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.

1. 1. Wang Y
   2. Lifshitz LM
   3. Luban J

   (2021) NCBI Gene Expression Omnibus

   ID GSE168212. Systematic analysis of innate lymphoid cells and natural killer cells in context of HIV-1 infection.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE168212

1. 1. Leslie A
   2. Khader S

   (2019) NCBI Gene Expression Omnibus

   ID GSE131031. Group 3 innate lymphoid cells mediate early protective immunity against Mycobacterium tuberculosis.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131031
2. 1. Yudanin N
   2. Latorre I
   3. Covington C

   (2019) NCBI Gene Expression Omnibus

   ID GSE126107. Spatial and Temporal Mapping of Human Innate Lymphoid Cells Reveals Elements of Tissue Specificity.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126107

1. 1. Alghamdi IG
   2. Hussain II
   3. Almalki SS
   4. Alghamdi MS
   5. Alghamdi MM
   6. El-Sheemy MA

   (2014) The pattern of Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive epidemiological analysis of data from the Saudi Ministry of Health

   International Journal of General Medicine 7:417–423.

   https://doi.org/10.2147/IJGM.S67061

   • PubMed
   • Google Scholar
2. 1. Alkhouli M
   2. Nanjundappa A
   3. Annie F
   4. Bates MC
   5. Bhatt DL

   (2020) Sex Differences in Case Fatality Rate of COVID-19: Insights From a Multinational Registry

   Mayo Clinic Proceedings 95:1613–1620.

   https://doi.org/10.1016/j.mayocp.2020.05.014

   • PubMed
   • Google Scholar
3. 1. Anegón I
   2. Cuturi MC
   3. Trinchieri G
   4. Perussia B

   (1988) Interaction of Fc receptor (CD16) ligands induces transcription of interleukin 2 receptor (CD25) and lymphokine genes and expression of their products in human natural killer cells

   The Journal of Experimental Medicine 167:452–472.

   https://doi.org/10.1084/jem.167.2.452

   • PubMed
   • Google Scholar
4. 1. Ardain A
   2. Domingo-Gonzalez R
   3. Das S
   4. Kazer SW
   5. Howard NC
   6. Singh A
   7. Ahmed M
   8. Nhamoyebonde S
   9. Rangel-Moreno J
   10. Ogongo P
   11. Lu L
   12. Ramsuran D
   13. de la Luz Garcia-Hernandez M
   14. K Ulland T
   15. Darby M
   16. Park E
   17. Karim F
   18. Melocchi L
   19. Madansein R
   20. Dullabh KJ
   21. Dunlap M
   22. Marin-Agudelo N
   23. Ebihara T
   24. Ndung’u T
   25. Kaushal D
   26. Pym AS
   27. Kolls JK
   28. Steyn A
   29. Zúñiga J
   30. Horsnell W
   31. Yokoyama WM
   32. Shalek AK
   33. Kløverpris HN
   34. Colonna M
   35. Leslie A
   36. Khader SA

   (2019) Group 3 innate lymphoid cells mediate early protective immunity against tuberculosis

   Nature 570:528–532.

   https://doi.org/10.1038/s41586-019-1276-2

   • PubMed
   • Google Scholar
5. 1. Arpaia N
   2. Green JA
   3. Moltedo B
   4. Arvey A
   5. Hemmers S
   6. Yuan S
   7. Treuting PM
   8. Rudensky AY

   (2015) A Distinct Function of Regulatory T Cells in Tissue Protection

   Cell 162:1078–1089.

   https://doi.org/10.1016/j.cell.2015.08.021

   • PubMed
   • Google Scholar
6. 1. Artis D
   2. Spits H

   (2015) The biology of innate lymphoid cells

   Nature 517:293–301.

   https://doi.org/10.1038/nature14189

   • PubMed
   • Google Scholar
7. 1. Ayres JS

   (2020a) Surviving COVID-19: A disease tolerance perspective

   Science Advances 6:eabc1518.

   https://doi.org/10.1126/sciadv.abc1518

   • PubMed
   • Google Scholar
8. 1. Ayres JS

   (2020b) The Biology of Physiological Health

   Cell 181:250–269.

   https://doi.org/10.1016/j.cell.2020.03.036

   • PubMed
   • Google Scholar
9. 1. Bailey LC
   2. Razzaghi H
   3. Burrows EK
   4. Bunnell HT
   5. Camacho PEF
   6. Christakis DA
   7. Eckrich D
   8. Kitzmiller M
   9. Lin SM
   10. Magnusen BC
   11. Newland J
   12. Pajor NM
   13. Ranade D
   14. Rao S
   15. Sofela O
   16. Zahner J
   17. Bruno C
   18. Forrest CB

   (2021) Assessment of 135 794 Pediatric Patients Tested for Severe Acute Respiratory Syndrome Coronavirus 2 Across the United States

   JAMA Pediatrics 175:176–184.

   https://doi.org/10.1001/jamapediatrics.2020.5052

   • PubMed
   • Google Scholar
10. 1. Bates D
    2. Mächler M
    3. Bolker B
    4. Walker S

    (2015) Fitting Linear Mixed-Effects Models Usinglme4

    Journal of Statistical Software 67:.

    https://doi.org/10.18637/jss.v067.i01

    • Google Scholar
11. 1. Biccard BM
    2. Gopalan PD
    3. Miller M
    4. Michell WL
    5. Thomson D
    6. Ademuyiwa A
    7. Aniteye E
    8. Calligaro G
    9. Chaibou MS
    10. Dhufera HT
    11. Elfagieh M
    12. Elfiky M
    13. Elhadi M
    14. Fawzy M
    15. Fredericks D
    16. Gebre M
    17. Bayih AG
    18. Hardy A
    19. Joubert I
    20. Kifle F
    21. Kluyts HL
    22. Macleod K
    23. Mekonnen Z
    24. Mer M
    25. Morais A
    26. Msosa V
    27. Mulwafu W
    28. Ndonga A
    29. Ngumi Z
    30. Omigbodun A
    31. Owoo C
    32. Paruk F
    33. Piercy JL
    34. Scribante J
    35. Seman Y
    36. Taylor E
    37. Straaten D
    38. Elfiky M
    39. Fawzy M
    40. Awad A
    41. Hussein H
    42. Shaban M
    43. Elbadawy M
    44. Elmehrath AO
    45. Cordie A
    46. Elganainy M
    47. El-Shazly M
    48. Essam M
    49. Abdelwahab OA
    50. Ali A
    51. Hussein AM
    52. Kamel EZ
    53. Monib FA
    54. Ahmed I
    55. Saad MM
    56. Al-Quossi MA
    57. Rafaat N
    58. Galal I
    59. Labib B
    60. Omran DO
    61. Fawzy M
    62. Elfiky M
    63. Ahmed A
    64. Azab M
    65. Tawheed A
    66. Gamal M
    67. El Kassas M
    68. Aml A
    69. Ahmed N
    70. NasrEldin Y
    71. Abdewahab O
    72. Elganainy M
    73. Elmandouh O
    74. Dhufera HT
    75. MeGebre M
    76. Bayih AG
    77. Kifle F
    78. Mekonnen Z
    79. Seman Y
    80. Addisie A
    81. Eshete A
    82. Kifle F
    83. Desita K
    84. Araya H
    85. Agidew Y
    86. Andabo AD
    87. Tesfaye E
    88. Yesuf EA
    89. Hailemariam G
    90. Mohammed MS
    91. Gebremedhin Y
    92. Taye Y
    93. Mebrate TA
    94. Gemechu TB
    95. Bedane TT
    96. Abera ET
    97. Teshome A
    98. Ernest Aniteye EA
    99. Christian Owoo CO
    100. Doku A
    101. Afriyie-Mensah JS
    102. Lawson A
    103. DYaw Sottie DA
    104. Addae E
    105. Ernest Ofosu-Appiah EOA
    106. William Obeng WO
    107. Ndonga A
    108. Ngumi Z
    109. Ndonga A
    110. Mugera A
    111. Bitta C
    112. Elfagieh M
    113. Elhadi M
    114. Huwaysh MA
    115. Yahya MMA
    116. Mohammed AAK
    117. Majeed AAM
    118. Mohammed AEM
    119. Majeed E
    120. Abusalama AA
    121. Altayr E
    122. Abubaker T
    123. Alkaseek AM
    124. Abdulhafith B
    125. Alziyituni Z
    126. Gamra MF
    127. Anaiba MM
    128. Khel S
    129. Abdelkabir M
    130. Abdeewi S
    131. Adam S
    132. Alhadi A
    133. Alsoufi A
    134. Hassan M
    135. Msherghi A
    136. Bouhuwaish AEM
    137. Msosa V
    138. Mulwafu W
    139. Masoo F
    140. Chikumbanje SS
    141. Mabedi D
    142. Morais A
    143. Carlos A
    144. Morais A
    145. Lorenzoni C
    146. Mambo J
    147. Isabel Chissaque I
    148. Mouzinho Saide M
    149. Chaibou MS
    150. Mamane M
    151. Amadou F
    152. Adesoji Ademuyiwa AA
    153. Akinyinka Omigbodun AO
    154. Adeyeye A
    155. Akinmade A
    156. Momohsani Y
    157. Bamigboye J
    158. Orshio D
    159. Isamade ES
    160. Embu H
    161. Nuhu S
    162. Ojiakor S
    163. Nuhu A
    164. Fowotade A
    165. Sanusi A
    166. Osinaike B
    167. Idowu O
    168. Amali AO
    169. Ibrahim S
    170. Adamu AA
    171. Kida I
    172. Otokwala J
    173. Essam M
    174. Alagbe-Briggs O
    175. Ojum S
    176. Fathima Paruk FP
    177. Juan Scribante JS
    178. Mdladla A
    179. Mabotja T
    180. Naidoo R
    181. Matos-Puig R
    182. Ramkillawan A
    183. Smith M
    184. Arnold-Day C
    185. Thomson D
    186. Calligaro G
    187. Joubert I
    188. Jagga J
    189. Piercy J
    190. Michell L
    191. Devenish L
    192. Miller M
    193. Fernandes N
    194. Gopalan D
    195. Pershad S
    196. Grabowski N
    197. Rammego M
    198. Zwane S
    199. Dhlamini ME
    200. Neuhoff M
    201. Fodo T
    202. Usenbo A
    203. Mrara B
    204. Kabambi F
    205. Cloete E
    206. De Caires L
    207. Dickerson R
    208. Louw C

