we care about for assessment of phenotype) depends both on shutoff of new expression and on perdurance.Yes. The reason we were reluctant to explore shutoff kinetics is that it is another complex story. In WTC846 controlled genes would be affected primarily by the rates of 3 reversible processes: aTc diffusion out of the cell, TetR binding to its operators, and sequestration of free aTc by newly synthesized TetR. These processes are fast: TetR binding and unbinding to DNA is fast  (doi: 10.1038/ncomms8357 (2015)). Moreover, in the same experiment, reverse TetR started associating with specific sites seconds after addition of doxycycline, indicating that, at least in mammalian cells inducer diffusion and inducer-TetR binding are also rapid. For this reason , we expect that shutoff of WTC846-controlled phenotypes will be limited by perdurance, governed by the degradation kinetics of the mRNA and the encoded protein. Given the rapidity of induction , we reasoned that the same reversible phenomena that govern WTC846 induction  will govern shutoff, so that shutoff of the WTC846 expressed phenotype will be dominated by perdurance of the controlled gene products. The consequence would be that for controlled genes , the predominant factor in achieving zero protein and observing the associated phenotype in the cell upon aTc removal will be the stability of the mRNA and protein of interest.We expected (and expect) that the speed of shutoff of WTC846 stopped producing the protein of interest upon aTc removal and (b) confirm that perdurance is the predominant factor in how quickly the desired loss of function is seen.Given the relative complexity of this explanation, in response to this reviewer we performed additional direct experiments to (a) determine how quickly WTC846 control to exponential phase and then induced with a high concentration (600ng/mL) of aTc. We then measured fluorescence signal every 30 minutes in flow cytometry. Additionally, after 30, 90, 150, and 210 minutes, we removed, washed, and resuspended a sample from the culture in (a) medium without aTc (to shut off WTC846) and (b) without aTc but with cyclohexamide (to shut off both WTC846 and new protein synthesis). After shutoff, we expected to see an initial increase in signal, followed by decline from this peak. The initial increase in fluorescence after shutoff would be due to 3 factors: the time it took for WTC846 to stop producing new transcripts, the time it took for the existing mRNA to be degraded, and continued fluorophore formation by already-synthesized but immature Citrine proteins. Whatever increase in fluorescence we observed above that found in the cycloheximide sample would be due to the other 2 factors outlined above, namely WTC846 shutoff speed and mRNA degradation speed.For (a) we grew a strain in which Citrine expression was under WTC846 directed expression is rapid, as we see stabilization of fluorescence within 60 minutes. And assuming a ~30 minute maturation time for Citrine (since its parent EYFP matures in ~30 minutes after denaturation (https://doi.org/10.1038/nbt0102-87)), WTC846 shutoff likely occurs within the first 30 minutes. These results also show that a stable protein like Citrine takes a long time to be eliminated from the cells, given that its level is halved every 90-120 minutes, consistent with dilution through cell division.Our results are presented as a supplementary figure in the revised submission. Shutoff of WTC846::CDC20 strain took 8 hours . Given that CDC20 is a very unstable protein, we attributed this long time to arrest to the high aTc concentration in which we precultured the cells (20ng/mL), which likely brought about high pre-arrest concentrations of Cdc20 that took a long time to become depleted. To confirm this hypothesis, we precultured the WTC846::CDC20 strain at a much lower aTc concentration (3ng/mL) and then placed cells in medium without aTc. At this lower concentration, arrest began at 1.5 hours after removal of aTc, consistent with the shutoff time of WTC846 established above, and between 3-4 hours all cells had arrested. The large difference between the arrest times of cells grown with 3 and 20ng/mL aTc, as well as the short shutoff time of WTC846 [WTC846 – Citrine expression] presented above, support the notion that perdurance of protein of interest will in most cases dominate the time to complete “phenotypic” shutoff, i.e. the time needed to see the zero expression "null" phenotype.In (b), we revisited cell cycle arrest and release by Cdc20. In the original experiment in Figure 5E, arrest of the WTCWe’ve included both experiments as supplementary figures  and added the following text to the manuscript.846 fulfils the criteria of an ideal transcriptional controller” we added:As the second paragraph of the Results section titled “WTC846 driven Citrine expression. […] Overall, we conclude that WTC846 shutoff is rapid, but the time required to see the phenotypic effects of the absence of the controlled gene product will primarily depend on the stability of the mRNA and expressed protein.”“To better characterize the system, we also measured the shutoff speed of WTC846 allows precise control over protein dosage and cellular physiology”.And we made the following changes in the last paragraph of the Results section titled “WTC846 to exert dynamic control of gene expression by constructing a WTC846-K3::CDC20 strain (Y2837) and using this allele to synchronize cells in batch culture by setting Cdc20 expression to zero and then restoring it. […] Given this, and the rapid shutoff kinetics of WTC846 presented in Figure 4—figure supplement 8, we conclude that the shutoff dynamics of WTC846 controlled phenotypes depend mostly on the speed of degradation of the controlled protein.”“Finally, we tested the ability of WTCWe also added a short comment on the speed of shutoff within the third to last paragraph of the Discussion section:846 are rapid, as both Citrine and Cdc20 expression occur within 30 minutes of induction, and shutoff of Citrine expression is observed within 60 minutes.”“Both induction and shutoff with WTCWe also adjusted our Materials and methods to explain these additional experiments.846 regulation of other genes implicated in cell size control.6. The expected results for the Whi5 experiment and how they might differ from the actual findings  are not clear and should be developed further, perhaps in the Results section itself. As the authors point out: "Whi5 controls cell volume by a complex mechanism", but they don't explain what I believe is a key feature, namely that its translation does not scale with cell volume (according to the Skotheim lab), which is an unusual feature. It would be interesting to examine WTCth paragraph of the last Results section). Revised text now reads.We thank the reviewer for the suggested clarification. We agree that the decoupling of the amount of translation from cell volume is a key feature of Whi5 control. We changed the results …”“Whi5 controls cell volume by a complex mechanism and unlike most other proteins its abundance does not scale with cell volume. […] To test whether we could control cell volume by controlling Whi5, we constructed haploid and diploid WTC846. We clarified this point by adding the text below to the Results:In terms of our results, we believe that while we alter the cell cycle stage at which Whi5 mRNA is expressed , the nuclear import/export regulation of Whi5 remains intact. The continued import/ export regulation allows cell size regulation by Whi5 to remain functional, and so that cell volume simply scales with the amount of Whi5 expression we induce via WTC846::WHI5 strains . […] We expected that the volume of these cells should scale with the concentration of the aTc inducer.”“Here, we constructed haploid and diploid WTCth paragraph of the subsection titled “WTC846 alleles allow precise control over protein dosage and cellular physiology”):While we observe that the cell size scaled with aTc concentration, we also found that overexpression of Whi5 in the haploid strain led to increased cell-to-cell variation in cell volume. In order to develop this aspect of the results further, we analysed DNA content of haploid cells grown in presence of high aTc and noticed an increase in the number of cells that had >2n DNA content. We believe this indicates a misregulation of the normal progression of the cell cycle, leading to endoreplication, which could then lead to the observed increase in cell-to-cell variation. We show this experiment in Figure 5—figure supplement 5. We also made the following addition to the Whi5 paragraph of the Results  expressing high levels of Whi5 showed increased variation in volume. […] We therefore believe that overexpression of Whi5 leads to endoreplication, and the increased variation in volume at high aTc concentrations in the haploid strain originates from these endoreplicated cells.”846 regulation of other cell size control genes, and we are attempting to recruit a student to address this work.It will be interesting to examine WTCNew figure legend reads:846-K1::WHI5 strain cells  at mid-exponential phase in S Ethanol media, grown with 4 different aTc concentrations as marked on top of each plot. […] Without Whi5 there was a reduction of G1 cells with 1n DNA content in the Y2791 strain, and upon overexpression of Whi5 aneuploid cells with >2n DNA content are observed.”“(C) DNA content of the haploid WTC846::CDC20 strain compares to the standard cdc20-ts allele used in the field.7. In Figure 5E, it would be great to show, or at least explain clearly in the text, how the WTCWe did so. We added the following text together with the relevant references to our Results section to compare our Cdc20 allele to already existing ones:846::CDC20 strain is more penetrant than that obtained using temperature sensitive (~25% unbudded cells) and transcriptionally controlled (~10% unbudded) alleles of cdc20. Release is at least just as fast as that observed for the temperature sensitive (~35 minutes) and the transcriptionally controlled allele (~40 minutes).”