Reff) to estimate the transmissibility. Daily meteorological data from 2008 to 2017 were used to determine Spearman's correlation with reported new cases and Reff. The SEIARW model fit the data well . The simulation results showed that the reservoir-to-person transmission of the shigellosis route has been interrupted. The Reff would be reduced to a transmission threshold of 1.00  0.82–1.19) in 2035. Reducing the infectious period to 11.25 days would also decrease the value of Reff to 0.99. There was a significant correlation between new cases of shigellosis and atmospheric pressure, temperature, wind speed and sun hours per day. The correlation coefficients, although statistically significant, were very low (<0.3). In Wuhan, China, the main transmission pattern of shigellosis is person-to-person. Meteorological factors, especially daily atmospheric pressure and temperature, may influence the epidemic of shigellosis.The article aims to estimate and forecast the transmissibility of shigellosis and explore the association of meteorological factors with shigellosis. The mathematical model named Susceptible–Exposed–Symptomatic/Asymptomatic–Recovered–Water/Food (SEIARW) was used to explore the feature of shigellosis transmission based on the data of Wuhan City, China, from 2005 to 2017. The study applied effective reproduction number ( High-risk groups in high-income countries include travellers and MSM (men who have sex with men), and in low- and middle-income countries include children aged 1–4 years [Shigella spp. are among the causes of diarrhoea in children [In 1892, dysentery was described as ‘one of the four great epidemic diseases of the world’ by the eminent physician William Osler [children . Despitechildren .With China's rapid economic growth, the disease once associated with poverty is becoming a chronic illness . EconomiR0) to evaluate infectious diseases [R0 is difficult to quantify, here, the effective reproduction number (Reff) was adopted to evaluate the shigellosis transmission instead [Kermack and McKendrick  built a diseases –17. Beca instead .Recently, studies have suggested a correlation between shigellosis incidence and climatic factors, including atmospheric pressure, sun hours, temperature, relative humidity, wind speed and precipitation , 19–22; The SEIARW dynamics model was used to simulate shigellosis transmission to further quantify the transmissibility and estimate its correlation with meteorological factors.https://www.ncmi.cn) and the demographic data including total population, birth rate and death rate were obtained from the Wuhan Statistical Yearbook.Data regarding shigellosis from January 2005 to December 2017 were obtained from Wuhan City through the China Information System for Disease Control and Prevention (CISDCP). Daily meteorological data  from 2008 to 2017 were obtained from the National Population Health Data Centre . Therefore, the transferred rate from exposed to asymptomatic and symptomatic is denoted by pωE and (1 – p)ωE, respectively.The average incubation period of illness is 1/I or A will be recovered after a symptomatic infectious period (1/γ) or an asymptomatic infectious period (1/γ'), respectively.People in shigella, with a μI and μ'A shedding rate, respectively.Symptomatic and asymptomatic individuals can shed the Shigella spp. die in water/food after a period, and the daily rate of decrease of the pathogen is ɛW.Based on our previous study , the popβ, Wβ, κ, p, ω, γ', γ, c, ɛ, μ, μ', br and dr, which were defined and valued in N denotes total number in population, we perform substitution b = βN, Wb = WN/ɛμβ, s = S/N, e = E/N, a = A/N, i = I/N, w = ɛW/μN, r = R/N and μ = cμ', and use the dimensionless differential equations of the model (Model 1):Reff to estimate transmissibility by the value of b simulated for each time segment such as seven parts in 2014 , 1.0000 (0.3333–1), 0.1000 (0.0037–0.27), 0.0741 (0.0477–0.1428), 0.0286 (0–0.0357), 0.3125 (0–1) and 0.6931 (0–1), respectively, based on previous research  and 2 (SEIAR) to previously reported shigellosis data.We set research  [12, 13,b and Wb were set to 0; (b) b was set to 0; (c) Wb was set to 0; and (d) b and Wb were taken value from the model fitting (None). The Wb = 0 means either μ = 0 or Wβ = 0 or both are 0 so this is not just setting water/food-to-person contact rate to 0. The total number of infected cases was monitored in each scenario.To evaluate the contribution of the two transmission routes, we employed a method called ‘knock-out’ simulation whose theory comes from the technique of gene ‘knock-out’, which could make one of an organism's genes inoperative. Furthermore, we performed four scenarios with the ‘knock-out’ simulation (cutting off diverse routes): (a) Reff = 1.16, γ = 0.0741 and infectious period (IP) 13.5 days. The DIP was assumed as follows: (1) an infected individual would go to a hospital immediately after becoming symptomatic, and (2) the hospital can diagnose and treat the patient. We calculated the value of Reff, IP and DIP with a change proportion (Δγ) of 10% and 20%, respectively. The DIP was calculated as follows:We adopted the decreasing the infectious period (DIP) method to estimate the intervention effectiveness in 2017 of Wuhan City. The baseline in 2017 was χ2 to evaluate the goodness of fit of the SEIARW model.Berkeley Madonna 8.3.18  was adopted for simulations. Calibration was performed via minimizing the root-mean-square deviation and the Runge–Kutta method of order four with tolerance set to 0.001 was adopted to solve the differential equations , 23, 24.Reff to estimate the seasonality of transmission.Data in 2014 were divided into seven parts that were called segments and the time step size of the simulation was set to be one day . We summrs), determined using SPSS 21.0 , was used to evaluate how meteorological variables were related to shigellosis. To consider the lag effect between risk factors and disease, in one study of meteorological variables and shigellosis, it was found to be 3 and 4 days [Reff of the corresponding period, then calculated the mean of the meteorological variables and estimated the correlation.The Spearman's correlation coefficient (d 4 days . We laggs.d.) and mean of the simulation were obtained. The sensitivity analysis was only performed for data in 2014 to avoid the yearly repeated simulation. Usually, the transmissibility of infectious diseases is different between rising and reduction trends. The year 2014 was divided into seven segments and each was calibrated . From 2005 to 2011, the incidence rate and the number of reported cases were stable. However, from 2011 to 2015, the incidence rate and the number of reported cases decreased quickly, followed by a steady trend after 2015.As shown in χ2 = 0.00046, p > 0.999). The Wb was 3.4331 × 10–10 (95% CI 1.4179 × 10–11–6.7245 × 10–10) and b was 0.0892 (95% CI 0.0741–0.1044). The values of b and Wb with no obvious seasonality in different segments per year are shown in Supplementary Table S1.In b = 0) as cutting off double routes (b = 0 and Wb = 0). Meanwhile, a consistent number of cases was observed between interrupting water/food-to-person route (Wb = 0) and control (b and Wb ≠ 0).We found the person-to-person transmission route is more significant  accordinReff had a relative fluctuation from 2005 to 2017, the trend was gradually decreasing. The mean values . It was forecasted to reach the epidemic threshold of 1.00 (95% CI 0.82–1.19) in the year 2035, according to the exponential model.Although n values  were calb had the same trend as Reff. Meanwhile, we found that the contribution of water/food-to-person had some effects in the previous 2 years (Reff) was mainly concentrated in October.We observed that the transmissibility of shigellosis fluctuated within the range of 1–2 . The par 2 years , but the 2 years . The sears = −0.282), temperature (rs = 0.299), relative humidity (rs = −0.178), wind speed (rs = −0.076) and sun hours (rs = 0.096); there was no significant correlation between new cases and precipitation (rs = −0.010). However, we just found a positive significant correlation between Reff and sun hours (rs = 0.211). Furthermore, there was no obvious change in rs when the meteorological variables were lagged by 1, 2, 3 and 4 days.Our results  showed sγ) and to determine the conditions to achieve Reff = 1.00. The results showed that the transmission of shigellosis would probably be interrupted (Reff = 0.99) if the infectious period were shortened by 2.25 days ; or managing shigellosis cases so that they rarely excrete pathogens to drinking water and food (it can be considered that μ = 0). Although transmission has been interrupted when b and Wb are set to zero, there is still a section of exposed people in compartment E who will develop cases. The SEIAR model across sex was employed in a study to discuss interpersonal transmissibility; different transmissibility was found in different genders [Several studies indicated a reduction of the incidence of shigellosis in China , 26, 27,smission . This is genders . Meanwhi genders , which iReff to be 1.09–1.29 in Wuhan City (higher than the epidemic threshold of 1.00). This finding suggests that one symptomatic/asymptomatic individual can infect at least 1.03 susceptible persons.Compared with influenza, Ebola virus disease and norovirus infection, shigellosis is not highly contagious –16, 28. Reff has a decreasing trend. We forecast that shigellosis in Wuhan City would not lose epidemic transmission capability until 2035 (Reff = 1.00). On one hand, meteorological factors can influence the transmissibility of shigellosis [Reff. However, we found that the transmission of shigellosis would be interrupted in Hubei Province after 2029 [According to an exponential model of the transmissibility of shigellosis, gellosis . On the ter 2029 . This maReff = 0.99. Therefore, reducing the infectious period is especially important for secondary prevention. We should take measures such as isolation or antibiotic treatment as early as possible.Simulated scenarios to shorten the infectious period showed that reducing the infectious period by 2.25 days achieved Reff), which was mainly concentrated in October. We consider that it may be related to the outbreak of shigellosis in October, and the seasonality of transmissibility needs to be further studied.The obvious seasonality of shigellosis onset was observed in this study (mainly concentrated from June to September). This may be related to the summer vacation of primary and middle school students from July to August. Furthermore, we found that there is a seasonality of peak transmissibility (rs = 0.878) and average rainfall (rs = 0.931) in Beijing City [rs = 0.010); and we found a weak correlation between new cases and temperature (rs = 0.299). These findings suggest that analysing the correlation with daily data might yield weaker results than with monthly data [Several studies have demonstrated the correlation between reported cases of shigellosis and climatic factors over many years, such as temperature (positive), rainfall (positive), relative humidity (positive), wind speed (negative), sunshine duration (negative) and atmospheric pressure (negative) , 29, 30. rs = 0.9 in Beijihly data . A previhly data . In our Reff is an indicator reflecting person-to-person transmissibility. Two previous studies found that person-to-person is an important mode of transmission [Reff. We found a weak positive correlation between Reff and sun hours. This is different from the results of the correlation between meteorological variables and new cases. Some studies have found no correlation or a small negative correlation between shigellosis cases and sun hours [Reff and new cases. The incidence of shigellosis could be related to meteorological and other variables.smission , 25, as un hours , 33. Sunα (person–reservoir contact rate (‘shedding’) and 1/ξ (pathogen lifetime in water reservoir)), which is very important for modelling results [ɛ and c of our model were from a reference, not first-hand data, which might underestimate the contribution of water/food in shigellosis transmission. We could not consider the population shifting in the compartmental model because we did not collect the internal migrants. Furthermore, further analysis such as Poisson regression should be performed to explore the association between shigellosis onset and several additional factors . For further work that the pathogen concentration in the water is measured on a regular basis, be considered and the incidence of shigellosis could be related to this and other variables.Some studies have indicated an effect of meteorological variables on shigellosis incidence with a lag of 3 or 4 days or even several weeks , 34. As  results . In thisIn Wuhan City, the incidence of shigellosis was relatively high from 2005 to 2017. The main transmission pattern is person-to-person. The transmission of shigellosis might be stopped in the year 2035 according to our forecast. Meteorological factors, especially daily atmospheric pressure and temperature, may influence the epidemic of shigellosis.