    (2021) Patient care and clinical outcomes for patients with COVID-19 infection admitted to African high-care or intensive care units (ACCCOS): a multicentre, prospective, observational cohort study

    Lancet (London, England) 397:1885–1894.

    https://doi.org/10.1016/S0140-6736(21)00441-4

    • PubMed
    • Google Scholar
12. 1. Bonnet B
    2. Cosme J
    3. Dupuis C
    4. Coupez E
    5. Adda M
    6. Calvet L
    7. Fabre L
    8. Saint-Sardos P
    9. Bereiziat M
    10. Vidal M
    11. Laurichesse H
    12. Souweine B
    13. Evrard B

    (2021) Severe COVID-19 is characterized by the co-occurrence of moderate cytokine inflammation and severe monocyte dysregulation

    EBioMedicine 73:103622.

    https://doi.org/10.1016/j.ebiom.2021.103622

    • PubMed
    • Google Scholar
13. 1. Branzk N
    2. Gronke K
    3. Diefenbach A

    (2018) Innate lymphoid cells, mediators of tissue homeostasis, adaptation and disease tolerance

    Immunological Reviews 286:86–101.

    https://doi.org/10.1111/imr.12718

    • PubMed
    • Google Scholar
14. 1. Brestoff JR
    2. Kim BS
    3. Saenz SA
    4. Stine RR
    5. Monticelli LA
    6. Sonnenberg GF
    7. Thome JJ
    8. Farber DL
    9. Lutfy K
    10. Seale P
    11. Artis D

    (2015) Group 2 innate lymphoid cells promote beiging of white adipose tissue and limit obesity

    Nature 519:242–246.

    https://doi.org/10.1038/nature14115

    • PubMed
    • Google Scholar
15. 1. Buggert M
    2. Vella LA
    3. Nguyen S
    4. Wu VH
    5. Chen Z
    6. Sekine T
    7. Perez-Potti A
    8. Maldini CR
    9. Manne S
    10. Darko S
    11. Ransier A
    12. Kuri-Cervantes L
    13. Japp AS
    14. Brody IB
    15. Ivarsson MA
    16. Gorin JB
    17. Rivera-Ballesteros O
    18. Hertwig L
    19. Antel JP
    20. Johnson ME
    21. Okoye A
    22. Picker L
    23. Vahedi G
    24. Sparrelid E
    25. Llewellyn-Lacey S
    26. Gostick E
    27. Sandberg JK
    28. Björkström N
    29. Bar-Or A
    30. Dori Y
    31. Naji A
    32. Canaday DH
    33. Laufer TM
    34. Wells AD
    35. Price DA
    36. Frank I
    37. Douek DC
    38. Wherry EJ
    39. Itkin MG
    40. Betts MR

    (2020) The Identity of Human Tissue-Emigrant CD8+ T Cells

    Cell 183:1946–1961.

    https://doi.org/10.1016/j.cell.2020.11.019

    • PubMed
    • Google Scholar
16. 1. Bunders MJ
    2. Altfeld M

    (2020) Implications of Sex Differences in Immunity for SARS-CoV-2 Pathogenesis and Design of Therapeutic Interventions

    Immunity 53:487–495.

    https://doi.org/10.1016/j.immuni.2020.08.003

    • PubMed
    • Google Scholar
17. 1. Califano D
    2. Furuya Y
    3. Roberts S
    4. Avram D
    5. McKenzie ANJ
    6. Metzger DW

    (2018) IFN-γ increases susceptibility to influenza A infection through suppression of group II innate lymphoid cells

    Mucosal Immunology 11:209–219.

    https://doi.org/10.1038/mi.2017.41

    • PubMed
    • Google Scholar
18. Website

    1. CDC Case Surveillance Task Force

    (2020) COVID-19 Case Surveillance Public Use Data

    Centers for Disease Control and Prevention. Accessed June 25, 2021.

    https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf
19. 1. Cevik M
    2. Tate M
    3. Lloyd O
    4. Maraolo AE
    5. Schafers J
    6. Ho A

    (2021) SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis

    The Lancet. Microbe 2:e13–e22.

    https://doi.org/10.1016/S2666-5247(20)30172-5

    • PubMed
    • Google Scholar
20. 1. Channappanavar R
    2. Fett C
    3. Mack M
    4. Ten Eyck PP
    5. Meyerholz DK
    6. Perlman S

    (2017) Sex-Based Differences in Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Infection

    Journal of Immunology 198:4046–4053.

    https://doi.org/10.4049/jimmunol.1601896

    • PubMed
    • Google Scholar
21. 1. Chen J
    2. Subbarao K

    (2007) The Immunobiology of SARS

    Annual Review of Immunology 25:443–472.

    https://doi.org/10.1146/annurev.immunol.25.022106.141706

    • PubMed
    • Google Scholar
22. 1. Chen G
    2. Wu D
    3. Guo W
    4. Cao Y
    5. Huang D
    6. Wang H
    7. Wang T
    8. Zhang X
    9. Chen H
    10. Yu H
    11. Zhang X
    12. Zhang M
    13. Wu S
    14. Song J
    15. Chen T
    16. Han M
    17. Li S
    18. Luo X
    19. Zhao J
    20. Ning Q

    (2020) Clinical and immunological features of severe and moderate coronavirus disease 2019

    The Journal of Clinical Investigation 130:2620–2629.

    https://doi.org/10.1172/JCI137244

    • PubMed
    • Google Scholar
23. 1. Cherrier DE
    2. Serafini N
    3. Di Santo JP

    (2018) Innate Lymphoid Cell Development: A T Cell Perspective

    Immunity 48:1091–1103.

    https://doi.org/10.1016/j.immuni.2018.05.010

    • PubMed
    • Google Scholar
24. 1. Cheung EW
    2. Zachariah P
    3. Gorelik M
    4. Boneparth A
    5. Kernie SG
    6. Orange JS
    7. Milner JD

    (2020) Multisystem Inflammatory Syndrome Related to COVID-19 in Previously Healthy Children and Adolescents in New York City

    JAMA 324:294–296.

    https://doi.org/10.1001/jama.2020.10374

    • PubMed
    • Google Scholar
25. 1. Cumnock K
    2. Gupta AS
    3. Lissner M
    4. Chevee V
    5. Davis NM
    6. Schneider DS

    (2018) Host Energy Source Is Important for Disease Tolerance to Malaria

    Current Biology 28:1635–1642.

    https://doi.org/10.1016/j.cub.2018.04.009

    • PubMed
    • Google Scholar
26. 1. Darboe A
    2. Nielsen CM
    3. Wolf AS
    4. Wildfire J
    5. Danso E
    6. Sonko B
    7. Bottomley C
    8. Moore SE
    9. Riley EM
    10. Goodier MR

    (2020) Age-Related Dynamics of Circulating Innate Lymphoid Cells in an African Population

    Frontiers in Immunology 11:594107.

    https://doi.org/10.3389/fimmu.2020.594107

    • PubMed
    • Google Scholar
27. 1. Diefenbach A
    2. Gnafakis S
    3. Shomrat O

    (2020) Innate Lymphoid Cell-Epithelial Cell Modules Sustain Intestinal Homeostasis

    Immunity 52:452–463.

    https://doi.org/10.1016/j.immuni.2020.02.016

    • PubMed
    • Google Scholar
28. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics (Oxford, England) 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
29. 1. Donnelly CA
    2. Ghani AC
    3. Leung GM
    4. Hedley AJ
    5. Fraser C
    6. Riley S
    7. Abu-Raddad LJ
    8. Ho LM
    9. Thach TQ
    10. Chau P
    11. Chan KP
    12. Lam TH
    13. Tse LY
    14. Tsang T
    15. Liu SH
    16. Kong JHB
    17. Lau EMC
    18. Ferguson NM
    19. Anderson RM

    (2003) Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong

    Lancet (London, England) 361:1761–1766.

    https://doi.org/10.1016/S0140-6736(03)13410-1

    • PubMed
    • Google Scholar
30. 1. D’Souza SS
    2. Shen X
    3. Fung ITH
    4. Ye L
    5. Kuentzel M
    6. Chittur SV
    7. Furuya Y
    8. Siebel CW
    9. Maillard IP
    10. Metzger DW
    11. Yang Q

    (2019) Compartmentalized effects of aging on group 2 innate lymphoid cell development and function

    Aging Cell 18:e13019.

    https://doi.org/10.1111/acel.13019

    • PubMed
    • Google Scholar
31. 1. Feldstein LR
    2. Rose EB
    3. Horwitz SM
    4. Collins JP
    5. Newhams MM
    6. Son MBF
    7. Newburger JW
    8. Kleinman LC
    9. Heidemann SM
    10. Martin AA
    11. Singh AR
    12. Li S
    13. Tarquinio KM
    14. Jaggi P
    15. Oster ME
    16. Zackai SP
    17. Gillen J
    18. Ratner AJ
    19. Walsh RF
    20. Fitzgerald JC
    21. Keenaghan MA
    22. Alharash H
    23. Doymaz S
    24. Clouser KN
    25. Giuliano JS
    26. Gupta A
    27. Parker RM
    28. Maddux AB
    29. Havalad V
    30. Ramsingh S
    31. Bukulmez H
    32. Bradford TT
    33. Smith LS
    34. Tenforde MW
    35. Carroll CL
    36. Riggs BJ
    37. Gertz SJ
    38. Daube A
    39. Lansell A
    40. Coronado Munoz A
    41. Hobbs CV
    42. Marohn KL
    43. Halasa NB
    44. Patel MM
    45. Randolph AG

    (2020) Overcoming COVID-19 Investigators, CDC COVID-19 Response Team. 2020. Multisystem Inflammatory Syndrome in U.S. Children and Adolescents

    The New England Journal of Medicine 383:NEJMoa2021680.

    https://doi.org/10.1056/NEJMoa2021680

    • Google Scholar
32. 1. Feldstein LR
    2. Tenforde MW
    3. Friedman KG
    4. Newhams M
    5. Rose EB
    6. Dapul H
    7. Soma VL
    8. Maddux AB
    9. Mourani PM
    10. Bowens C
    11. Maamari M
    12. Hall MW
    13. Riggs BJ
    14. Giuliano JS
    15. Singh AR
    16. Li S
    17. Kong M
    18. Schuster JE
    19. McLaughlin GE
    20. Schwartz SP
    21. Walker TC
    22. Loftis LL
    23. Hobbs CV
    24. Halasa NB
    25. Doymaz S
    26. Babbitt CJ
    27. Hume JR
    28. Gertz SJ
    29. Irby K
    30. Clouser KN
    31. Cvijanovich NZ
    32. Bradford TT
    33. Smith LS
    34. Heidemann SM
    35. Zackai SP
    36. Wellnitz K
    37. Nofziger RA
    38. Horwitz SM
    39. Carroll RW
    40. Rowan CM
    41. Tarquinio KM
    42. Mack EH
    43. Fitzgerald JC
    44. Coates BM
    45. Jackson AM
    46. Young CC
    47. Son MBF
    48. Patel MM
    49. Newburger JW
    50. Randolph AG
    51. Overcoming COVID-19 Investigators

    (2021) Characteristics and Outcomes of US Children and Adolescents With Multisystem Inflammatory Syndrome in Children (MIS-C) Compared With Severe Acute COVID-19

    JAMA 325:1074–1087.

    https://doi.org/10.1001/jama.2021.2091

    • PubMed
    • Google Scholar
33. 1. Flanagan KL
    2. Fink AL
    3. Plebanski M
    4. Klein SL

    (2017) Sex and Gender Differences in the Outcomes of Vaccination over the Life Course

    Annual Review of Cell and Developmental Biology 33:577–599.

    https://doi.org/10.1146/annurev-cellbio-100616-060718

    • PubMed
    • Google Scholar
34. 1. Gallo Marin B
    2. Aghagoli G
    3. Lavine K
    4. Yang L
    5. Siff EJ
    6. Chiang SS
    7. Salazar-Mather TP
    8. Dumenco L
    9. Savaria MC
    10. Aung SN
    11. Flanigan T
    12. Michelow IC

    (2021) Predictors of COVID-19 severity: A literature review

    Reviews in Medical Virology 31:1–10.

    https://doi.org/10.1002/rmv.2146

    • PubMed
    • Google Scholar
35. 1. García M
    2. Kokkinou E
    3. Carrasco García A
    4. Parrot T
    5. Palma Medina LM
    6. Maleki KT
    7. Christ W
    8. Varnaitė R
    9. Filipovic I
    10. Ljunggren H-G
    11. Björkström NK
    12. Folkesson E
    13. Rooyackers O
    14. Eriksson LI
    15. Sönnerborg A
    16. Aleman S
    17. Strålin K
    18. Gredmark-Russ S
    19. Klingström J
    20. Mjösberg J
    21. Karolinska KI/K COVID‐19 Study Group

    (2020) Innate lymphoid cell composition associates with COVID-19 disease severity

    Clinical & Translational Immunology 9:e1224.

    https://doi.org/10.1002/cti2.1224

    • PubMed
    • Google Scholar
36. 1. Giamarellos-Bourboulis EJ
    2. Netea MG
    3. Rovina N
    4. Akinosoglou K
    5. Antoniadou A
    6. Antonakos N
    7. Damoraki G
    8. Gkavogianni T
    9. Adami ME
    10. Katsaounou P
    11. Ntaganou M
    12. Kyriakopoulou M
    13. Dimopoulos G
    14. Koutsodimitropoulos I
    15. Velissaris D
    16. Koufargyris P
    17. Karageorgos A
    18. Katrini K
    19. Lekakis V
    20. Lupse M
    21. Kotsaki A
    22. Renieris G
    23. Theodoulou D
    24. Panou V
    25. Koukaki E
    26. Koulouris N
    27. Gogos C
    28. Koutsoukou A

    (2020) Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure

    Cell Host & Microbe 27:992–1000.

    https://doi.org/10.1016/j.chom.2020.04.009

    • PubMed
    • Google Scholar
37. 1. Giefing-Kröll C
    2. Berger P
    3. Lepperdinger G
    4. Grubeck-Loebenstein B

    (2015) How sex and age affect immune responses, susceptibility to infections, and response to vaccination

    Aging Cell 14:309–321.

    https://doi.org/10.1111/acel.12326

    • PubMed
    • Google Scholar
38. 1. Gu Z
    2. Eils R
    3. Schlesner M

    (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data

    Bioinformatics (Oxford, England) 32:2847–2849.

    https://doi.org/10.1093/bioinformatics/btw313

    • PubMed
    • Google Scholar
39. 1. Gupta RK
    2. Harrison EM
    3. Ho A
    4. Docherty AB
    5. Knight SR
    6. van Smeden M
    7. Abubakar I
    8. Lipman M
    9. Quartagno M
    10. Pius R
    11. Buchan I
    12. Carson G
    13. Drake TM
    14. Dunning J
    15. Fairfield CJ
    16. Gamble C
    17. Green CA
    18. Halpin S
    19. Hardwick HE
    20. Holden KA
    21. Horby PW
    22. Jackson C
    23. Mclean KA
    24. Merson L
    25. Nguyen-Van-Tam JS
    26. Norman L
    27. Olliaro PL
    28. Pritchard MG
    29. Russell CD
    30. Scott-Brown J
    31. Shaw CA
    32. Sheikh A
    33. Solomon T
    34. Sudlow C
    35. Swann OV
    36. Turtle L
    37. Openshaw PJM
    38. Baillie JK
    39. Semple MG
    40. Noursadeghi M
    41. ISARIC4C Investigators

    (2021) Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study

    The Lancet. Respiratory Medicine 9:349–359.

    https://doi.org/10.1016/S2213-2600(20)30559-2

    • PubMed
    • Google Scholar
40. 1. Hashimshony T
    2. Senderovich N
    3. Avital G
    4. Klochendler A
    5. de Leeuw Y
    6. Anavy L
    7. Gennert D
    8. Li S
    9. Livak KJ
    10. Rozenblatt-Rosen O
    11. Dor Y
    12. Regev A
    13. Yanai I

    (2016) CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

    Genome Biology 17:77.

    https://doi.org/10.1186/s13059-016-0938-8

    • PubMed
    • Google Scholar
41. 1. He J
    2. Guo Y
    3. Mao R
    4. Zhang J

    (2020) Proportion of asymptomatic coronavirus disease 2019: A systematic review and meta-analysis

    Journal of Medical Virology 93:820–830.

    https://doi.org/10.1002/jmv.26326

    • PubMed
    • Google Scholar
42. 1. Heald-Sargent T
    2. Muller WJ
    3. Zheng X
    4. Rippe J
    5. Patel AB
    6. Kociolek LK

    (2020) Age-Related Differences in Nasopharyngeal Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Levels in Patients With Mild to Moderate Coronavirus Disease 2019 (COVID-19)

    JAMA Pediatrics 174:902–903.

    https://doi.org/10.1001/jamapediatrics.2020.3651

    • PubMed
    • Google Scholar
43. 1. Huang I
    2. Pranata R

    (2020) Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis

    Journal of Intensive Care 8:36.

    https://doi.org/10.1186/s40560-020-00453-4

    • PubMed
    • Google Scholar
44. 1. Huang C
    2. Wang Y
    3. Li X
    4. Ren L
    5. Zhao J
    6. Hu Y
    7. Zhang L
    8. Fan G
    9. Xu J
    10. Gu X
    11. Cheng Z
    12. Yu T
    13. Xia J
    14. Wei Y
    15. Wu W
    16. Xie X
    17. Yin W
    18. Li H
    19. Liu M
    20. Xiao Y
    21. Gao H
    22. Guo L
    23. Xie J
    24. Wang G
    25. Jiang R
    26. Gao Z
    27. Jin Q
    28. Wang J
    29. Cao B

    (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

    Lancet (London, England) 395:497–506.

    https://doi.org/10.1016/S0140-6736(20)30183-5

    • PubMed
    • Google Scholar
45. 1. Jamieson AM
    2. Pasman L
    3. Yu S
    4. Gamradt P
    5. Homer RJ
    6. Decker T
    7. Medzhitov R

    (2013) Role of tissue protection in lethal respiratory viral-bacterial coinfection

    Science (New York, N.Y.) 340:1230–1234.

    https://doi.org/10.1126/science.1233632

    • PubMed
    • Google Scholar
46. 1. Jhaveri KA
    2. Trammell RA
    3. Toth LA

    (2007) Effect of environmental temperature on sleep, locomotor activity, core body temperature and immune responses of C57BL/6J mice

    Brain, Behavior, and Immunity 21:975–987.

    https://doi.org/10.1016/j.bbi.2007.03.007

    • PubMed
    • Google Scholar
47. 1. Jones TC
    2. Biele G
    3. Mühlemann B
    4. Veith T
    5. Schneider J
    6. Beheim-Schwarzbach J
    7. Bleicker T
    8. Tesch J
    9. Schmidt ML
    10. Sander LE
    11. Kurth F
    12. Menzel P
    13. Schwarzer R
    14. Zuchowski M
    15. Hofmann J
    16. Krumbholz A
    17. Stein A
    18. Edelmann A
    19. Corman VM
    20. Drosten C

    (2021) Estimating infectiousness throughout SARS-CoV-2 infection course

    Science (New York, N.Y.) 373:eabi5273.

    https://doi.org/10.1126/science.abi5273

    • PubMed
    • Google Scholar
48. 1. Kaneko N
    2. Kuo HH
    3. Boucau J
    4. Farmer JR
    5. Allard-Chamard H
    6. Mahajan VS
    7. Piechocka-Trocha A
    8. Lefteri K
    9. Osborn M
    10. Bals J
    11. Bartsch YC
    12. Bonheur N
    13. Caradonna TM
    14. Chevalier J
    15. Chowdhury F
    16. Diefenbach TJ
    17. Einkauf K
    18. Fallon J
    19. Feldman J
    20. Finn KK
    21. Garcia-Broncano P
    22. Hartana CA
    23. Hauser BM
    24. Jiang C
    25. Kaplonek P
    26. Karpell M
    27. Koscher EC
    28. Lian X
    29. Liu H
    30. Liu J
    31. Ly NL
    32. Michell AR
    33. Rassadkina Y
    34. Seiger K
    35. Sessa L
    36. Shin S
    37. Singh N
    38. Sun W
    39. Sun X
    40. Ticheli HJ
    41. Waring MT
    42. Zhu AL
    43. Alter G
    44. Li JZ
    45. Lingwood D
    46. Schmidt AG
    47. Lichterfeld M
    48. Walker BD
    49. Yu XG
    50. Padera RF
    51. Pillai S
    52. Massachusetts Consortium on Pathogen Readiness Specimen Working Group

    (2020) Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19

    Cell 183:143–157.

    https://doi.org/10.1016/j.cell.2020.08.025

    • PubMed
    • Google Scholar
49. 1. Karlberg J
    2. Chong DSY
    3. Lai WYY

    (2004) Do Men Have a Higher Case Fatality Rate of Severe Acute Respiratory Syndrome than Women Do

    American Journal of Epidemiology 159:229–231.

    https://doi.org/10.1093/aje/kwh056

    • PubMed
    • Google Scholar
50. Book

    1. Kassambara A.

    (2020)

    ggpubr: “ggplot2

    Based Publication Ready Plots.

    • Google Scholar
51. 1. Klein SL
    2. Flanagan KL

    (2016) Sex differences in immune responses

    Nature Reviews. Immunology 16:626–638.

    https://doi.org/10.1038/nri.2016.90

    • PubMed
    • Google Scholar
52. 1. Klose CSN
    2. Artis D

    (2016) Innate lymphoid cells as regulators of immunity, inflammation and tissue homeostasis

    Nature Immunology 17:765–774.

    https://doi.org/10.1038/ni.3489

    • PubMed
    • Google Scholar
53. 1. Kløverpris HN
    2. Kazer SW
    3. Mjösberg J
    4. Mabuka JM
    5. Wellmann A
    6. Ndhlovu Z
    7. Yadon MC
    8. Nhamoyebonde S
    9. Muenchhoff M
    10. Simoni Y
    11. Andersson F
    12. Kuhn W
    13. Garrett N
    14. Burgers WA
    15. Kamya P
    16. Pretorius K
    17. Dong K
    18. Moodley A
    19. Newell EW
    20. Kasprowicz V
    21. Abdool Karim SS
    22. Goulder P
    23. Shalek AK
    24. Walker BD
    25. Ndung’u T
    26. Leslie A

    (2016) Innate Lymphoid Cells Are Depleted Irreversibly during Acute HIV-1 Infection in the Absence of Viral Suppression

    Immunity 44:391–405.

    https://doi.org/10.1016/j.immuni.2016.01.006

    • PubMed
    • Google Scholar
54. 1. Kompaniyets L
    2. Goodman AB
    3. Belay B
    4. Freedman DS
    5. Sucosky MS
    6. Lange SJ
    7. Gundlapalli AV
    8. Boehmer TK
    9. Blanck HM

    (2021) Body Mass Index and Risk for COVID-19–Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death — United States, March–December 2020

    MMWR. Morbidity and Mortality Weekly Report 70:355–361.

    https://doi.org/10.15585/mmwr.mm7010e4

    • PubMed
    • Google Scholar
55. 1. Kuri-Cervantes L
    2. Pampena MB
    3. Meng W
    4. Rosenfeld AM
    5. Ittner CAG
    6. Weisman AR
    7. Agyekum RS
    8. Mathew D
    9. Baxter AE
    10. Vella LA
    11. Kuthuru O
    12. Apostolidis SA
    13. Bershaw L
    14. Dougherty J
    15. Greenplate AR
    16. Pattekar A
    17. Kim J
    18. Han N
    19. Gouma S
    20. Weirick ME
    21. Arevalo CP
    22. Bolton MJ
    23. Goodwin EC
    24. Anderson EM
    25. Hensley SE
    26. Jones TK
    27. Mangalmurti NS
    28. Luning Prak ET
    29. Wherry EJ
    30. Meyer NJ
    31. Betts MR

    (2020) Comprehensive mapping of immune perturbations associated with severe COVID-19

    Science Immunology 5:eabd7114.

    https://doi.org/10.1126/sciimmunol.abd7114

    • PubMed
    • Google Scholar
56. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest package: tests in linear mixed effects models

    Journal of Statistical Software 82:1–26.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
57. 1. Laxminarayan R
    2. Wahl B
    3. Dudala SR
    4. Gopal K
    5. Mohan B C
    6. Neelima S
    7. Jawahar Reddy KS
    8. Radhakrishnan J
    9. Lewnard JA

    (2020) Epidemiology and transmission dynamics of COVID-19 in two Indian states

    Science (New York, N.Y.) 370:691–697.

    https://doi.org/10.1126/science.abd7672

    • PubMed
    • Google Scholar
58. 1. Lee S
    2. Kim T
    3. Lee E
    4. Lee C
    5. Kim H
    6. Rhee H
    7. Park SY
    8. Son HJ
    9. Yu S
    10. Park JW
    11. Choo EJ
    12. Park S
    13. Loeb M
    14. Kim TH

    (2020) Clinical Course and Molecular Viral Shedding Among Asymptomatic and Symptomatic Patients With SARS-CoV-2 Infection in a Community Treatment Center in the Republic of Korea

    JAMA Internal Medicine 180:1447–1452.

    https://doi.org/10.1001/jamainternmed.2020.3862

    • PubMed
    • Google Scholar
59. 1. Leist SR
    2. Dinnon KH
    3. Schäfer A
    4. Tse LV
    5. Okuda K
    6. Hou YJ
    7. West A
    8. Edwards CE
    9. Sanders W
    10. Fritch EJ
    11. Gully KL
    12. Scobey T
    13. Brown AJ
    14. Sheahan TP
    15. Moorman NJ
    16. Boucher RC
    17. Gralinski LE
    18. Montgomery SA
    19. Baric RS

    (2020) A Mouse-Adapted SARS-CoV-2 Induces Acute Lung Injury and Mortality in Standard Laboratory Mice

    Cell 183:1070–1085.

    https://doi.org/10.1016/j.cell.2020.09.050

    • PubMed
    • Google Scholar
60. Preprint

    1. Lennon NJ
    2. Bhattacharyya RP
    3. Mina MJ
    4. Rehm HL
    5. Hung DT
    6. Smole S
    7. Woolley A
    8. Lander ES
    9. Gabriel SB

    (2020) Comparison of Viral Levels in Individuals with or without Symptoms at Time of COVID-19 Testing among 32,480 Residents and Staff of Nursing Homes and Assisted Living Facilities in Massachusetts

    medRxiv.

    https://doi.org/10.1101/2020.07.20.20157792

    • Google Scholar
61. 1. Lennon NJ
    2. Bhattacharyya RP
    3. Mina MJ
    4. Rehm HL
    5. Hung DT
    6. Smole S
    7. Woolley A
    8. Lander ES
    9. Gabriel SB

    (2021) Cross-sectional assessment of SARS-CoV-2 viral load by symptom status in Massachusetts congregate living facilities

    The Journal of Infectious Diseases 224:1658–1663.

    https://doi.org/10.1093/infdis/jiab367

    • PubMed
    • Google Scholar
62. 1. Li B
    2. Dewey CN

    (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

    BMC Bioinformatics 12:323.

    https://doi.org/10.1186/1471-2105-12-323

    • PubMed
    • Google Scholar
63. 1. Li B
    2. Zhang S
    3. Zhang R
    4. Chen X
    5. Wang Y
    6. Zhu C

    (2020) Epidemiological and Clinical Characteristics of COVID-19 in Children: A Systematic Review and Meta-Analysis

    Frontiers in Pediatrics 8:591132.

    https://doi.org/10.3389/fped.2020.591132

    • PubMed
    • Google Scholar
64. 1. Licciardi F
    2. Pruccoli G
    3. Denina M
    4. Parodi E
    5. Taglietto M
    6. Rosati S
    7. Montin D

    (2020) SARS-CoV-2-Induced Kawasaki-Like Hyperinflammatory Syndrome: A Novel COVID Phenotype in Children

    Pediatrics 146:e20201711.

    https://doi.org/10.1542/peds.2020-1711

    • PubMed
    • Google Scholar
65. 1. López-Otín C
    2. Kroemer G

    (2021) Hallmarks of Health

    Cell 184:33–63.

    https://doi.org/10.1016/j.cell.2020.11.034

    • PubMed
    • Google Scholar
66. Preprint

    1. LoTempio JE
    2. Billings EA
    3. Draper K
    4. Ralph C
    5. Moshgriz M
    6. Duong N
    7. Bard JD
    8. Gai X
    9. Wessel D
    10. DeBiasi RL
    11. Campos JM
    12. Vilain E
    13. Delaney M
    14. Michael DG

    (2021) Novel SARS-CoV-2 spike variant identified through viral genome sequencing of the pediatric Washington D.C. COVID-19 outbreak

    medRxiv.

    https://doi.org/10.1101/2021.02.08.21251344

    • Google Scholar
67. 1. Love M
    2. Anders S
    3. Huber W

    (2014) Differential analysis of count data--the DESeq2 package

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • Google Scholar
68. 1. Lu X
    2. Zhang L
    3. Du H
    4. Zhang J
    5. Li YY
    6. Qu J
    7. Zhang W
    8. Wang Y
    9. Bao S
    10. Li Y
    11. Wu C
    12. Liu H
    13. Liu D
    14. Shao J
    15. Peng X
    16. Yang Y
    17. Liu Z
    18. Xiang Y
    19. Zhang F
    20. Silva RM
    21. Pinkerton KE
    22. Shen K
    23. Xiao H
    24. Xu S
    25. Wong GWK
    26. Chinese Pediatric Novel Coronavirus Study Team

    (2020) SARS-CoV-2 Infection in Children

    The New England Journal of Medicine 382:1663–1665.

    https://doi.org/10.1056/NEJMc2005073

    • PubMed
    • Google Scholar
69. 1. Lucas C
    2. Wong P
    3. Klein J
    4. Castro TBR
    5. Silva J
    6. Sundaram M
    7. Ellingson MK
    8. Mao T
    9. Oh JE
    10. Israelow B
    11. Takahashi T
    12. Tokuyama M
    13. Lu P
    14. Venkataraman A
    15. Park A
    16. Mohanty S
    17. Wang H
    18. Wyllie AL
    19. Vogels CBF
    20. Earnest R
    21. Lapidus S
    22. Ott IM
    23. Moore AJ
    24. Muenker MC
    25. Fournier JB
    26. Campbell M
    27. Odio CD
    28. Casanovas-Massana A
    29. Team YI
    30. Herbst R
    31. Shaw AC
    32. Medzhitov R
    33. Schulz WL
    34. Grubaugh ND
    35. Dela Cruz C
    36. Farhadian S
    37. Ko AI
    38. Omer SB
    39. Iwasaki A