“When compared to published data, arrest at G2/M using the WTC846 actually gives much lower cell-to-cell variation than all other current methods (at least where this has been measured).8. In Discussion  the authors need to show clearly that WTCWe've done the best we can. When evaluating gene expression tools, cell-to- cell variation has not been routinely measured. This is possibly because all systems thus far have relied on a simple repression or activation configuration, and cell-to-cell variation has been quite high. Moreover, the best means to quantify the different contributions to cell-to-cell variation in expression require microscopic quantification of expression of two reporters in sets of hundreds of single cells tracked over time  and even the best flow cytometric methods for achieving variation (Pesce 2018) involve dual reporter strains rather than the single reporter strains used here.846. The β-estradiol induced system had high cell to cell variation (>0.4) throughout its entire dynamic range, whereas WTC846 shows high variation only at a small portion of its dynamic range at low expression levels. Additionally, we constructed a strain where the commonly used, galactose inducible PGAL1 promoter directed Citrine expression. We quantified cell-tocell variation in a dose response to galactose, where upon induction variability was always very high (>0.6). We included these comparisons as a supplementary figure.To address this point, we have included , relevant instances in which variation in single reporter strains can be assessed. In these, cell-to-cell variation was not explicitly quantified, but induction resulted in highly variable  distributions of expression. One of those references is to a transcriptional controller induced by β-estradiol (LexA-hER-B112) we published previously. In response to the reviewer's direction, we quantified cell-to-cell variation in gene expression by this controller compared to expression by WTC846 fulfills the criteria of an ideal transcriptional controller”):We also included the following text in our Results section (last paragraph of the section titled “WTC846::citrine strain (Y2759) grown in YPD, and compared it to variation in a β-estradiol (LexA-hER-B112) activation based transcriptional control system we previously described, and the commonly used galactose activated PGAL1 promoter.”“We quantified the cell-to-cell variability in Citrine expression using the single reporter (ViV) measure for the WTC846 alleles display no uninduced basal expression" . I think that it would be more correct to point out, at least in the9. In the Discussion (pg. 11 top right) the authors state that "WTC846 alleles display no uninduced basal expression as measured here, by the Citrine fluorescent read-out. Producing null phenotypes in cases where this was not possible with previous systems is a valuable demonstration of the utility of the new method, but not a demonstration of complete repression. The authors might try to measure mRNA steady-state levels by RT-qPCR to estimate copy number per cell, and together with any information on mRNA stability, to estimate transcription rate, if they really want to pin a number on the basal expression level.Discussion, that WTC846 is intended as a tool for controlling gene expression, and our functional tests with low expressed genes did not show any problematic basal expression, we opted not to further quantify mRNA, but to change the wording so that it says what we did observe. Discussion now reads.The reviewer's qualification is of course correct. Since WTC846 alleles also exhibit high maximum expression, low cell-to-cell variation, and operation in different media conditions without adverse effects on cell physiology.”“It can set protein levels across a large input and output dynamic range. […] WTCInstead of the old text which read:846 alleles display no uninduced basal expression, high maximum expression, low cell-to-cell variation, and operate in different media conditions without adverse effects on cell physiology.”“It can set protein levels across a large input and output dynamic range. WTC846 system described here, as pointed out by the authors, is genome-wide screening, that authors should at least discuss how this might be efficiently implemented. The generation of a library of WTC846 fusions to each protein-coding gene in yeast by standard methods would be extremely laborious and expensive. An alternative worth mentioning is the SWAp-Tag strategy described by Schuldiner and colleagues . There may very well be others.10. Since one of the major potential applications of the WTC846 controlled endogenous genes would be to use existing whole genome collections such the SWAP-Tag as a starting point. SWAP-Tag allows insertion of N or C-terminal sequences to most ORFs in the yeast genome by creating standard sites for homologous recombination. Generating a whole genome collection of WTC846 alleles would be accomplished crossing the existing SWAP-Tag N terminal collection with a WTC846 donor strain, followed by sporulation on the appropriate selective medium. We’ve therefore added a short section and the necessary references in our discussion on this subject using the text below.We thank the reviewer for the suggested addition. As the reviewer suggested, one good way to generate a library of WTCLast paragraph of the Discussion previously read:846- controlled genes define a new type of conditional allele that allows precise control of gene dosage. […] We also expect them to find use in genome-wide gene-by-gene and gene-bychemical epistasis screens to detect protein dosage independent interactions,…”“Taken together, our results show that WTCNew version reads:846- controlled genes define a new type of conditional allele, one that allows precise control of gene dosage. S. cerevisiae […] We therefore suggest that growth rate-based assays using WTC846 or any other inducible system pre-induce cells several generations before plating or pinning. WTC846- alleles may find use in engineering applications, for example in directed evolution of higher affinity antibodies expressed in yeast surface display, where precise ability to lower surface concentration should aid selection of….”“Taken together, our results show that WTC846 regulation for generating precisely graded expression levels of a given protein with minimal secondary effects growth or physiology, they devoted considerable effort towards achieving maximal shut-off without much discussion of the utility of this feature. In light of anchoring and induced degradation methods that operate at the protein level to generate loss-of-function effects, the authors should consider how WTC846 regulation might be a useful complement to these methods, particularly when their efficiency or rapidity is in question.11. Although the authors emphasize the utility of WTC846 could certainly a useful complement to such methods, especially in cases where rapid shutoff of expression is required together with precise dosage control. We have added this to the last part of our discussion using the text below:We thank the reviewer for the suggestion. WTC846 can also be a useful complement boost the efficiency of methods that act at the protein level such as auxin induced degradation or AnchorAway techniques. […] For example, an experimenter might simultaneously induce depletion of the product of a controlled gene by such a method while adjusting aTc downward to rapidly reset the level of an expressed protein to a new, lower level.”“WTCAdditional comments and suggestions1. Pg. 5 right column: "…and we developed a second measure (explained in SI) that normalized variation in dosage with respect to a key confounding variable, cell volume, to correct for its effect on protein concentration. In this, we measured the Residual Standard Deviation (RSD) in signal from cells after normalization of output for volume estimated by a vector of forward and side scatter signals…"I think it would be a good idea to give the second measure a name here and make it clearer that Figure 2D reports the RSD of this value if I understand correctly. The sentence "In this, we measured the RSD in signal…" is awkward and unclear. Please re-word.Yes. We named the method "Volume Independent Variation". We reworded the unclear sentence as shown below, and clarified in the legend that Figure 2D reports this measure. We modified all other figure legends in the manuscript and the supplementary material where RSD was reported to reflect the new name for the measure.Old text read:“In this, we measured RSD in signal from cells after normalization of output for volume estimated by a vector of forward and side scatter signals.”New text reads:“In ViV, we estimated cell volume by a vector of forward and side scatter signals, and measured the remaining  Standard Deviation of the single reporter output after normalization with this estimated volume.”The new Figure 2 legend now reads:TDH3 and dashed line indicates VIV of autofluorescence in the parent strain without Citrine.”“(D) Cell-to-cell variation of expression by these three architectures. We calculated single-reporter cell to cell variation (VIV) as described. […] Dot-dash line indicates VIV of the strain where Citrine is constitutively expressed from P2. In Figure 3A the fluorescence curves should be better aligned with the strain designations.Yes, with thanks. We have made the necessary change to the alignment.3. Why is there no curve shown for repressed P3tet in Figure 1B? .We thank the reviewer for pointing out the alignment issue and have fixed it in the figure.There is no curve for P3tet because the curve is centred around fluorescence value ~20000 and thus lies outside the range of the xaxis we used in main Figure 1. If we extend the x-axis to include this curve, the difference between all the other curves becomes difficult to perceive. Therefore, we show the data for P3tet separately again in Supplementary Figure 1 .We have now added a note also in the figure legend (see below) directing the reader to Figure S1.3tet is above the x axis depicted in this figure, but can be seen in Figure S1.”