The outcome of SARS-CoV-2 infection is highly variable with only a minority progressing to severe COVID-19, characterized by acute respiratory distress syndrome, multi-organ dysfunction, elevated inflammatory cytokines, lymphopenia, and other abnormalities of the immune system (Bonnet et al., 2021; Giamarellos-Bourboulis et al., 2020; Huang and Pranata, 2020; Kaneko et al., 2020; Kuri-Cervantes et al., 2020; Lucas et al., 2020; Mathew et al., 2020; Mudd et al., 2020; Zhou et al., 2020). The risk of severe COVID-19 and death in people infected with SARS-CoV-2 increases with age and is greater in men than in women (Alkhouli et al., 2020; Bunders and Altfeld, 2020; Gupta et al., 2021; Laxminarayan et al., 2020; Mauvais-Jarvis, 2020; O’Driscoll et al., 2021; Peckham et al., 2020; Richardson et al., 2020; Scully et al., 2020). These trends have been observed in people infected with SARS-CoV (Chen and Subbarao, 2007; Donnelly et al., 2003; Karlberg et al., 2004), or with MERS-CoV (Alghamdi et al., 2014), and in laboratory animals challenged with SARS-CoV or SARS-CoV-2 (Channappanavar et al., 2017; Leist et al., 2020). The mechanisms underlying these effects of age and sex on COVID-19 morbidity and mortality remain poorly understood.

The composition and function of the human immune system changes with age and exhibits sexual dimorphism (Darboe et al., 2020; Klein and Flanagan, 2016; Márquez et al., 2020; Patin et al., 2018; Solana et al., 2012), with consequences for survival of infection, response to vaccination, and susceptibility to autoimmune disease (Flanagan et al., 2017; Giefing-Kröll et al., 2015; Márquez et al., 2020; Mauvais-Jarvis, 2020; Patin et al., 2018; Piasecka et al., 2018). Better understanding of these effects might provide clues as to why the clinical outcome of SARS-CoV-2 infection is so variable, ranging from asymptomatic to lethal (Cevik et al., 2021; He et al., 2020; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2020; Ra et al., 2021; Richardson et al., 2020; Yang et al., 2021).

Survival after infection with a pathogenic virus such as SARS-CoV-2 requires not only that the immune system control and eliminate the pathogen, but that disease tolerance mechanisms limit tissue damage caused by the pathogen or by host inflammatory responses (Ayres, 2020a; McCarville and Ayres, 2018; Medzhitov et al., 2012; Schneider and Ayres, 2008). Research with animal models has demonstrated that genetic and environmental factors can promote host fitness without directly inhibiting pathogen replication (Ayres, 2020a; Cumnock et al., 2018; Jhaveri et al., 2007; McCarville and Ayres, 2018; Medzhitov et al., 2012; Råberg et al., 2007; Sanchez et al., 2018; Schneider and Ayres, 2008; Wang et al., 2016). Although in most cases the underlying mechanism is unknown, some of these models suggest that subsets of innate lymphoid cells (ILCs) contribute to disease tolerance (Artis and Spits, 2015; Branzk et al., 2018; Califano et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011). Some ILC subsets produce the epidermal growth factor family member amphiregulin (AREG) that maintains the integrity of epithelial barriers in the lung and intestine (Branzk et al., 2018; Jamieson et al., 2013; Monticelli et al., 2015; Monticelli et al., 2011), and promotes tissue repair (Artis and Spits, 2015; Cherrier et al., 2018; Klose and Artis, 2016; Rak et al., 2016). In models of influenza infection in mice, homeostatic ILCs and exogenous AREG promote lung epithelial integrity, decrease disease severity, and increase survival, without decreasing pathogen burden (Califano et al., 2018; Jamieson et al., 2013; Monticelli et al., 2011).

Little is known about disease tolerance in the context of human infectious diseases. Interestingly, SARS-CoV-2 viral load does not reliably discriminate symptomatic from asymptomatic infection (Cevik et al., 2021; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2021; Ra et al., 2021; Yang et al., 2021). This discrepancy between SARS-CoV-2 viral load and the severity of COVID-19 is especially pronounced in children, who rarely have severe COVID-19 (Bailey et al., 2021; Li et al., 2020; Lu et al., 2020; Poline et al., 2020), although viral load may be comparable to that in adults with severe COVID-19 (Heald-Sargent et al., 2020; LoTempio et al., 2021; Yonker et al., 2020). These observations suggest that age-dependent, disease tolerance mechanisms influence the severity of COVID-19. In mice, homeostatic ILCs decrease in abundance in the lung with increasing age, and lose their ability to maintain disease tolerance during influenza infection (D’Souza et al., 2019). Although the distribution of ILCs within human tissues differs from mice and is heterogeneous among individuals (Yudanin et al., 2019), human ILCs share many features with those in mice (Vivier et al., 2018) and therefore may perform similar roles in maintaining tissue homeostasis and disease tolerance.

