Functional proteomic profiling links deficient DNA clearance with increased mortality in individuals with severe COVID-19 pneumonia

doi:10.1016/j.immuni.2022.11.007

. 2022 Dec 13;55(12):2436-2453.e5.

doi: 10.1016/j.immuni.2022.11.007. Epub 2022 Nov 17.

Functional proteomic profiling links deficient DNA clearance with increased mortality in individuals with severe COVID-19 pneumonia

Iker Valle Aramburu¹, Dennis Hoving¹, Spyros I Vernardis², Martha C F Tin¹, Marianna Ioannou¹, Mia I Temkin¹, Nathalia M De Vasconcelos¹, Vadim Demichev², Elisa Theresa Helbig³, Lena Lippert³, Klaus Stahl⁴, Matthew White², Helena Radbruch⁵, Jana Ihlow⁶, David Horst⁶, Scott T Chiesa⁷, John E Deanfield⁷, Sascha David⁸, Christian Bode⁹, Florian Kurth³, Markus Ralser¹⁰, Venizelos Papayannopoulos¹¹

Affiliations

¹ The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK.
² The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK.
³ Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany.
⁴ Department of Gastroenterology, Hepatology and Endocrinology, Medical School Hannover, Hannover, Germany.
⁵ Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Neuropathology, Charitéplatz 1, 10117 Berlin, Germany.
⁶ Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany.
⁷ Institute of Cardiovascular Science, University College London, London, UK.
⁸ Institute for Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland.
⁹ Department of Anaesthesiology and Critical Care, University Hospital Bonn, Bonn, Germany.
¹⁰ The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK; Charité - Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany.
¹¹ The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK. Electronic address: veni.p@crick.ac.uk.

PMID: 36462503
PMCID: PMC9671605
DOI: 10.1016/j.immuni.2022.11.007

Functional proteomic profiling links deficient DNA clearance with increased mortality in individuals with severe COVID-19 pneumonia

Iker Valle Aramburu et al. Immunity. 2022.

. 2022 Dec 13;55(12):2436-2453.e5.

doi: 10.1016/j.immuni.2022.11.007. Epub 2022 Nov 17.

Authors

Affiliations

¹ The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK.
² The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK.
³ Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany.
⁴ Department of Gastroenterology, Hepatology and Endocrinology, Medical School Hannover, Hannover, Germany.
⁵ Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Neuropathology, Charitéplatz 1, 10117 Berlin, Germany.
⁶ Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany.
⁷ Institute of Cardiovascular Science, University College London, London, UK.
⁸ Institute for Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland.
⁹ Department of Anaesthesiology and Critical Care, University Hospital Bonn, Bonn, Germany.
¹⁰ The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK; Charité - Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany.
¹¹ The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK. Electronic address: veni.p@crick.ac.uk.

PMID: 36462503
PMCID: PMC9671605
DOI: 10.1016/j.immuni.2022.11.007

Abstract

The factors that influence survival during severe infection are unclear. Extracellular chromatin drives pathology, but the mechanisms enabling its accumulation remain elusive. Here, we show that in murine sepsis models, splenocyte death interferes with chromatin clearance through the release of the DNase I inhibitor actin. Actin-mediated inhibition was compensated by upregulation of DNase I or the actin scavenger gelsolin. Splenocyte death and neutrophil extracellular trap (NET) clearance deficiencies were prevalent in individuals with severe COVID-19 pneumonia or microbial sepsis. Activity tracing by plasma proteomic profiling uncovered an association between low NET clearance and increased COVID-19 pathology and mortality. Low NET clearance activity with comparable proteome associations was prevalent in healthy donors with low-grade inflammation, implicating defective chromatin clearance in the development of cardiovascular disease and linking COVID-19 susceptibility to pre-existing conditions. Hence, the combination of aberrant chromatin release with defects in protective clearance mechanisms lead to poor survival outcomes.

