What is the actual relationship between neutrophil extracellular traps and COVID-19 severity? A longitudinal study

doi:10.21203/rs.3.rs-3227029/v1

Download PDF

Research Article

What is the actual relationship between neutrophil extracellular traps and COVID-19 severity? A longitudinal study

https://doi.org/10.21203/rs.3.rs-3227029/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 19 Jan, 2024

Read the published version in Respiratory Research →

You are reading this latest preprint version

Background

Neutrophil extracellular traps (NETs), have repeatedly been related to COVID-19 severity and mortality. However, there is no consensus on their quantification, and there are scarce data on their evolution during the disease. We studied circulating NET markers in patients with COVID-19 throughout their hospitalization.

Methods

We prospectively included 93 patients (201 blood samples), evaluating the disease severity in 3 evolutionary phases (viral, early, and late inflammation). Of these, 72 had 180 samples in various phases. We also evaluated 55 controls with similar age, sex and comorbidities. We measured 4 NET markers: cfDNA, CitH3, and MPO-DNA and NE-DNA complexes; as well as neutrophil-related cytokines IL-8 and G-CSF.

Results

The COVID-19 group had higher CitH3 (p = 0.022), and cfDNA, MPO-DNA, and NE-DNA (p < 0.001) than the controls throughout hospitalisation. cfDNA was the only NET marker clearly related to severity, and it remained higher in non-survivors during the 3 phases. Only cfDNA was an independent risk factor for mortality and need for intensive care. Neutrophil count, IL-8, and G-CSF were significantly related to severity. MPO-DNA and NE-DNA showed significant correlations in all 3 phases and across all severity grades, and they only remained significantly higher on days 10–16 of evolution in those who died. Correlations among the other NET markers were lower than expected.

Conclusions

Although NETs were present in patients with COVID-19 throughout hospitalization, their markers, except cfDNA, showed little or no association with severity and mortality. Neutrophil activity and neutrophil count were also associated with severity. MPO-DNA and NE-DNA better reflected NET formation. cfDNA appeared to be more associated with overall tissue damage; previous widespread use of this marker could have overestimated the relationship between NETs and severity. Currently, there are limitations to accurate NET markers measurement that make it difficult to assess its true role in COVID-19 pathogenesis.

COVID-19

Neutrophil extracellular traps

NET Markers

Longitudinal study

Severity

Mortality

Circulating markers evidenced NETs formation by Covid-19 throughout hospitalization and in all grades of severity, but showed limited relationship with severity and mortality, and different degrees of specificity, making it difficult to assess their true role in the disease outcomes.

Elevated neutrophil levels are early indicators of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe disease (1–5). Their effector functions are multiple, including enzyme degranulation, pathogen destruction by reactive oxygen species (ROS), phagocytosis, and neutrophil extracellular traps (NETs). NETs are networks of chromatin, intranuclear proteins (histones), and granule enzymes present in extracellular medium after neutrophilic destruction, to further control bacterial, fungal, viral, or parasitic infection (6). This cell death mechanism, or NETosis, is distinct from apoptosis and necrosis, with an alternative mechanism, exocytosis of granules and nuclear material, termed vital or non-lytic NETosis (7, 8). Peptidylarginine deiminase (PAD4) activation results in histone citrullination (9). All these products, in cases of excessive production and/or reduced clearance (10, 11), can cause significant tissue damage. NETosis triggers are multiple, including virus-damaged epithelial cells, activated platelets and endothelial cells, and inflammatory cytokines, including interleukin (IL)-1, IL-8, and granulocyte colony-stimulating factor (GCSF) (2).

During the COVID-19 pandemic, certain NET components, such as cell-free DNA (cfDNA) and extracellular histones, have been shown to be damage-associated molecular patterns (DAMPs) from damaged cells, which regardless of their origin have been involved in its pathogenesis¹². The presence of potential circulating NET markers (2, 4, 5, 12–14), in respiratory samples (13, 15) or in tissues (5, 15–18) is associated with severity and mortality.