However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes – groups of biochemically similar TCRs – that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele , demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package,  An individual’s unique repertoire of T-cell receptors (TCRs) is shaped by antigen exposure and is a critical component of immunological memory . With thtcrdist3 that brings flexibility to distance-based repertoire analysis, allowing customization of the distance metric, and at-scale computation with sparse data representations and parallelized, byte-compiled code.In this study, we describe a framework for engineering ‘meta-clonotypes’: groupings of TCRs sharing biochemically similar complementarity determining regions (CDRs), which enable population-level biomarker development . PreviouThe framework is based on TCR sequences that have been experimentally enriched for antigen recognition, most commonly by sorting T cells labeled by peptide–MHC multimers or by activation-induced markers upon stimulation (we refer to these as ‘antigen-associated’ TCRs). Each meta-clonotype is defined by an antigen-associated centroid TCR and a TCRdist radius chosen so that the expected frequency of antigen-naive receptors within the radius is low. A CDR3 ‘motif’ is constructed from the subset of antigen-associated TCRs within the radius to further refine the meta-clonotype’s specificity. Together the centroid receptor, radius, and the CDR3 motif can be used to search for conformant TCRs in large bulk-sequenced repertoires and quantify their frequency . As inteTo demonstrate one potential application of meta-clonotypes and to characterize their ability to estimate the frequency of similar antigen-specific T cells in bulk-sequenced TCR repertoires, we apply the meta-clonotype framework to a large publicly available dataset of SARS-CoV-2 antigen-associated TCRs. The dataset comes from a recent study that sought to elucidate the role of cellular immune responses in acute SARS-CoV-2 infection and examined the TCR repertoires of patients diagnosed with COVID-19 disease. Researchers used an assay based on antigen stimulation and flow cytometric sorting of activated CD8+ T cells to identify SARS-CoV-2 peptide-associated TCR β-chains; the assay is called ‘multiplex identification of TCR antigen specificity’ or MIRA  and the Searching for identical TCRs within a repertoire – arising either from clonal expansion or convergent nucleotide encoding of amino acids in the CDR3 – is a common strategy for identifying functionally important receptors. However, in the absence of experimental enrichment procedures, observing T cells with the same amino acid TCR sequence in a bulk sample is rare. For example, in 10,000 β-chain TCRs from an umbilical cord blood sample, less than 1 % of TCR amino acid sequences were observed more than once, inclusive of possible clonal expansions . By contWe investigated the degree to which the MIRA assay employed by tcrdist3 reimplementation of TCRdist); a one amino acid mismatch in the CDR3 results in a maximal distance of 12 TCRdist units (tdus). As the radius about a TCR centroid expands, the number of TCRs it encompasses naturally increases. The increase was greater among the sets of antigen-associated TCRs compared to the ‘background’ repertoires that were not experimentally enriched for antigen-specific T cells . The proportion of these TCRs with sparse or empty neighborhoods (ECDF proportion <0.001) is indicated by the height of the gray area plotted below the ECDF  for each unique TCR . The ECDthe ECDF . We obsethe ECDF . Notablythe ECDF , we assugen; gen during genomic recombination of the TCR locus . First, an ECDF is constructed for each centroid TCR showing the relationship between the meta-clonotype radius and its sensitivity: its inclusion of similar antigen-recognizing TCRs, approximated by the proportion of TCRs in the antigen-associated MIRA set that are within the centroid’s radius . Next, a–4) TCRs are within a TCR’s radius, but that they are sampled from a V–J gene combination with 1%  prevalence, the estimated frequency in the full background would be 1 in 1 million (10−4 × 10−2 = 10−6). Across all V–J gene defined strata in the synthetic background, the adjusted frequency BGP can be estimated as a weighted average, with V–J gene strata frequencies from the full background as the weights (iw):We noted that, based on germline encoded CDR residues alone, much of the TCR background is too distant from a single TCR to be relevant and therefore realized that efficiency could be gained by focusing on TCRs that share the same TRBV and TRBJ genes. Therefore, for each set of antigen-associated TCRs identified using MIRA, we created a two part background. One part consisted of 100,000 synthetic TCRs whose TRBV- and TRBJ-gene frequencies matched those of the antigen-associated TCRs; TCRs were generated using the software OLGA with slight modification to allow V–J gene directed sequence generation . The othiY is the number of TCRs within the radius of the centroid in the ith V–J gene defined strata and in is the number of total sampled TCRs in the strata. Estimating neighbor frequency from a V–J gene-matched background alone, however, assumes there are zero neighbors in the unobserved strata with nonmatched V–J genes. Therefore, to avoid overlooking other regions of TCR space, we combined the synthetic background with a uniformly sampled background from antigen-naive cord blood repertoires. This is potentially important because with TCRdist – in contrast to metrics that require a matched V-gene – it is possible to find biochemically similar TCRs with different V-genes.where gen TCRs. With a large radius, most TCR centroids had high sensitivity but low specificity, indicated by the meta-clonotypes including both a high proportion of TCRs from the antigen-associated and background repertoires. Some TCRs had low sensitivity even at a radius of 24 tdus, which is indicative of a low pgen or a ‘snowflake’ TCR: a seemingly unique TCR among the antigen-associated and background TCRs. However, radius-defined neighborhoods around many TCRs in the MIRA55:ORF1ab repertoire included 1–10% of the antigen-associated TCRs (5–50 clonotypes) with a radius that included fewer than 0.0001 % of background TCRs , demonstrating a level of sensitivity and specificity that would be favorable for the development of a TCR biomarker. In We found that for each TCR, its radius-defined meta-clonotype was more abundant within a repertoire and more prevalent in a human cohort than the exact clonotype; for example, TCR meta-clonotypes formed from the MIRA55:ORF1ab TCR set were detected in 3–12 (median 6) of 15 HLA-A*01 participants in the MIRA cohort, despite 34 of the 46 centroid clonotype TCRs being private  . Generaltcrdist3, computing radii for 500 unique MIRA TCRs using a synthetic background of 1,000,000 TCRs  can be completed in 1 min using 12 CPUs and 48 GB of memory.We conducted sensitivity analyses to evaluate how the optimal radius was dependent upon the size of the synthetic background. Using meta-clonotype centroids from the MIRA55:ORF1ab TCR set we recomputed the optimal radius for each meta-clonotype using 2,000,000 background sequences , then drew repeated random samples from that background varying in size from 25,000 to 1,000,000 TCRs. We found that with a smaller background the optimal radius tended to be greater and was more variable over repeat samples; this is consistent with a lower and more variable chance of finding a sequence in the background that would help constrain the optimal radius. As the size of the background increased to 1,000,000 the estimates of the optimal radius generally approached the estimate using a 2,000,000 TCR background . For larNext, for a fixed background size , we evaluated the ability to estimate the optimal radius using three background sets: (1) 100,000 V–J gene-matched OLGA + 100,000 cord blood (primary background), (2) 200,000 V–J gene-matched OLGA, or (3) 200,000 OLGA without V–J gene matching . Each me6, using a synthetic background as described above. For each MIRA set we then ranked the meta-clonotypes by their sensitivity, approximated as the proportion of TCRs in the set that were within the meta-clonotype radius and matched by CDR3 motif (diagrammed in n = 35), S (n = 27), N (n = 10), M (n = 7), ORF3a (n = 7), ORF7a (n = 4), E (n = 2), ORF8 (n = 2), ORF6 (n = 1), ORF7b (n = 1), and ORF10 (n = 1) – we engineered 4548 public meta-clonotypes, which spanned 15 %  of the original TCR sequences  included TCR clonotypes from 15 individuals. From the 449 potential centroids, we defined 40 public meta-clonotypes. Among these features, the radii ranged from 10 to 36 tdus (median 22 tdus), and the publicity – the number of unique subjects spanned by the meta-clonotype – ranged from 3 to 12 individuals (median 6). Meta-clonotype summary statistics for other antigen-associated repertoires are provided in Supplemental Materials . The res−7). We found that for 17 of the MIRA sets, the patients contributing TCRs were significantly enriched for at least 1 HLA-A or HLA-B allele   . Strong q value of these associations were orders of magnitude larger  than those obtained from using the engineered RADIUS or RADIUS + MOTIF feature with the same clonotype as a centroid  radius-defined meta-clonotypes (RADIUS), (2) radius and motif-defined meta-clonotypes (RADIUS + MOTIF), and (3) centroid clonotypes alone, using TRBV-CDR3 amino acid matching (EXACT). Using the features in each set we screened TCRs from the bulk TCR β-chain repertoires of 694 COVID-19 patients whose repertoires were publicly released as part of the immuneRACE datasets (see Methods for details); these patients were not part of the smaller