ILCs in peripheral blood have been reported to be depleted in individuals with severe COVID-19 (García et al., 2020; Kuri-Cervantes et al., 2020), but it is difficult to determine the extent to which ILCs are decreased independently from the overall lymphopenia associated with COVID-19 (Chen et al., 2020; Huang et al., 2020; Huang and Pranata, 2020; Zhang et al., 2020; Zhao et al., 2020), or from changes in other blood cell lineages (Giamarellos-Bourboulis et al., 2020; Huang et al., 2020; Kuri-Cervantes et al., 2020; Lucas et al., 2020; Mathew et al., 2020; Mudd et al., 2020; Zheng et al., 2020). In addition, assessment of lymphoid cell abundance, in the context of a disease for which age and sex are risk factors for severity, is confounded by programmed differences in lymphocyte abundance with age and sex (Márquez et al., 2020; Patin et al., 2018). The goal of this study was to determine whether the abundance of any blood lymphoid cell population was altered in COVID-19, independent of age, sex, and global lymphopenia, and whether abundance of any lymphoid cell population correlated with clinical outcome in SARS-CoV-2 infection.

The outcome of SARS-CoV-2 infection ranges from entirely asymptomatic to lethal COVID-19 (Cevik et al., 2021; He et al., 2020; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2020; Ra et al., 2021; Richardson et al., 2020; Yang et al., 2021). Yet, viral load does not reliably discriminate asymptomatic from symptomatic or hospitalized populations (Cevik et al., 2021; Jones et al., 2021; Lee et al., 2020; Lennon et al., 2020; Ra et al., 2021; Yang et al., 2021). In contrast, demographic factors, including increasing age and male sex, predict worse outcome of SARS-CoV-2 infection (Alkhouli et al., 2020; Bunders and Altfeld, 2020; Gupta et al., 2021; Laxminarayan et al., 2020; Mauvais-Jarvis, 2020; O’Driscoll et al., 2021; Peckham et al., 2020; Richardson et al., 2020; Scully et al., 2020). These demographic risk factors could be due to sexual dimorphism and changes with aging in composition and function of the human immune system (Darboe et al., 2020; Klein and Flanagan, 2016; Márquez et al., 2020; Patin et al., 2018; Solana et al., 2012). Therefore it is necessary to account for effects of age and sex to determine if there are additional, independent, effects of SARS-CoV-2-associated disease.

This study collected and analyzed 245 blood samples from 177 adult and 58 pediatric patients and controls, spanning the ages of 0.7–83 years, with approximately equal numbers of males and females. It was therefore possible to characterize the independent effects of age, sex, COVID-19, and MIS-C on blood lymphoid cell populations. After accounting for effects of age and sex, ILCs, but not CD4⁺ or CD8⁺ T cells, were lower in individuals hospitalized with COVID-19 when compared with controls (Table 2 and Figure 3A, B). Lower numbers of ILCs were also observed in children with COVID-19 (Table 5 and Figure 4A, B), as well as in an independent cohort of adult patients (Figure 3—figure supplement 2). Among adults infected with SARS-CoV-2, lower abundance of ILCs, but not of the other lymphoid cell subsets, was associated with increased odds of hospitalization, longer duration of hospitalization, and higher blood level of factors associated with systemic inflammation, including CRP (Tables 3 and 4, and Figure 3C). This inverse relationship between ILC abundance and CRP was also evident in children with COVID-19 or MIS-C (Figure 4C).

The identification of reduced ILC numbers as uniquely related to COVID-19 severity is important as these cells mediate disease tolerance in animal models (Artis and Spits, 2015; Branzk et al., 2018; Califano et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011). The results here therefore indicate that loss of ILCs from blood correlates with loss of ILC-associated homeostatic functions, thereby allowing more severe COVID-19. Although this study examined circulating blood lymphoid cells, and does not provide direct information about processes occurring within tissues, transcriptional and functional characterization of blood ILCs demonstrated that these cells are similar to ILCs isolated from lung tissue (Figure 5). Human ILCs circulate in lymphatic fluid draining from the tissues to the blood via the thoracic duct (Buggert et al., 2020), raising the possibility that some ILCs in the blood originate from, or traffic to, lung tissue. Further characterization of these blood ILCs showed that they are functional ILC2s capable of producing the protein AREG (Figure 6A and B). Given the tissue homeostatic role AREG plays in animal models of disease tolerance (Branzk et al., 2018; Diefenbach et al., 2020; Jamieson et al., 2013; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), the discovery here that males have a smaller fraction than females of blood ILCs capable of producing AREG (Figure 6C) could explain why males are at greater risk of death from SARS-CoV-2 infection (O’Driscoll et al., 2021). This sexual dimorphism in ILC function would be amplified further by the lower overall abundance of ILCs in males (Figure 2B and Table 2).