NET quantification typically assesses the simultaneous presence of its components as biomarkers, including cfDNA, histones (H3) or citrullinated histones (CitH3), myeloperoxidase (MPO) isolated or in complexes with DNA (MPO-DNA), neutrophil elastase (NE), and NE-DNA complexes. Those considered more specific are those less likely to originate from activation and death of neutrophils other than NETs, such as from other cells or damaged tissues, or incidentally from molecular interactions in plasma. The choice of markers is a potential source of contradictory conclusions, and few studies exist in longitudinal cohorts to improve knowledge of their predictive power.

After having demonstrated the usefulness of cfDNA as a prognostic marker in patients hospitalised for COVID-19 (19), we aimed to check whether these elevated figures were related to NETs, studying the most specific NET markers longitudinally and throughout the hospitalization of our patient group.

From the onset of symptoms, there is an initial viral replication and elimination phase lasting approximately 7 days, followed by a decrease in viral presence and increased inflammation (early inflammation phase); in the case of progression, there is disbalance of the immune response by day 16 and later (late inflammation phase), and potentially life-threatening pulmonary and systemic alterations can occur (20).

We conducted a study of 4 NET markers: cfDNA and 3 specific ones [CitH3, MPO-DNA complex, NE-DNA complex (21)] in serum from 93 hospitalised patients with COVID-19 categorized into 3 severity groups, longitudinally and throughout the 3 phases of the disease from its onset, including 72 patients with samples at various stages, as well as 55 age-matched and comorbid uninfected participants.

Our hypotheses:

NETs markers are increased in patients with COVID-19 throughout their hospital evolution.

These markers are associated with severity or mortality at some point in the evolution of hospitalised patients with COVID-19.

Patients and controls

Patients with COVID-19 hospitalised in April and May 2020 were prospectively enrolled. Exclusion criteria were refusal to sign informed consent, concomitant infection during hospitalization, and immunosuppression (transplant recipients, hematologic malignancies, chemotherapy, or prednisone equivalent ≥ 20 mg/day). The patients were grouped according to severity, based on the Chinese Center for Disease Control and Prevention (CCDC) classification (22) and the World Health Organization Ordinal Scale (WHO OS) (23) as follows: moderately ill (MI), severely ill (SI), and critically ill (CI).

According to the day of symptom onset, 3 phases were considered: viral (1–9 days from symptom onset), early inflammation (10–16 days from symptom onset), and late inflammation (> 16 days from symptom onset).

We selected 55 non-hospitalised controls without any infection, of similar age and chronic pathologies as the patients, all of whom underwent routine laboratory tests. All met the same exclusion criteria as the patient group. To compare the three dependent groups (severity groups or the three disease progression phases), we would need a total sample size of 100 (33 by group).

Blood samples

The samples were obtained from blood requested for healthcare reasons. Patients with at least 2 blood samples in 2 consecutive phases were included. We measured neutrophil count, cytokines related to neutrophil activation and recruitment (IL-8, G-CSF), and 4 NET markers: cfDNA, CitH3, and MPO-DNA and NE-DNA complexes.

IL-8 and G-CSF

IL-8 and G-CSF in serum were quantified employing a bead-based multiplex immunoassay for R&D (LXSAHM-07) and a LABSCAN 100 (Luminex corporation). Data were analysed with Xponent software.

NE-DNA and MPO-DNA complexes

Serum samples were analysed by enzyme-linked immunosorbent assay (ELISA) of NE-DNA and MPO-DNA complexes, employing anti-NE and anti-MPO antibodies (07-496-I and MABS461 from Sigma Aldrich) and peroxidase conjugate anti-DNA antibody (Cell Death Detection ELISA Kit 154467500 Roche) as previously described (24).

Citrullinated histone H3

We employed a citrullinated histone H3 (Clone 11D3) ELISA kit (Cayman 501620) for quantification according to the manufacturer’s instructions, and 450-nm ODs were read in a Sinergy HT reader (Biotek).

Cell-free DNA

DNA was purified following the manufacturer’s procedure (ChargeSwitch gDNA 1 mL Serum Kit), adjusting the reactant quantity to the serum sample volume (100 µL). PicoGreen reagent was added in a 1:1 ratio to the purified sample (previously diluted to 1/5 in TE buffer), and the mix was incubated 5 min at room temperature in the dark (Quant-iT PicoGreen dsDNA Assay Kit, Invitrogen P7589). The Synergy HT reader (Biotek) was used to read the sample’s fluorescence (excitation 485 nm, emission 528 nm), and cfDNA concentrations were calculated with the standard provided in the kit.