cohort that contributed samples for TCR identification in MIRA experiments. Testing the HLA restriction hypothesis required having the HLA genotype of each individual, which was not provided in the dataset. To overcome this, we inferred each participant’s HLA genotype with a classifier that was based on previously published HLA-associated TCR β-chain sequences  and theicentroid . The impcentroid . Moreovecentroid .q = 2.4e−10) versus as a RADIUS  or an individual TCR .Since meta-clonotypes cluster similar antigen-annotated TCRs and allow further clustering of similar TCRs from bulk repertoires, they provide an opportunity to visualize and refine hypotheses about the characteristics of TCRs that confer antigen specificity. After observing that in many instances, the strength of evidence of HLA-restriction in bulk repertoires was greater for RADIUS + MOTIF versus RADIUS meta-clonotypes, we sought to directly inspect the differences between MOTIF-conformant and non-MOTIF-conformant TCRs and utilize one meta-clonotype as an illustrative example. The MIRA55 meta-clonotype based on the centroid TRBV28*01 + TRBJ27*01 + CASSLKTDAYEQFY provided substantially stronger evidence of HLA association in bulk samples when applied as a RADIUS + MOTIF  and conformed to the motif constraint (SL[RK][ST][ND].YEQ) together with a ‘background-adjusted’ logo plot. The background-adjusted plot shows the position-specific Kullback–Leibler divergence from an alignment of background CDR3s that were sampled from cord blood and constrained to use the same V and J genes; it emphasizes the uncommon amino acid residues in the meta-clonotype, reducing the size in particular of residues encoded by the germline V and J genes . Inspectq values)  fully overlaps several A*01-associated GLIPH2 patterns including S.GQGAFTDT , QGAF , and SLG.GAFTDT . Similarly, the A*01-associated meta-clonotype motif S[RLMF][RK][ST][ND].YEQ  covers 21 global GLIPH motifs including SFRTD.YE , SLRTD.YE , and SF.TDSYE   . MIRA55:ue 1E−4) . These ogen) are likely to be detected reliably across individuals . We reasoned that we could compare the abundance of meta-clonotypes in COVID-19 patients with and without the restricting HLA genotype, and that a significant positive association of abundance with the restricting genotype would provide confirmatory evidence of the meta-clonotype’s SARS-CoV-2 antigen specificity in addition to its HLA restriction. Overall, we found significant confirmatory evidence of the HLA restriction of meta-clonotype abundance for a majority of the MIRA sets we analyzed  and for a majority  of the individual meta-clonotypes tested using the RADIUS + MOTIF approach; importantly, there were no meta-clonotypes significantly associated with the absence of expression of the restricting HLA allele. There are several plausible explanations for the remaining meta-clonotypes that did not have a significant signal of HLA restriction in this study. One possibility is that meta-clonotype definitions were not sufficiently specific for the target antigen; the radius is optimized for specificity, but not all amino acid substitutions accommodated within the radius are guaranteed to preserve antigen recognition, and while the motif constraint increases specificity, it is likely that meta-clonotype definitions could be further refined with more antigen-associated TCR data and enhanced motif refinement methods. Also, subdominant SARS-CoV-2 epitopes may not be ubiquitously presented, even among participants that share the required HLA genotype, which weakens the signal of HLA restriction detectable by regression analysis.To evaluate the properties of radius-defined meta-clonotypes we utilized the immuneRACE dataset and focused on the SARS-CoV-2 epitopes with the strongest evidence of HLA restriction  might be statistically enriched compared to a background, yet they may still be abundant in the background and lack sufficient specificity for subsequent analyses of bulk repertoires. We saw evidence of this in our comparison with GLIPH2; while many ‘global’ GLIPH2 groups performed similar to meta-clonotypes, the short ‘local’ groups identified by the algorithm were often too short to be specific, and they were generally not as strongly associated with the restricting HLA in our analysis. The meta-clonotype approach is distinct because it estimates an optimal radius to control the probability of finding conformant TCRs in a naive background at a prespecified level . This probability is further reduced by the CDR3 motif constraint, which requires that conformant TCRs match at least one of the residues in the antigen-associated sequences at critical conserved positions; other methods developed primarily for identifying TCRs under antigenic selection from bulk repertoire data do not leverage this information. Finding a radius to control the frequency of meta-clonotype conformant TCRs in the background was also made more efficient by a background sampling algorithm that focused on TCRs with matching V and J genes with weighting to account for the sampling bias. Ultimately, it is the focus on controlling the absolute frequency of meta-clonotype conformant TCRs in an antigen-naive background that gives the meta-clonotype definitions portability to be applied to analyses of bulk repertoires, where quantification of similar antigen-specific TCRs is required.The meta-clonotype framework we present joins a class of commonly used methods for TCR analysis that depend on comparisons to an antigen-naive background repertoire. For example, GLIPH2 attempts to find CDR3 n = 62; HLA-typed n = 47), with TCRs conformant to these meta-clonotypes frequently detectable in a larger independent cohort. We propose that meta-clonotypes constitute a set of potential features that could be leveraged in developing TCR-based clinical biomarkers that go beyond detection of infection or exposure. For example, biomarkers predictive of infection, disease severity, or vaccine protection may each require different TCR features. We note that meta-clonotypes are often overlapping in that a single TCR may be conformant with the definition of multiple meta-clonotypes, which should be a consideration when applying a set of meta-clonotypes together. For instance, tallying conformant TCRs in a repertoire should avoid counting a TCR more than once, while in a biomarker context many statistical and machine learning algorithms may benefit from a set of partially redundant features to amplify the clinically relevant signals. We also note that it may be more immunologically relevant to quantitate the frequency of unique clonotypes conforming to a meta-clonotype in each repertoire  in addition to the overall frequency; it is plausible both may carry important signals. Much like any biomarker study, to establish a TCR-based predictor of a particular outcome, the features must be measured among a sufficiently large cohort of individuals, with a sufficient mix of outcomes; meta-clonotypes offer a way to build public features that are suitable for this process.Recently, Though demonstrating HLA restriction of the SARS-CoV-2 meta-clonotypes establishes their potential utility, it also highlighted how HLA diversity could be a major hurdle to biomarker development. The sensitivity of a TCR-based biomarker in a diverse population may depend on combining meta-clonotypes with diverse HLA restrictions since individuals with different HLA genotypes often target different epitopes using divergent TCRs. Our analysis shows that having HLA genotype information for TCR repertoire analysis can be critical to interpreting results. The simple HLA classifier we developed suggests that soon it may be possible to infer high-resolution HLA genotype from bulk TCR repertoires, but until then it is valuable to have sequenced-based HLA genotyping. In the absence of HLA genotype information, it may still be feasible to generate informative TCR meta-clonotypes. For example, a poly-antigenic TCR-enrichment strategy  could help generate meta-clonotypes that broadly cover HLA diversity if the sample donors are racially, ethnically, and geographically representative of the ultimate target population. For these reasons, donor unrestricted T cells and their receptors  may also be good targets for TCR biomarker development.tcrdist3, which provides open-source (https://github.com/kmayerb/tcrdist3), documented (https://tcrdist3.readthedocs.io) computational building blocks for a wide array of TCR repertoire workflows in Python3. The software is highly flexible, allowing for: (1) customization of the distance metric with position and CDR-specific weights and amino acid substitution matrices, (2) inclusion of CDRs beyond the CDR3, (3) clustering based on single-chain or paired-chain data for α/β or γ/δ TCRs, and (4) use of default as well as user-provided TCR repertoires as background for controlling meta-clonotype specificity . tcrdist3 makes efficient use of available CPU and memory resources; as a reference, identification of meta-clonotypes from the MIRA55:ORF1ab dataset (n = 479 TCRs) was completed in less than 5 min using 2 CPUs and <4 GB of memory including distance computation and radius optimization. Quantification of the identified meta-clonotypes (n = 40) conformant TCRs in 694 bulk β-chain repertoires, ranging in size from 10,395 to 1,038,012 in-frame clones  could be completed in less than 2 hr using 2 CPUs and <6 GB memory. The package also can generate multiple types of publication-ready figures . The continued maturation of multiple adaptive immune receptor repertoire sequencing technologies will open possibilities for basic immunology and clinical applications, and tcrdist3 provides a flexible tool that researchers can use to integrate the data sources needed to detect and quantify antigen-specific TCR features.To enable TCR biomarker development and innovative extensions of distance-based immune repertoire analysis, we developed subject-metadata.csv file. When assessing the frequency of neighboring TCRs in antigen-associated MIRA sets, we also used TCRs evaluated by MIRA from 26 COVID-19-negative control subjects activate by SARS-CoV-2 peptides that were part of ImmuneCODE-MIRA-Release002. We analyzed the 253 MIRA sets with at least six unique TCRs contributed by ≥2 people, referred to as MIRA0-MIRA252 in rank order by their size (n = 374); COVID-19-HUniv12Oct ; COVID-19-NIH/NIAID + COVID-19-ISB . The sampling depth of these repertoires varied from 15,626 to 1,220,991 productive templates  and 10,395–1,038,012 productive rearrangements . We did not use bulk samples from the COVID-19-ADAPTIVE dataset as the average age was substantially lower than other immuneRACE populations and to avoid possible overlap with individuals that contributed samples to the MIRA experiments.The study utilized two primary sources of TCR data . The fireir size ; each ‘Mhttps://github.com/kmayerb/hla3, copy archived at swh:1:rev:daaa03b89883629e53974c8e5cab2563971acfa0, No publicly available HLA genotyping was available for the 694 bulk-sequenced immuneRACE T-cell repertoires . Before n = 47). From these data, we derived two-digit HLA-binding predictions  for each MIRA set by pooling the predictions for all the four-digit HLA variants  across all the derivative peptides and selecting the lowest IC50 (strongest affinity). Predictions with IC50 <50 nM were considered strong binders and IC50 <500 nM were considered weak binders . Position with gaps, where