Keywords: COVID-19; DNA; DNase I; NETs; actin; degradation; histone; inflammation; proteomics; sepsis.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

**Figure 1**
T cell death promotes actin release and DNase I upregulation in systemic candidiasis (A) Immunofluorescence confocal micrographs of spleens from WT and TCRα^−/− mice infected intravenously with C. *albicans*, 3 days post-infection stained for CD169 (marginal-zone macrophages, yellow), CD3 (T cells, cyan), and TUNEL (apoptosis, magenta). Scale bars: 50 μm. (B) Principal-component analysis of plasma proteomes from naive and infected WT and TCRα^−/− mice analyzed by mass spectrometry. (C) Heatmap depicting unbiased hierarchical clustering of plasma proteomes of mice in (B). (D) Heatmap of plasma proteins that are upregulated in infected WT but not in infected TCRα^−/− mice. N, naive; I, infected. (E–I) Naive (N) or infected (Inf) WT or TCRα^−/− mice untreated or treated with DNase I or anti-histone H3 and H4 antibodies. (E) NET degradation (NETase) activity. (F) DNase activity expressed as the dilution of murine plasmas required for 50% plasmid degradation (D₅₀). (G and H) DNase I and DNA concentrations in murine plasma. (I) Correlations between plasma DNase I, DNA, DNase (D₅₀), or NETase activities. (J) Plasma DNA, DNase I, and actin concentrations in naive, control liposome (PBS-L)-, or clodronate liposome (Clo-L)-treated mice or infected with *C. albicans*, 1 or 2 days post-challenge. (K) Kinetics of plasma DNA, DNase I, and actin in naive (T = 0) and C. *albicans*-infected (T = 1, 2 days) WT mice in two separate experiments. (L) Correlation analysis between plasma actin and DNA or DNase I in experiment 1 (left panels, blue circles) and experiment 2 (right panels, pink circles). (M and N) TUNEL⁺ splenic cells and plasma actin in naive or infected WT mice treated with control IgG antibody, anti-histone H3 and H4 antibodies, or DNase I. Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations. See also Figure S1.

**Figure 2**
Splenic cell death and reduced plasma DNA degradation capacity in sepsis and COVID-19 pneumonia (A) Representative immunofluorescence confocal micrographs of post-mortem spleens from 1 control and 9 individuals with CP, stained for T cells (CD3, cyan), TUNEL (magenta), and myeloperoxidase (MPO, neutrophils; yellow). Scale bars: 100 μm. (B) High-magnification micrographs from (A). Scale bars: 20 μm. (C) Quantification of the percentage of TUNEL⁺ cells that are CD3⁺ in the splenic tissues shown in (A) (left) and distribution of TUNEL⁺ T cells (CD3⁺), neutrophils (MPO⁺), or other cells in (A) (right). (D) Correlation between plasma actin (ACTA2; ACTB; ACTG1) relative protein abundance (RPA) and blood lymphocyte counts in the final sample obtained from 63 participants with CP who reached WHO-7. (E) Plasma DNA in healthy (HD, gray), CP (green), and sepsis participants (SP, magenta) or mice infected with C. *albicans* (SM, yellow). (F) Plasma samples from HD, SP, CP, and SM immunoblotted for actin and histone H2B. (G) Correlation between actin abundance measured by densitometric analysis of the western immunoblots in (F) or mass spectrometry in SP plasma. (H) Correlation between SP plasma actin and DNA. (I) Correlation between histone H3 measured by western immunoblot (left) or actin (right) and platelet factor 4 (PF4) measured by mass spectrometry. (J) Plasmid DNA degradation by 30 HD, 40 CP, and 36 SP plasmas. D₅₀ values were calculated from raw data in Figures S2D and S2E. Low activity range (D₅₀ > 15) depicted by the purple bar on the y axis. (K) NETase activity in 3% plasma from 30 HD, 87 CP, and 36 SP measured by timelapse microscopy as the loss in human NET DNA intensity over 8 h. (L) Correlation between DNase activity at 10% plasma dilution and NETase activity in CP plasmas. (M) DNase I concentrations in HD (blue) or CP plasmas with WHO-3, -4 (green), or -7 (orange) and SP plasmas (pink). Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations. See also Figure S2.