Statistical analysis

According to normal criteria (Shapiro–Wilk test), data were presented as mean (standard deviation) or median (interquartile range) and analyzed with the Mann–Whitney U, Kruskal–Wallis, Student’s, or analysis of variance test, as appropriate. Correlations between biomarkers and clinical variables were analysed with Spearman’s rho correlation. Receiver operating characteristic (ROC) analysis was performed to determine the predictive power of biomarkers for various outcomes. A Wilcoxon signed-rank test was performed for the longitudinal study.

Logistic regression was used for specific outcomes, adjusting for age > 60 years, male sex, hypertension, obesity, diabetes, and corticosteroid therapy. The variables selected for the multivariate model were age, sex, and those with a p-value less than 0.05 in the bivariate analysis. All statistical analyses were performed with R version 4.0.5 (R Core Team 2021), and P-values < 0.05 were considered significant. The study was approved (03/04/2020) by IRB Aragón (Aragon Research Ethics Committee [CEICA]) (07/2020).

Patients and controls

We collected 201 samples from 93 patients, 72 of whom provided samples (180) from at least 2 disease progression stages. Of the 180 samples from the 72 patients, 87 corresponded to the viral phase, 53 to the early inflammatory phase and 40 to the late inflammatory phase. Thirty-two patients had three or more samples, and eight patients had samples from all three disease progression phases. The median number of days from symptom onset for the viral, early inflammatory, and late inflammatory phase samples were 6 (5–8), 12 (4–11), and 20 (18–24), respectively. Twelve patients required admission to the intensive care unit (ICU); 19 died. There were no statistically significant differences in the patients’ (n = 93) and controls’ (n = 55) demographics (age and sex) or comorbidities (hypertension; diabetes; obesity; respiratory, cardiac, renal, or hepatic disease; dementia; dyslipidemia) (p > 0.05 for all) (Additional file 1. Table S1). At admission, 56 of the study 93 patients had moderate disease, 29 had severe disease, and 8 were initially classified as critical. Table 1 presents the patients’ information according to severity and samples at each stage.

Table 1

Number of patients in each severity group and number of samples of the patients followed longitudinally, according to time from symptom onset. Median comparison was performed using Wilcoxon signed-rank test. Abbreviations: CCDC scale: Chinese Center for Disease Control and Prevention classification; WHO OS: World Health Organization Ordinal Scale.
WHO OS	1	2	3		4	5	6	7	8	TOTAL
Patients (n)	0	0	7		41	20	1	5	19	93
CCDC scale	Mild-Moderate					Severe		Critical		TOTAL
Patients (n)	51					15		27		93
Illness onset	Viral phase			Early inflammation			Late inflammation			TOTAL
Samples (n) from 93 patients	97			59			45			201
Samples (n) from 72 patients	87			53			40			180

NETs markers in patients and controls

On admission, we observed elevated levels compared with controls in 3 of the 4 NET markers studied: cfDNA and MPO-DNA and NE-DNA complexes (p < 0.001 for all) (Table 2). The same occurred in the first 9 days of disease evolution (viral phase), and from days 10–16 (early inflammation phase), in which we also observed elevated CitH3 values (p = 0.003). These differences were maintained from day 17 (late inflammation phase) (p < 0.001 for all 4 markers) (Table 3). Even the MI patients (according to WHO OS) presented increased values in the 4 NET markers: MPO-DNA (p < 0.001), NE-DNA (p < 0.001), CitH3 (p = 0.05), and cfDNA (p < 0.001). In the SI and CI patients together, all 4 NETs markers remained significantly elevated relative to controls (p = 0.041 for CitH3 and p < 0.001 for the others).