some sequences are missing a residue, are accommodated by making that position optional . Since the motif constraints form regular expressions, they can be used to rapidly scan large repertoires for conformant TCRs and easily be combined with a radius constraint. When applied to bulk repertoires, the motif constraint eliminates CDR3s that did not match key conserved residues.tcrdist3 documentation page (https://tcrdist3.readthedocs.io).After defining a set of meta-clonotypes using antigen-associated TCRs, we searched for similar TCRs in 694 bulk repertoires from COVID-19 patients 0–30 days from diagnosis. Association with predicted HLA was tested based on the count TCRs conformant with each meta-clonotype individually; however, we note that meta-clonotypes are often overlapping in that a single TCR may be conformant with the definition of multiple meta-clonotypes. A full example of tabulating EXACT, RADIUS, and RADUS+ MOTIF meta-clonotypes conformant TCRs in a bulk repertoire, while avoiding such double counting, is provided in corncob, which provides maximum likelihood methods for inference and hypothesis testing with beta-binomial regression models  as statistically significant; adjustment was performed across meta-clonotypes within each MIRA set and within each variable class . The HLA regression coefficients from the beta-binomial models indicate log-fold differences in meta-clonotype abundance between patients with and without the HLA genotype.Similar to bulk RNA sequencing data, TCR frequencies are count data drawn from samples of heterogeneous size. Thus, we initially attempted to fit a negative binomial model to the data, for example, DESEQ2 . We founn models . Due to GLIPH2  and 54 global k-mer , associated with CDR3 patterns enriched relative the program’s default CD8+ TCR background . The GLIPH2 patterns and their associated ‘specificity group’ TRBV gene usages and sequence length were then used to search for conforming TCRs in the 694 bulk-sequenced COVID-19 repertoires, allowing comparison to exact and meta-clonotype features. GLIPH2 represents degenerate positions using the ‘%’ character, which we represent throughout this study by the ‘.’ character.GLIPH2  softwaretcrdist3 is an open-source Python3 package for TCR repertoire analysis and visualization. The core of the package is the TCRdist, a distance metric for relating two TCRs, which has been expanded beyond what was previously published . On a modern laptop, distances can be computed at a rate of ~70 M per minute, per CPU.ublished  to inclutcrsampler is a tool for subsampling large bulk datasets to estimate the frequency of TCRs and TCR neighborhoods in background repertoires. The module comes with large, bulk sequenced, default databases for human TCR α, β, γ, and δ and mouse TCR β  to a preselected centroid sequence  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) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Benny Chain as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Aleksandra Walczak as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Tahel Ronel (Reviewer #3).Thank you for submitting your article "TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted, SARS-CoV-2 associated TCR features" for consideration by The reviewers are all agreed that the content of the paper is of interest to the readership of the journal. However, they are also agreed that significant revisions are necessary to increase the impact of the paper, and we would like to invite you to submit a revised version of the manuscript.Please address the detailed comments from all three reviewers and provide a point-by-point response. In particular, the emphasis of the paper needs to be shifted from COVID to the idea of the meta-clonotype. In order to achieve this, and to make the maximum impact, the paper must be thoroughly revised, to provide much more detail and clarity on the definition/construction of meta-clones, and how they can be used to define antigen-specific responses.Reviewer #1 (Recommendations for the authors):The authors should sharpen the manuscript to focus it exclusively on evaluating their new concept of a meta-clonotype, and not on evaluating the MIRA-based SARS-Cov-2 data set. For example, Lines 302-325 – what is the relevance of this section ? It analyses the MIRA dataset, and suggests the peptide-specific responses show HLA preference (hardly surprising) but doesn't say anything about the meta clonotypes.The extension of TCRdist to gamma/delta cells seems irrelevant to this paper. No gamma-delta data are evaluated.6". How do you reach 106 with only 200000 background TCRs?I found the section 560-574 (the details of generating the meta-clonotypes) very hard to follow. This is surely the crux of the whole paper, and the method for generating meta-clonotypes needs to be crystal clear. For example what does "With each candidate centroid, a meta-clonotype was engineered by selecting the maximum distance radius that still controlled the number of neighboring TCRs in the weighted unenriched background to 1 in 10Critically , in order to increase the impact of this study, it would be important to show that meta-clonotypes perform better than public clonotypes in identifying COVID-infected individuals .Reviewer #2 (Recommendations for the authors):Title: It's a bit strange to refer to meta-clonotypes as features . Also. the term feature does not have the same meaning throughout the manuscript. Can you adjust the title to be a bit more direct and less confusing?Abstract: "As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode" → this sentence doesn't make sense. There are many mechanisms involved in immune recognition , TCR sequences are certainly a part of it but I would hardly call them the "mechanistic basis" – can you rephrase? I would like the term "mechanistic" to go – this paper is not mechanistic in any way."17 SARS-CoV-2 antigen-enriched repertoires" → antigen-enriched doesn't mean what you think it means. Can you please rephrase throughout the manuscript? What you mean is antigen-annotated/antigen-specific/etc…Introduction: The introduction seems a bit verbose (the entire paper is quite verbose…more streamlining by making text and captions more precise would greatly enhance readability). This is not a covid-centric paper – please dramatically reduce the SARS-CoV-2 section. Also, the first paragraph can be written in 5 instead of 35 lines. Please try to get closer to one page overall.Results Figure 1A: what does "searchable public meta-clonotype" mean? It's not mentioned anywhere else in the text.I think it would be much more useful to the reader if you illustrate how tcrdist calculates tdus, how they are to be interpreted .Figure 1B In my opinion, the figure does not reflect what you want it to say (quantification of the frequency of putative meta-clonotypes). Please adjust the figure accordingly.Figure 2A "As the radius about a TCR centroid expands, the number of TCRs it encompasses naturally increases; the rate of increase is more rapid in the antigen-enriched 167 repertoires compared to the unenriched repertoires" → can you quantify this rate? How does the rate depend on sequencing depth? Can you also add the OLGA-generated data to this plot? Is your assumption that the OLGA-generated data would perform close to that of the cord blood data?For all figures: can you change the bold text of the caption to the main result of the figure. As of now, the bold text doesn't really say much.If I do a text search in the main text for "Dash BMLF", I don't find anything. Please define all named datasets in the methods/main text.Regarding the antigen-specific data – is the higher rate in antigen-specific data due to the fact that you are focusing on only a few peptides here? Would the rate be similar to the baseline data if you somehow normalized for that? Like for example only taking one tcr per peptide? Probably not possible, right, given the sparsity of the data..?How does Tcrdist3 normalize for length differences? Do these curves differ across tcr length, germline genes?Figure 2BCan you explain in the main text and the methods what MIRA M48 means –specifically, what do each of the numbers mean?Figure 3"This suggests that TCRs within sparse neighborhoods represent less common modes of antigen recognition and highlights the broad heterogeneity of neighborhood densities even among TCRs recognizing a single pMHC." → to what extent can TCRs with sparse neighborhood be a result of undersampling?Can you add OLGA-generated data to Figure 3 as well?Figure 4Can you mention in the caption how many TCRs you are investigating in this subfigure? Please also check for all other figures where relevant."We also noted that TCRs with 191 empty neighborhoods tended to have longer CDR3 loops " Where and how do I see this in Figure 4C?"To be useful, a meta-clonotype definition should be broad enough 206 to capture multiple biochemically similar TCRs" → what do you mean by biochemically similar? I think, in the AIRR field, when people speak of biochemically similar, they mean something related to Atchley/Kidera factors. I don't think this is what you mean here, right? Maybe rephrase to avoid misunderstandings?"This is similar to previous approaches taken by tools like ALICE and TCRNET, except that we employ a biochemically informed distance measure (TCRdist)" → this statement is a bit indirect for my taste. Can you rephrase and make it really clear in what respect tcrdist3 differs from Alice et al.?How did you decide on the number of 100000 IGOR and cord blood TCRs?"One part consisted of 100,000 synthetic TCRs whose TRBV- and TRBJ-gene frequencies matched those in the antigen-enriched repertoire; TCRs were generated using the software OLGA" → how did you make sure that frequencies matched?"Using this approach, we are able to estimate the abundance of TCRs similar to a centroid TCR in an unenriched background repertoire of effectively ~1,000,000 TCRs," → Where does the number of 1M TCRs come from? Can you show that calculations don't change if you sample another 100000? To what extent do you think it plays a role that the cord blood data has been generated with a completely different experimental protocol than the MIRA data?Figure 5Can you please add to "HLA genotype inferences" section in Methods the prediction accuracies for HLA-B alleles used in Figure 5A? If prediction accuracies are low, please remove those data also from Figure 5A.Is the HLA-classifier publicly available? Does a package for it exist as well? Which HLA alleles other than those mentioned are quite safe to predict from sequencing data?Independently of Figure 5, is there a Figure where I see how much meta-clonotypes make up of a repertoire in terms of sequences and sequencing reads? Basically, how much more of the antigen-specific portion of a repertoire does one capture if looking at meta-clonotypes?Furthermore, how do you compare meta-clonotypes across individuals (publicity)? Can a TCR be part of several meta-clonotypes? Can you explain all of this more in Figure 1 and the main text?Figure 7To what extent is this figure needed in the main text?Since your approach is actually quite similar to Alice, especially, since you include in your analysis pgens, wouldn't it be more interesting to relate in depth to Alice than to GLIPH?GLIPH2 was used with the default TCR background (line 629). Would this give the TCRdist3 meta-clonotypes an advantage, since the used background