Although the inverse relationship between the number of blood ILCs and severity of COVID-19 suggests that loss of ILC homeostatic function results in breakdown of disease tolerance (Arpaia et al., 2015; Artis and Spits, 2015; Branzk et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), this observational study cannot determine whether ILC depletion preceded SARS-CoV-2 infection or whether ILC numbers are depleted as a consequence of SARS-CoV-2 infection. However, several observations support the hypothesis that individuals with lower ILC numbers at the time of SARS-CoV-2 infection are at greater risk of developing severe disease. ILC numbers in uninfected controls decrease exponentially with age; this decrease is much larger than that seen with other lymphoid cell types (Figure 2A), and much more closely mirrors the exponential increase in COVID-19 mortality with age (O’Driscoll et al., 2021; Figure 2C). In addition, the greater risk of COVID-19 mortality in males (O’Driscoll et al., 2021) correlates with lower abundance of blood ILCs (Figure 2B and Table 2) and smaller fraction of ILCs capable of producing AREG (Figure 6C). Finally, patients hospitalized with COVID-19 have a smaller fraction of AREG⁺ ILCs than controls (Figure 6D) and conditions independently associated with lower ILC abundance, such as HIV-1 infection (Kløverpris et al., 2016; Wang et al., 2020) and obesity (Brestoff et al., 2015; Yudanin et al., 2019), increase the risk for worse outcomes from SARS-CoV-2 infection (Biccard et al., 2021; Kompaniyets et al., 2021; Tesoriero et al., 2021).

In contrast to individuals with COVID-19, children with MIS-C had lower numbers of T cells as well as ILCs (Table 5 and Figure 4A and B), and longitudinal follow-up samples for pediatric COVID-19 and MIS-C patients showed persistence of low ILC numbers after COVID-19, but normalization of all depleted cell types after recovery from MIS-C (Figure 4D and E and Figure 4—figure supplement 2). These differences imply that the reversible lymphopenia in MIS-C is due to different underlying processes than the more specific and persistent lower ILC abundance seen in individuals with COVID-19. This difference is made more interesting by the fact that none of the children with MIS-C had required hospitalization for COVID-19 and only one experienced any COVID-19 symptoms. The other children with MIS-C were therefore unaware that they had been infected. It is possible that children with pre-existing lower ILC numbers are at risk of developing COVID-19 if infected with SARS-CoV-2, while other factors such as prolonged exposure to SARS-CoV-2 antigens in the gastrointestinal tract (Yonker et al., 2021), or rare inborn errors of immunity (Sancho-Shimizu et al., 2021), promote inflammatory processes in MIS-C that drive nonspecific lymphoid cell depletion, which ultimately normalizes after recovery.

Although ILC depletion and recovery has been reported in rheumatoid arthritis (Rauber et al., 2017), inflammation-driven ILC-depletion is not necessarily reversible, as ILCs appear permanently depleted after HIV-1 infection, possibly by the high levels of common γ-chain cytokines that are present during acute infection (Wang et al., 2020). Better understanding of the processes that drive down ILC abundance in populations susceptible to COVID-19 could potentially allow for development of interventions that increase ILC abundance and restore homeostatic disease tolerance mechanisms.

In conclusion, considering the established functions of ILCs (Artis and Spits, 2015; Branzk et al., 2018; Klose and Artis, 2016; Monticelli et al., 2015; Monticelli et al., 2011), and the host homeostatic responses necessary to survive pathogenic infection (López-Otín and Kroemer, 2021; McCarville and Ayres, 2018; Medzhitov et al., 2012; Schneider and Ayres, 2008), the findings reported here support the hypothesis that loss of disease tolerance mechanisms attributable to ILCs increase the risk of morbidity and mortality with SARS-CoV-2 infection. The findings of this observational study warrant establishment of prospective cohorts to determine whether abundance of ILCs or of other lymphoid cell subsets associated with disease tolerance (Arpaia et al., 2015; Artis and Spits, 2015; Branzk et al., 2018; Diefenbach et al., 2020; McCarville and Ayres, 2018; Monticelli et al., 2015; Monticelli et al., 2011), predict clinical outcome for infection with SARS-CoV-2 or other lethal pathogens. Understanding the mechanisms that allow an individual to tolerate high-level viral replication without experiencing symptoms, and how these mechanisms can fail and thereby allow for progression to severe disease, will provide the foundation for development of therapeutic interventions that maintain health and improve survival of pathogenic viral infection (Ayres, 2020b).

The data that support the findings of this study are available within the manuscript and in its supplementary information data files. Bulk RNA-Seq datasets generated here can be found at: NCBI Gene Expression Omnibus (GEO): GSE168212. Bulk RNA-Seq data generated by previously published studies are available from NCBI GEO: GSE131031 and GSE126107. This study did not generate unique code.

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Decision letter after peer review:

Thank you for submitting your article "Innate lymphoid cells and disease tolerance in SARS-CoV-2 infection" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Evangelos J Giamarellos-Bourboulis as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Satyajit Rath as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Evdoxia Kyriazopoulou (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

– The Introduction is too long and it is based on references of the past before the arrival of COVID-19.

– One main concern is that the authors suggest that depletion of innate lymphoid cells (ILC) is the main driver of the lymphopenia of severe COVID-19. To prove this the authors need to provide the absolute counts of all subpopulations and to provide serial follow-up data. They also need to explain how hypoglobulinemia of patients is explained if depletion involves only ILC. There are at least two publications (PMID 32320677 and 34678611) suggesting defect in antigen presentation and they are not even referenced in this submission.

– The authors need to provide data on the cytokine production capacity of ILC.