Table 2

Comparison of biomarkers between patients and controls for all samples and for only the first sample after admission. Median comparison was performed using Wilcoxon signed-rank test
All samples	Patients N = 201	Controls N = 55	p-value
IL-8 (pg/mL)	54.29 [40.43;89.22]	56.21 [43.26;107.75]	0.412
G-CSF (pg/mL)	143.97 [123.70;159.45]	130.47 [124.32;138.97]	< 0.001
MPO-DNA	0.80 [0.64;1.02]	0.52 [0.43;0.72]	< 0.001
NE-DNA	1.04 [0.88;1.25]	0.72 [0.60;0.89]	< 0.001
cfDNA (ng/mL)	7.87 [4.45;14.40]	2.56 [1.71;3.61]	< 0.001
CitH3 (ng/mL)	28.43 [17.02;54.13]	20.91 [15.92;31.94]	0.022
1st samples	Patients N = 93	Controls N = 55	p-value
IL-8 (pg/mL)	46.33 [33.73;65.81]	56.21 [43.26;107.75]	0.008
G-CSF (pg/mL)	141.37 [120.38;160.92]	130.47 [124.32;138.97]	0.039
MPO-DNA	0.75 [0.63;1.03]	0.52 [0.43;0.72]	< 0.001
NE-DNA	1.07 [0.90;1.22]	0.72 [0.60;0.89]	< 0.001
cfDNA (ng/mL)	7.55 [4.24;13.80]	2.56 [1.71;3.61]	< 0.001
CitH3 (ng/mL)	23.32 [10.42;35.73]	20.91 [15.92;31.94]	0.627

Table 3

Comparison of biomarkers between patients and controls during the three disease progression phases.
Viral phase (1–9 days)	Patients N = 93	Controls N = 55	p-value
IL-8 (pg/mL)	50.31 [34.62;69.05]	56.21 [43.26;107.75]	0.025
G-CSF (pg/mL)	138.42 [120.43;159.45]	130.47 [124.32;138.97]	0.032
MPO-DNA	0.75 [0.63;0.95]	0.52 [0.43;0.72]	< 0.001
NE-DNA	1.06 [0.87;1.25]	0.72 [0.60;0.89]	< 0.001
cfDNA (ng/mL)	6.67 [4.04;13.69]	2.56 [1.71;3.61]	< 0.001
CitH3 (ng/mL)	23.35 [11.32;34.99]	20.91 [15.92;31.94]	0.599
Early inflammation (10–16 days)	Patients N = 59	Controls N = 55	p-value
IL-8 (pg/mL)	55.02 [42.51;88.89]	56.21 [43.26;107.75]	0.725
G-CSF (pg/mL)	146.39 [129.14;162.05]	130.47 [124.32;138.97]	0.001
MPO-DNA	0.90 [0.64;1.10]	0.52 [0.43;0.72]	< 0.001
NE-DNA	1.03 [0.87;1.25]	0.72 [0.60;0.89]	< 0.001
cfDNA (ng/mL)	7.51 [5.04;11.66]	2.56 [1.71;3.61]	< 0.001
CitH3 (ng/mL)	35.89 [21.49;68.04]	20.91 [15.92;31.94]	0.003
Late inflammation (> 16 days)	Patients N = 45	Controls N = 55	p-value
IL-8 (pg/mL)	78.18 [50.36;111.21]	56.21 [43.26;107.75]	0.093
G-CSF (pg/mL)	149.48 [132.43;157.64]	130.47 [124.32;138.97]	< 0.001
MPO-DNA	0.87 [0.74;1.05]	0.52 [0.43;0.72]	< 0.001
NE-DNA	1.00 [0.88;1.32]	0.72 [0.60;0.89]	< 0.001
cfDNA (ng/mL)	11.24 [5.77;17.77]	2.56 [1.71;3.61]	< 0.001
CitH3 (ng/mL)	54.54 [28.72;89.53]	20.91 [15.92;31.94]	< 0.001

NET markers, neutrophil count, IL-8, G-CSF, and severity and mortality

Among the 4 NETs markers, we only found significant cfDNA increases in MI vs SI and CI, but not in CitH3, MPO-DNA, or NE-DNA complexes. The same occurred comparing MI and SI vs CI (CCDC) (Table 4). Of the 4 NET markers, only cfDNA showed significant increases in CI vs non-CI (CCDC and WHO OS scales), in the first sample and in the 3 phases of the study. Neutrophil count, IL-8, and G-CSF were significantly related to severity (Table 4). cfDNA was also the single NET marker with higher levels in non-survivors compared with survivors, including in the total sample (p < 0.001), at admission, and in the 3 phases of the disease. The same occurred with neutrophil count. MPO-DNA and NE-DNA showed higher values in non-survivors, but only in the early inflammation phase (Table 5). The non-survivors showed elevated neutrophils, IL-8, and cfDNA in the first sample after admission. There were no differences in CitH3 levels between non-survivors and survivors at any time. In our multivariate model, only cfDNA and obesity were independent risk factors for mortality and need for ICU (Table 6, and Additional file 2. Table S2). The area under the curve (AUC) ROC for critical status and mortality was clearly higher for cfDNA than for the other parameters (Table 7).