for TCRdist3 has been more densely sampled around the biochemical neighborhoods of interest (line 563)?In the comparison to k-mer based CDR3 features, were meta-clonotypes defined by RADIUS or RADIUS +MOTIF? Please also mention this in Figure 7.More general comments:The paper presents meta-clonotypes as a novel approach to comparing similar TCRs across repertoires and mainly compares the meta-clonotypes approach to the use of public exact TCRs. This comparison seems a bit trivial since any sequence similarity clustering approach is expected to perform better than exact TCR matches. I think it's very interesting to show that meta-clonotype spaces differ between antigen-specific and non-specific repertoires. It would have been nice to focus the analysis more on this and to actually discover something cool about the repertoire biology than trying to relate exclusively to covid.How does your method compare to this recently published approach based on TCR sub-repertoires shared across individuals? https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04087-7Reviewer #3 (Recommendations for the authors):eLife's policies.The methodology proposed in this manuscript (tcrdist3) has been made publicly available through GitHub. The application to COVID-19 is based on public datasets and is clearly referenced. Data derived in the analysis, such as NetMHCpan predictions and the set of derived meta-clonotypes are included as supplementary material, which is helpful and adheres to The dataset of derived COVID-19 related meta-clonotypes is a valuable resource for the analysis of other bulk repertoire COVID-19 datasets, and the proposed method should be applicable in a variety of antigenic settings. This dataset could be further characterised, perhaps as a supplementary/additional figure: the distribution of optimal radii, distribution of number of TCRs conforming to each meta-clonotype, number of people contributing TCRs to the meta-clonotype, are these different between the strong HLA meta-clonotypes and weak HLA meta-clonotypes? This would help when applying the method in other contexts.tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs" for further consideration by eLife. Your revised article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Aleksandra Walczak as the Senior Editor.Thank you for resubmitting your work entitled "TCR meta-clonotypes for biomarker discovery with The reviewers agree that the manuscript has been considerably improved but there are a few remaining issues of clarity that need to be addressed, as outlined below by reviewer 3.Specifically:1. Please include a clear and consistent definition of a meta-clone in the Discussion. If in fact there are multiple alternative definitions, please clearly set these out, with an indication of when each would be used.2. Clearly indicate which background set is used in each figure/section of the paper.3. Further sharpen the Discussion around comparing the meta-clonotype approach to other existing methods. Specifically, please clarify the relative importance to the novel meta-clonotype defining which derives from the new TCRdist3 metric, the motif, and the novel approach to establishing background comparisons.The comments of the reviewers are listed below.Reviewer #2 (Recommendations for the authors):The authors have addressed all of my comments.Reviewer #3 (Recommendations for the authors):The authors have placed more focus in the revised manuscript on the definition and generation of meta-clonotypes, as suggested, with the covid data used as an example application. They have included a couple of new analyses to this effect . While I still think that the idea of meta-clonotypes is both interesting and potentially useful, I find some of the paper a bit long and difficult to follow.For example, it looks to me like the definition of meta-clonotype changes along the paper: e.g. In Figure 1/Figure 6 a meta-clonotype is centroid (TRBV + CDR3) + radius +/- motif; in Figure 10 it also includes what I think is an identifier for the set it comes from; and in Figure 12 it is TRBV + TRBJ + CDR3 + radius. As this is the main focus of the paper I think this definition should be made clear, consistent and explained somewhere.Secondly, it was unclear to me when the authors use which set for background: e.g. does Figure 1 and caption 'synthetic set' refer to the set of 100k OLGA V-J biased + 100k cord used later? cf Lines 535-537 "background CDR3s that were sampled from cord blood and constrained to use the same V and J genes", and elsewhere unadjusted cord blood is used without the OLGA adjusted set.I also think that the advantage / unique usage of the proposed meta-clonotype method over existing methods for grouping antigen-specific TCRs should be further clarified and emphasised: I personally don't find the Results section 'Comparison to k-mer based CDR3 features' or Figures 11 and 12 very convincing to this effect, and think this message should be made clearer in the paper before the sentence in the discussion "Our framework is designed for a different task than these algorithms…".I do think the meta-clonotype method is useful, these changes are addressable and the paper has the potential to be made more readable and more impactful, if the authors streamline the definition and explanations, remove repetitive sections, and cross-reference to the relevant places in the text where things are referred to in the text before their explanations. Reviewer #1 (Recommendations for the authors):The authors should sharpen the manuscript to focus it exclusively on evaluating their new concept of a meta-clonotype, and not on evaluating the MIRA-based SARS-Cov-2 data set. For example, Lines 302-325 – what is the relevance of this section ? It analyses the MIRA dataset, and suggests the peptide-specific responses show HLA preference (hardly surprising) but doesn't say anything about the meta clonotypes.We agree with you and understand why you think it’s important to focus on the concept of the meta-clonotype and have revised the manuscript towards this goal. Lines 333-337 help to establish prior evidence of HLA restriction for a subset of the MIRA datasets, which becomes critical later when we provide evidence of consistent HLA restriction of meta-clonotype conforming TCRs in COVID-19 patients; it’s this consistency that helps demonstrate the value of meta-clonotypes over individual clonotypes. However, we recognized that HLA analysis section in our original version distracted from our main point, so we have shortened this section in the main text.The extension of TCRdist to gamma/delta cells seems irrelevant to this paper. No gamma-delta data are evaluated.We agree that this is not a focus of the paper, however we included this information as the ability to analyze gamma/delta TCRs differentiate tcrdist3 from other currently available tools. The ability to perform analysis on paired-chain data as well as gamma/delta TCRs are important features of the software and therefore we’ve left one brief reference to these features in the Discussion section.I found the section 560-574 (the details of generating the meta-clonotypes) very hard to follow.Thank you for bringing this to our attention. We have substantially revised the Results paragraph that describes how meta-clonotypes are generated, including the radius selection procedure (lines 178-258). The additions include a formula (line 235) for implementing the adjustment required for the sampling design and a sensitivity analysis (lines 259-294) supporting the choice of background size and make-up. With all details in the Results, we have removed this section from the Methods.6". How do you reach 106 with only 200000 background TCRs?This is surely the crux of the whole paper, and the method for generating meta-clonotypes needs to be crystal clear. For example what does "With each candidate centroid, a meta-clonotype was engineered by selecting the maximum distance radius that still controlled the number of neighboring TCRs in the weighted unenriched background to 1 in 10This is achieved by sampling background TCRs from regions of “TCR space” that are proximal to the centroid, based on VJ-gene matching. This greatly increases the efficiency of the background comparison since the vast majority of TCR space is not relevant and can be more sparsely sampled. We then use weighting to account for this sampling bias; previously we referred to this technique as inverse probability weighting (IPW), and while the concept is derived from IPW we think it’s more clearly communicated without this statistical terminology, and therefore have removed this from the manuscript. This is explained in greater detail in the Results section, “Meta-clonotype radius can be tuned to balance sensitivity and specificity” on lines 178-258.Critically , in order to increase the impact of this study, it would be important to show that meta-clonotypes perform better than public clonotypes in identifying COVID-infected individuals .While it may seem that building a repertoire-based classifier for SARS-CoV-2 infection would be a great way to demonstrate the value of meta-clonotypes, there are good reasons not to include such an analysis in the manuscript. One reason is that we would not necessarily expect that meta-clonotypes would be better than public clonotypes for identifying SARS-CoV-2 infected individuals. In fact, others have shown  that with a massive dataset of cases and controls it is possible to identify a relatively small number of individual clones that are capable of identifying individuals with prior infection. Instead, we propose that meta-clonotypes would be more useful than individual clonotypes when, (a) less case-control data is available to train a classifier, (b) focus on a specific epitope is required, or (c) when it would be useful to gain insight into the underlying biology . We also note that the uninfected control dataset that enabled the above analysis at the clonotype level has not been made publicly available, preventing us and others from making further improvements. Lastly, we agree with previous comments that this manuscript would benefit from a sharpened focus on the concept of the meta-clonotype. Therefore, we have chosen not to make efforts to build a classifier for prior infection and instead have emphasized the existing application, which demonstrates how meta-clonotypes retain antigen-specificity and how they can be used for population-level analysis.Reviewer #2 (Recommendations for the authors):Title: It's a bit strange to refer to meta-clonotypes as features . Also. the term feature does not have the same meaning throughout the manuscript. Can you adjust the title to be a bit more direct and less confusing?We see how referring to meta-clonotypes as features may be confusing and have revised the title accordingly: “TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs”Abstract: "As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode" → this sentence doesn't make sense. There are many mechanisms involved in immune recognition , TCR sequences are certainly a part of it but I would hardly call them the "mechanistic basis" – can you rephrase? I would like the term "mechanistic" to go – this paper is not mechanistic in any way.We agree and removed that phrasing."17 SARS-CoV-2 antigen-enriched repertoires" → antigen-enriched doesn't mean what you think it means. Can you please rephrase throughout the manuscript? What you