“Repressed activity of P4. Yeast gene names should be in italics throughout (including the figure legends). Deletions that a recessive should also by italics, and with the "Δ", essentially an allele designation, placed after the gene name.Necessary changes made in text and the supplementary material.5. Reference no. 34 refers to Rap1 and Grf2 binding sites (not Gcr1). Please clarify.TDH3 UAS, but also includes in the last portion of its Results section the identification of the CATCC consensus binding sites. The authors of the paper suggest in their discussion that these are likely to be binding sites for the recently discovered Gcr1. This paper was the earliest reference we could find to the CATCC binding sites in the PTDH3 UAS, and therefore we cited it in this context.We thank the reviewer for pointing this out. Yes, the paper refers to Rap1 and Grf2 binding sites and finds them in the PTDH3 UAS in the main text, next to the original reference 34. We also added a short note “Gcr1 binding sites were found in reference 13 and confirmed in reference 14“ in the legend of Figure S21.We added a more recent reference corroborating the GCR1 binding sites in the P 6. Bottom pg. 11, right, I would suggest "…when inducer is absent, the measured basal expression of the controlled gene is abolished…"We thank the reviewer and have made the suggested change.7. References no. 28-30 and 35 are incomplete .We thank the reviewer for pointing this out. We fixed the references.8. Bottom pg. 5, left should be >50,000.We thank the reviewer for pointing this out. We fixed the symbol.9. Page 8 right: "…according to,53 to enable…" could be replaced by "…to enable…" with the reference placed at the end of the sentence.We thank the reviewer for the suggested change which we have implemented in the text.10. Page 8, right: again, replace inverted "?" with ">" (?)We thank the reviewer for catching these formatting errors. We have replaced the symbol.Reviewer #1 (Significance (Required)):846 system described here represents a genuine improvement on current methods and is likely to be an important addition to the yeast toolbox for gene function analysis. It may also motivate similar developments in mammalian cell culture systems, and eventually in whole animals.The increasing interest in quantitative analysis of cell function emphasizes the need for systems that allow the graded and precise control of gene expression to address dosage effects on phenotype. Although systems of this sort have been developed in yeast, they all have specific drawbacks, as clearly described here. The WTCWe thank the reviewer for this assessment. Just as qualitative means to bring about conditional gene expression enabled experiments that produced important qualitative insights into cell function, so development of precise quantitative means to bring about conditional gene expression will enable better quantitative analysis of cell function. It will also enable new technical approaches such as the threshold phenotypes, epistasis approaches, and evolutionary methods mentioned above. We suspect that development of analogous methods in in vertebrate cells will likely prove even more important in making vertebrate models of human diseases.Reviewer #2 ):Summary:Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).Saccharomyces cerevisiae. This system is very precise and allows for dynamic gene expression by adjusting the amount of inducer (aTc) and/or by changing the translation initiation sequences. In addition, it is equally good at being turned on and off in all the media tested and has low cellto-cell variation. The authors tested their transcriptional controller using different genes  and recapitulated various known phenotypes. They also identified a novel phenotype when overexpressing IPL1 in yeast which mimics a previously identified phenotype in mammalian cells when overexpressing Aurora B. This system will be very useful for the yeast community.In this manuscript, Azizoglu and co-authors developed an autorepression-based transcriptional controller of gene expression for a given gene of interest in We also want to stress that now that we have proof of concept, we can do this in higher cells and multicellular organisms including vertebrates, which need it more.Major commentsAre the key conclusions convincing?Yes.Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?No. They do a good job backing up their claims in their supplementary files.Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.No. See above.Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.No suggested experiments.Are the data and the methods presented in such a way that they can be reproduced?Yes.Are the experiments adequately replicated and statistical analysis adequate?Yes.Minor comments:846-driven IPL1 overexpression experiment ? It might be easier for the reader to understand why the exciting phenotype the authors found was not reported before.Specific experimental issues that are easily addressable. Could the authors add the pGAL1-10-IPL1 control to their WTC846::IPL1 allele manifests an overexpression phenotype because it produces more protein than PGAL1 would have, replacing the promoter of Ipl1 with PGAL1 would be the best control. However, since IPL1 is an essential gene, and its misregulation affects chromosomal integrity, we weren't confident we could replace its promoter with PGAL1 and still achieve the right expression level to keep the cells healthy. In fact, in previously published work on IPL1, overexpression of IPL1 was achieved by introducing a PGAL1-IPL1 producing plasmid into an IPL1+ background.We thank the reviewer for the suggested control. Since we believe that the WTCGAL1 promoter upstream of Citrine in a centromeric plasmid, then grew the strain that carried the (PGAL1Citrine) plasmid in the same  media used by M. MunozBarrera and F. Monje-Casas 2014, to observe effects of Ipl1 overexpression on growth rate and colony formation. The authors did not observe detrimental effects of Ipl1 overexpression in either case.To address the reviewer's concern we therefore took the same approach. We placed the PGAL1-Citrine plasmid, as well as the WTC846::citrine strain grown in YPD with 400ng/mL aTc (the same concentration we used in the Ipl1 overexpression experiment).Here, we used flow cytometry to measure single cell fluorescence from the strain bearing the PGAL1-Citrine strain was approximately two fold lower than that in the integrated WTC846::citrine strain, and that it displayed much higher cell to cell variation. As measured by the single reporter cell-to-cell variation measure (ViV), PGAL1-Citrine variation was 0.9, whereas WTC846::citrine variation was only 0.19. Ipl1 overexpression phenotype does not lead to a complete arrest of growth in the entire population- some cells are affected while others are not. With PGal1 driven overexpression, overall Ipl1 level is lower compared to WTC846 driven overexpression. Additionally, due to high cell-to-cell variability only a handful of cells are actually expressing Ipl1 at this level, which would have made it difficult to observe the growth arrest/delay phenotype that is already not a uniform phenotype in the population. Therefore, we believe the combination of lower expression and higher cell-to-cell variation in the PGAL1-Citrine strain likely accounts for the failure of Munoz-Barrera and Monje-Casas to observe a growth arrest phenotype.Our results showed that the median expression level of the centromeric Pth paragraph of the Results section titled “WTC846 alleles allow precise control over protein dosage and cellular physiology“.We present the results in Figure 5—figure supplement 7. We explain the results in additional sentences at the end of the 4The paragraph in the main text now reads:846-K2:IPL1 strain formed colonies with lower plating efficiency than the parent strain. […] Either the lower expression or the higher variation, or both, might account for the fact that PGAL1 driven Ipl1 overexpression does not result in the mammalian Aurora B phenotype in S. cerevisiae."“At high aTc concentrations, the WTCAre prior studies referenced appropriately?Yes.Are the text and figures clear and accurate? The authors used the strain names in the text and it is hard to figure out what each strain contains. They could transmit this information by using the construction "strain Y### (contains [specify relevant details]).We thank the reviewer for the suggestion. During the revision process, we took care to mention the relevant details as much as possible together with the yeast strain number within the text.In Figure 3, specify that the sentence that explains the errors bars refers to section A and B. or move the sentence before C.Right. There are actually error bars also around the data points in C, although they are very narrow and difficult to see. This sentence therefore refers to Figure 3C as well. In response to this comment, we adapted the figure to make it easier to see the error bars in Figure 3C.In page 3, the authors mention ts and cs mutations. The authors should define what ts and cs are and italicize them.We thank the reviewer for pointing this out. We have included the long format of these abbreviations where they were first mentioned, and have italicized the abbreviations.Old text read:“During the 20th century, workhorse methods to ensure the presence or absence of gene products have included use of ts and cs mutations within genes, for example to give insight into ordinality of cell biological events.”New text reads:“During the 20th century, workhorse methods to ensure the presence or absence of gene products have included use of temperature sensitive (ts) and cold sensitive (cs) mutations within genes, for example to give insight into ordinality of cell biological events.”On page 4, the authors defined PIC and aTc, but both terms were previously defined.Fixed.On page 4, in the following sentence "We also combined the operators in theseconstructs to generate P5tet ", the figure is wrongly referenced, the reference should be to Figure 1B.We thank the reviewer for catching this, and have fixed the reference.On page 7, in the following sentence "The PACT1, PVPH1, PRNR2 strains showed no uninduced expression,.…" please add a reference to Figure 3B.We thank the reviewer for the suggestion and have added the reference.846-K1::TOR2) grew more slowly than the otherwise-isogenic control parent strain with WT TOR2 " the figure is wrongly referenced, the reference should be to Figure S18D. I also suggest the authors add the following text: .On page 10, in the following sentence "…at high aTc concentrations, cells bearing the higher translational efficiency TOR2 allele .We thank the reviewer for the correction of the naming nomenclature. We have fixed the gene names throughout the paper and the supplementary material.Throughout the paper, the authors have random question marks (which may be the result of converting a file in another format to a PDF) that may be removed.We thank the reviewer for catching these formatting errors. We replaced the question marks with the correct symbols.On Supplementary Text page 1, the following sentence: "However single reporter studies have a major, confounding contribution to measured variation in gene expression that multi-reporter studies don't:" is missing a comma after However and change don't to do not.We thank the reviewer for both corrections which we have implemented as suggested.In Supplemental Figure 7, the authors may want to add P2tet and P3tet for better main text that the promoter for essential genes was replaced with another promoter in the presence of the inducer (aTc) before referencing Figure 5, or in the legend of figure 5.We believe the reviewer wanted to raise two separate points here. One about Figure S7, the other about clarifying the essential endogenous gene promoter replacement protocol we used. We address these two points separately below.2tet and P3tet compared to the autofluorescence. Therefore, given that the focus of this supplementary figure is narrowly on the repressed expression, we believe it is best to omit the unrepressed expression curves in this figure.First. We thank the reviewer for the suggestion about Figure S7. In Figure S7, inclusion of the unrepressed promoter fluorescence curves would widen the x-axis range we need to show, and therefore would make it more difficult for the reader to perceive the small differences between the repressed expression of P3tet is weaker than P2tet, this fact is not the only reason that P3tet shows a lower level of repressed expression. Unrepressed P2tet is 1.14 times stronger than P3tet (data from main figure 1). However, when repressed, P2tet expression is 1.38 higher than that for P3tet. This suggests that promoter strength is not the explanation for the lower repressed expression from P3tet. To emphasize this point, in response to the reviewer direction, we added fold comparison numbers to the figure legend.It is important to note that, although PThe new text in the figure legend of Supplementary Figure 7 reads as follows:2tet strain (Y2662) was 1.38 fold higher than in the P3tet strain (Y2702), even though the expression in the unrepresssed P2tet strain was only 1.14 fold more than P3tet. This fact shows that, even given the difference in promoter strength, P3tet is repressed more effectively than P2tet.”“For comparison, repressed expression in the PSecond, we thank the reviewer for the suggested clarification to the strain generation protocol. We changed the main text to indicate that the strains were generated by replacing the promoter of the endogenous gene in cells growing in medium containing aTc., using the text presented below:846 controlled the expression of these genes by replacing the promoter of the endogenous gene. Before transformation the cells were grown in liquid medium containing aTc, and then plated on solid medium containing aTc .”“We constructed strains in which WTCReviewer #2 (Significance (Required)):Describe the nature and significance of the advance  for the field. The authors provide a new tool to dynamically control the expression of genes. The previous systems that have been developed are hard to modulate or are "leaky". The system developed in this work allows for precise control of gene dosage, has low cell-to-cell variation, and has a true OFF state.Static steady state expression around an adjustable setpoint, i.e. clamped expression is good too.Place the work in the context of the existing literature .Regulating gene expression in a targeted way has been a common method in yeast research for decades. However, the current gene expression systems have deficiencies . The authors in this paper developed a new system that shore up these deficiencies by enabling tight on and off controlled gene expression.S. cerevisiae.State what audience might be interested in and influenced by the reported findings. The yeast community in general, as it is a transcriptional controller developed for We also believe the ability to make alleles of this type will be extended higher cells and multicellular organisms, particularly vertebrates.Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Genetics, yeast, evolution, chromosome segregation.


What can be learned from a crowdsourced challenge for the prediction of COVID-19 diagnosis and hospitalization?This diagnostic and prognostic study used a model-to-data approach to implement a continuous benchmarking challenge that has enabled 482 participants to join in the effort to use regularly updated COVID-19 patient data to build machine learning models for COVID-19 diagnosis and hospitalization prediction. Machine learning models showed high accuracy in COVID-19 outcome prediction, but analysis of subgroups and prospective data revealed limitations and bias in the models.This study suggests that crowdsourced clinical algorithms can predict COVID-19 diagnosis and hospitalization, but evaluation of the submitted models using reserved data sets is necessary to avoid self-assessment traps. Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups.This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries.Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated.In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776  and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796  and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger.In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models. This diagnostic and prognostic study describes the development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provides an assessment of model performance, and benchmarks model performance on subgroups. These necessary restrictions hinder data accessibility for researchers, limiting their ability to develop models and to externally validate their models. In cases where researchers have access to patient health data, models developed by isolated teams with no objective evaluation oversight can lead to self-assessment bias and overfit models.13 Using this infrastructure, we organized a response to the COVID-19 pandemic by launching the COVID-19 EHR DREAM Challenge. The feasibility and utility of this approach was previously demonstrated in the Patient Mortality EHR DREAM Challenge, leading to the unbiased assessment of machine learning models applied to EHRs to predict patient mortality.15 In our COVID-19 Challenge, we asked participants to address 2 clinically pressing questions. Diagnostic Question 1 (Q1): Of patients who received a test for COVID-19, who will have positive test results? Prognostic Question 2 (Q2): Of patients who have positive test results for COVID-19 in an outpatient setting, who is at risk for hospitalization within 21 days? The questions were motivated by the need to triage patients prior to widespread diagnostic and treatment capabilities. We evaluated models’ performance and generalizability to patient subgroups stratified by age, sex, race, ethnicity, and time of COVID-19 test.To overcome these challenges, we provided a solution for lowering the accessibility barrier to private patient data, while maintaining privacy protections, by implementing the model-to-data framework, under which investigators can build models on but never have direct access to sensitive health data.TRIPOD) reporting guideline.All work was reviewed and approved by the University of Washington (UW) institutional review board and UW Medicine leadership. We curated 2 challenge data sets  separately for the purpose of model training and evaluation. The COVID-19 EHR DREAM challenge was run as a continuous benchmarking exercise where the data sets were updated every 2 to 5 weeks to incorporate new patients and update existing patients’ clinical trajectory. The Q1 challenge data set has 6 versions that accumulated over 30 weeks since May 6, 2020, and the Q2 challenge data set has 4 versions over 18 weeks since August 19, 2020. Each data set version was named by the challenge week during which the data set was used . This stIn contrast to the last version of the Q1 and Q2 challenge data sets that were both updated November 18, 2020, we gathered all the data that had accumulated by January 21, 2021, and referred to this data set as the “cumulative data set.” This represented 108 500 patients who underwent testing for COVID-19, 4980 who received a positive test result, 3100 who received a positive test result during an outpatient visit, and 170 who were hospitalized within 21 days after receiving a positive test result during that outpatient visit. We split the cumulative data set in a way where 50% of patients who most recently underwent testing for COVID-19 were incorporated into the cumulative evaluation data set  and the other 50% were incorporated into the cumulative training data set . The cumulative evaluation data set was split evenly and prospectively into 3 subevaluation data sets based on the patients’ COVID-19 measurement date to evaluation 1 , evaluation 2 , and evaluation 3 . . The cumWe built an ensemble validation data set to evaluate the performance of ensemble models. This data set comprised 12 870 patients who had been tested for COVID-19 between January 22 and March 19, 2021, among which 278 had positive results, 208 had positive results in outpatient settings, and 16 were hospitalized within 21 days. .We implemented the model-to-data approach for the COVID-19 challenge to facilitate the delivery of participants’ models to the sensitive challenge data sets. COVID-19 patient data sets were hosted on a UW Medicine Information