– The authors should clarify when exactly blood sampling took place (at admission? during hospitalization? Before any treatment start?).

– According to Table 1 median duration of symptoms is about 20 days which is quiet a long period; normally patients are admitted after the first week. If sampling is not done at admission, administered therapy especially corticosteroids may influence flow cytometry results. If therapy was administered this has to be part of the regression results in Table 3.

– Another concern is the absence in presentation of comorbidities as they can perhaps also influence results. If these data are unavailable, this is for sure a limitation and should be discussed.

– In SARS-CoV2 infected adults, as reported widely, infections were highest in young adults, and mortality was highest in the elderly and increased approximately 1 log for each 20 year age group. Linear regression revealed that age, sex, and ILC and NK cell frequency were inversely correlated with hospitalization, and the association between ILC and NK frequency was retained when age and sex were adjusted for. Subsequent multiple logistic regression analysis showed that adjusting for age, sex, and symptom duration, only ILC frequency was significantly associated with an increased risk of hospitalization, the duration of hospital stay, and an increase in CRP. The same association between ILC frequency, but not other lymphocytes, and COVID was observed in an additional paediatric cohort. However, in rare Paediatric cases who developed MIS-C, both ILCs and T cells were significantly reduced. To link blood ILCs to what is happening on the lung, bioinformatic analysis of sequence data generated from sorted blood ILCS in compared to that of published gut and lung ILC datasets. Although, the authors argue this shows blood ILCs are transcriptionally more closely aligned to lung ILCs, the validity of this analysis is hard to judge without more detail and, as the data does not come from COVID subjects, its value in supporting the manuscript is unclear. Finally, ILCs from uninfected males produced less amphiregulin, important in lung homeostasis and repair.

– The loss of ILCs from circulation has been shown in several diseases, including HIV, TB, and COVID, as has the association between circulating ILCs and age. The strength of this manuscript is in using multiple regression to show that the association between ILC loss and disease severity is retained when age is controlled for, as age is such an important factor in COVID. However, the overall conclusion that elevated circulating ILCs support disease tolerance, while interesting, is not directly supported by any data. For example, a reduction in blood ILCs may indicate the recruitment of these cells to the lung, as has been shown for TB (Ardain et al., Nature 2019). In which case, an increased frequency of blood ILCs may not be protective per se, but just reflective of less lung involvement. Therefore, I suggest the title and abstract be altered to reflect the data presented more directly.

– The study consistently refers to cell abundance but is actually reporting the frequency of cells. It is an important distinction in this context, as the total lymphocyte count declines with age. Therefore, if there are differences in the rates of decline, it can create the impression of an increase in specific subsets which are actually declining but just at a slower than the median (as may be happening with NK cells for example). To avoid any potential confusion, I suggest the authors present and discuss the data as frequencies of total lymphocytes (or PBMC as appropriate).

– For the lymphocyte specific analysis, what is lacking is reporting of the other lymphocyte subsets, such as B-cells. It may not be possible to represent these as individual populations, depending on the markers used, but they could be presented as the remainder of the whole. In this way, the reader can get a sense of what is happening in the lymphocyte compartment as a whole. This is important given that absolute values are not available.

-I am a bit concerned that the synchronization of the paeds sample is too big an issue in this small sample size to give much confidence in the effects observed – the MISC subjects are measured at about 2months and 6-7 months, and the COVID at 6-8 months and one at 11 months. Given this, how much weight do the authors think this analysis carries?

– The authors compare the RNA read-out of the peripheral ILCs and they use previous arrays from lung ILCs to suggest that they are coming from the lung. This is far too arbitrary since this is a different cohort. Ardain et al., sorted ILC2s and ILC3s from the lung whilst Yudanin sorted ILC1, 2 and 3 from the gut – and I think only sequenced 3 and 1. ILC2 are the dominant population in blood. Is it possible that the greater apparent overlap between the blood and lung relates to the type of ILCs sequenced rather than the biological overlap between these compartments?. More generally, does this analysis add useful information to the current study? Analysis of the transcriptional activity of ILCs during COVID may have provided evidence of their role in disease tolerance, as hypothesised by the authors. However, as the PBMC sequenced were uninfected healthy controls, this is impossible.

– Figure 2.B – it might be informative to plot males and females separately in each age bin. We know there is an equal ratio of males and females but we don’t know if this is equally distributed by age. From Figure 3 it looks like there may be a bias toward younger female and older male controls? Presenting it this way will make it easy for the reader to assess.

Essential Revisions (for the authors):

– The Introduction is too long and it is based on references of the past before the arrival of COVID-19.

To address these points, the Introduction was edited to start with a brief but more general statement about the features of severe COVID-19 (page 3, lines 89-92) and to include the two references suggested in comment #2 (PMID 32320677 and 34678611). Several sentences on innate lymphoid cells were deleted to focus the Introduction on COVID-19.

– One main concern is that the authors suggest that depletion of innate lymphoid cells (ILC) is the main driver of the lymphopenia of severe COVID-19. To prove this the authors need to provide the absolute counts of all subpopulations and to provide serial follow-up data. They also need to explain how hypoglobulinemia of patients is explained if depletion involves only ILC. There are at least two publications (PMID 32320677 and 34678611) suggesting defect in antigen presentation and they are not even referenced in this submission.