**Figure 3**
DNA and NET clearance activities correlate with regulators and disease severity markers in sepsis and COVID-19 pneumonia (A) Correlation between DNase activity (D₅₀) and DNA (left) or histone H3 (right) in SP samples. (B) Correlation between DNase I and DNase activity at 10% plasma (left) or NETase activity in SP samples (right). (C) Correlation between SP plasma actin and DNase activity (D₅₀) (left) or NETase activity (right). (D) Correlation between SP plasma gelsolin (GSN) and DNase activity (D₅₀) (left) or NETase activity (right). (E) Correlation between CP plasma actin measured by mass spectrometry and DNase activity (D₅₀) (left) or NETase activity (right). (F) CP plasma DNase I plotted against DNase activity (D₅₀) (left) or NETase activity (right). (G) From left to right, HI values, thrombocyte counts, neutrophil to lymphocyte ratio (NLR), and red cell blood distribution width (RDW-CV) plotted against their corresponding NETase activity in plasmas of individuals with COVID-19. (H) Correlation between raw DNase activity measurements at 10% plasma dilution and CRP (left) or FGG protein (right) in WHO-7 (orange circle) and WHO-3 and -4 (green circle) CP samples. (I) Correlation between ORM1;ORM2 and DNase activity measurements at 10% plasma dilution (left) or NETase activity (right) in WHO-7 and WHO-3 and -4 CP samples. (J) Correlation between ORM2 (RPA) and DNase I measured in CP by ELISA. Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations. See also Figure S3.

**Figure 4**
Relationship between plasma actin, ORM proteins, and infection outcomes Analysis of 465 samples from 63 CP with maximum WHO-7. (A) Daily cumulative average relative to time post-admission for actin (ACTB;ACTG1), ORM1;ORM2, HI, and CRP separated by outcome: survivors (gray) or deceased (red). Right: Daily cumulative average FGG in individuals with (TE+ red) or without thromboembolism (TE− gray). (B) Change in NETase activity over time in 44 CP samples (left). NETase activity in early and late samples connected by a line and the corresponding paired Wilcoxon test for the difference per participant (right). (C) DNase activity (D₅₀) values obtained from CP samples segregated by 0–30 and 30–60 days post-admission. (D) Actin (ACTB;ACTG1) abundance in the sample closest to day 20 post-admission (D20^+/−) and the final plasma sample obtained from each participant grouped by survival outcome (survived: S, blue; deceased: D, pink). (E) Average, maximum, D20^+/−, and final plasma ORM1;ORM2 per participant grouped by survival outcome. (F) Fitted linear regression per participant of the correlation obtained from the values for ORM1;ORM2 and actin (ACTA2;ACTB;ACTG1) (left) and violin plot of the corresponding slopes (right). (G) Correlation between the final values for ORM1;ORM2 and actin (ACTB;ACTG1) per participant who either survived (gray circles) or died (red circles). (H) Difference between the final and longitudinal maximum ORM1;ORM2 abundance segregated by the corresponding longitudinal average actin values above (red circles) or below (gray circles)150 RPA per participant. (I) Correlation between the actin and CRP abundance in the final plasma sample collected from each participant (left) or between the longitudinal average ORM1;ORM2 and CRP values (right). (J) Correlation between the average ORM1;ORM2 and the final HI readings (left). Difference between the final and minimum HI values per participant segregated by whether their final ORM1;ORM2 values were above or below 30,000 RPA (right). (K) Correlation between longitudinal maximum actin and FGG values. (L) Longitudinal maximum D-dimer readings in participants with maximum ORM1;ORM2 values above or below 43,000 RPA (right). (M) Probability of survival in CP clusters according to the longitudinal average values of ORM1;ORM2 (left) or the final actin values (right). Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations, and Mantel-Cox survival analysis. See also Figure S4.