Table 4

Comparisons of biomarkers according to severity (CCDC scale and WHO OS). Median comparison was performed using Wilcoxon signed-rank test.
		Severe and Critical	Moderate	p-value
CCDC scale	Neutrophils/mm3	6700 [4575;10725]	4600 [3500;6400]	< 0.001
	IL-8 (pg/mL)	58.22 [46.90;97.08]	49.89 [35.59;80.94]	0.015
	G-CSF (pg/mL)	148.16 [126.10;163.97]	142.01 [122.64;156.55]	0.162
	MPO-DNA	0.84 [0.64;1.03]	0.78 [0.63;0.99]	0.563
	NE-DNA	0.98 [0.86;1.29]	1.06 [0.91;1.24]	0.394
	cfDNA (ng/mL)	9.44 [6.15;20.19]	6.21 [3.46;10.44]	< 0.001
	CitH3 (ng/mL)	28.28 [16.19;57.92]	28.43 [17.93;51.13]	0.670
WHO OS	Neutrophils/mm3	6600 [4500;10775]	4600 [3550;6350]	< 0.001
	IL-8 (pg/mL)	58.80 [46.72;102.29]	49.15 [36.85;72.20]	0.007
	G-CSF (pg/mL)	150.75 [131.66;168.10]	136.76 [120.48;153.66]	0.002
	MPO-DNA	0.80 [0.64;1.02]	0.80 [0.64;1.00]	0.873
	NE-DNA	1.04 [0.87;1.31]	1.05 [0.89;1.24]	0.945
	cfDNA (ng/mL)	9.08 [5.54;19.38]	6.88 [3.61;10.73]	0.001
	CitH3 (ng/mL)	31.79 [15.08;57.53]	26.80 [18.37;49.48]	0.388
		Critical	Moderate and Severe	p-value
CCDC scale	Neutrophils/mm3	8250 [5400;12100]	4900 [3700;6600]	< 0.001
	IL-8 (pg/mL)	68.60 [47.40;94.41]	52.57 [37.56;82.75]	0.058
	G-CSF (pg/mL)	152.80 [131.66;177.42]	140.17 [122.64;156.55]	0.009
	MPO-DNA	0.84 [0.68;1.01]	0.78 [0.63;1.01]	0.470
	NE-DNA	0.99 [0.86;1.35]	1.05 [0.89;1.24]	0.669
	cfDNA (ng/mL)	14.11 [7.13;22.67]	7.01 [4.01;10.93]	< 0.001
	CitH3 (ng/mL)	27.67 [12.88;53.68]	30.58 [19.08;54.70]	0.324

Table 5. Comparisons of median biomarker levels between survivors and non-survivors. A: total samples and first sample; B: throughout the 3 evolutionary phases. Median comparison was performed using Wilcoxon signed-rank test

Total samples (n=201)	Survivors (n=158)	Non-survivors (n=43)	p-value
Neutrophils/mm3	5100 [3700;6700]	8400 [6050;13600]	<0.001
IL-8 (pg/mL)	52.82 [37.84;83.46]	69.12 [49.89;104.92]	0.043
G-CSF (pg/mL)	142.57 [122.64;157.28]	149.55 [130.26;165.07]	0.090
MPO-DNA	0.80 [0.63;1.00]	0.80 [0.71;1.02]	0.517
NE-DNA	1.04 [0.87;1.23]	1.05 [0.90;1.48]	0.439
cfDNA (ng/mL)	7.06 [4.12;11.40]	14.98 [7.42;25.74]	<0.001
CitH3 (ng/mL)	28.29 [18.10;53.97]	29.29 [12.34;58.58]	0.745
1st sample (n=93)	Survivors (n=74)	Non-survivors (n=19)	p-value
Neutrophils/mm3	5050 [3725;6550]	8400 [4950;11750]	0.009
IL-8 (pg/mL)	42.11 [33.18;56.48]	61.83 [52.88;89.06]	0.016
G-CSF (pg/mL)	134.22 [118.12;153.99]	160.09 [139.92;184.10]	0.012
MPO-DNA	0.75 [0.63;1.01]	0.77 [0.71;1.01]	0.508
NE-DNA	1.07 [0.88;1.19]	1.02 [0.94;1.57]	0.559
cfDNA (ng/mL)	6.87 [4.01;10.81]	14.98 [7.73;22.51]	0.002
CitH3 (ng/mL)	24.51 [12.61;34.99]	14.80 [6.70;29.79]	0.129