We thank the reviewers for the chance to clarify these points.

– It was not our intention to suggest that ILC defects are the primary cause of immune abnormalities such as lymphopenia or the defects in antigen presentation that the reviewer mentioned. As we wrote (page 6, lines 157-161), “The goal of this study was to determine whether the abundance of any blood lymphoid cell population was altered in COVID-19, independent of age, sex, and global lymphopenia, and whether abundance of any lymphoid cell population correlated with clinical outcome in SARS-CoV-2 infection.” After taking into account the effects of age and sex, our main finding was that reduction in ILC number was significantly associated with COVID-19 severity, risk and duration of hospitalization, and with increase in blood inflammatory markers such as CRP.

– To clarify this point we modified our abstract to say that our study “suggests” that lower ILC abundance, “contributes” to increased COVID-19 severity with age and in males (page 2, line 76).

– As stated above in response to point #1, we have expanded our introduction about the abnormalities associated with severe COVID-19 and have incorporated the two suggested references (page 3, lines 89-92).

– The authors need to provide data on the cytokine production capacity of ILC.

In response to this comment we have added a new panel showing that, in addition to being decreased in abundance, blood ILCs in people hospitalized for COVID-19 have significantly decreased capacity to produce amphiregulin (Figure 6D). The text of the abstract (page 2, line 69-71), results (page 44, lines 676-681), discussion (page 49, lines 786-788), and figure legends (page 46, line 703-706), have been modified accordingly.

– The authors should clarify when exactly blood sampling took place (at admission? During hospitalization? Before any treatment start?).

We now mention that samples from hospitalized patients were collected during admission (page 12, lines 165-168). In addition, we now mention that the COVID-19 blood samples were collected during the first wave of COVID-19, between March 31 and June 3^rd, 2020 (page 12, lines 165-166); this time was before corticosteroids were demonstrated to be efficacious for severe COVID-19. Records of treatment interventions other than intubation were not available to us for these patients and this point has been added to the text (page 17, lines 271-272).

– According to Table 1 median duration of symptoms is about 20 days which is quiet a long period; normally patients are admitted after the first week. If sampling is not done at admission, administered therapy especially corticosteroids may influence flow cytometry results. If therapy was administered this has to be part of the regression results in Table 3.

We agree that timing of corticosteroids would be an important variable to include in Table 3. As discussed in response to point #4, this information was not available, and could not be included in the analysis. That being said, as indicated in the text (page 30, lines 457-463) and in the footnote to Table 3 (page 30), we included symptom duration at the time of sample collection as a covariate in the regression analysis presented in Table 3.

– Another concern is the absence in presentation of comorbidities as they can perhaps also influence results. If these data are unavailable, this is for sure a limitation and should be discussed.

In response to this suggestion we obtained information about diabetes mellitus. These data are now listed in Table 1 (page 18), mentioned in the text (page 17, lines 276-278), and included in the calculations for the odds of hospitalization presented Table 3 (page 30) and discussed on page 30 (lines 457-463). Accounting for diabetes mellitus diagnosis had minimal effect on the statistical results.

– In SARS-CoV2 infected adults, as reported widely, infections were highest in young adults, and mortality was highest in the elderly and increased approximately 1 log for each 20 year age group. Linear regression revealed that age, sex, and ILC and NK cell frequency were inversely correlated with hospitalization, and the association between ILC and NK frequency was retained when age and sex were adjusted for. Subsequent multiple logistic regression analysis showed that adjusting for age, sex, and symptom duration, only ILC frequency was significantly associated with an increased risk of hospitalization, the duration of hospital stay, and an increase in CRP. The same association between ILC frequency, but not other lymphocytes, and COVID was observed in an additional paediatric cohort. However, in rare Paediatric cases who developed MIS-C, both ILCs and T cells were significantly reduced. To link blood ILCs to what is happening on the lung, bioinformatic analysis of sequence data generated from sorted blood ILCS in compared to that of published gut and lung ILC datasets. Although, the authors argue this shows blood ILCs are transcriptionally more closely aligned to lung ILCs, the validity of this analysis is hard to judge without more detail and, as the data does not come from COVID subjects, its value in supporting the manuscript is unclear. Finally, ILCs from uninfected males produced less amphiregulin, important in lung homeostasis and repair.

We thank the reviewer for the extensive summary of our findings. The comment they make here is addressed in point #12 below.

– The loss of ILCs from circulation has been shown in several diseases, including HIV, TB, and COVID, as has the association between circulating ILCs and age. The strength of this manuscript is in using multiple regression to show that the association between ILC loss and disease severity is retained when age is controlled for, as age is such an important factor in COVID. However, the overall conclusion that elevated circulating ILCs support disease tolerance, while interesting, is not directly supported by any data. For example, a reduction in blood ILCs may indicate the recruitment of these cells to the lung, as has been shown for TB (Ardain et al., Nature 2019). In which case, an increased frequency of blood ILCs may not be protective per se, but just reflective of less lung involvement. Therefore, I suggest the title and abstract be altered to reflect the data presented more directly.