**Figure 5**
Low DNase activity is associated with increased COVID-19 mortality (A) DNase activity in 25 WHO-7 CP samples measured at 2.5% dilution and segregated by survivors (S, gray) or deceased (D, pink). The pink bar in the y-axis indicates low DNase activity. (B) AUC analysis of CP samples in (A) segregated by low and high DNase activity. (C) Distribution of survivors and deceased CP in low and high DNase activity groups. (D) DNase activity measured at 2.5% and 10% CP plasma dilution. The samples clustered as low degraders (L) are shown in pink, and the high degraders (H) are shown in blue. (E) MBL2, CFHR1, APOC1 abundance in low (L, pink circles) and high (H, blue circles) DNase activity samples. (F) AUC analysis of MBL2, CFHR1, APOC1 in low (L, pink circles) and high (H, blue circles) DNase activity samples. (G) Gating strategy for segregation of measured DNase activity samples by proteomic measurements. In the first gating step CHFR1< 100 RPA; APOC1 < 200 RPA gating profiles a high DNase activity group. A second gating step is applied to the remaining samples profiling CHFR1 > 100 RPA; MBL2 < 80 RPA as low DNase activity samples and the excluded are grouped as high DNase activity samples. (H) Illustration of how gating strategies in (G) apply on 300 WHO-7 CP samples segregated by survival and plotted for MBL2 and APOC1. The top plots illustrate the profiling of low DNase activity using a MBL2 < 80 RPA gate, and the plots below depict the second application of a CHFR1 > 100 RPA; APOC1 < 200 RPA correction filter to identify high DNase activity samples within the low DNase activity group from step one. (I) CRP, procalcitonin (PCT), creatinine, and lipase values in 300 WHO-7 CP samples segregated by profiled high and low DNase activity. (J) Distribution of low and high DNase activity assigned by proteomic profiling of 300 WHO-7 samples from 52 participants with maximum WHO-7 in survivors (blue circle) and deceased participants (pink circle). (K) Fraction of low DNase activity samples per participant segregated by survival outcome. (L) Heatmap of 52 participants ranked by their content of profiled DNase activity WHO-7 samples. Each row depicts one individual. The first column depicts the percentage of low DNase activity (%L) per participant and the second column depicts the survival outcome where yellow indicates deceased and purple indicates the survivors. Individuals containing only high profiled DNase activity samples (blue bar) or both high and low DNase activity samples (pink bar) are marked on the right. (M) Probability of survival segregated by whether participants contained low profiled DNase activity samples (pink). The blue curve depicts participants that contained only high profiled DNase activity samples. (N) Comparison of the longitudinal minimum and final HI values per participant segregated by the whether they contain low profiled DNase activity samples. Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations, and Mantel-Cox survival analysis.

**Figure 6**
Low NETase activity is associated with increased COVID-19 mortality (A) Correlation between NETase activity and ACTBL2, SAA1;SAA2, SERPINA1, and C6 protein measured by mass spectrometry in 41 WHO-7 CP plasmas. Significantly correlating proteins were identified by proteome-wide linear regression. (B) Plot of measured and profiled NETase activity from averaging profiled NETase activity calculated from corresponding linear regression curves of ACTA2;ACTB1;ACTG1;ACTG2, SAA1;SAA2, SERPINA1, SERPINA10, C8A, and C8B. (C) Graphs for CRP, NLR, HI, and RDW-CV against the corresponding profiled NETase activity in 300 WHO-7 CP plasmas, color coded by low (purple), medium (green), and high (yellow) profiled NETase activity. Violin plot of D-dimer and bar plot of creatinine kinase readings segregated by low, medium, or high profiled NETase activity. (D–G) (D) Violin plot of the distribution of profiled NETase activity; (E) the profiled NETase low (L < 0.25), medium (M = 0.25–0.4), and high (H > 0.4) activity score or (F) the average NETase activity score per participant; or (G) the distribution of low (L), medium (M), and high (H) NETase activity samples per participant in 465 samples from 63 individuals with maximum WHO-7, segregated by survival outcome (S, survivors; D, deceased). (H) Heatmap depicting the distribution of all L, M, and H NETase activity samples per participant in 63 individuals with maximum WHO-7, alongside the survival outcome where yellow indicates deceased and purple indicates survivors. Individuals containing high profiled NETase activity samples (blue bar) and individuals that contain only low and medium NETase activity samples (pink bar) are marked on the right. Each row represents 1 participant. (I) Survival analysis of participants segregated by whether they contain high profiled NETase activity samples as shown in (H). (J) Distribution of combined scoring for profiled DNase activity and NETase activity in 465 samples of all severity grades segregated by survival outcome. (K) Heatmap displaying the low profiled DNase activity WHO-7 sample content and profiled NETase activity content per participant. In the last column, yellow indicates deceased and purple indicates survivors. (L) Distribution of combined scoring for profiled DNase and NETase activities in 465 samples of all severities from 63 maximum WHO-7 participants segregated by whether they were receiving extracorporeal membrane oxygenation (ECMO), hydrocortisone, albumin, or anti-inflammatory therapies at the time of sample collection. (M−O) Distribution of 465 samples of all WHO severity grades segregated by sex depicting (M) profiled DNase activity, (N) profiled NETase activity, or (O) plasma actin segregated by survival outcome. Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations, and Mantel-Cox survival analysis. See also Figure S5 and S6.