mean is antigen-annotated/antigen-specific/etc…We appreciate that the term “antigen-enriched” may not mean the same thing to everyone. We have adopted the term antigen-associated TCRs to refer to a set of TCRs that have an overrepresentation of those that recognize a particular antigen. We have provided an explicit definition of this usage in the Introduction and use the term throughout.Introduction: The introduction seems a bit verbose (the entire paper is quite verbose…more streamlining by making text and captions more precise would greatly enhance readability). This is not a covid-centric paper – please dramatically reduce the SARS-CoV-2 section. Also, the first paragraph can be written in 5 instead of 35 lines. Please try to get closer to one page overall.We appreciate the need for brevity. The Introduction has been streamlined and any background on COVID-19 that was not directly related to the analyses was removed.Results Figure 1A: what does "searchable public meta-clonotype" mean? It's not mentioned anywhere else in the text.This phrase has been replaced with “Public meta-clonotype”: we had meant that the meta-clonotype was something that could be used to search for conformant sequences in bulk repertoires, but this point is now clarified elsewhere.I think it would be much more useful to the reader if you illustrate how tcrdist calculates tdus, how they are to be interpreted .We agree that having an intuition for tdus is important to understanding the definition of a meta-clonotype and its radius. We have added a new Figure 2 that plots the distances between illustrative pairs of antigen-associated TCR CDR3s (MIRA55) using units of edit-distance versus TCR distance units.Figure 1B In my opinion, the figure does not reflect what you want it to say (quantification of the frequency of putative meta-clonotypes). Please adjust the figure accordingly.The Figure 1B caption now begins: “Quantifying meta-clonotype conformant TCRs in a bulk repertoire” and Figure 1C “Population-level analysis of TCR meta-clonotype frequency”Figure 2A "As the radius about a TCR centroid expands, the number of TCRs it encompasses naturally increases; the rate of increase is more rapid in the antigen-enriched 167 repertoires compared to the unenriched repertoires" → can you quantify this rate? How does the rate depend on sequencing depth? Can you also add the OLGA-generated data to this plot? Is your assumption that the OLGA-generated data would perform close to that of the cord blood data?Figure 2 is now Figure 3. What we meant here was to make the observation that the proportion of TCRs with a neighbor within a set of antigen-associated TCRs was greater than for TCRs among a set that has not been experimentally enriched for T cells recognizing a single antigen , across a range of neighborhood radii. Indeed, the size of the set of TCRs does affect the proportion of TCRs with at least one neighbor, which is why we chose to include cord blood repertoires with 1K and 10K TCRs each; the bigger a set of TCRs the more likely it is to find at least one that is a neighbor. As we push this observation further in the manuscript, it’s helpful to consider the proportion of TCRs included in each TCR’s neighborhood rather than the proportion with at least one neighbor; this is what we plot in Figure 3 , again for antigen-associated and cord blood TCRs. The goal of Figure 2  is to start with a more intuitive observation and then move to Figure 3  which is a better way to quantify the observation of neighborhood density as a function of radius; the statistic plotted in Figure 3  for each TCR, “proportion of TCRs within a specified radius” is also now robust to the set size compared to a simple indicator of whether or not a neighbor is present.We have conducted several sensitivity analyses evaluating cord blood, OLGA sequences and a mix of the two, as background repertoires for selecting the optimal radius for each meta-clonotype (lines 259-294). These analyses show efficiently sampled OLGA sequences behave similarly to cord blood sequences when estimating meta-clonotype neighborhood densities. We concluded that additional lines on Figure 3A for OLGA repertoires of comparable sizes wouldn’t help make the intended point that neighborhoods around antigen-associated TCRs are more densely populated than those around individual TCRs in an unselected repertoire (e.g. cord blood). We have added a sub-panel with ECDFs from OLGA generated TCRs in the new Figure 4D.For all figures: can you change the bold text of the caption to the main result of the figure. As of now, the bold text doesn't really say much.The first line of the figure captions have been revised to be more descriptive.If I do a text search in the main text for "Dash BMLF", I don't find anything. Please define all named datasets in the methods/main text.The Dash et al. publicly available source of these tetramer-sorted TCRs is now included in the figure caption.Regarding the antigen-specific data – is the higher rate in antigen-specific data due to the fact that you are focusing on only a few peptides here? Would the rate be similar to the baseline data if you somehow normalized for that? Like for example only taking one tcr per peptide? Probably not possible, right, given the sparsity of the data..?We’re not sure we fully understand your point, but will try to offer further clarification. We think you are making the point that the size of the TCR set matters. For instance, you could take one TCR from either an antigen-associated set or a background set and count how many neighbors it has within its set for a given radius. The number tallied would depend on the size of the set AND on the density of the neighborhood. We show 1K and 10K cord blood repertoires because it shows that as the set size decreases there are fewer TCRs in the neighborhood. If we used 100 or 500 TCRs, which would be similar to some of the antigen-associated sets, it would probably result in an even flatter line; we included 1K and 10K because they are conservatively larger than the antigen-associated sets and yet are still generally flatter lines. Also, keep in mind that the line represents an average across all TCRs in the set, which will also be more variable for smaller sets. We’ve now noted this last point of clarification in the Figure 3 caption.How does Tcrdist3 normalize for length differences? Do these curves differ across tcr length, germline genes?The TCRdist metric penalizes differences in CDR3 length as a non-conservative amino-acid substitution; this has been clarified in the methods. We show in Figures 3 and 4  that neighborhood density is related to CDR3 length by plotting curves by centroid generation probability. TCRs with lower probability of generation typically have fewer neighbors in the background. We have not systematically studied different VJ-genes but their usage in a background repertoire would have a similar effect on neighborhood density.Figure 2BCan you explain in the main text and the methods what MIRA M48 means – specifically, what do each of the numbers mean?This is an important point so we have pulled the definition from the Methods and added a definition in the Introduction: “we refer to these sets as MIRA1 through MIRA252 in rank order by their size” (Lines 95-96).Figure 3"This suggests that TCRs within sparse neighborhoods represent less common modes of antigen recognition and highlights the broad heterogeneity of neighborhood densities even among TCRs recognizing a single pMHC." → to what extent can TCRs with sparse neighborhood be a result of undersampling?If we make an assumption that sampling is uniform across the repertoire space, then, while the absolute number of neighbors may be affected by under/over sampling, the relative number of neighbors for one TCR compared to another should be controlled for within a set of antigen-associated TCRs. It’s possible that near the level of detection (e.g. frequencies near 1/n) the presence of a neighbor may be observed in this experiment, but not if we had repeated the experiment (i.e. high variability), but it shouldn’t be biased to underestimate or overestimate density. Therefore, we believe that the heterogeneity we see within each MIRA set represents real variation in neighborhood density.Can you add OLGA-generated data to Figure 3 as well?Yes. We have added OLGA-generated data to revised Figure 3D, now Figure 4D.Figure 4Can you mention in the caption how many TCRs you are investigating in this subfigure? Please also check for all other figures where relevant.Yes, added and addressed throughout."We also noted that TCRs with 191 empty neighborhoods tended to have longer CDR3 loops " Where and how do I see this in Figure 4C?We have clarified this observation in the text. Revised Figure 4C shows that TCRs with sparse or empty neighborhoods tend to have longer CDR3 loops."To be useful, a meta-clonotype definition should be broad enough 206 to capture multiple biochemically similar TCRs" → what do you mean by biochemically similar? I think, in the AIRR field, when people speak of biochemically similar, they mean something related to Atchley/Kidera factors. I don't think this is what you mean here, right? Maybe rephrase to avoid misunderstandings?While a sequence-based distance could simply count substitutions, with TCRdist we are using a substitution matrix (BLOSUM62) that down-weights substitutions of biochemically similar amino acids. It also upweights the CDR3 because of its importance in making contacts with the antigen. Therefore, we think that referring to TCRdist defined neighbors as biochemically similar is appropriate."This is similar to previous approaches taken by tools like ALICE and TCRNET, except that we employ a biochemically informed distance measure (TCRdist)" → this statement is a bit indirect for my taste. Can you rephrase and make it really clear in what respect tcrdist3 differs from Alice et al.?Yes, please see response above. A complete comparison of these methods now appears in the Discussion.How did you decide on the number of 100000 IGOR and cord blood TCRs?"One part consisted of 100,000 synthetic TCRs whose TRBV- and TRBJ-gene frequencies matched those in the antigen-enriched repertoire; TCRs were generated using the software OLGA" → how did you make sure that frequencies matched?"Using this approach, we are able to estimate the abundance of TCRs similar to a centroid TCR in an unenriched background repertoire of effectively ~1,000,000 TCRs," → Where does the number of 1M TCRs come from? Can you show that calculations don't change if you sample another 100000? To what extent do you think it plays a role that the cord blood data has been generated with a completely different experimental protocol than the MIRA data?Please see response above. We have conducted sensitivity analyses studying the impact of the size of the background on meta-clonotype radius optimization, which appear in the revised manuscript. To your specific questions that are not answered above: V and J gene frequencies of the OLGA set were matched to their frequencies in the cord blood dataset. Also, OLGA was modified to generate synthetic TCRs with specific V and J genes (this modified version is available on GitHub). We don’t know to what extent the technical factors play a role in sampling from the background and using it as a comparator for the MIRA data. Ideally, one could generate a background that is relevant to the experiment and employ identical sequencing protocols. We think its promising that the OLGA (after VJ-gene matching) and cord blood background performed consistently. Ultimately, we think the choice of background should be informed by the experiment, including possible technical factors. We’ve added this commentary to the manuscript.Figure 5Can you please add to "HLA genotype inferences" section in Methods the prediction accuracies for HLA-B alleles used in Figure 5A? If prediction accuracies are low, please remove those data also from Figure 5A.Is the HLA-classifier publicly available? Does a package for it exist as well? Which HLA alleles other than those mentioned are quite safe to predict from sequencing data?Yes, this classifier has been made available on GitHub (github.com/kmayerb/hla3). Prediction accuracies and sensitivity vary by allele, but generally sensitivity ranged between 0.85-0.97 and specificity 0.97-1 for the major HLA-A and HLA-B alleles we have analyzed here: HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*24:02, HLA-A*11:01, HLA-B*07:02, HLA-B*15:01, HLA-B*35:03, HLA-B*57:01, HLA-B*40:01.Independently of Figure 5, is there a Figure where I see how much meta-clonotypes make up of a repertoire in terms of sequences and sequencing reads? Basically, how much more of the antigen-specific portion of a repertoire does one capture if looking at meta-clonotypes?This is an important question, but we do not know the answer. With something like a tetramer-sorted dataset we can answer that question and show what proportion of the antigen-specific sequences are captured by meta-clonotypes. We are essentially doing this with the MIRA ECDFs in Figures 3, 4, and 5 , but this is circular because the meta-clonotypes were of course defined to identify these TCRs. Perhaps with a large hold-out dataset one could look to see how well these meta-clonotypes perform; experimentally one might want to generate large tetramer-sorted datasets from many individuals to show that the meta-clonotypes work in a population. With the COVID-19 analysis we have attempted to demonstrate this statistically, and while it doesn’t provide an estimate of how much of the antigen-specific repertoire we’re capturing, it does show that the antigen-specific signal is statistically significant.Furthermore, how do you compare meta-clonotypes across individuals (publicity)? Can a TCR be part of several meta-clonotypes? Can you explain all of this more in Figure 1 and the main text?Yes, see response above about overlapping meta-clonotypes.Figure 7To what extent is this figure needed in the main text?Figure 7 is now Figure 11. This figure compares meta-clonotype to k-mere based features for 16 MIRA sets.Since your approach is actually quite similar to Alice, especially, since you include in your analysis pgens, wouldn't it be more interesting to relate in depth to Alice than to GLIPH?Our impression is that GLIPH2 is a more commonly used tool  for finding potentially interesting groups of TCRs and the feedback we’ve received is that it would be helpful to compare the two in the manuscript. Therefore, we have kept Figure 7  in the manuscript. To clarify, we do not explicitly use Pgen in forming meta-clonotypes; instead we are estimating the likelihood of observing a meta-clonotype conformant TCR in the background empirically. As we understand it, ALICE would not be applicable to this context when trying to find public features among TCRs that have already been enriched for sharing an antigen specificity; this enrichment would make most if not all groupings seem significantly unexpected in an ALICE model.GLIPH2 was used with the default TCR background (line 629). Would this give the TCRdist3 meta-clonotypes an advantage, since the used background for TCRdist3 has been more densely sampled around the biochemical neighborhoods of interest (line 563)?The GLIPH2 default background is large , which may actually give GLIPH2 the advantage. Instead, we use a smaller background that has greater efficiency, due to the dense sampling of biochemically similar TCRs. We decided that using the default GLIPH2 background is the most relevant for comparison since that’s what most people seem to use.In the comparison to k-mer based CDR3 features, were meta-clonotypes defined by RADIUS or RADIUS+MOTIF? Please also mention this in Figure 7.The comparisons were based on RADIUS+MOTIF meta-clonotypes. This has been clarified in Figure 7 .More general comments:The paper presents meta-clonotypes as a novel approach to comparing similar TCRs across repertoires and mainly compares the meta-clonotypes approach to the use of public exact TCRs. This comparison seems a bit trivial since any sequence similarity clustering approach is expected to perform better than exact TCR matches. I think it's very interesting to show that meta-clonotype spaces differ between antigen-specific and non-specific repertoires. It would have been nice to focus the analysis more on this and to actually discover something cool about the repertoire biology than trying to relate exclusively to covid.We’ve revised the manuscript to show how meta-clonotypes can be to form interesting biological hypotheses about antigen specificity from experiments that enrich antigen-specific TCRs. We also think it’s important that meta-clonotypes can be used for population-level statistics and biomarker development, a necessary step towards clinical translation. Still we’ve de-emphasize the COVID-19 analysis, focusing on how it demonstrates the performance of meta-clonotypes.How does your method compare to this recently published approach based on TCR sub-repertoires shared across individuals? https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04087-73) 8000-dimension vector represents each CDR3s. Within-repertoire diversity is reduced by clustering vectors via agglomerative clustering. Mean valued vector represents each cluster . Centroid verctors from multiple samples are clustered across samples to identify clusters representing common repertoire features. This novel approach was applied to a small cohort of patients with Celiac disease and was able to identify clusters of similar but non-identical TCRs across samples that were similar to antigen-associated TCRs from prior studies. This and other k-mer methods  and GLIPH (Glanville 2017) and GLIPH2 (Huang 2020), have shown that k-mer representation of CDR3s encode information that generalizes across samples.Yohannes et al. 2021 recently published an interesting methodology. Briefly, Yohannes and colleagues use 4-nt-mers or 3-aa-mers to cluster sets of CDR3 sequences within a sample from a single TCR repertoire. In the 3-mer aa amino acid case, a :The methodology proposed in this manuscript (tcrdist3) has been made publicly available through GitHub. The application to COVID-19 is based on public datasets and is clearly referenced. Data derived in the analysis, such as NetMHCpan predictions and the set of derived meta-clonotypes are included as supplementary material, which is helpful and adheres to eLife's policies.The dataset of derived COVID-19 related meta-clonotypes is a valuable resource for the analysis of other bulk repertoire COVID-19 datasets, and the proposed method should be applicable in a variety of antigenic settings. This dataset could be further characterised, perhaps as a supplementary/additional figure: the distribution of optimal radii, distribution of number of TCRs conforming to each meta-clonotype, number of people contributing TCRs to the meta-clonotype, are these different between the strong HLA meta-clonotypes and weak HLA meta-clonotypes? This would help when applying the method in other contexts.These are interesting points. We provide characteristics of the other MIRA sets in supporting Table S6 including the number of unique TCRs contributing to each meta-clonotype and the number of participants contributing to each meta-clonotype. The additional sensitivity analyses focused on radius optimization now show the distribution of radii across meta-clonotypes; radius and the number of subjects contributing to each meta-clonotype is also included in Tables S7 and S8. In general, the highest quality meta-clonotypes include many input TCRs and are highly public.The reviewers agree that the manuscript has been considerably improved but there are a few remaining issues of clarity that need to be addressed, as outlined below by reviewer 3.Specifically:1. Please include a clear and consistent definition of a meta-clone in the Discussion. If in fact there are multiple alternative definitions, please clearly set these out, with an indication of when each would be used.We appreciate the opportunity to further refine the manuscript. We agree that clarity on the definition of a meta-clonotype is critical. As a result, we have moved a clear description to the introduction; technical discussion of the definition and its contrast with other TCR groupings is in the Discussion, with greater clarity.We define a meta-clonotype to be a grouping of biochemically similar clonotypes surrounding a centroid clonotype, which enables population-level analyses that can consider all members of the meta-clonotype as a functional unit. In this study we developed a framework for defining meta-clonotypes from antigen associated TCRs based on a similarity threshold  and optionally, a CDR3 motif, which represent a portable set of criteria that can be used to identify meta-clonotype conformant TCRs across datasets. As experimental and statistical approaches evolve, we welcome new methods for defining TCR meta-clonotypes, and hope that the new term and concept continues to be useful, in so much that it captures the need for defining groups of functionally similar clonotypes for analysis. See lines 53-80 for clarification of this definition in the manuscript.2. Clearly indicate which background set is used in each figure/section of the paper.Throughout the manuscript, the background used to generate meta-clonotypes refers to the following, which we now state explicitly:“A synthetic background was generated using 100,000 OLGA-generated TCRs and 100,000 TCRs sub-sampled from umbilical cord blood; OLGA-generated TCRs were sampled to match the V-J gene frequency in each MIRA receptor set, with weighting to account for the sampling bias (see Methods for details).”In one separate instance, a different type of “background” is used to create motif visualization as shown as Figure 10. In Figure 10, the “background” used to refers to a background-subtracted CDR3 logo motif. Here the background is a random sampling of TCR beta receptors from cord blood with the same TRBV and TRBJ gene usage frequency as the receptors found within a TCRdist 20 radius of meta-clonotype centroid TRBV28*01 CASSLKTDAYEQYF.To ensure clarity, we have modified figure captions with explicit descriptions of the backgrounds:– We have added this description to Figure 1A caption to match wording in Figure 6:“A synthetic background was generated using 100,000 OLGA-generated TCRs and 100,000 TCRs sub-sampled from umbilical cord blood; OLGA-generated TCRs were sampled to match the V-J gene frequency in each MIRA receptor set, with weighting to account for the sampling bias (see Methods for details).”– Figure 4C and 4D, respectively shows proportion of neighbors in 9866 OLGA and 10,000 Cord Blood receptors as now specified in the caption text.– Figure 7 involves sensitivity testing varying background size and type as specified in the caption.