Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19
Authors: Yi-Ling Chen
Lance Turtle
Malcolm G Semple
Teresa Lockett
ISARIC4C Investigators
Juthathip Mongkolsapaya
De-Sheng Ker
Jeremy Ratcliff
David I Stuart
Ushani Rajapaksa
Graham Ogg
Christina Dold
Persephone Borrow
John Frater
Krishanthi Subramaniam
Dannielle Wellington
Piyada Supasa
Susanna Dunachie
Timothy Rostron
Paul Thomson
Philip Goulder
Xuan Yao
Wanwisa Dejnirattisai
Yuguang Zhao
Peter J M Openshaw
Mariolina Salio
Oxford Immunology Network Covid-19 Response T cell Consortium
Peter Simmonds
Thushan de Silva
Jose Slon-Campos
Yanchun Peng
Andrew McMichael
Alfred A Antson
Ronghua Jing
Wayne Paes
Ling-Pei Ho
Richard J Cornall
Julian C Knight
Oliver W Bayfield
Chang Liu
Eleanor Barnes
Tao Dong
Benedikt M Kessler
Yonghong Zhang
César López-Camacho
Jeremy W Fry
Guihai Liu
Danning Dong
Paul Klenerman
Ping Zhang
Giorgio Napolitani
Beibei Wang
Robert Levin
Zixi Yin
Nikolai F Schwabe
Alexander J Mentzer
Paul Sopp
Guido C Paesen
Jonathan M Grimes
J Kenneth Baillie
Georgina Kerr
M Azim Ansari
Gavin R Screaton
Christopher P Conlon
Dorothy E D P Hawkins
Shona C Moore
Journal: Nature Immunology (2020)
Discipline: IMMUNOLOGY
Keywords: SARS-CoV-2; T cell memory; COVID-19; immunity; peptides
Affiliations: MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK., Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK., Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK., Nuffield Department of Medicine, University of Oxford, Oxford, UK., Sequencing and Flow Cytometry Facility, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK., York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK., Tropical and Infectious Diseases Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK., NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK., Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK., The Florey Institute for Host-Pathogen Interactions, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK., ProImmune, Oxford, UK., Anaesthesia, Critical Care and Pain Medicine Division of Health Sciences, University of Edinburgh, Edinburgh, UK., National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK., Oxford University Hospitals NHS Foundation Trust, Oxford, UK., Worthing Hospital, Worthing, UK., Beijing You'an Hospital, Capital Medical University, Beijing, China., MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. .

Abstract:  The development of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines and therapeutics will depend on understanding viral immunity. We studied T cell memory in 42 patients following recovery from COVID-19 (28 with mild disease and 14 with severe disease) and 16 unexposed donors, using interferon-γ-based assays with peptides spanning SARS-CoV-2 except ORF1. The breadth and magnitude of T cell responses were significantly higher in severe as compared with mild cases. Total and spike-specific T cell responses correlated with spike-specific antibody responses. We identified 41 peptides containing CD4+ and/or CD8+ epitopes, including six immunodominant regions. Six optimized CD8+ epitopes were defined, with peptide-MHC pentamer-positive cells displaying the central and effector memory phenotype. In mild cases, higher proportions of SARS-CoV-2-specific CD8+ T cells were observed. The identification of T cell responses associated with milder disease will support an understanding of protective immunity and highlights the potential of including non-spike proteins within future COVID-19 vaccine design. 