Viral phase (1-9 days)	Survivors N=78	Non-survivors N=19	p-value
Neutrophils/mm3	4500 [3150;6600]	7000 [4600;10550]	0.011
IL-8 (pg/mL)	49.03 [34.24;61.20]	54.29 [42.30;74.82]	0.269
G-CSF (pg/mL)	136.45 [118.12;153.91]	155.74 [128.91;168.02]	0.069
MPO-DNA	0.72 [0.63;0.95]	0.75 [0.65;0.90]	0.988
NE-DNA	1.07 [0.91;1.23]	0.96 [0.78;1.34]	0.342
cfDNA (ng/mL)	6.11 [3.48;11.45]	7.87 [5.08;22.21]	0.037
CitH3 (ng/mL)	24.51 [13.76;34.99]	16.19 [7.76;33.13]	0.295
Early inflammation (10-16 days)	Survivors N=48	Non-survivors N=11	p-value
Neutrophils/mm3	5600 [4275;7000]	11400 [7650;14450]	<0.001
IL-8 (pg/mL)	51.93 [40.97;81.89]	77.53 [60.24;110.09]	0.052
G-CSF (pg/mL)	146.64 [129.14;159.15]	142.57 [125.89;175.26]	0.731
MPO-DNA	0.85 [0.61;1.03]	1.61 [0.90;2.87]	0.026
NE-DNA	0.99 [0.86;1.14]	1.37 [1.10;1.68]	0.016
cfDNA (ng/mL)	7.11 [4.46;9.75]	12.59 [7.88;26.36]	0.008
CitH3 (ng/mL)	35.89 [21.92;65.96]	34.49 [17.23;67.53]	1.000
Late inflammation (>16 days)	Survivors N=32	Non-survivors N=13	p-value
Neutrophils/mm3	5250 [4075;6625]	8400.00 [6500.00;16400.00]	0.001
IL-8 (pg/mL)	79.03 [49.63;110.03]	75.08 [51.72;107.04]	0.966
G-CSF (pg/mL)	149.55 [135.01;156.55]	149.42 [131.66;161.75]	0.920
MPO-DNA	0.87 [0.77;1.05]	0.78 [0.69;1.02]	0.466
NE-DNA	0.99 [0.88;1.33]	1.06 [0.90;1.11]	0.548
cfDNA (ng/mL)	8.35 [4.91;12.94]	24.62 [19.21;39.06]	<0.001
CitH3 (ng/mL)	54.13 [29.46;79.69]	73.28 [34.28;102.09]	0.520

Table 6

Multivariate model for severity, mortality and ICU need
	YES	NO	OR Univariable	OR Multivariable
Age > 60 years n (%)	51 (85.0)	9 (15.0)	1.76 (0.48–8.41, p = 0.421)	0.40 (0.01–9.74, p = 0.599)
Male sex n (%)	34 (85.0)	8 (19.0)	2.76 (0.80-11.06, p = 0.119)	1.84 (0.08–63.05, p = 0.695)
Hypertension n (%)	40 (88.9)	5 (11.1)	0.73 (0.20–2.48, p = 0.619)	-
Obesity n (%)	7 (53.8)	6 (46.2)	10.57 (2.69–43.67, p = 0.001)	46.21 (3.76-2138.71, p = 0.011)
Corticosteroid therapy n (%)	36 (76.6)	11 (23.4)	13.75 (2.50-257.24, p = 0.014	19.62 (1.07-2584.67, p = 0.114)
Diabetes n (%)	25 (89.3)	3 (10.7)	0.75 (0.16–2.75, p = 0.680)	-
IL-8 Mean (SD)	71.3 (97.6)	67.9 (32.7)	1.00 (0.99–1.01, p = 0.909)	-
G-CSF Mean (SD)	199.5 (324.7)	170.9 (41.8)	1.00 (0.99-1.00, p = 0.775)	-
cfDNA Mean (SD)	8.7 (6.5)	23.7 (17.4)	1.14 (1.06–1.25, p = 0.001)	1.22 (1.07–1.53, p = 0.025)
MPO-DNA Mean (SD)	0.9 (0.6)	1.1 (0.8)	1.57 (0.61–3.46, p = 0.272)	-
NE-DNA Mean (SD)	1.1 (0.4)	1.4 (0.7)	2.49 (0.78–7.95, p = 0.100)	-
CitH3 Mean (SD)	30.1 (25.8)	18.8 (15.3)	0.97 (0.92-1.00, p = 0.174)	-