    (2020) Longitudinal analyses reveal immunological misfiring in severe COVID-19

    Nature 584:463–469.

    https://doi.org/10.1038/s41586-020-2588-y

    • PubMed
    • Google Scholar
70. 1. Luo X
    2. Zhou W
    3. Yan X
    4. Guo T
    5. Wang B
    6. Xia H
    7. Ye L
    8. Xiong J
    9. Jiang Z
    10. Liu Y
    11. Zhang B
    12. Yang W

    (2020) Prognostic Value of C-Reactive Protein in Patients With Coronavirus 2019

    Clinical Infectious Diseases 71:2174–2179.

    https://doi.org/10.1093/cid/ciaa641

    • PubMed
    • Google Scholar
71. 1. Márquez EJ
    2. Chung CH
    3. Marches R
    4. Rossi RJ
    5. Nehar-Belaid D
    6. Eroglu A
    7. Mellert DJ
    8. Kuchel GA
    9. Banchereau J
    10. Ucar D

    (2020) Sexual-dimorphism in human immune system aging

    Nature Communications 11:751.

    https://doi.org/10.1038/s41467-020-14396-9

    • PubMed
    • Google Scholar
72. 1. Mathew D
    2. Giles JR
    3. Baxter AE
    4. Oldridge DA
    5. Greenplate AR
    6. Wu JE
    7. Alanio C
    8. Kuri-Cervantes L
    9. Pampena MB
    10. D’Andrea K
    11. Manne S
    12. Chen Z
    13. Huang YJ
    14. Reilly JP
    15. Weisman AR
    16. Ittner CAG
    17. Kuthuru O
    18. Dougherty J
    19. Nzingha K
    20. Han N
    21. Kim J
    22. Pattekar A
    23. Goodwin EC
    24. Anderson EM
    25. Weirick ME
    26. Gouma S
    27. Arevalo CP
    28. Bolton MJ
    29. Chen F
    30. Lacey SF
    31. Ramage H
    32. Cherry S
    33. Hensley SE
    34. Apostolidis SA
    35. Huang AC
    36. Vella LA
    37. UPenn COVID Processing Unit
    38. Betts MR
    39. Meyer NJ
    40. Wherry EJ

    (2020) Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

    Science (New York, N.Y.) 369:eabc8511.

    https://doi.org/10.1126/science.abc8511

    • PubMed
    • Google Scholar
73. 1. Mauvais-Jarvis F

    (2020) Aging, Male Sex, Obesity, and Metabolic Inflammation Create the Perfect Storm for COVID-19

    Diabetes 69:1857–1863.

    https://doi.org/10.2337/dbi19-0023

    • PubMed
    • Google Scholar
74. 1. McCarville JL
    2. Ayres JS

    (2018) Disease tolerance: concept and mechanisms

    Current Opinion in Immunology 50:88–93.

    https://doi.org/10.1016/j.coi.2017.12.003

    • PubMed
    • Google Scholar
75. 1. Medzhitov R
    2. Schneider DS
    3. Soares MP

    (2012) Disease tolerance as a defense strategy

    Science (New York, N.Y.) 335:936–941.

    https://doi.org/10.1126/science.1214935

    • PubMed
    • Google Scholar
76. 1. Monticelli LA
    2. Sonnenberg GF
    3. Abt MC
    4. Alenghat T
    5. Ziegler CGK
    6. Doering TA
    7. Angelosanto JM
    8. Laidlaw BJ
    9. Yang CY
    10. Sathaliyawala T
    11. Kubota M
    12. Turner D
    13. Diamond JM
    14. Goldrath AW
    15. Farber DL
    16. Collman RG
    17. Wherry EJ
    18. Artis D

    (2011) Innate lymphoid cells promote lung-tissue homeostasis after infection with influenza virus

    Nature Immunology 12:1045–1054.

    https://doi.org/10.1031/ni.2131

    • PubMed
    • Google Scholar
77. 1. Monticelli LA
    2. Osborne LC
    3. Noti M
    4. Tran SV
    5. Zaiss DMW
    6. Artis D

    (2015) IL-33 promotes an innate immune pathway of intestinal tissue protection dependent on amphiregulin–EGFR interactions

    PNAS 112:10762–10767.

    https://doi.org/10.1073/pnas.1509070112

    • PubMed
    • Google Scholar
78. 1. Mudd PA
    2. Crawford JC
    3. Turner JS
    4. Souquette A
    5. Reynolds D
    6. Bender D
    7. Bosanquet JP
    8. Anand NJ
    9. Striker DA
    10. Martin RS
    11. Boon ACM
    12. House SL
    13. Remy KE
    14. Hotchkiss RS
    15. Presti RM
    16. O’Halloran JA
    17. Powderly WG
    18. Thomas PG
    19. Ellebedy AH

    (2020) Distinct inflammatory profiles distinguish COVID-19 from influenza with limited contributions from cytokine storm

    Science Advances 6:eabe3024.

    https://doi.org/10.1126/sciadv.abe3024

    • PubMed
    • Google Scholar
79. 1. O’Driscoll M
    2. Ribeiro Dos Santos G
    3. Wang L
    4. Cummings DAT
    5. Azman AS
    6. Paireau J
    7. Fontanet A
    8. Cauchemez S
    9. Salje H

    (2021) Age-specific mortality and immunity patterns of SARS-CoV-2. Nature

    Nature 590:140–145.

    https://doi.org/10.1038/s41586-020-2918-0

    • PubMed
    • Google Scholar
80. 1. Patin E
    2. Hasan M
    3. Bergstedt J
    4. Rouilly V
    5. Libri V
    6. Urrutia A
    7. Alanio C
    8. Scepanovic P
    9. Hammer C
    10. Jönsson F
    11. Beitz B
    12. Quach H
    13. Lim YW
    14. Hunkapiller J
    15. Zepeda M
    16. Green C
    17. Piasecka B
    18. Leloup C
    19. Rogge L
    20. Huetz F
    21. Peguillet I
    22. Lantz O
    23. Fontes M
    24. Di Santo JP
    25. Thomas S
    26. Fellay J
    27. Duffy D
    28. Quintana-Murci L
    29. Albert ML
    30. Milieu Intérieur Consortium

    (2018) Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors

    Nature Immunology 19:302–314.

    https://doi.org/10.1038/s41590-018-0049-7

    • PubMed
    • Google Scholar
81. 1. Peckham H
    2. de Gruijter NM
    3. Raine C
    4. Radziszewska A
    5. Ciurtin C
    6. Wedderburn LR
    7. Rosser EC
    8. Webb K
    9. Deakin CT

    (2020) Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission

    Nature Communications 11:6317.

    https://doi.org/10.1038/s41467-020-19741-6

    • PubMed
    • Google Scholar
82. 1. Petrilli CM
    2. Jones SA
    3. Yang J
    4. Rajagopalan H
    5. O’Donnell L
    6. Chernyak Y
    7. Tobin KA
    8. Cerfolio RJ
    9. Francois F
    10. Horwitz LI

    (2020) Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study

    BMJ (Clinical Research Ed.) 369:m1966.

    https://doi.org/10.1136/bmj.m1966

    • PubMed
    • Google Scholar
83. 1. Piasecka B
    2. Duffy D
    3. Urrutia A
    4. Quach H
    5. Patin E
    6. Posseme C
    7. Bergstedt J
    8. Charbit B
    9. Rouilly V
    10. MacPherson CR
    11. Hasan M
    12. Albaud B
    13. Gentien D
    14. Fellay J
    15. Albert ML
    16. Quintana-Murci L
    17. Consortium MI

    (2018) Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges

    PNAS 115:E488–E497.

    https://doi.org/10.1073/pnas.1714765115

    • PubMed
    • Google Scholar
84. 1. Poline J
    2. Gaschignard J
    3. Leblanc C
    4. Madhi F
    5. Foucaud E
    6. Nattes E
    7. Faye A
    8. Bonacorsi S
    9. Mariani P
    10. Varon E
    11. Smati-Lafarge M
    12. Caseris M
    13. Basmaci R
    14. Lachaume N
    15. Ouldali N

    (2020) Systematic SARS-CoV-2 screening at hospital admission in children:a French prospective multicenter study

    Clinical Infectious Diseases 72:2215–2217.

    https://doi.org/10.1093/cid/ciaa1044

    • Google Scholar
85. Software

    1. R Development Core Team

    (2020) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.R-project.org
86. 1. Ra SH
    2. Lim JS
    3. Kim GU
    4. Kim MJ
    5. Jung J
    6. Kim SH

    (2021) Upper respiratory viral load in asymptomatic individuals and mildly symptomatic patients with SARS-CoV-2 infection

    Thorax 76:61–63.

    https://doi.org/10.1136/thoraxjnl-2020-215042

    • PubMed
    • Google Scholar
87. 1. Råberg L
    2. Sim D
    3. Read AF

    (2007) Disentangling genetic variation for resistance and tolerance to infectious diseases in animals

    Science (New York, N.Y.) 318:812–814.

    https://doi.org/10.1126/science.1148526

    • PubMed
    • Google Scholar
88. 1. Rak GD
    2. Osborne LC
    3. Siracusa MC
    4. Kim BS
    5. Wang K
    6. Bayat A
    7. Artis D
    8. Volk SW

    (2016) IL-33-Dependent Group 2 Innate Lymphoid Cells Promote Cutaneous Wound Healing

    The Journal of Investigative Dermatology 136:487–496.

    https://doi.org/10.1038/JID.2015.406

    • PubMed
    • Google Scholar
89. 1. Rauber S
    2. Luber M
    3. Weber S
    4. Maul L
    5. Soare A
    6. Wohlfahrt T
    7. Lin NY
    8. Dietel K
    9. Bozec A
    10. Herrmann M
    11. Kaplan MH
    12. Weigmann B
    13. Zaiss MM
    14. Fearon U
    15. Veale DJ
    16. Cañete JD
    17. Distler O
    18. Rivellese F
    19. Pitzalis C
    20. Neurath MF
    21. McKenzie ANJ
    22. Wirtz S
    23. Schett G
    24. Distler JHW
    25. Ramming A

    (2017) Resolution of inflammation by interleukin-9-producing type 2 innate lymphoid cells

    Nature Medicine 23:938–944.

    https://doi.org/10.1038/nm.4373

    • PubMed
    • Google Scholar
90. 1. Richardson S
    2. Hirsch JS
    3. Narasimhan M
    4. Crawford JM
    5. McGinn T
    6. Davidson KW
    7. Barnaby DP
    8. Becker LB
    9. Chelico JD
    10. Cohen SL
    11. Cookingham J
    12. Coppa K
    13. Diefenbach MA
    14. Dominello AJ
    15. Duer-Hefele J
    16. Falzon L
    17. Gitlin J
    18. Hajizadeh N
    19. Harvin TG
    20. Hirschwerk DA
    21. Kim EJ
    22. Kozel ZM
    23. Marrast LM
    24. Mogavero JN
    25. Osorio GA
    26. Qiu M
    27. Zanos TP
    28. the Northwell COVID-19 Research Consortium

    (2020) Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

    JAMA 323:2052–2059.

    https://doi.org/10.1001/jama.2020.6775

    • PubMed
    • Google Scholar
91. 1. Riphagen S
    2. Gomez X
    3. Gonzalez-Martinez C
    4. Wilkinson N
    5. Theocharis P

    (2020) Hyperinflammatory shock in children during COVID-19 pandemic

    Lancet (London, England) 395:1607–1608.

    https://doi.org/10.1016/S0140-6736(20)31094-1

    • PubMed
    • Google Scholar
92. 1. Sanchez KK
    2. Chen GY
    3. Schieber AMP
    4. Redford SE
    5. Shokhirev MN
    6. Leblanc M
    7. Lee YM
    8. Ayres JS

    (2018) Cooperative Metabolic Adaptations in the Host Can Favor Asymptomatic Infection and Select for Attenuated Virulence in an Enteric Pathogen

    Cell 175:146–158.

    https://doi.org/10.1016/j.cell.2018.07.016

    • PubMed
    • Google Scholar
93. 1. Sancho-Shimizu V
    2. Brodin P
    3. Cobat A
    4. Biggs CM
    5. Toubiana J
    6. Lucas CL
    7. Henrickson SE
    8. Belot A
    9. Tangye SG
    10. Milner JD
    11. Levin M
    12. Abel L
    13. Bogunovic D
    14. Casanova JL
    15. Zhang SY
    16. MIS-C@CHGE

    (2021) SARS-CoV-2–related MIS-C: A key to the viral and genetic causes of Kawasaki disease

    The Journal of Experimental Medicine 218:e20210446.

    https://doi.org/10.1084/jem.20210446

    • PubMed
    • Google Scholar
94. 1. Schneider DS
    2. Ayres JS

    (2008) Two ways to survive infection: what resistance and tolerance can teach us about treating infectious diseases

    Nature Reviews. Immunology 8:889–895.

    https://doi.org/10.1038/nri2432

    • PubMed
    • Google Scholar
95. 1. Scully EP
    2. Haverfield J
    3. Ursin RL
    4. Tannenbaum C
    5. Klein SL

    (2020) Considering how biological sex impacts immune responses and COVID-19 outcomes

    Nature Reviews. Immunology 20:442–447.

    https://doi.org/10.1038/s41577-020-0348-8

    • PubMed
    • Google Scholar
96. 1. Solana R
    2. Tarazona R
    3. Gayoso I
    4. Lesur O
    5. Dupuis G
    6. Fulop T

    (2012) Innate immunosenescence: effect of aging on cells and receptors of the innate immune system in humans

    Seminars in Immunology 24:331–341.

    https://doi.org/10.1016/j.smim.2012.04.008

    • PubMed
    • Google Scholar
97. 1. Tesoriero JM
    2. Swain CAE
    3. Pierce JL
    4. Zamboni L
    5. Wu M
    6. Holtgrave DR
    7. Gonzalez CJ
    8. Udo T
    9. Morne JE
    10. Hart-Malloy R
    11. Rajulu DT
    12. Leung SYJ
    13. Rosenberg ES

    (2021) COVID-19 Outcomes Among Persons Living With or Without Diagnosed HIV Infection in New York State

    JAMA Network Open 4:e2037069.

    https://doi.org/10.1001/jamanetworkopen.2020.37069

    • PubMed
    • Google Scholar
98. 1. Verdoni L
    2. Mazza A
    3. Gervasoni A
    4. Martelli L
    5. Ruggeri M
    6. Ciuffreda M
    7. Bonanomi E
    8. D’Antiga L

    (2020) An outbreak of severe Kawasaki-like disease at the Italian epicentre of the SARS-CoV-2 epidemic: an observational cohort study

    Lancet (London, England) 395:1771–1778.

    https://doi.org/10.1016/S0140-6736(20)31103-X

    • PubMed
    • Google Scholar
99. 1. Vivier E
    2. Artis D
    3. Colonna M
    4. Diefenbach A
    5. Di Santo JP
    6. Eberl G
    7. Koyasu S
    8. Locksley RM
    9. McKenzie ANJ
    10. Mebius RE
    11. Powrie F
    12. Spits H

    (2018) Innate Lymphoid Cells: 10 Years On

    Cell 174:1054–1066.

    https://doi.org/10.1016/j.cell.2018.07.017

    • PubMed
    • Google Scholar
100. 1. Wang A
     2. Huen SC
     3. Luan HH
     4. Yu S
     5. Zhang C
     6. Gallezot JD
     7. Booth CJ
     8. Medzhitov R

     (2016) Opposing Effects of Fasting Metabolism on Tissue Tolerance in Bacterial and Viral Inflammation

     Cell 166:1512–1525.

     https://doi.org/10.1016/j.cell.2016.07.026

     • PubMed
     • Google Scholar
101. 1. Wang Y
     2. Lifshitz L
     3. Gellatly K
     4. Vinton CL
     5. Busman-Sahay K
     6. McCauley S
     7. Vangala P
     8. Kim K
     9. Derr A
     10. Jaiswal S
     11. Kucukural A
     12. McDonel P
     13. Hunt PW
     14. Greenough T
     15. Houghton J
     16. Somsouk M
     17. Estes JD
     18. Brenchley JM
     19. Garber M
     20. Deeks SG
     21. Luban J

     (2020) HIV-1-induced cytokines deplete homeostatic innate lymphoid cells and expand TCF7-dependent memory NK cells

     Nature Immunology 21:274–286.