Retracted: No

PubPeer comments:
 The study has two main components: a survey (observational) and an experiment (manipulative). There is missing data from the former, and several aspects of the latter s methodology require clarification (in addition to some missing data). First, the data uploaded to dryad only contains the experimental data, and therefore the survey data are missing in their entirety. A corrigendum containing the survey data is warranted, or alternatively a link to where this data may be obtained if they were previously published. In addition, the gall density variable used to compare density across treatments in the experiment is not provided. Regarding the experiment, I found the methodology confusing and from what is written it would be difficult to replicate. Much more detail is needed. The author writes that  Patches varied in size by a factor of ~2, but treatments were applied randomly... . Galls were seeded by placing 30 mature (i.e., overwintered) galls in each of 48 patches in May of 2017, and the flies emerged to generate the focal populations that were manipulated in June 2017. The author created three different mean size treatments and two variance in size treatments. To apply treatments, the author:  removed large galls, small galls, or both to create a three-by-two factorial design with low, medium, or high average gall size . The author also writes that  that treatments did not differ in gall density (P=.68 in a linear model) and that [he] removed a similar number of galls from all populations. Examining the data, simple removal of large or small galls would not create the desired pattern. In particular, there are stark differences in the maximum or minimum gall sizes between *variance treatments* within the same *mean treatment*. The cut-offs of gall size occur at seemingly arbitrary values with odd decimal cut-offs that would have had to have been estimated well in advance (e.g. the maximum size in the low mean and low var treatment was approximately 19.1 or 19.2, and the maximum size of the low mean and high variance treatment was approximately 20.6 or 20.7). See the plot below for reference. Color here is just the variance treatment. Clearly there is more to what was done. Can the author please clarify exactly how the treatments were implemented? ![file] I next wanted to infer how many galls must have been present in each patch before treatment application. For the purposes of this analysis, I am assuming that the statistical distribution of gall sizes is approximately similar to the distribution that is observed in the low variance treatments (and that the high variance treatments were thinned out). I estimated the number of galls per mm in various ranges of the data and generated a null distribution. Some quick math suggests that the starting density of each patch was no lower than 209 galls before treatments were applied. This implies that at least 8000 galls were culled in total. Visually, the author s experiment involved making a distribution of galls in the field that originally resembled the BLUE dots into the observed distribution in RED: ![file] My estimation of the original densities is conservative because the dense bits drop off very rapidly. It s more likely that 10,000 galls were culled in total and that the starting density in each patch was more like ~250. This is much greater than the average number of galls in the same study area documented by Start Gilbert 2016 (mean of 26 galls; and mean of 91 stems per patch). Were different definitions of patch used here than in Start Gilbert 2016? It would be useful to clarify. Here is a plot from that earlier study of mean and SD gall sizes across patches: ![file] Given this distribution and the fact that most *Eurosta* were local (see next paragraph), it would seem likely that each patch would have required a thorough survey in order to obtain the virtually indistinguishable distributions of gall sizes within treatments that are observed. Was this done? Note that most patches have a low density of galls above 25 mm, which would make obtaining sufficient galls for this treatment especially difficult. With these necessary starting point of at least 200 galls, the ~30 galls added per patch would have had a very minor effect on total gall densities. This means that the majority of the galls were from local *Eurosta*. Given that the previous work by this author (eg Start and Gilbert 2016) shows massive differences among nearby populations in gall size, with some barely overlapping, it is surprising how consistent the different populations were given that they were assigned randomly. If treatments were applied in June 2017, and galls grew until mid-July, it is remarkable how constant the maximum and minimum values were across populations. This is especially surprising given the strongly different amounts of early larval death observed (expected to reduce gall size) between the different treatments (e.g., 2.4% in the high-mean low-variance treatment compared to 32% in the low-mean low-variance treatment). Wouldn t a high amount of ELD cause more variation in gall sizes? I don t fully understand ELD so I might have this wrong. Concluding: there is missing data that requires an update to the data package. And there are many aspects of the experiment methodology that require more explanation. How many galls were culled per patch? How large were patches and were they defined differently from previous papers at this study location? How is it that the minimum and maximum values are so similar in spite of ~1 month of variation in growth? Were most of the focal *Eurosta* locally adapted instead of from the ~30 gall randomized seed ? , 
 # Means and variances of the treatments Table S1, reproduced here for convenience, gives the mean and variance (incorrectly given units of mm rather than mm^2^ throughout the paper) of the gall diameters by treatment. Table: Table S1 Mean_Treat | Var_Treat | Mean | Variance -----------| ---------- |------ |--------- High | High | 25.27 | 3.76 High | Low | 25.04 | 0.55 Medium | High | 22.01 | 3.95 Medium | Low | 22.12 | 0.41 Low | High | 17.99 | 4.11 Low | Low | 17.88 | 0.38 These statistics match the description of the experimental design Population mean gall size spanned from ~18 mm (small) to ~22 mm (medium) to ~25 mm (large). Within-population variance in gall size ranged from ~0.4 to ~4 mm. but they do not match the mean and variance of the archived data. Table: Reproduction of table S1 from the archived data. mean_treat | var_treat | Mean | Variance ----------- | ---------- |--------- |----------- high | high | 25.04204 | 2.7588909 high | low | 25.57222 |0.5551020 mid | high | 22.01839 | 11.5104891 mid | low | 21.22555 | 0.1893685 low | high | 17.99571 | 4.7587577 low | low | 17.58545 | 1.4071299 None of the entries are an exact match. Some of the means could be generated by incorrectly rounding the data. Several of the variances are very different from that reported. The supplementary material also includes histograms of gall diameter by treatment (figs S1-S6). ![file] Figure: Reproduction of figures S1-S6. Mean treatments are arranged left to right, variance treatments vertically Even though I have tried to match the bin width and centres, none of my histograms exactly match those in the supplementary material, but all are similar enough that, at least for the high variance treatments, gross difference between the reported and actual variance are not expected. Unless there is an error in my code, either table S1 is incorrect and the treatments do not match what was reported, or the archived data are not the correct data. In any case, table S1 and figure S5 cannot both be correct. Please can the author confirm that the correct data are archived, and archive the code needed to reproduce the supplementary material. Please can the author also please archive the code used for the simulation as the described rejection algorithm will be exceptionally slow for generating samples with a mean far from the population mean. Code to reproduce these analyses can be found at https: github.com Sorbus-torminalis start blob main R individual_pop.Rmd , 
 An expression of concern has been issued. See [here] . , 
  Key data to reproduce results are missing incomplete from the Dryad data repository for several papers by an author, and cannot be obtained due to laptop theft (I confirmed this with the Dept of Justice) . Source: https: twitter.com DanielBolnick status 1379177536656187406 , 
 It appears the computer and hard drive with all of the data were stolen (confirmed by investigation by Daniel Bolnick), but this occurred in 2019. While this paper was published in 2019, I feel it is safe to assume that the data would have been available at time of publication and deposition of the partial data. What has not been addressed by any expression of concern or erratum is why the full and complete data were not deposited from the beginning? We cannot validate the analyses in the papers, yet the deposited data still document clear errors and problems. , 
 See https: retractionwatch.com 2021 05 03 ecologist-who-lost-thesis-awards-earns-expressions-of-concern-after-laptop-stolen for backgrounds. ,