In response to this comment we have added new data and changed the text as recommended:

– The term “disease tolerance” has been removed from the title (page 1, line 3).

– The abstract now mentions that blood ILCs from people hospitalized with COVID-19 have significantly decreased capacity to produce amphiregulin (page 2, lines 69-71). See point number 3 above regarding new Figure 6D.

– The possibility that ILCs are sequestered in the lung is now mentioned in the text (page 40, lines 614-615).

– The study consistently refers to cell abundance but is actually reporting the frequency of cells. It is an important distinction in this context, as the total lymphocyte count declines with age. Therefore, if there are differences in the rates of decline, it can create the impression of an increase in specific subsets which are actually declining but just at a slower than the median (as may be happening with NK cells for example). To avoid any potential confusion, I suggest the authors present and discuss the data as frequencies of total lymphocytes (or PBMC as appropriate).

As suggested by the reviewer, the majority of the data are reported as ILCs per million lymphocytes. This is stated in the text and figure legends, and indicated in the Y axis labels on the figures. As the reviewer also points out, certain analyses were per million PBMCs; critical findings such as the effect of COVID-19 on ILCs were essentially the same per million lymphocytes or PBMCs (page 27, lines 418-420; Supplementary file 2e). The same was true for the other lymphoid cell subsets, including the significant increase in NK cells with age that was referred to by the reviewer.

– For the lymphocyte specific analysis, what is lacking is reporting of the other lymphocyte subsets, such as B-cells. It may not be possible to represent these as individual populations, depending on the markers used, but they could be presented as the remainder of the whole. In this way, the reader can get a sense of what is happening in the lymphocyte compartment as a whole. This is important given that absolute values are not available.

The reviewer makes an important point. To provide a sense of the lymphocyte compartment as a whole, we had analyzed lymphocyte abundance as a fraction of total PBMCs. This was described in the text on page 25, line 379-384,

“the effect of COVID-19 on total lymphocyte abundance was addressed with multiple linear regression. After accounting for effects of age and sex, individuals hospitalized with severe COVID-19 had 1.33-fold (95%CI: 1.49–1.19; p = 1.22 x 10^-6) fewer total lymphocytes among PBMCs than did controls (Supplementary file 2d)”.

-I am a bit concerned that the synchronization of the paeds sample is too big an issue in this small sample size to give much confidence in the effects observed – the MISC subjects are measured at about 2months and 6-7 months, and the COVID at 6-8 months and one at 11 months. Given this, how much weight do the authors think this analysis carries?

We thank the reviewer for raising this question regarding the difference in timing of follow-up blood collections for the MISC-C subjects vs the COVID-19 subjects in Figure 4E (page 38). If the results had been different, the difference in timing would have been a problem. But, given the directionality of the effect we observed, this difference in timing highlights the divergence in rate of recovery of ILC numbers between these two groups: ILC abundance rebounded by 2 months of follow-up for the MIS-C patients, whereas ILC abundance had still not rebounded after 9 months of follow-up for the COVID-19 patients. We now emphasize this point in the text (page 36, lines 570-572).

– The authors compare the RNA read-out of the peripheral ILCs and they use previous arrays from lung ILCs to suggest that they are coming from the lung. This is far too arbitrary since this is a different cohort. Ardain et al., sorted ILC2s and ILC3s from the lung whilst Yudanin sorted ILC1, 2 and 3 from the gut – and I think only sequenced 3 and 1. ILC2 are the dominant population in blood. Is it possible that the greater apparent overlap between the blood and lung relates to the type of ILCs sequenced rather than the biological overlap between these compartments?. More generally, does this analysis add useful information to the current study? Analysis of the transcriptional activity of ILCs during COVID may have provided evidence of their role in disease tolerance, as hypothesised by the authors. However, as the PBMC sequenced were uninfected healthy controls, this is impossible.

Several changes were made to the manuscript in response to these comments:

– We agree that it would have been better if we had presented RNA-Seq data on lung ILCs from people hospitalized with COVID-19. The text now states that our attempts to profile ILCs from COVID-19 lung samples were unsuccessful (page 40, lines 610-612). In our previous work concerning intestinal ILCs from people living with HIV-1, biopsies had to be processed fresh within the hour (Nat Immunol 21:274); we were unable to profile intestinal ILCs from frozen or fixed tissues and suspect a similar issue holds for the viability of lung ILCs in COVID-19.

– We also agree that it would have been better if we had RNA-Seq data for ILCs isolated from the blood of people hospitalized with COVID-19. This was not possible because the limited sample available was prioritized for the FACS experiments. These samples had low numbers of ILCs which increased the difficulty of sorting viable cells from this population that is already rare in the blood of uninfected people.

– Given the significant reductions in blood ILCs in our study, and our inability to profile these rare cells from people with severe COVID, we felt it was important to provide some additional characterization of blood ILCs, even if only from uninfected individuals. Our analysis of global gene expression in blood ILCs from 9 donors shows close similarity to the expression profile of ILCs from the lung. While the reviewer is correct that this is driven in part by the ILC2 character of these cells, many of the 355 genes that distinguish blood and lung ILCs from ILCs of other tissues are not obvious ILC2 genes and could indicate other biologically relevant features in common between blood and lung ILCs (Figure 5B and C, page 42).

– The transcriptional analysis guided our phenotypic analysis of the blood ILCs (Figure 6 page 45), and, in the revised manuscript, we now show that blood ILCs in people hospitalized for COVID-19 have significantly decreased capacity to produce amphiregulin (Figure 6D). The text of the abstract (page 2, line 69-71), results (page 44, lines 676-681), discussion (page 49, lines 786-788), and figure legends (page 46, line 703-706), have been modified accordingly.

– Figure 2.B – it might be informative to plot males and females separately in each age bin. We know there is an equal ratio of males and females but we don't know if this is equally distributed by age. From Figure 3 it looks like there may be a bias toward younger female and older male controls? Presenting it this way will make it easy for the reader to assess.

To address the reviewer’s question about our control group we compared the male/female ratio for each age group against each of the other age groups in Figure 2. There were no significant differences in this ratio among any of the groups. A table showing this analysis has been added to the supplement (Supplementary file 2c).

Author details

1. Noah J Silverstein

   1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
   2. Medical Scientist Training Program, University of Massachusetts Medical School, Worcester, United States
   3. Massachusetts Consortium on Pathogen Readiness, Boston, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Yetao Wang

   For correspondence

   noah.silverstein@umassmed.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5688-9978

2. Yetao Wang

   1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
   2. Massachusetts Consortium on Pathogen Readiness, Boston, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Noah J Silverstein

   For correspondence

   yetao.wang@umassmed.edu

   Competing interests

   No competing interests declared

3. Zachary Manickas-Hill

   1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
   2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

4. Claudia Carbone

   Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Ann Dauphin

   Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Brittany P Boribong

   1. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
   2. Massachusetts General Hospital, Department of Pediatrics, Boston, United States
   3. Harvard Medical School, Boston, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

7. Maggie Loiselle

   Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1051-2072

8. Jameson Davis

   Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

9. Maureen M Leonard

   1. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
   2. Massachusetts General Hospital, Department of Pediatrics, Boston, United States
   3. Harvard Medical School, Boston, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

10. Leticia Kuri-Cervantes

    1. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    2. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

11. MGH COVID-19 Collection & Processing Team

    Contribution

    Resources

    Competing interests

    No competing interests declared

    1. Kendall Lavin-Parsons, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    2. Blair Parry, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    3. Brendan Lilley, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    4. Carl Lodenstein, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    5. Brenna McKaig, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    6. Nicole Charland, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    7. Hargun Khanna, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    8. Justin Margolin, Department of Emergency Medicine, Massachusetts General Hospital, Boston, United States
    9. Anna Gonye, Massachusetts General Hospital Cancer Center, Boston, United States
    10. Irena Gushterova, Massachusetts General Hospital Cancer Center, Boston, United States
    11. Tom Lasalle, Massachusetts General Hospital Cancer Center, Boston, United States
    12. Nihaarika Sharma, Massachusetts General Hospital Cancer Center, Boston, United States
    13. Brian C Russo, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, United States
    14. Maricarmen Rojas-Lopez, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, United States
    15. Moshe Sade-Feldman, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    16. Kasidet Manakongtreecheep, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    17. Jessica Tantivit, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    18. Molly Fisher Thomas, Massachusetts General Hospital Center for Immunology and Inflammatory Diseases, Boston, United States
    19. Betelihem A Abayneh, Massachusetts General Hospital, Boston, United States
    20. Patrick Allen, Massachusetts General Hospital, Boston, United States
    21. Diane Antille, Massachusetts General Hospital, Boston, United States
    22. Katrina Armstrong, Massachusetts General Hospital, Boston, United States
    23. Siobhan Boyce, Massachusetts General Hospital, Boston, United States
    24. Joan Braley, Massachusetts General Hospital, Boston, United States
    25. Karen Branch, Massachusetts General Hospital, Boston, United States
    26. Katherine Broderick, Massachusetts General Hospital, Boston, United States
    27. Julia Carney, Massachusetts General Hospital, Boston, United States
    28. Andrew Chan, Massachusetts General Hospital, Boston, United States
    29. Susan Davidson, Massachusetts General Hospital, Boston, United States
    30. Michael Dougan, Massachusetts General Hospital, Boston, United States
    31. David Drew, Massachusetts General Hospital, Boston, United States
    32. Ashley Elliman, Massachusetts General Hospital, Boston, United States
    33. Keith Flaherty, Massachusetts General Hospital, Boston, United States
    34. Jeanne Flannery, Massachusetts General Hospital, Boston, United States
    35. Pamela Forde, Massachusetts General Hospital, Boston, United States
    36. Elise Gettings, Massachusetts General Hospital, Boston, United States
    37. Amanda Griffin, Massachusetts General Hospital, Boston, United States
    38. Sheila Grimmel, Massachusetts General Hospital, Boston, United States
    39. Kathleen Grinke, Massachusetts General Hospital, Boston, United States
    40. Kathryn Hall, Massachusetts General Hospital, Boston, United States
    41. Meg Healy, Massachusetts General Hospital, Boston, United States
    42. Deborah Henault, Massachusetts General Hospital, Boston, United States
    43. Grace Holland, Massachusetts General Hospital, Boston, United States
    44. Chantal Kayitesi, Massachusetts General Hospital, Boston, United States
    45. Vlasta LaValle, Massachusetts General Hospital, Boston, United States
    46. Yuting Lu, Massachusetts General Hospital, Boston, United States
    47. Sarah Luthern, Massachusetts General Hospital, Boston, United States
    48. Jordan Marchewka, Massachusetts General Hospital, Boston, United States
    49. Brittani Martino, Massachusetts General Hospital, Boston, United States
    50. Roseann McNamara, Massachusetts General Hospital, Boston, United States
    51. Christian Nambu, Massachusetts General Hospital, Boston, United States
    52. Susan Nelson, Massachusetts General Hospital, Boston, United States
    53. Marjorie Noone, Massachusetts General Hospital, Boston, United States
    54. Christine Ommerborn, Massachusetts General Hospital, Boston, United States
    55. Lois Chris Pacheco, Massachusetts General Hospital, Boston, United States
    56. Nicole Phan, Massachusetts General Hospital, Boston, United States
    57. Falisha A Porto, Massachusetts General Hospital, Boston, United States
    58. Edward Ryan, Massachusetts General Hospital, Boston, United States
    59. Kathleen Selleck, Massachusetts General Hospital, Boston, United States
    60. Sue Slaughenhaupt, Massachusetts General Hospital, Boston, United States
    61. Kimberly Smith Sheppard, Massachusetts General Hospital, Boston, United States
    62. Elizabeth Suschana, Massachusetts General Hospital, Boston, United States
    63. Vivine Wilson, Massachusetts General Hospital, Boston, United States
    64. Galit Alter, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    65. Alejandro Balazs, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    66. Julia Bals, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    67. Max Barbash, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    68. Yannic Bartsch, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    69. Julie Boucau, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    70. Josh Chevalier, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    71. Fatema Chowdhury, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    72. Kevin Einkauf, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    73. Jon Fallon, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    74. Liz Fedirko, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    75. Kelsey Finn, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    76. Pilar Garcia-Broncano, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    77. Ciputra Hartana, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    78. Chenyang Jiang, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    79. Paulina Kaplonek, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    80. Marshall Karpell, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    81. Evan C Lam, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    82. Kristina Lefteri, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    83. Xiaodong Lian, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    84. Mathias Lichterfeld, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    85. Daniel Lingwood, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    86. Hang Liu, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    87. Jinqing Liu, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    88. Natasha Ly, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    89. Ashlin Michell, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    90. Ilan Millstrom, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    91. Noah Miranda, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    92. Claire O’Callaghan, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    93. Matthew Osborn, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    94. Shiv Pillai, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    95. Yelizaveta Rassadkina, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    96. Alexandra Reissis, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    97. Francis Ruzicka, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    98. Kyra Seiger, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    99. Libera Sessa, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    100. Christianne Sharr, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    101. Sally Shin, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    102. Nishant Singh, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    103. Weiwei Sun, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    104. Xiaoming Sun, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    105. Hannah Ticheli, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    106. Alicja Trocha-Piechocka, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    107. Daniel Worrall, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    108. Alex Zhu, Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    109. George Daley, Harvard Medical School, Boston, United States
    110. David Golan, Harvard Medical School, Boston, United States
    111. Howard Heller, Harvard Medical School, Boston, United States
    112. Arlene Sharpe, Harvard Medical School, Boston, United States
    113. Nikolaus Jilg, Brigham and Women’s Hospital, Boston, United States
    114. Alex Rosenthal, Brigham and Women’s Hospital, Boston, United States
    115. Colline Wong, Brigham and Women’s Hospital, Boston, United States

12. Nuala J Meyer

    Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States

    Contribution

    Data curation, Resources

    Competing interests

    No competing interests declared

13. Michael R Betts

    1. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    2. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation, Resources

    Competing interests

    No competing interests declared

14. Jonathan Z Li

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Department of Medicine, Brigham and Women’s Hospital, Boston, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

15. Bruce D Walker

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    3. Howard Hughes Medical Institute, Chevy Chase, United States
    4. Department of Biology and Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Xu G Yu

    1. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    3. Department of Medicine, Brigham and Women’s Hospital, Boston, United States

    Contribution

    Data curation, Investigation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

17. Lael M Yonker

    1. Massachusetts General Hospital, Mucosal Immunology and Biology Research Center, Boston, United States
    2. Massachusetts General Hospital, Department of Pediatrics, Boston, United States
    3. Harvard Medical School, Boston, United States

    Contribution

    Data curation, Funding acquisition, Investigation, Resources, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

18. Jeremy Luban

    1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    2. Massachusetts Consortium on Pathogen Readiness, Boston, United States
    3. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States
    4. Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Medical School, Worcester, United States
    5. Broad Institute of Harvard and MIT, Cambridge, United States

    Contribution

    Conceptualization, Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing

    For correspondence

    jeremy.luban@umassmed.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5650-4054

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