     https://doi.org/10.1038/s41590-020-0593-9

     • PubMed
     • Google Scholar
102. 1. Whittaker E
     2. Bamford A
     3. Kenny J
     4. Kaforou M
     5. Jones CE
     6. Shah P
     7. Ramnarayan P
     8. Fraisse A
     9. Miller O
     10. Davies P
     11. Kucera F
     12. Brierley J
     13. McDougall M
     14. Carter M
     15. Tremoulet A
     16. Shimizu C
     17. Herberg J
     18. Burns JC
     19. Lyall H
     20. Levin M
     21. PIMS-TS Study Group and EUCLIDS and PERFORM Consortia

     (2020) Clinical Characteristics of 58 Children With a Pediatric Inflammatory Multisystem Syndrome Temporally Associated With SARS-CoV-2

     JAMA 324:259–269.

     https://doi.org/10.1001/jama.2020.10369

     • PubMed
     • Google Scholar
103. Book

     1. Wickham H

     (2016) Ggplot2: Elegant Graphics for Data Analysis

     Springer.

     https://doi.org/10.1007/978-3-319-24277-4

     • Google Scholar
104. 1. Wickham H
     2. Averick M
     3. Bryan J
     4. Chang W
     5. McGowan L
     6. François R
     7. Grolemund G
     8. Hayes A
     9. Henry L
     10. Hester J
     11. Kuhn M
     12. Pedersen T
     13. Miller E
     14. Bache S
     15. Müller K
     16. Ooms J
     17. Robinson D
     18. Seidel D
     19. Spinu V
     20. Takahashi K
     21. Vaughan D
     22. Wilke C
     23. Woo K
     24. Yutani H

     (2019) Welcome to the Tidyverse

     Journal of Open Source Software 4:1686.

     https://doi.org/10.21105/joss.01686

     • Google Scholar
105. 1. Wieduwilt F
     2. Lenth C
     3. Ctistis G
     4. Plachetka U
     5. Möller M
     6. Wackerbarth H

     (2020) Evaluation of an on-site surface enhanced Raman scattering sensor for benzotriazole

     Scientific Reports 10:8260.

     https://doi.org/10.1038/s41598-020-65181-z

     • PubMed
     • Google Scholar
106. 1. Yang Q
     2. Monticelli LA
     3. Saenz SA
     4. Chi AWS
     5. Sonnenberg GF
     6. Tang J
     7. De Obaldia ME
     8. Bailis W
     9. Bryson JL
     10. Toscano K
     11. Huang J
     12. Haczku A
     13. Pear WS
     14. Artis D
     15. Bhandoola A

     (2013) T cell factor 1 is required for group 2 innate lymphoid cell generation

     Immunity 38:694–704.

     https://doi.org/10.1016/j.immuni.2012.12.003

     • PubMed
     • Google Scholar
107. 1. Yang Q
     2. Saldi TK
     3. Gonzales PK
     4. Lasda E
     5. Decker CJ
     6. Tat KL
     7. Fink MR
     8. Hager CR
     9. Davis JC
     10. Ozeroff CD
     11. Muhlrad D
     12. Clark SK
     13. Fattor WT
     14. Meyerson NR
     15. Paige CL
     16. Gilchrist AR
     17. Barbachano-Guerrero A
     18. Worden-Sapper ER
     19. Wu SS
     20. Brisson GR
     21. McQueen MB
     22. Dowell RD
     23. Leinwand L
     24. Parker R
     25. Sawyer SL

     (2021) Just 2% of SARS-CoV-2−positive individuals carry 90% of the virus circulating in communities

     PNAS 118:e2104547118.

     https://doi.org/10.1073/pnas.2104547118

     • PubMed
     • Google Scholar
108. 1. Yonker LM
     2. Neilan AM
     3. Bartsch Y
     4. Patel AB
     5. Regan J
     6. Arya P
     7. Gootkind E
     8. Park G
     9. Hardcastle M
     10. St John A
     11. Appleman L
     12. Chiu ML
     13. Fialkowski A
     14. De la Flor D
     15. Lima R
     16. Bordt EA
     17. Yockey LJ
     18. D’Avino P
     19. Fischinger S
     20. Shui JE
     21. Lerou PH
     22. Bonventre JV
     23. Yu XG
     24. Ryan ET
     25. Bassett IV
     26. Irimia D
     27. Edlow AG
     28. Alter G
     29. Li JZ
     30. Fasano A

     (2020) Pediatric Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Clinical Presentation, Infectivity, and Immune Responses

     The Journal of Pediatrics 227:45–52.

     https://doi.org/10.1016/j.jpeds.2020.08.037

     • PubMed
     • Google Scholar
109. 1. Yonker LM
     2. Gilboa T
     3. Ogata AF
     4. Senussi Y
     5. Lazarovits R
     6. Boribong BP
     7. Bartsch YC
     8. Loiselle M
     9. Rivas MN
     10. Porritt RA
     11. Lima R
     12. Davis JP
     13. Farkas EJ
     14. Burns MD
     15. Young N
     16. Mahajan VS
     17. Hajizadeh S
     18. Lopez XIH
     19. Kreuzer J
     20. Morris R
     21. Martinez EE
     22. Han I
     23. Griswold K Jr
     24. Barry NC
     25. Thompson DB
     26. Church G
     27. Edlow AG
     28. Haas W
     29. Pillai S
     30. Arditi M
     31. Alter G
     32. Walt DR
     33. Fasano A

     (2021) Multisystem inflammatory syndrome in children is driven by zonulin-dependent loss of gut mucosal barrier

     The Journal of Clinical Investigation 131:149633.

     https://doi.org/10.1172/JCI149633

     • PubMed
     • Google Scholar
110. 1. Yu G
     2. Wang LG
     3. Han Y
     4. He QY

     (2012) clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

     OMICS 16:284–287.

     https://doi.org/10.1089/omi.2011.0118

     • PubMed
     • Google Scholar
111. 1. Yudanin NA
     2. Schmitz F
     3. Flamar AL
     4. Thome JJC
     5. Tait Wojno E
     6. Moeller JB
     7. Schirmer M
     8. Latorre IJ
     9. Xavier RJ
     10. Farber DL
     11. Monticelli LA
     12. Artis D

     (2019) Spatial and Temporal Mapping of Human Innate Lymphoid Cells Reveals Elements of Tissue Specificity

     Immunity 50:505–519.

     https://doi.org/10.1016/j.immuni.2019.01.012

     • PubMed
     • Google Scholar
112. 1. Yukselen O
     2. Turkyilmaz O
     3. Ozturk AR
     4. Garber M
     5. Kucukural A

     (2020) DolphinNext: a distributed data processing platform for high throughput genomics

     BMC Genomics 21:310.

     https://doi.org/10.1186/s12864-020-6714-x

     • PubMed
     • Google Scholar
113. 1. Zhang ZL
     2. Hou YL
     3. Li DT
     4. Li FZ

     (2020) Laboratory findings of COVID-19: a systematic review and meta-analysis

     Scandinavian Journal of Clinical and Laboratory Investigation 80:441–447.

     https://doi.org/10.1080/00365513.2020.1768587

     • PubMed
     • Google Scholar
114. 1. Zhao Q
     2. Meng M
     3. Kumar R
     4. Wu Y
     5. Huang J
     6. Deng Y
     7. Weng Z
     8. Yang L

     (2020) Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A systemic review and meta-analysis

     International Journal of Infectious Diseases 96:131–135.

     https://doi.org/10.1016/j.ijid.2020.04.086

     • PubMed
     • Google Scholar
115. 1. Zheng M
     2. Gao Y
     3. Wang G
     4. Song G
     5. Liu S
     6. Sun D
     7. Xu Y
     8. Tian Z

     (2020) Functional exhaustion of antiviral lymphocytes in COVID-19 patients

     Cellular & Molecular Immunology 17:533–535.

     https://doi.org/10.1038/s41423-020-0402-2

     • PubMed
     • Google Scholar
116. 1. Zhou F
     2. Yu T
     3. Du R
     4. Fan G
     5. Liu Y
     6. Liu Z
     7. Xiang J
     8. Wang Y
     9. Song B
     10. Gu X
     11. Guan L
     12. Wei Y
     13. Li H
     14. Wu X
     15. Xu J
     16. Tu S
     17. Zhang Y
     18. Chen H
     19. Cao B

     (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

     Lancet (London, England) 395:1054–1062.

     https://doi.org/10.1016/S0140-6736(20)30566-3

     • PubMed
     • Google Scholar

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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