**Figure 7**
Low NETase activity is associated with chronic inflammation in healthy individuals (A) CRP in plasma from sepsis participants (SP) or healthy ALSPAC donors segregated by low (HD) or high (HD_infl) plasma concentrations of the inflammatory marker GlycA. Pie chart represents the number of high (colored) and low (white) CRP abundance samples. (B–D) DNase activity (D₅₀), NETase activity, and plasma DNA in ALSPAC HD and HD_infl plasma samples. (E) Principal-component analysis of SP and ALSPAC HD and HD_infl plasma proteomes measured simultaneously by mass spectrometry. (F and G) Correlation between measured NETase activity in HD (blue) and HD_infl (yellow) and plasma proteins selected based on CP correlation analysis. (H) Comparison of plasma haptoglobin (HP), serpinA4, complement factor C9, and actin (ACTBL2 or ACTG1) protein abundance in HD (blue), HD_infl (yellow), and SP (pink) samples. Statistics by Mann-Whitney or Kruskal-Wallis test for single comparisons, one-way ANOVA for multiple comparisons, simple linear or non-linear regression for correlations. See also Figure S7.

See this image and copyright information in PMC

Cited by

Development of a Clonal and High-Yield Mammalian Cell Line for the Manufacturing of a Hyperactive Human DNase I with Extended Plasma Half-Life Using PASylation^® Technology.
Stamm SM, Wagner R, Lang DA, Skerra A, Gebauer M. Stamm SM, et al. Pharmaceutics. 2024 Jul 22;16(7):967. doi: 10.3390/pharmaceutics16070967. Pharmaceutics. 2024. PMID: 39065664 Free PMC article.
Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation.
Pagani L, Chinello C, Risca G, Capitoli G, Criscuolo L, Lombardi A, Ungaro R, Mangioni D, Piga I, Muscatello A, Blasi F, Favalli A, Martinovic M, Gori A, Bandera A, Grifantini R, Magni F. Pagani L, et al. Int J Mol Sci. 2023 Feb 10;24(4):3570. doi: 10.3390/ijms24043570. Int J Mol Sci. 2023. PMID: 36834989 Free PMC article.
Impaired balance between neutrophil extracellular trap formation and degradation by DNases in COVID-19 disease.
Garcia G, Labrouche-Colomer S, Duvignaud A, Clequin E, Dussiau C, Trégouët DA, Malvy D, Prevel R, Zouine A, Pellegrin I, Goret J, Mamani-Matsuda M, Dewitte A, James C. Garcia G, et al. J Transl Med. 2024 Mar 7;22(1):246. doi: 10.1186/s12967-024-05044-7. J Transl Med. 2024. PMID: 38454482 Free PMC article.
You are what you eat and how you digest it! A discussion on inflammatory efferocytosis.
Tanzer MC. Tanzer MC. Front Cell Dev Biol. 2023 Feb 9;11:1132696. doi: 10.3389/fcell.2023.1132696. eCollection 2023. Front Cell Dev Biol. 2023. PMID: 36846584 Free PMC article. Review.
Visualization of Nuclease- and Serum-Mediated Chromatin Degradation with DNA-Histone Mesostructures.
Wasielewski ML, Nguyen K, Yalavarthi S, Ekbote P, Weerappuli PD, Knight JS, Takayama S. Wasielewski ML, et al. Int J Mol Sci. 2023 Feb 6;24(4):3222. doi: 10.3390/ijms24043222. Int J Mol Sci. 2023. PMID: 36834634 Free PMC article.

