Local and systemic responses to SARS-CoV-2 infection in children and adults.
Adult
Bronchi
/ immunology
COVID-19
/ blood
Chicago
Cohort Studies
Dendritic Cells
/ immunology
Disease Progression
Epithelial Cells
/ cytology
Female
Humans
Immunity, Innate
Interferons
/ immunology
Killer Cells, Natural
/ immunology
London
Male
Nasal Mucosa
/ immunology
SARS-CoV-2
/ growth & development
Single-Cell Analysis
T-Lymphocytes, Cytotoxic
/ immunology
Trachea
/ virology
Young Adult
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
received:
06
03
2021
accepted:
14
12
2021
pubmed:
23
12
2021
medline:
24
2
2022
entrez:
22
12
2021
Statut:
ppublish
Résumé
It is not fully understood why COVID-19 is typically milder in children
Identifiants
pubmed: 34937051
doi: 10.1038/s41586-021-04345-x
pii: 10.1038/s41586-021-04345-x
pmc: PMC8828466
mid: NIHMS1775797
doi:
Substances chimiques
Interferons
9008-11-1
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
321-327Subventions
Organisme : Medical Research Council
ID : MC_PC_17230
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/W014556/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT211276/Z/18/Z
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : P01 AG049665
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI135964
Pays : United States
Organisme : NHLBI NIH HHS
ID : P01 HL154998
Pays : United States
Organisme : Medical Research Council
ID : MR/S036113/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_20022
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S005579/1
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL149883
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL153122
Pays : United States
Organisme : Medical Research Council
ID : MR/S035842/1
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL153312
Pays : United States
Organisme : Medical Research Council
ID : MR/K017047/1
Pays : United Kingdom
Organisme : NCATS NIH HHS
ID : UL1 TR001422
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL076139
Pays : United States
Organisme : Medical Research Council
ID : MR/R015635/1
Pays : United Kingdom
Investigateurs
G R Scott Budinger
(GRS)
Helen K Donnelly
(HK)
Nikolay S Markov
(NS)
Ziyan Lu
(Z)
Informations de copyright
© 2021. The Author(s).
Références
Swann, O. V. et al. Clinical characteristics of children and young people admitted to hospital with covid-19 in United Kingdom: prospective multicentre observational cohort study. Brit. Med. J. 370, m3249 (2020).
pubmed: 32960186
doi: 10.1136/bmj.m3249
Castagnoli, R. et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children and adolescents: a systematic review. JAMA Pediatr. 174, 882–889 (2020).
pubmed: 32320004
doi: 10.1001/jamapediatrics.2020.1467
Ledford, H. Deaths from COVID ‘incredibly rare’ among children. Nature 595, 639–639 (2021).
doi: 10.1038/d41586-021-01897-w
Hoffmann, M. et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181, 271–280 (2020).
pubmed: 32142651
pmcid: 7102627
doi: 10.1016/j.cell.2020.02.052
Pang, L. et al. Influence of aging on deterioration of patients with COVID-19. Aging 12, 26248–26262 (2020).
pubmed: 33232272
pmcid: 7803552
doi: 10.18632/aging.202136
Muus, C. et al. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics. Nat. Med.27, 546–559 (2021).
pubmed: 33654293
pmcid: 9469728
doi: 10.1038/s41591-020-01227-z
Sungnak, W. et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat. Med. 26, 681–687 (2020).
pubmed: 32327758
pmcid: 8637938
doi: 10.1038/s41591-020-0868-6
Bunyavanich, S., Do, A. & Vicencio, A. Nasal gene expression of angiotensin-converting enzyme 2 in children and adults. JAMA 323, 2427–2429 (2020).
pubmed: 32432657
pmcid: 7240631
doi: 10.1001/jama.2020.8707
Saheb Sharif-Askari, N. et al. Airways expression of SARS-CoV-2 receptor, ACE2, and TMPRSS2 is lower in children than adults and increases with smoking and COPD. Mol. Ther. Methods Clin. Dev. 18, 1–6 (2020).
pubmed: 32537478
pmcid: 7242205
doi: 10.1016/j.omtm.2020.05.013
Koch, C. M. et al. Age-related differences in the nasal mucosal immune response to SARS-CoV-2. Am. J. Respir. Cell Mol. Biol. https://doi.org/10.1165/rcmb.2021-0292OC (2021).
Loske, J. et al. Pre-activated antiviral innate immunity in the upper airways controls early SARS-CoV-2 infection in children. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01037-9 (2021).
Schultze, J. L. & Aschenbrenner, A. C. COVID-19 and the human innate immune system. Cell 184, 1671–1692 (2021).
pubmed: 33743212
pmcid: 7885626
doi: 10.1016/j.cell.2021.02.029
Schoggins, J. W. Interferon-stimulated genes: what do they all do? Annu. Rev. Virol. 6, 567–584 (2019).
pubmed: 31283436
doi: 10.1146/annurev-virology-092818-015756
Ziegler, C. G. K. et al. Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19. Cell 184, 4713–4733 (2021).
pubmed: 34352228
pmcid: 8299217
doi: 10.1016/j.cell.2021.07.023
Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718–724 (2020).
pubmed: 32661059
pmcid: 7402632
doi: 10.1126/science.abc6027
Wang, E. Y. et al. Diverse functional autoantibodies in patients with COVID-19. Nature 595, 283–288 (2021).
pubmed: 34010947
doi: 10.1038/s41586-021-03631-y
Major, J. et al. Type I and III interferons disrupt lung epithelial repair during recovery from viral infection. Science 369, 712–717 (2020).
pubmed: 32527928
pmcid: 7292500
doi: 10.1126/science.abc2061
Broggi, A. et al. Type III interferons disrupt the lung epithelial barrier upon viral recognition. Science 369, 706–712 (2020).
pubmed: 32527925
pmcid: 7292499
doi: 10.1126/science.abc3545
Berlin, D. A., Gulick, R. M. & Martinez, F. J. Severe COVID-19. N. Engl. J. Med. 383, 2451–2460 (2020).
pubmed: 32412710
doi: 10.1056/NEJMcp2009575
Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).
pubmed: 32398875
doi: 10.1038/s41591-020-0901-9
Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070–1076 (2020).
pubmed: 32514174
pmcid: 7382903
doi: 10.1038/s41591-020-0944-y
Zhang, J.-Y. et al. Single-cell landscape of immunological responses in patients with COVID-19. Nat. Immunol. 21, 1107–1118 (2020).
pubmed: 32788748
doi: 10.1038/s41590-020-0762-x
Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021).
pubmed: 33879890
pmcid: 8121667
doi: 10.1038/s41591-021-01329-2
Pierce, C. A. et al. Immune responses to SARS-CoV-2 infection in hospitalized pediatric and adult patients. Sci. Transl. Med. 12, eabd5487 (2020).
pubmed: 32958614
pmcid: 7658796
doi: 10.1126/scitranslmed.abd5487
Weisberg, S. P. et al. Distinct antibody responses to SARS-CoV-2 in children and adults across the COVID-19 clinical spectrum. Nat. Immunol. 22, 25–31 (2021).
pubmed: 33154590
doi: 10.1038/s41590-020-00826-9
Deprez, M. et al. A single-cell atlas of the human healthy airways. Am. J. Respir. Crit. Care Med. 202, 1636–1645 (2020).
pubmed: 32726565
doi: 10.1164/rccm.201911-2199OC
Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018).
pubmed: 30069044
pmcid: 6295155
doi: 10.1038/s41586-018-0393-7
Chua, R. L. et al. COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nat. Biotechnol. 38, 970–979 (2020).
pubmed: 32591762
doi: 10.1038/s41587-020-0602-4
Grant, R. A. et al. Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia. Nature 590, 635–641 (2021).
pubmed: 33429418
pmcid: 7987233
doi: 10.1038/s41586-020-03148-w
Ziegler, C. G. K. et al. SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues. Cell 181, 1016–1035 (2020).
pubmed: 32413319
pmcid: 7252096
doi: 10.1016/j.cell.2020.04.035
Wang, W. et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 323, 1843–1844 (2020).
pubmed: 32159775
pmcid: 7066521
Yu, F. et al. Quantitative detection and viral load analysis of sars-cov-2 in infected patients. Clin. Infect. Dis. 71, 793–798 (2020).
pubmed: 32221523
doi: 10.1093/cid/ciaa345
Vieira Braga, F. A. et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25, 1153–1163 (2019).
pubmed: 31209336
doi: 10.1038/s41591-019-0468-5
Zhu, N. et al. Morphogenesis and cytopathic effect of SARS-CoV-2 infection in human airway epithelial cells. Nat. Commun. 11, 3910 (2020).
pubmed: 32764693
pmcid: 7413383
doi: 10.1038/s41467-020-17796-z
Fang, Y. et al. Distinct stem/progenitor cells proliferate to regenerate the trachea, intrapulmonary airways and alveoli in COVID-19 patients. Cell Res. 30, 705–707 (2020).
pubmed: 32606347
pmcid: 7325636
doi: 10.1038/s41422-020-0367-9
Ruiz García, S. et al. Novel dynamics of human mucociliary differentiation revealed by single-cell RNA sequencing of nasal epithelial cultures. Development 146, dev177428 (2019).
pubmed: 31558434
pmcid: 6826037
doi: 10.1242/dev.177428
Ygberg, S. & Nilsson, A. The developing immune system—from foetus to toddler. Acta Paediatr. 101, 120–127 (2012).
pubmed: 22003882
doi: 10.1111/j.1651-2227.2011.02494.x
Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036–1045 (2020).
pubmed: 32416070
pmcid: 7227586
doi: 10.1016/j.cell.2020.04.026
Chen, G. et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J. Clin. Invest. 130, 2620–2629 (2020).
pubmed: 32217835
pmcid: 7190990
doi: 10.1172/JCI137244
Aschenbrenner, A. C. et al. Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients. Genome Med. 13, 7 (2021).
pubmed: 33441124
pmcid: 7805430
doi: 10.1186/s13073-020-00823-5
Silvin, A. et al. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell 182, 1401–1418 (2020).
pubmed: 32810439
pmcid: 7405878
doi: 10.1016/j.cell.2020.08.002
Galani, I.-E. et al. Untuned antiviral immunity in COVID-19 revealed by temporal type I/III interferon patterns and flu comparison. Nat. Immunol. 22, 32–40 (2021).
pubmed: 33277638
doi: 10.1038/s41590-020-00840-x
Lee, J. S. et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci. Immunol. 5, eabd1554 (2020).
pubmed: 32651212
pmcid: 7402635
doi: 10.1126/sciimmunol.abd1554
Diebold, S. S. et al. Viral infection switches non-plasmacytoid dendritic cells into high interferon producers. Nature 424, 324–328 (2003).
pubmed: 12819664
doi: 10.1038/nature01783
Saichi, M. et al. Single-cell RNA sequencing of blood antigen-presenting cells in severe COVID-19 reveals multi-process defects in antiviral immunity. Nat. Cell Biol. 23, 538–551 (2021).
pubmed: 33972731
doi: 10.1038/s41556-021-00681-2
Zhou, R. et al. Acute SARS-CoV-2 infection impairs dendritic cell and T cell responses. Immunity 53, 864–877 (2020).
pubmed: 32791036
pmcid: 7402670
doi: 10.1016/j.immuni.2020.07.026
Lokugamage, K. G. et al. Type I interferon susceptibility distinguishes SARS-CoV-2 from SARS-CoV. J. Virol. 94, e01410-20 (2020).
pubmed: 32938761
pmcid: 7654262
doi: 10.1128/JVI.01410-20
Schurink, B. et al. Viral presence and immunopathology in patients with lethal COVID-19: a prospective autopsy cohort study. Lancet Microbe 1, e290–e299 (2020).
pubmed: 33015653
pmcid: 7518879
doi: 10.1016/S2666-5247(20)30144-0
Kumar, B. V., Connors, T. J. & Farber, D. L. Human T cell development, localization, and function throughout life. Immunity 48, 202–213 (2018).
pubmed: 29466753
pmcid: 5826622
doi: 10.1016/j.immuni.2018.01.007
Naylor, K. et al. The influence of age on T cell generation and TCR diversity. J. Immunol. 174, 7446–7452 (2005).
pubmed: 15905594
doi: 10.4049/jimmunol.174.11.7446
Hagai, T. et al. Gene expression variability across cells and species shapes innate immunity. Nature 563, 197–202 (2018).
pubmed: 30356220
pmcid: 6347972
doi: 10.1038/s41586-018-0657-2
Li, S. et al. SARS-CoV-2 triggers inflammatory responses and cell death through caspase-8 activation. Signal Transduct. Target. Ther. 5, 235 (2020).
pubmed: 33037188
pmcid: 7545816
doi: 10.1038/s41392-020-00334-0
Lee, P. Y. et al. Distinct clinical and immunological features of SARS-CoV-2-induced multisystem inflammatory syndrome in children. J. Clin. Invest. 130, 5942–5950 (2020).
pubmed: 32701511
pmcid: 7598077
doi: 10.1172/JCI141113
Worlock, K. B. Cell dissociation from nasal, bronchial and tracheal brushings with cold-active protease for single-cell RNA-seq. protocols.io https://doi.org/10.17504/protocols.io.btpunmnw (2021).
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).
pubmed: 30902100
pmcid: 6431044
doi: 10.1186/s13059-019-1662-y
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).
pubmed: 30954476
pmcid: 6625319
doi: 10.1016/j.cels.2018.11.005
Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).
pubmed: 30787436
pmcid: 6522369
doi: 10.1038/s41586-019-0933-9
Popescu, D.-M. et al. Decoding human fetal liver haematopoiesis. Nature 574, 365–371 (2019).
pubmed: 31597962
pmcid: 6861135
doi: 10.1038/s41586-019-1652-y
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
pubmed: 30914743
pmcid: 6435756
doi: 10.1038/s41598-019-41695-z
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).
pubmed: 33367645
pmcid: 7763177
doi: 10.1093/gigascience/giaa151
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
pubmed: 31779668
pmcid: 6883579
doi: 10.1186/s13059-019-1891-0
Wood, D. kraken2, https://github.com/DerrickWood/kraken2 (2018).
Bost, P. Viral-Track, https://github.com/PierreBSC/Viral-Track (2020).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Quinlan, A. bedtools2, https://github.com/arq5x/bedtools2 (2021).
Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).
pubmed: 31400197
doi: 10.1093/bioinformatics/btz625
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
pubmed: 32747759
doi: 10.1038/s41587-020-0591-3
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Heaton, H. et al. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. Methods 17, 615–620 (2020).
pubmed: 32366989
doi: 10.1038/s41592-020-0820-1
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
pubmed: 30954475
pmcid: 6853612
doi: 10.1016/j.cels.2019.03.003
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177 1888–1902 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2020).
doi: 10.1016/j.cell.2021.04.048
Reimand, J. et al. g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 44, W83–W89 (2016).
pubmed: 27098042
pmcid: 4987867
doi: 10.1093/nar/gkw199
Cantuti-Castelvetri, L. et al. Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity. Science 370, 856–860 (2020).
pubmed: 33082293
pmcid: 7857391
doi: 10.1126/science.abd2985
Daly, J. L. et al. Neuropilin-1 is a host factor for SARS-CoV-2 infection. Science 370, 861–865 (2020).
pubmed: 33082294
pmcid: 7612957
doi: 10.1126/science.abd3072
Wang, K. et al. CD147-spike protein is a novel route for SARS-CoV-2 infection to host cells. Signal Transduct. Target. Ther. 5, 283 (2020).
pubmed: 33277466
pmcid: 7714896
doi: 10.1038/s41392-020-00426-x
Tang, X. et al. Transferrin receptor is another receptor for SARS-CoV-2 entry. Preprint at https://doi.org/10.1101/2020.10.23.350348 (2020).
Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021).
pubmed: 34083789
pmcid: 7610960
doi: 10.1038/s41588-021-00875-2
Sturm, G. et al. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics 36, 4817–4818 (2020).
pubmed: 32614448
pmcid: 7751015
doi: 10.1093/bioinformatics/btaa611