Technology provisioned secure server. Challenge participants never had direct access to patient data; instead, they were required to build and submit Dockerized (containerized) models. A synthetic data set was provided to the participants to help them become familiar with the format of the data and to aid in technical debugging. Models submitted by participants would first go through a validation process in an Amazon Web Service cloud environment, running against synthetic data. Once validated, the models would be transferred to the UW environment, training and evaluating on real patient data. Area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) were 2 performance metrics we used to assess models. Synapse collaboration platform was used to receive submissions and host the challenge leaderboard .To evaluate and compare models submitted with different versions of challenge data sets, we retrained and evaluated Q1 and Q2 models separately on the cumulative data set. A full protocol of model retraining and selection is in eAppendix 4 in t tests were used to examine if the top 10 models’ performances were consistently different and P < .001 was considered significant.To evaluate and study the potential bias of the top 10 models from Q1 . Trained on the cumulative training data set, each individual model outputs a probability between 0 and 1 indicating the likelihood of a patient receiving a positive COVID-19 test result (Q1) or being hospitalized within 21 days (Q2). A logistic regression model with 10-fold cross-validation ingested individual models’ probability for the cumulative evaluation data set to build an ensemble model. The ensemble validation data set was used to assess ensemble models’ performance   and an AUPRC of 0.297. We observed considerable variation in models’ AUROCs. We then applied the top 10 retrained models to longitudinally ordered subsets of the cumulative evaluation data set  to understand how models trained on previous patient data will generalize to future patients. The results for all the top 10 models showed that the performance on the evaluation 1 data set was significantly better than on the evaluation 2 and evaluation 3 data sets ( < .001) .P < .001)  . The PeaThe top 10 models did not show a consistent pattern of model performance on the subdata sets split based on ethnicity (Hispanic or Latino and not Hispanic or Latino) or race . Among the top 3 teams, the first team (“Home-Sweet-Home”) outperformed the second team (“UWisc-Madison-BMI”) and third team (“Bryson-and-Yao-Team”) in all race groups. However, the third team outperformed the second team in the White race group B and D.When Q2 models were retrained and evaluated on the cumulative data set, the best AUROC achieved was 0.796 , with an AUPRC of 0.188. We asked whether the models could be generalized to patients who received positive COVID-19 test results during all visit types, not just outpatient settings. When the Q2 models were trained and applied to patients who received positive test results in either inpatient or outpatient settings, 4 of 7 models’ performances decreased, and only 1 observed performance increased significantly compared with the prediction for only outpatient patients . Applying the Q1 ensemble model combining the top 10 models to the ensemble validation data set resulted in higher AUROC performance compared with any single model, with an AUROC of 0.714  and AUPRC of 0.106, compared with Q1 best individual model’s AUROC of 0.699  and AUPRC of 0.112. When stratifying the ensemble validation data set based on demographic profile, the Q1 ensemble model outperformed the best individual model in 10 of total 13 subgroups significantly  it protected the patient data while enabling model development on private data; (2) it forced model developers to standardize their models, enabling model transferability and reproducibility for rigorous evaluation; and (3) it enabled an unbiased third party to evaluate these standardized models on previously unseen data.We saw performance degradation on the temporally evolving data set, indicating limitations in the models’ generalizability on prospective data. However, this performance degradation was expected, given the rapid changes to the challenge data set caused by ever-changing clinical practice and variance in age distribution and prevalence of COVID-19–positive individuals. We observed better model performance in the female group compared with the male group, which could be owing to more female patients than male patients and more EHR history data for the female patients in the cumulative data set. We observed that model performance for the 25- to 49-year-old age group was the best and for the age group 17 years or younger was the worst among all age groups. This finding was consistent with the number of patients in the 2 age groups, in that the 25- to 49-year-old group was the largest and the group 17 years or younger was the smallest. However, with White patients making up most of the data set, the model performance on the White group was not always better than the other race groups, indicating that COVID-19 diagnosis prediction for White patients was difficult even with more training samples for race. We also identified that top teams could have inferior model performance on some subpopulations compared with other teams who ranked lower. This finding could be ameliorated with a model ensemble based on the strength of each team to maximize prediction accuracy. The Q1 ensemble model outperformed the best individual model in most demographic subgroups.18) and incorporate the models into a live clinical workflow setting for providing clinical decision support.The high-performing models we received in the challenge indicate potential clinical utility. To achieve that, we will need to further test the generalizability of those models in a larger and multisite data set , indicating the existence of potential bias in machine learning approaches, which warrants attention prior to implementation of such models in clinical practice.


Background: Emergency Medicine didactic teaching has traditionally been delivered through face-to-face (F2F) lectures. However, during the pandemic of COVID-19, the didactic teaching was switched to online through using Microsoft Teams. The aim of this study was to assess the impact of online learning in the knowledge and skills acquisition of millennial learners based within emergency medicine.Methodology: This was a retrospective review of assessment data. Over a period of 10 months (August 2019 to June 2020), each resident was exposed to traditional F2F teaching for a period of four months and then online teaching in a crossover manner. After each method, there were two types of assessments, multiple-choice questions (MCQs) and computer-based objective structured clinical examination (OSCE). A total of 20 MCQs with one correct answer, totaling 20 marks, and 20 OSCEs consisting of an image or a video with five options, each option carrying one mark, totaling 100 marks were used at each assessment point. A student t-test was used to compare the two groups of results.Results: The total number of participants was 49 (n=49). All residents belonged to the millennial generation. Fourteen were female and 35 were male. The mean MCQ 1 score after F2F teaching was 12.16 (SD=1.688), whilst the mean MCQ 2 score after online teaching was 13.40 (SD=1.861). The mean computer-based OSCE 1 score after F2F teaching was 64.45 (SD=5.895), whilst the mean OSCE 2 score after online teaching was 65.57 (SD=5.969). Ten out of 49 students (20.4%) failed the MCQ exam after F2F teaching, whilst 6/49 students (12.2%) failed the MCQ test after online teaching. Seven out of 49 students (14.3%) failed the OSCE exam after F2F teaching, while six out of 49 students (12.2%) failed the OSCE exam after online teaching. There was a statistically significant improvement in the MCQ score after online teaching as compared to F2F teaching , whilst there was no statistically significant change in the OSCE between the two-teaching methods .Conclusion: Both F2F and online teaching methods resulted in a significant improvement in the knowledge and skills of emergency medicine residents. Online education resulted in a statistically significant improvement of MCQ score as compared to F2F teaching. The difference in MCQ score may be due to millennial learners, who traditionally benefit proportionately more from self-learning that is primarily online. Online learning is a term that describes a learning process that uses the internet and other technological tools to impart knowledge and skills outside of a conventional classroom. This platform of learning is in contrast to the traditional face-to-face F2F) learning process that takes place within the confines of a classroom. Medical institutions around the world have utilized various innovative and creative strategies to teach and train their students especially at the emergence of the COVID-19 pandemic, using available applications and software such as Google Classroom, Zoom, and Microsoft Teams to deliver courses online. To ensure not only completion of a course and delivery of lectures but also to stay in constant contact with the learners, online learning through virtual classes was initiated to grow the certainty and confidence of the students within their Faculty even as the world was battling the COVID-19 pandemic [F learninEmergency medicine specialists are trained to handle unscheduled and undifferentiated patients of all ages. They serve as frontline workers, in coordination with Emergency Medical Services and are primarily responsible for initiating resuscitation, stabilization, and performing the germinal investigations and interventions necessary for the diagnosis and treatment of illnesses or injuries within the acute phase. This specialty requires adequate knowledge and skills in the prevention, diagnosis, and management of acute and urgent aspects of illness and injury affecting patients belonging to all age groups. The scope of this aspect of medicine also entails an understanding of the development of pre-hospital and in-hospital emergency medical systems and the skills to support this.Millennials, also called Generation Y or Gen Y, are the demographic cohort following Generation X and preceding Generation Z. Researchers and popular media classify those born between the early 1980s and mid-1990s to early 2000s as Millennials, with the generation typically being defined as people born from 1981 to 1996 . MillennThe World Health Organization (WHO) in January 2020 announced COVID-19, a new coronavirus disease outbreak and was later declared as a pandemic . This diThe imparting of knowledge and skills in emergency medicine has always been done through the traditional F2F method of learning and due to its wide scope and the technicalities involved, experts in this field believe it must remain that way, but the emergence of COVID-19 has forced even the stakeholders in emergency medicine to accept other learning platforms outside of the classroom and embrace online learning. This study is designed to assess the impact of online learning on the level of knowledge and skills of emergency medicine millennial residents and review if this has consequently improved, stayed stable, or deteriorated their performance within two different forms of assessments.The emergency medicine residency training program (EMRTP) runs for four years and had 49 residences at the time of the study, including rotating residents from other specialties. The EMRTP has been recognized by the Accreditation Council of Graduate Medical Education-international (ACGEM-I). Every year, the residency program starts on July 1. The first formative assessment was done in December 2019. In February 2020, the education went online to comply with the national pandemic guidelines. The second formative assessment was completed in July 2020. The comparison between the two assessments was done by using the student's t-test. The multiple-choice question (MCQ) exam consisted of 20 questions with one correct answer. The MCQs were prepared by the EMRTP program director (PD) from the curriculum blueprint. Each MCQ carried one mark (Tabel 1). There were a total of 20 marks for the MCQ.The computer-based objective structured clinical examination (OSCE) question consisted of a clinical scenario, an image, or video followed by five questions Figure , each quThe retrospective data were collected in Microsoft XL Microsoft Corporation Redmond WA. The data were then transferred to the statistical package “Stata” . The data were normally distributed and presented as mean and standard deviation. Statistical significant parameters were set at P 0.05 and the confidence interval was reported at the 95% level. A student t-test was used to compare the two groups.A total of 49 residents’  data were collected Table . Ten outThe study looked at knowledge and skills assessment outcomes through MCQ and computer-based OSCE after two different educational approaches, F2F and online. Both educational approaches resulted in improving the knowledge and skills of residence. The improvement in the MCQ test was statistically better after online learning. However, there was no statistical difference in the improved outcomes of knowledge in computer-based OSCE tests between the two educational approaches.The emergence of COVID-19 has caused a wide acceptance of online learning by the various stakeholders within education as against F2F learning. This acceptance is due to the fact that social distancing serves as one of the major preventing measures against COVID-19 and though learning must continue it cannot be done in close proximity. Hence, the adoption of online learning as means of imparting knowledge and skills during the pandemic. This study was designed to assess the impact of online learning on emergency medicine millennial learners.This study revealed that online learning was more effective in improving the knowledge and skills of emergency medicine millennial learners than traditional F2F learning as measured by MCQs, and thus, suggesting online learning as a better tool in imparting knowledge to millennial learners in emergency medicine. The result from this study agrees with other research on millennial learners which showed that they learn more and appreciate knowledge and skills passed across to them using the internet and other technological tools. Oomen-Early and Early in their report in 2015 stated that most millennial learners entering college in this present day, do not know a world without computers or new media. Hence, one of the biggest challenges facing higher education today is bridging the gap between these “digital natives” and faculty who may see themselves as digital immigrants. According to their report, some scholars believe that higher education faculty, in general, are using 20th-century methods to teach 21st-century students . This gaThough the study suggests online learning as a better tool in improving knowledge and skills in emergency medicine millennial learners, it is relevant to be aware of a paradox  that exist among Millennials. It is accepted that even though some millennial learners may be immersed in new media and technologies, they may not fully understand how to harness this technology to benefit their learning . Many reSeveral limitations need to be considered in relation to this study. First, the study looked at the results of the MCQ exam and computerized OSCE which may not represent the full effort by the residents. The residents may have spent extra time spent online resulting in better MCQ results. A focus group could have been a better approach to capture this information. The focus group was not possible, as this was a retrospective review of the data, and was difficult to interview residents during the height of the pandemic. Second, this was a single hospital study and the results may not be generalizable to other hospitals. Finally, we have only used MCQs and OCCE tests for the assessment; it would have been better to use short answer questions and course work as recommended blooms taxonomy . Due to The study has compared the results of MCQs and OSCE after F2F and online educational methods. The traditional F2F and online teaching approach resulted in a significant improvement in the knowledge and skills of emergency medicine residents. There was a statistically significant improvement in the MCQ score following online education as compared to F2F teaching. The difference in MCQ score may be due to Millennial learners, who traditionally benefit more from online-based self-learning.


A gene signature was previously found to be correlated with mosaic adenovirus 26 vaccine protection in simian immunodeficiency virus and simian-human immunodeficiency virus challenge models in non-human primates. In this report, we investigated the presence of this signature as a correlate of reduced risk in human clinical trials and potential mechanisms of protection. The absence of this gene signature in the DNA/rAd5 human vaccine trial, which did not show efficacy, strengthens our hypothesis that this signature is only enriched in studies that demonstrated protection. This gene signature was enriched in the partially effective RV144 human trial that administered the ALVAC/protein vaccine, and we find that the signature associates with both decreased risk of HIV-1 acquisition and increased vaccine efficacy (VE). Total RNA-seq in a clinical trial that used the same vaccine regimen as the RV144 HIV vaccine implicated antibody-dependent cellular phagocytosis (ADCP) as a potential mechanism of vaccine protection. CITE-seq profiling of 53 surface markers and transcriptomes of 53,777 single cells from the same trial showed that genes in this signature were primarily expressed in cells belonging to the myeloid lineage, including monocytes, which are major effector cells for ADCP. The consistent association of this transcriptome signature with VE represents a tool both to identify potential mechanisms, as with ADCP here, and to screen novel approaches to accelerate the development of new vaccine candidates. The only HIV vaccine in ongoing human efficacy trials employs an adenovirus serotype 26 (Ad26)-based vector, but tests a different route of infection and geographic population compared to African women in whom it failed to show efficacy . VaccineTNFSF13 (APRIL), were also enriched in uninfected rhesus monkeys in the NHP studies (We previously showed that a vaccine-induced gene signature identified in B cells by an unbiased transcriptome-wide RNA-seq approach associated with decreased risk against simian immunodeficiency virus (SIV)/simian-human immunodeficiency virus (SHIV) infection in NHP studies evaluating the Ad26 vaccine . This ge studies . Althoug studies , we were studies . To dete studies . We propSince the gene signature associated with protection within the vaccinated group in multiple studies from different sources and regimens, we wanted to further confirm that this signature was truly associated with protection by looking for its presence or absence in a human vaccine trial that failed to show efficacy . We screIn previous analyses of NHP preclinical studies, we utilized a composite gene expression score (GES) consisting of an average of standardized expression of the specific number of enriched genes in one study to predict infection status in an independent study using the overlapping expressed genes from the first study . While tTYROBP, SIRPA, SIRPB1, and RAB20)  .TYROBP, FPR1, CD14, CCR1, TNFRSF1B, CD68, CD63, CEBPD, and LST1. Clustering on the enriched genes to identify highly interconnected regions in the GeneMANIA network showed that TYROBP, FPR1, CD14, TNFRSF1B, CD68, and LST1 were all members of the cluster with the greatest number of genes  to have independent associations with reduced risk of acquisition and was mainly expressed in the monocyte compartment  technology in a subset of the vaccinated RV306 participants . This tepartment . CD14+ mpartment .Though an effective vaccine has been a challenge for the HIV field, we see a glimpse of optimism in partially protective NHP and human studies . These sThe gene signature was previously associated with HIV vaccine protection in a number of studies with partial protection . We hypoNext, we developed a method to assess this gene signature compared to other correlates of risk in the human RV144 study. This method employs an analytical method using a GES which is computational score generated from the average expression of all genes enriched in the signature and associating with a phenotype. This method was tested across different NHP studies and RV144 and showed consistent association with reduced risk of infection based on the study-specific GES. The composite GES computed from RV144 consisting of the standardized expression of 63 genes had the strongest association with decreased risk of infection and increased vaccine efficacy. The RV144 GES was also able to accurately predict infection status in the study. This study shows that the GES composite score provides a robust analytical measurement to explore the effect of genes as a continuous variable in immune-correlates analyses, and that it could be applied to other ongoing efficacy studies. Further, the analysis of RV144 breakthrough infections was consistent with GES being lower in the vaccinated infected participants compared to placebos and hence protective. These observations, albeit only significant in the group infected with single founder viruses, strengthen the premise of the RV144 GES being a correlate of reduced risk of infection.SIRPA, SIRPB1, RAB20, and TYROBP in the phagocytosis GO term (http://amigo.geneontology.org/amigo/term/GO:0006909#display-lineage-tab). These four genes were also connected to an additional 44 genes from the total 178 genes enriched in at least one of our GSEA analyses, supporting a possible role in phagocytosis  which was the most protective gene in the NHP studies. Although the geneset of interest was first seen in sorted B cells from vaccinated NHP, it was subsequently identified in transcriptomic data from PBMC in the RV144 study  and monocytes (Aqua live/dead−CD20−CD3−CD56−HLA-DR+CD14+) from PBMC of HVTN 505 vaccinees using RNAzolRT (MRC Inc) as per recommendations from the manufacturer. For the preparation of mRNA libraries, polyadenylated transcripts were purified on oligo-dT magnetic beads, fragmented, reverse transcribed using random hexamers, and incorporated into barcoded cDNA libraries based on the Illumina TruSeq platform. Next, libraries were validated by electrophoresis, quantified, pooled, and clustered on Illumina TruSeq v2 flow cells. Clustered flow cells were sequenced on an Illumina HiSeq (2000/4000) using 2×75 base paired-end runs. Total RNA from RV306 participants was extracted from whole blood collected in PAXgene Blood RNA tubes using associated RNA extraction  and GlobinClear purification kits  as per the manufacturer’s suggestions. RNA-seq was performed using the SMART-Seq technology  technology from 10× Genomics, based on the CITE-seq technology . Cell hahttps://cran.r-project.org/web/packages/dsb/index.html). Only cells with <10% mitochondrial genes were retained, and cells were assigned to specific donors using the HTO demultiplexing results. A total of 53,777 single cells remained after the quality control process. The gene expression matrices for all samples were normalized and integrated into a single object in Seurat (FASTQ files were demultiplexed with bcl2fastq v2.20 (Illumina). Alignment and counting were performed using Cell Ranger v3.1.0 (10× Genomics) and the human reference files provided by 10× Genomics (human genome GRCh38 and Ensembl annotation v93). The average number of genes per cell was 1453 and the average number of unique molecular identifiers was 4248. The mean read depth per cell was approximately 65,000–84,000. The minimum fraction of reads mapped to the genome was 88% and sequencing saturation was above 85% for all lanes, with an average of 88%. The computational analysis of ADT data was performed using the Seurat v3.1 package . HTO expn Seurat . Based on Seurat . This maThe antibody effector function ADCP was measured as previously described . BrieflyAssociation of the protective gene signature with infection (HVTN 505) or magnitude of median ADCP (RV306) responses were analyzed using the Gene Set Enrichment Analysis (GSEA) method as described previously . GSEA waComposite GES was computed as the average of standardized expression of normalized enriched genes in the gene signature in different vaccine studies. The samples in each vaccine study were grouped into outcomes after challenge or infection status after immunization . LogistiAmong 121 RV144 participants who became infected during the trial and had their HIV-1 genome sequenced at diagnosis, 43 had GES measurements computed from microarray data . VaccineLogistic regression that accounted for the sampling design was used for the univariate analyses of the 63 enriched genes. A radar plot of the significant genes was generated to illustrate OR and 95 % CIs. All ORs were reported per 1-SD increase. Significant genes resulted from univariate logistic regressions of the 63 enriched genes were further analyzed with a multivariate stepwise logistic regression to identify genes that were independently associated with HIV protection. Akaike information criterion was used to identify the optimal set of genes. The expressed enriched genes associated with higher magnitude of ADCP in RV306 at day 3 and 2 weeks post the RV144 vaccine regimen were used to compute the ADCP GES in RV144. Spearman correlation was calculated between the ADCP GES from the two time points and the infection GES, respectively.For prediction analyses, a GES was computed using the 93 genes enriched at the day 3 time point associating with magnitude of ADCP in RV306. The performance of the classifier was assessed using AUC with 95% CI. This model was then tested at the week 2 time point. The prediction accuracy of the model was also assessed on the week 2 test data set. The probability that gives minimum misclassification error was chosen as the cutoff. Similar analysis was done using the 82 genes overlapping between the 118 enriched genes from week 2 and the 93 enriched genes from day 3 post the 4th RV144 vaccination series that were associated with ADCP in RV306. The average expression values for 82 overlapping genes in RV306 at the day 3 and week 2 post vaccination time points were stratified by ADCP scores to generate a heatmap with the R package pheatmap. Values for each gene were scaled and the resulting z-scores were hierarchically clustered using the ‘complete’ method.All descriptive and inferential statistical analyses were performed using GraphPad Prism 8 (GraphPad Software) and R 3.6.1 (or later) software packages. Comparison of groups was performed using Mann-Whitney tests or t-tests when assumptions were met. All logistic regression models were adjusted for gender and baseline risk behavior and one significant principal component axis . A two-s public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Acceptance summary:Attempts to produce effective vaccines against HIV have not yet been successful with a lack of understanding of the correlates of protection being a significant limitation. This paper analyses gene expression in a number of human and non-human primate vaccine trials and identifies a profile that appears to correlate with protection from infection. This profile is linked primarily to monocytes and the ability of these cells to mediate antibody dependent cellular phagocytosis. The work has implications for ongoing attempts to generate effective vaccines against HIV and perhaps other viral diseases.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Päivi Ojala as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Genoveffa Franchini (Reviewer #2).Thank you for submitting your article "Monocyte-derived transcriptome signature indicates antibody-dependent cellular 1 phagocytosis as the primary mechanism of vaccine-induced protection against HIV-1" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The NHP data on ALVAC/gp120 /MF59 (that was not effective) , as requested by reviewer 2 should be included.2) Further information should be provided on the protective gene set in the various trials and the overlapping genes in these gene sets. Van Diagrams and Tables summarizing the common or private genes in the different NHP, and human trials would be very informative, as requested by reviewer 2.3) The title should be tempered, as suggested by two of the reviewers.4) You should respond to all of the points raised by the reviewers, either by the inclusion of additional data, modifications to the text or in a rebuttal letter.Reviewer #1 (Recommendations for the authors):In Vaccari et al. Nat Med. 2016 Jul; 22(7): 762-770 the RAS pathway is reported associated with resistance to infection and favorable innate immunity. Is this pathway observed as significant in the trials reported here? How are the pathways reveled here used to interpreted biologically how they can provide better protection based on the function they are involved with?All human samples described as RV306 are samples are derived 2 weeks after last RV144 vaccination, therefore the trial at that point was still RV144, no reason to call it RV306 and this trial should be left out, as that implies sampling after the additional boosting of RV144  and becomes confusing for the reader.When looking at Methods, it is confusing what is new to this paper and what was already part of previous publications . For instance, RNA transcriptomics appear to have been done for the previous publication. If so, it should not be here. The same is true for ADCP that was also reported in Ehrenberg et al., 2019. Methods listed here should only be those used for the new analyses reported here, not those used to generate data already reported in previous publications.Reviewer #2 (Recommendations for the authors):This is an interesting paper and i provide my suggestions to improve it. Most of all I believe that the choice of the vaccine modalities to be compared needs to be more comprehensive and the data are available.1)The NHP data on ALVAC/gp120 /alum ((that was efficacious) was analyzed, but not that of the ALVAC/gp120 /MF59 (that was not effective) .Question to be addressed: Was the "protective" gene set not enriched in the MF59 arm?This would be particularly important to strengthen the hypothesis put forward by the authors, in light of the failure of human HIV vaccine trial (HVTN702) that used the same vaccine modalities in RV144 except for the adjuvant (MF59 rather than Alum).2) Germane to the "protective" gene set described here and the conclusion that monocytes are important in protection is a study that tested, in parallel to the DNA prime /ALVAC/gp120/alum boost vaccine (Table 1) to the Ad26 prime / ALVAC /gp120/alum boost. The DNA prime was protective and vaccine efficacy correlated with classical monocyte frequency as well as with a monocyte gene signature. The Ad26 prime was not protective Question to be addressed: Was the "protective" gene set not enriched in the Ad26/ALVAC /gp120/alum arm?3) In the text it is difficult to grasp what is the protective gene set in the various trials and what are the overlapping genes in these gene sets. Van Diagrams and Tables summarizing the common or private geneses in the different NHP, and human trials would be very informative.4) There are several trials described here and all need to be summarized in Table 1Table 1 should include also RV306, and the origin of the samples analyzed 5) The title of the paper refers to …' phagocytosis as the primary mechanism of vaccine induced protection'a) The data on phagocytosis before and after vaccination in this subset of patients should be shownb) The Title should also be temperedMonocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis…. Please clarify in the text which gene set defines specifically phagocytosis?…as the primary mechanism of vaccine -induced protection …The conclusion is based on a correlation of a correlation between two independent trials.It should not be referred as the primary mechanism… Essential revisions:1) The NHP data on ALVAC/gp120 /MF59 (that was not effective) , as requested by reviewer 2 should be included.The premise of our findings is that in studies where there is partial efficacy, we find a common protective gene signature when comparing between uninfected and infected vaccine recipients. A similar analysis is not possible in the ALVAC-SIV/gp120/MF59 arm mentioned by the reviewer, since all 27 vaccinated animals became infected . We now clarify this at the beginning of the Results section and also by adding a sentence observing that we test this signature across studies/trials that show partial efficacy, and find the signature associates with trials that showed significant protection, but cannot test non-efficacious regimens where all animals/recipients were infected (Discussion section).2) Further information should be provided on the protective gene set in the various trials and the overlapping genes in these gene sets. Van Diagrams and Tables summarizing the common or private genes in the different NHP, and human trials would be very informative, as requested by reviewer 2.The overlapping genes in these gene sets are now detailed in an added Supplementary File 1b. This table shows the 200 genes in the SE29618_BCELL_VS_MONOCYTE_DAY7_FLU_VACCINE_DN geneset and the genes that were expressed or enriched in each of the different NHP or human trials. Venn diagrams of overlapping genes, as well as pathway analyses of enriched genes in different studies are shown in new Figures 4A, C-D and 5A.3) The title should be tempered, as suggested by two of the reviewers.We agree and have updated the title to “Monocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis as a potential mechanism of vaccine-induced protection against HIV-1”.4) You should respond to all of the points raised by the reviewers, either by the inclusion of additional data, modifications to the text or in a rebuttal letter.We have responded to all the points raised by the reviewers.Reviewer #1 (Recommendations for the authors):In Vaccari et al. Nat Med. 2016 Jul; 22(7): 762-770 the RAS pathway is reported associated with resistance to infection and favorable innate immunity. Is this pathway observed as significant in the trials reported here? How are the pathways reveled here used to interpreted biologically how they can provide better protection based on the function they are involved with?There are several pathways in current literature that associate with innate immunity and favorable responses in specific vaccine studies. We did not choose to look for specific pathway(s) but used an unbiased method to identify the strongest correlates of vaccine efficacy in multiple NHP and human studies. We also investigated immune responses associating with this signature, which showed that ADCP might be a potential lead for mechanistic investigation. The associated pathways show leukocyte activation, lysosomes and signaling by cytokines to be important in these partially protective vaccine responses. We think that such omics approaches can potentially be used to assist with down-selection of promising vaccine candidates in preclinical and early clinical trials, and to identify potential mechanisms of protection that might be targeted in future vaccine designs.All human samples described as RV306 are samples are derived 2 weeks after last RV144 vaccination, therefore, the trial at that point was still RV144, no reason to call it RV306 and this trial should be left out, as that implies sampling after the additional boosting of RV144  and becomes confusing for the reader.The RV306 trial is independent from the RV144 trial, and there are no overlapping participants. The RV306 trial conducted in Thailand used the same RV144 vaccine series but included additional boosts after the fourth vaccine . We focused on timepoints that overlapped with the original RV144 vaccine series for generating RNA-seq and ADCP data. We now include supplementary file 1a to clarify the different studies described in this manuscript.When looking at Methods, it is confusing what is new to this paper and what was already part of previous publications . For instance, RNA transcriptomics appear to have been done for the previous publication. If so, it should not be here. The same is true for ADCP that was also reported in Ehrenberg et al., 2019. Methods listed here should only be those used for the new analyses reported here, not those used to generate data already reported in previous publications.We have included in the methods specifically bulk transcriptomics for RNA-seq in the HVTN 505 and RV306 studies, and ADCP assay data for RV306. These are new datasets and have not been published previously, but to clarify further we have included supplementary file 1a showing the new transcriptomic datasets that were generated for this manuscript.Reviewer #2 (Recommendations for the authors):This is an interesting paper and i provide my suggestions to improve it. Most of all I believe that the choice of the vaccine modalities to be compared needs to be more comprehensive and the data are available.1)The NHP data on ALVAC/gp120 /alum ((that was efficacious) was analyzed, but not that of the ALVAC/gp120 /MF59 (that was not effective) .Question to be addressed: Was the "protective" gene set not enriched in the MF59 arm?Please see our response above, ‘essential revisions point 1’.This would be particularly important to strengthen the hypothesis put forward by the authors, in light of the failure of human HIV vaccine trial (HVTN702) that used the same vaccine modalities in RV144 except for the adjuvant (MF59 rather than Alum).2) Germane to the "protective" gene set described here and the conclusion that monocytes are important in protection is a study that tested, in parallel to the DNA prime /ALVAC/gp120/alum boost vaccine (Table 1) to the Ad26 prime / ALVAC /gp120/alum boost. The DNA prime was protective and vaccine efficacy correlated with classical monocyte frequency as well as with a monocyte gene signature. The Ad26 prime was not protective Question to be addressed: Was the "protective" gene set not enriched in the Ad26/ALVAC /gp120/alum arm?Please see our response above, Essential revisions point 1. We were unable to test the presence of the gene signature in the Ad26/ALVAC arm from the Vaccari et al. 2018 study, since all 22 monkeys became infected.3) In the text it is difficult to grasp what is the protective gene set in the various trials and what are the overlapping genes in these gene sets. Van Diagrams and Tables summarizing the common or private geneses in the different NHP, and human trials would be very informative.We have now included the overlapping genes in an added supplementary file 1b, which shows the 200 genes in the GSE29618_BCELL_VS_MONOCYTE_DAY7_FLU_VACCINE_DN geneset. Status of each gene  is shown for each study. We have additionally created a column in the list which provides the total number of genes expressed and overlapping in each study.4) There are several trials described here and all need to be summarized in Table 1Table 1 should include also RV306, and the origin of the samples analyzed We now include a table to summarize all studies specifically investigated in this paper, along with their source of origin, in an added supplementary file 1a. To help differentiate this from studies with vaccine efficacy or protection data, we have separated this from Table 1 that includes the vaccine preclinical and clinical trials.5) The title of the paper refers to …' phagocytosis as the primary mechanism of vaccine induced protection'a) The data on phagocytosis before and after vaccination in this subset of patients should be shownWe examined the presence of the gene signature with magnitude of ADCP scores only in the post vaccination samples. This analysis is not possible prior to vaccination, since there are only 4 participants (active arm) who met the threshold of positive ADCP scores in the absence of vaccination.b) The Title should also be temperedMonocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis…. Please clarify in the text which gene set defines specifically phagocytosis?…as the primary mechanism of vaccine -induced protection …The conclusion is based on a correlation of a correlation between two independent trials.It should not be referred as the primary mechanism…We have updated the title per the reviewer’s suggestion (please see above).