See all "Cited by" articles

References

1. Afzali B., Noris M., Lambrecht B.N., Kemper C. The state of complement in COVID-19. Nat. Rev. Immunol. 2021;22:77–84. - PMC - PubMed
1. Belsky J.B., Rivers E.P., Filbin M.R., Lee P.J., Morris D.C. Thymosin beta 4 regulation of actin in sepsis. Expert Opin. Biol. Ther. 2018;18:193–197. - PMC - PubMed
1. Boyd A., Golding J., Macleod J., Lawlor D.A., Fraser A., Henderson J., Molloy L., Ness A., Ring S., Davey Smith G. Cohort profile: the 'children of the 90s'--the index offspring of the avon longitudinal study of parents and children. Int. J. Epidemiol. 2013;42:111–127. - PMC - PubMed
1. Chang K., Svabek C., Vazquez-Guillamet C., Sato B., Rasche D., Wilson S., Robbins P., Ulbrandt N., Suzich J., Green J., et al. Targeting the programmed cell death 1: programmed cell death ligand 1 pathway reverses T cell exhaustion in patients with sepsis. Crit. Care. 2014;18:R3. - PMC - PubMed
1. Chiesa S.T., Charakida M., Georgiopoulos G., Roberts J.D., Stafford S.J., Park C., Mykkänen J., Kähönen M., Lehtimäki T., Ala-Korpela M., et al. Glycoprotein acetyls: a novel inflammatory biomarker of early cardiovascular risk in the young. J. Am. Heart Assoc. 2022;11 e024380. - PMC - PubMed

Publication types

Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Substances

Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
Medical
- MedlinePlus Health Information
Research Materials
- NCI CPTC Antibody Characterization Program

[1] Afzali B., Noris M., Lambrecht B.N., Kemper C. The state of complement in COVID-19. Nat. Rev. Immunol. 2021;22:77–84. - PMC - PubMed

[2] Afzali B., Noris M., Lambrecht B.N., Kemper C. The state of complement in COVID-19. Nat. Rev. Immunol. 2021;22:77–84. - PMC - PubMed

[3] Belsky J.B., Rivers E.P., Filbin M.R., Lee P.J., Morris D.C. Thymosin beta 4 regulation of actin in sepsis. Expert Opin. Biol. Ther. 2018;18:193–197. - PMC - PubMed

[4] Belsky J.B., Rivers E.P., Filbin M.R., Lee P.J., Morris D.C. Thymosin beta 4 regulation of actin in sepsis. Expert Opin. Biol. Ther. 2018;18:193–197. - PMC - PubMed

[5] Boyd A., Golding J., Macleod J., Lawlor D.A., Fraser A., Henderson J., Molloy L., Ness A., Ring S., Davey Smith G. Cohort profile: the 'children of the 90s'--the index offspring of the avon longitudinal study of parents and children. Int. J. Epidemiol. 2013;42:111–127. - PMC - PubMed

[6] Boyd A., Golding J., Macleod J., Lawlor D.A., Fraser A., Henderson J., Molloy L., Ness A., Ring S., Davey Smith G. Cohort profile: the 'children of the 90s'--the index offspring of the avon longitudinal study of parents and children. Int. J. Epidemiol. 2013;42:111–127. - PMC - PubMed

[7] Chang K., Svabek C., Vazquez-Guillamet C., Sato B., Rasche D., Wilson S., Robbins P., Ulbrandt N., Suzich J., Green J., et al. Targeting the programmed cell death 1: programmed cell death ligand 1 pathway reverses T cell exhaustion in patients with sepsis. Crit. Care. 2014;18:R3. - PMC - PubMed

[8] Chang K., Svabek C., Vazquez-Guillamet C., Sato B., Rasche D., Wilson S., Robbins P., Ulbrandt N., Suzich J., Green J., et al. Targeting the programmed cell death 1: programmed cell death ligand 1 pathway reverses T cell exhaustion in patients with sepsis. Crit. Care. 2014;18:R3. - PMC - PubMed

[9] Chiesa S.T., Charakida M., Georgiopoulos G., Roberts J.D., Stafford S.J., Park C., Mykkänen J., Kähönen M., Lehtimäki T., Ala-Korpela M., et al. Glycoprotein acetyls: a novel inflammatory biomarker of early cardiovascular risk in the young. J. Am. Heart Assoc. 2022;11 e024380. - PMC - PubMed

[10] Chiesa S.T., Charakida M., Georgiopoulos G., Roberts J.D., Stafford S.J., Park C., Mykkänen J., Kähönen M., Lehtimäki T., Ala-Korpela M., et al. Glycoprotein acetyls: a novel inflammatory biomarker of early cardiovascular risk in the young. J. Am. Heart Assoc. 2022;11 e024380. - PMC - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Functional proteomic profiling links deficient DNA clearance with increased mortality in individuals with severe COVID-19 pneumonia

Affiliations

Functional proteomic profiling links deficient DNA clearance with increased mortality in individuals with severe COVID-19 pneumonia

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Related information

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials