Harnessing A3G for efficient and selective C-to-T conversion at C-rich sequences
Authors: Jianan Li
Xiangyang Li
Guanglei Li
Wenxia Yu
Ping Li
Aibin Liang
Shisheng Huang
Xingxu Huang
Tian Chi
Journal: BMC Biology (2021)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Apobec 3G; Base editing; C-rich; Motif.
Affiliations: School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China., University of Chinese Academy of Sciences, Beijing, 100049, China., Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China., Department of Hematology, Tongji Hospital of Tongji University, Shanghai, 200092, China., School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. ., Department Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA. ., CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China. .

Abstract:  Background: Site-specific C&gt;T DNA base editing has been achieved by recruiting cytidine deaminases to the target C using catalytically impaired Cas proteins; the target C is typically located within 5-nt editing window specified by the guide RNAs. The prototypical cytidine base editor BE3, comprising rat APOBEC1 (rA1) fused to nCas9, can indiscriminately deaminate multiple C's within the editing window and also create substantial off-target edits on the transcriptome. A powerful countermeasure for the DNA off-target editing is to replace rA1 with APOBEC proteins which selectively edit C's in the context of specific motifs, as illustrated in eA3A-BE3 which targets TC. However, analogous editors selective for other motifs have not been described. In particular, it has been challenging to target a particular C in C-rich sequences. Here, we sought to confront this challenge and also to overcome the RNA off-target effects seen in BE3. Results: By replacing rA1 with an optimized human A3G (oA3G), we developed oA3G-BE3, which selectively targets CC and CCC and is also free of global off-target effects on the transcriptome. Furthermore, we created oA3G-BE4max, an upgraded version of oA3G-BE3 with robust on-target editing. Finally, we showed that oA3G-BE4max has negligible Cas9-independent off-target effects at the genome. Conclusions: oA3G-BE4max can edit C(C)C with high efficiency and selectivity, which complements eA3A-editors to broaden the collective editing scope of motif selective editors, thus filling a void in the base editing tool box. 

Retracted: No

PubPeer comments:
 Everyone knows that things have gone very wrong in the activities labelled  science; that they have come to lack the fundamental characteristic of science, that is, reliable control over phenomena, such that we may produce a given effect over and over again, at will. For a number of years now, we have been constantly hearing about the various  replication crises. While there have been many expressions of concern over this, it doesn t look as though anything is going to change anytime soon. Reasons of convenience created the crisis, and reasons of convenience will ensure the conditions that produced it continue to prevail. Recently, a bunch of academics penned an article suggesting that, as a step toward greater rigor, the permissive p .05 criterion, considered to produce too many  false positives, be tightened to p .005. In the present article, a bunch of academics argue that no, we should keep it where it is. (I think that s called going around in circles). The authors don t offer much of a rationale - at least, not one that makes real science a priority. Essentially, a thick slice of their rather vague argument is that if you raise the bar too high u2013 if you make it harder to publish false positives (see below) u2013 you ll discourage u201cresearch.u201d A higher bar, they say, would HARM scientific practice. Why? First, there will be fewer replication studies, because to achieve (publishable) results you ll need bigger sample sizes. So, better more replications that you can t count on than less. Second, a higher bar entails a  risk of reduced generalizability and breadth ! This doesn t seem to make any sense. Wouldn t a more reliable result be more likely to generalize? Again, the rationale isn t scientific, but practical. Basically, achieving an adequate number of false positives to publish would become more difficult, in part because more expensive, and: u201cThus, researchers may become less motivated to study unique populations or collect difficult-to-obtain data, reducing the generalisability and breadth of findings.u201d My head is spinning. So basically they re saying we need to keep the bar low or u201cresearchu201d - the non-replicable kind, that is - won t get done because it would be too expensive or too difficult. The only kind of research both the pro-.005 and the anti-.005 group are referring to; the only kind that seems to be conceivable in the current culture, is post hoc correlation-fishing. This type of activity has *never* produced reliable results, only chance correlations specific to particular datasets. So the only difference a p .005 standard makes is a reduction in the publishable output, not an improvement in the reliability of the (false) positives. The concern of the anti-.005 crowd is to keep up the appearance of productivity, not improve quality. The u201cprior probabilityu201d of any improvement is... I predict business-as-usual, non-replicability-as-usual. . , 
 This paper was criticised as part of a self-reflection posted on Twitter on 2nd-3rd April, 2021 (twitter TheHandLab status 1377760796130869248) Criticism: I ve been hanging on the citation coat-tails of this crowd-sourced paper. My contribution to this paper was mostly replacing with u2264 in the Google document (I joke you not!), but I heartily-agree with the message. I find the comment from Maniatis here far too negative, too dismissive of the real changes that are happening in science, and too dismissive of (what I believe is) the majority of scientists who want to do good work. Science is far from perfect, but I believe it s getting better. Even PubPeer is helping give a platform to anyone to express disagreement! One rationale is that changing alpha to reduce false-positives will increase false-negatives; some fields, populations, or methods will then find it very difficult to work (e.g., what do you do when your population N=1, for example subjects with a unique brain lesion?). There s nothing wrong with considering practical implications alongside lofty scientific ideals. And it is easy for a scientist to get smaller p-values: just do worse control conditions (e.g., in TMS brain stimulation research: don t do a control, or use a weak control). Leaving more things uncontrolled is worse science, but effect-sizes will be bigger and p-values smaller. Changing alpha is not the solution: improving science is. , 
 #2 You say I was far too negative, too dismissive of the real changes, without touching on what these real changes are. And after all, all I said was that after years of discussion and dispute over alpha level, it seemed odd to me that a new paper comes out saying...let s keep things the same. I agree that science needs to be improved, I just don t see any signs that it is or that the right moves are being made to make it better. , 
 The improvements in science and society are clear and many, in my view. Here s a list of a few things that were not available during previous replication crises in Psychology (e.g. when Barber wrote his 1976 manifesto: https: doi.org 10.1016 C2013-0-02792-2): - the internet (speeds and opens up everything) and many open paper repositories at Universities and elsewhere - pubpeer blogs social media - much easier to criticise others, no gatekeepers - DOI altmetric ORCID PubMed GoogleScholar - all free e-databases that link work together allowing critiques to be found - DORA Leiden other multi-national manifestos, signed by thousands of institutions - low-cost independent journals can start without major gatekeepers funders (http: rescience.org x, https: online.ucpress.edu collabra, https: www.jtrialerror.com ) - preprints - the Open Access Open Science movements - The UK reproducibility network - RIOT science clubs (http: riotscience.co.uk ) - OSF and the various multi-lab replication projects in the last 5-10 years I can see that it *feels* like science is not improving, because much of the commentary is about fraud, bias, spin, and hype; that many new entrants are problematic (predatory journals, paper-mills); and that many dinosaur-like practices remain. But in general the systems of science are improving and working much better, in my view. We are at least talking about the problems much more than I can ever remember. When I started in the 1990s, it was acceptable to publish a (high-profile) neuroscience paper making general claims about the human brain with N=4 subjects, replicated or with control conditions only in N=2. Now the same sort of experiments would likely be rejected by reviewers. Sometimes N=4 is sufficient, and sometimes alpha=0.10 is sufficient. It just depends, and no blanket rule-changes should be introduced. 5% works fine for me! , 
 From the perspective of a physicist, I have to ask: why bother with an alpha value at all? Seriously. Every now and again Nature publishes some paper(s) on improving science in various ways, and I must say, dropping p-values (or at least alpha values) is always the most appealing to me. My favourite of these is linked below (written after the paper discussed by this thread). My favourite: https: www.nature.com articles d41586-019-00857-9 To me, it makes much more sense to simply state your findings, with a discussion of your interpretations. Binning research in statistically significant if it hits some arbitrary threshold really doesn t make any sense. Bringing the discussion back to this paper, forcing people to justify (preferably with some numerical background) for their cutoff seems totally valid, if not necessary if people insist on using p-values and alpha values. In fact I agree with almost everything in #4, right up to the point 5% works fine for me! -- like, how do you know it works fine for you? Is your work reproducible? 5% could mean a significant false positive rate, depending on the effect size you are studying. , 
 #5 Note that physicists are also not immune to using these arbitrary boundaries. The discovery of the Higgs boson comes to mind, using a 5 sigma cut-off (p = 0.00003%). See e.g. https: medium.com @chris.m.pease the-higgs-boson-and-5-sigma-eec238b43f93 or https: iopscience.iop.org article 10.1088 1742-6596 510 1 012001 pdf. I believe there are even examples of beamlines trying to beat each other in getting to 5 sigma significance to claim particle discovery. Still, the above is very much different from hypothesis-poor A-B significant correlation finding that is a bit too common. I think https: retractionwatch.com 2021 07 13 in-hindsight-the-mistake-was-quite-stupid-authors-retract-paper-on-stroke is an interesting warning on interpreting significance. An association was found between A and B. Except that after publication it was found that the A- B assignment was random and hence the association a fluke. Could only read the abstract though, fortunately no mention of significance there. , 
 For an explanation of The Epistemic and Pragmatic Function of Dichotomous Claims Based on Statistical Hypothesis Tests see https: psyarxiv.com af9by . Yes, you can also just describe findings - but you can also adopt a philosophy of science where you make claims. Everyone is free to choose their preferred approach. If you want to evaluate what works best, read up on the idea by Meehl of a cliometric metatheory. , 
 #7 Where in the space between  making [dichotomous] claims and  just describing findings does theory and prediction, in the sense that these concepts have been used, successfully, in the past, come in? Also, what bearing does any of this have on the replicability problems? , 
 #6  Note that physicists are also not immune to using these arbitrary boundaries. -- As you point out, that is sort of true, however papers that are nowhere near 5 sigma are printed all the time because they still advance our collective scientific understanding, usually in the context, of hey, this effect doesn t exist . #7  what bearing does any of this have on the replicability problems? -- My point simply was that I don t think the binary labeling of significant or insignificant is a good measure; if we just printed the results of all experiments, regardless of whether they are  statistically significant , we might get a more complete picture. That being said, I don t think this is going to be a one-size-fits-all solution to fix science , one could argue that better peer review, or better oversight, or better sharing of data, or better experimental design, or... etc. may all have impacts on replicability. ,