– Figure 8: we added the caption text for clarity:“Meta-clonotype radii were engineered using synthesized backgrounds developed for each MIRA set. Each background contained 100,000 OLGA-generated TCRs and 100,000 TCRs sub-sampled from umbilical cord blood; OLGA-generated TCRs were sampled to match to the V-J gene frequency in each MIRA receptor set  with weighting to account for the sampling bias (see Methods for details).”– Figure 10: The background-adjusted logos are constructed by randomly sampling TCR β receptors from cord blood with the same TRBV and TRBJ gene usage, with 100 V-J matched TCRs sampled for every receptor in the foreground set.– Figure 13: we added the caption text for clarity:“Meta-clonotype radii were engineered using synthesized backgrounds developed for each MIRA set. Each background contained 100,000 OLGA-generated TCRs and 100,000 TCRs sub-sampled from umbilical cord blood; OLGA-generated TCRs were sampled to match to the V-J gene frequency in each MIRA receptor set with weighting to account for the sampling bias (see Methods for details).”3. Further sharpen the Discussion around comparing the meta-clonotype approach to other existing methods. Specifically, please clarify the relative importance to the novel meta-clonotype defining which derives from the new TCRdist3 metric, the motif, and the novel approach to establishing background comparisons.We agree that its critical to provide clarity about how this framework to develop antigen-associated meta-clonotypes is different from other published methods of grouping TCRs and also about when meta-clonotypes are a suitable tool for analysis. The fundamental difference is that other tools like GLIPH2, TCRNET and ALICE were not developed to find groups of similar TCRs among TCRs that have been experimentally pre-enriched for antigen-specificity. We’ve included a paragraph in the Discussion (quoted below), which emphasizes this distinction and the consequence for analyses.We also address how the TCRdist radius, the motif and the novel approach to efficient background comparisons all contribute to differentiating our meta-clonotype framework from existing methods. In the revised manuscript, we write:“The meta-clonotype framework we present joins a class of commonly used methods for TCR analysis that depend on comparisons to an antigen-naïve background repertoire. […] Ultimately, it is the focus on controlling the absolute frequency of meta-clonotype conformant TCRs in an antigen-naïve background that gives the meta-clonotype definitions portability to be applied to analyses of bulk repertoires, where quantification of similar antigen-specific TCRs is required.”Reviewer #3 (Recommendations for the authors):The authors have placed more focus in the revised manuscript on the definition and generation of meta-clonotypes, as suggested, with the covid data used as an example application. They have included a couple of new analyses to this effect . While I still think that the idea of meta-clonotypes is both interesting and potentially useful, I find some of the paper a bit long and difficult to follow.For example, it looks to me like the definition of meta-clonotype changes along the paper: e.g. In Figure 1/Figure 6 a meta-clonotype is centroid (TRBV + CDR3) + radius +/- motif; in Figure 10 it also includes what I think is an identifier for the set it comes from; and in Figure 12 it is TRBV + TRBJ + CDR3 + radius. As this is the main focus of the paper I think this definition should be made clear, consistent and explained somewhere.The nomenclature for defining meta-clonotypes is specified in Supplemental Table 1G and 1H:Set Name+Background Frequency+ Centroid V-Gene+Centroid CDR3+TCRdist Radius+MOTIF Constraint )– Set Name – Background Frequency Estimate Used to Set Radius – Centroid V-Gene – Centroid CDR3 – TCRdist Radius – MOTIF Constraint )The reviewer is correct for Figure 10 we do include the meta-clonotype nomenclature: M_55_1E6+TRBV28*01+CASSLKTDAYEQYF+20+(SL[RK][ST][ND].YEQ)We have addressed the reviewer’s comment and made the identifiers consistent in the updated Figure 12. We have listed identifiers as TRBV27*01+CASSDRGPPDTQYF+22+([AST][DE]RG[GP].[DE]TQ). Because all of these come from a single MIRA set we omit the M_55_1E6 portion of the tag for visual clarity.Secondly, it was unclear to me when the authors use which set for background: e.g. does Figure 1 and caption 'synthetic set' refer to the set of 100k OLGA V-J biased + 100k cord used later? cf Lines 535-537 "background CDR3s that were sampled from cord blood and constrained to use the same V and J genes", and elsewhere unadjusted cord blood is used without the OLGA adjusted set.Thank you for the careful review. See comment above about clarifying the captions to explicitly explain the background in each figure. Addressing the previous sentences at line 398-400:“The background-adjusted plot shows the position-specific Kullback-Leibler divergence from an alignment of background CDR3s that were sampled from cord blood and constrained to use the same V and J genes”The source of confusion is that the backgrounds used to estimate an optimal meta-clonotype radius are based on V-J gene usage of each of the set of antigen-annotated TCRs. This includes the 200K TCRs: 100,000 OLGA V-J matched combined with a random 100,000 sampled from cord blood.Whereas for background-adjusted logo motifs and computation of position specific Kullback-Leibler divergence, we use TCRsampler to randomly select TCRs from cord blood with V-J gene usage matching that of the TCRs used for the motif. The new caption states:“The background-adjusted logos are constructed by randomly sampling TCR β receptors from cord blood with the same TRBV and TRBJ gene usage, with 100 V-J matched TCRs sampled for every receptor in the foreground set.”The methodology to select a 200K synthetic background is more extensive than that used to generate a background to visualize CDR3 logo motif. More sequences and sampling adjustments are needed to estimate the optimal radius lengths for a large set of antigen-annotated TCRs. By contrast 1000 V-J matched clones is sufficient for the CDR3 logo visualization.I also think that the advantage / unique usage of the proposed meta-clonotype method over existing methods for grouping antigen-specific TCRs should be further clarified and emphasised: I personally don't find the Results section 'Comparison to k-mer based CDR3 features' or Figures 11 and 12 very convincing to this effect, and think this message should be made clearer in the paper before the sentence in the discussion "Our framework is designed for a different task than these algorithms…".We have clarified in the Discussion the differences in the design of GLIPH2 and other tools compared to the meta-clonotype framework.I do think the meta-clonotype method is useful, these changes are addressable and the paper has the potential to be made more readable and more impactful, if the authors streamline the definition and explanations, remove repetitive sections, and cross-reference to the relevant places in the text where things are referred to in the text before their explanations.Thanks, for your feedback. See above for the changes we’ve made to the Introduction and Discussion which address several of these issues.


A 46-year-old male was admitted to the trauma department after a motor vehicle accident. He presented with severe abdominal pain and a distended abdomen with peritonitis. His past surgical history included total proctocolectomy with ileal J-pouch anal anastomosis for ulcerative colitis 20 years previously. Computed tomography showed free peritoneal air and fluid in the abdomen mandating an exploratory laparotomy. A perforation at the ileal J-pouch blind end was found. Primary closure with diverting loop ileostomy was performed. The patient had an uneventful recovery and underwent closure of the ileostomy two months later. The case and management are discussed after reviewing the literature. Restorative proctocolectomy with ileal J-pouch anal anastomosis has become a standard operation for ulcerative colitis in consideration of the patient's quality of life. Although the long-term outcome of this procedure is generally satisfactory , occasioA 46-year-old male was admitted to the trauma shock room due to his involvement in a motor vehicle accident. He presented with severe abdominal pain and a distended abdomen with peritoneal signs. The patient underwent a total proctocolectomy with ileal J-pouch anal anastomosis for ulcerative colitis, 20 years previously. He also suffered several events of pouchitis.Laboratory workup on admission revealed a white blood cell count of 12,500/mcL , an arterial blood gas analysis lactate level of 4.1 mmol/L (0-1.3). Abdominal computed tomography with IV contrast injection but no oral or rectal contrast fluid added, in accordance to trauma imaging protocol, showed free intraperitoneal air and fluid.The patient underwent emergency exploratory laparotomy on admission, which revealed a large amount of turbid ascites in the abdominal cavity and peritoneal adhesions. After careful adhesiolysis, we did not identify the perforation site. We injected methylene blue dye through the nasogastric tube, which did not reveal perforation of the stomach or duodenum.We then injected air rectally after soaking the pelvis in sterile water and witnessed a “positive bubble test,” revealing a leak of air and feces through a 10 mm perforation at the blind end of the J-pouch . There wTraumatic perforation of the ileal pouch is rare. We found only one case report in the literature of ileal pouch perforation , caused The intestine is the third most commonly injured abdominal organ in blunt trauma. Minor injury may result in only a serosal tear or mild contusion of the intestine. Major injury may cause transmural perforation or transection of the bowel, mesenteric injury resulting in ischemic bowel, contusion of the bowel wall with seromuscular damage, or injury to the root of the mesentery, resulting in a tear of the mesentery or vessels. The mechanism of the injury has been suggested by Moty  as folloproctocolectomy, the pouch was brought down to the anal area for anastomosis. Thus, the vascular pedicle to the pouch is under much more tension and is fixed [In patients with restorative is fixed , 12. Thiis fixed , and wheis fixed , 13, 14.To decrease the morbidity after perforation of a pouch, the following guidelines should be employed: (1) a high index of suspicion is essential; (2) a diagnostic aid , and peritoneal lavage) should be used properly; (3) antibiotics for aerobic and anaerobic bacteria should be given; (4) early surgery is mandatory; (5) appropriate surgical drainage is important for the outcome. With limited experience, it remains however difficult to draw conclusions as to whether proximal diversion of the ileostomy is necessary.In our injured patient, pouch perforation occurred after traumatic seat belt injury.Transmission of the trauma forces to a distended, possibly inflamed ileal pouch is probably the main reason for perforation.In conclusion, we report a rare case of perforation in the blind end of the ileal J-pouch occurring after a severe seat belt injury. We present our thought process and diagnostic workup. It is mandatory to rule out a blow out of an ileal pouch after a severe abdominal trauma if there is evidence of perforation of hollow viscus viscera.