Table 7

AUC ROC of NETs markers for severity and mortality
AUC ROC for critical status
Marker	AUC	Threshold	Specificity	Sensitivity
cfDNA	0.7162 (0.63–0.8)	14099.03900	0.5094340	0.8175182
MPO-DNA	0.5347 (0.44–0.63)	0.70700	0.7400000	0.3787879
NE-DNA	0.5203 (0.42–0.62)	0.94250	0.4313725	0.6911765
CitH3	0.5461 (0.45–0.65)	17.13575	0.3962264	0.8029197
AUC ROC for mortality
cfDNA	0.7482 (0.66–0.84)	20170.72520	0.9466667	0.4500000
MPO-DNA	0.4654 (0.36–0.57)	0.74850	0.5793103	0.4594595
NE-DNA	0.5403 (0.43–0.65)	1.53000	0.8986486	0.2564103
CitH3	0.4832 (0.37–0.6)	73.17575	0.8800000	0.2500000

Evolution of NET markers throughout the disease course

In the longitudinal study, the MPO-DNA and NE-DNA complexes remained elevated and stable during the 3 phases of the disease. cfDNA increased, especially in the late inflammation phase (p = 0.043) compared with the viral phase; levels of CitH3 progressively increased throughout the 3 phases (p < 0.001), doubling between the first and last sample obtained (p < 0.001) (Table 8).

Table 8

Comparison of biomarkers among the three disease progression phases. Before conducting median comparison, a Kruskal-Wallis test was performed to assess the overall differences among groups. Median comparison was performed using Wilcoxon signed-rank test
	Viral phase (1) N = 87	Early inflammation (2) N = 53	Late inflammation (3) N = 40	p-value	p-value 1 vs 2	p-value 1 vs 3	p-value 2 vs 3
Neutrophils/mm3	5100 [3350;6950]	6000 [4400;8700]	6050 [4100;8225]	0.069	0.119	0.119	0.874
IL-8 (pg/mL)	52.01 [34.96;68.72]	55.52 [45.64;91.01]	77.32 [50.36;106.06]	0.002	0.048	0.002	0.108
G-CSF (pg/mL)	139.08 [120.98;160.64]	147.85 [129.84;162.19]	148.79 [132.43;157.64]	0.478	0.488	0.488	0.964
MPO-DNA	0.75 [0.62;0.94]	0.87 [0.63;1.03]	0.87 [0.72;1.03]	0.092	0.211	0.112	0.833
NE-DNA	1.05 [0.88;1.25]	1.03 [0.86;1.25]	0.97 [0.88;1.27]	0.909	0.868	0.868	0.868
cfDNA (ng/mL)	6.40 [3.61;13.99]	7.87 [5.34;14.65]	10.53 [5.56;17.30]	0.041	0.200	0.043	0.200
CitH3 (ng/mL)	23.64 [12.73;35.10]	35.89 [21.92;61.36]	54.54 [28.72;91.81]	< 0.001	0.001	< 0.001	0.043

Correlations

There were significant correlations between MPO-DNA and NE-DNA complexes (r = 0.483, p = 0.000), and less correlation between cfDNA and CitH3 (r = 0.277, p = 0.000) and between cfDNA and NE-DNA (r = 0.168, p = 0.022) (Table 9). We found no other correlations among the 4 NET markers. There were significant correlations between MPO-DNA and NE-DNA (r = 0.793, p = 0.000 in MI; r = 0.472, p = 0.000 in SI; and r = 0.627, p = 0.000 in CI [WHO OS]), which remained significant from the sample at hospital arrival (r = 0.586, p = 0.000) and during the 3 phases (r = 0.705, p = 0.000 in viral; r = 0.581, p = 0.000 in early inflammation; and r = 0.476, p = 0.002 in late inflammation). We found no correlation between CitH3 and MPO-DNA or NE-DNA. There were less strong, but significant, correlations between cfDNA and NE-DNA in CI (r = 0.297, p = 0.043) and between cfDNA and CitH3 in CI (r = 0.371, p = 0.009) and SI (r = 0.289, p = 0.004) (WHO OS). In the total sample, cfDNA was the only NET marker that correlated with typical clinical severity factors, such as lymphopenia, neutrophil/lymphocyte ratio, C-reactive protein, lactate dehydrogenase levels, and baseline SaO2 (See Additional file 3. Table S3).

Table 9

Correlations between 4 circulating NET biomarkers
Total samples (n = 201)	r	p
cfDNA vs MPO-DNA	0.095	0.203
cfDNA vs NE-DNA	0.168	0.022
cfDNA vs CitH3	0.277	< 0.001
MPO-DNA vs NE-DNA	0.483	< 0.001
MPO-DNA vs CitH3	0.019	0.797
NE-DNA vs CitH3	-0.010	0.891

The most important results of our study included a clear increase in circulating NET markers in patients with COVID-19 compared with controls, confirming NET formation in hospitalised patients with COVID-19, from admission throughout hospitalization, including less severe cases. cfDNA was the NET marker most strongly associated with severity and mortality, from the first sample and throughout the disease. It was an independent risk factor and showed the best AUC ROC for both outcomes. However, we found less association than expected between the other 3 NET markers and poor outcomes: only increased MPO-DNA and NE-DNA complexes during the second week (days 10–16 from symptom onset) in non-surviving patients. We observed a significant correlation between markers considered highly specific for NETosis: MPO-DNA complexes and NE-DNA, during the entire evolution and at all severity levels, were much higher than that of the other markers among each other. Throughout the evolution, neutrophil count increased with severity and mortality, and IL-8 increased with severity.

The presence of NET biomarkers in patients with COVID-19 has generated interest (2, 4, 5, 12, 18, 25); most studies have found increased markers in these patients with respect to healthy controls (2, 5, 12, 13, 26). Increased NET formation from ex vivo neutrophils of patients with COVID-19 (27) and impaired NET degradation in those with COVID-19 have been reported (5, 25).

In our study, every NETosis marker remained elevated from admission (CitH3 from day 10), during the entire hospitalization and at the 3 levels of severity. This, and the high MPO-DNA and NE-DNA correlations in the 3 levels of severity and during the entire evolution, offer little doubt as to the existence of NETosis in hospitalised patients with COVID-19, including in less severe patients. Interestingly, the delayed onset and increased CitH3 suggest that citrullination is a consequence rather than a cause of histone externalization, which is a matter of debate (9).

Other studies have concluded that circulating NET marker levels are related to severity and mortality in COVID-19, and that NETs could play an important role in pathogenesis (2, 4, 5, 12, 14, 15, 26, 28–30), including their identification in fatal disease tissues (5, 30) and the observation of small pulmonary vessel occlusion by NET aggregates (5, 16). This NETs–severity relationship is the dominant opinion today.

However, contradictory results have been observed for the relationship between circulating NET markers and COVID-19 severity (5, 31), viral load (13, 30), or associated obstructive vascular phenomena (5, 13, 16). Some reasons could include the non-standardized choice of NET markers, which varies between studies (including some less specific ones); an apparent discrepancy between the markers in tissues and in peripheral blood (13); levels can vary significantly with underlying chronic diseases (5); and some appear to be associated with severity more than others (5). In addition, the heterogeneity of the populations, the short half-life of NET markers (32), and the variety of enzymatic pathways triggering NETs (13) can modify the markers and lead to divergent results.

In addition to cfDNA, included in most of the previous studies, we chose 3 NET markers considered the most specific: CitH3, and MPO-DNA and NE-DNA complexes (21). It is difficult to distinguish NETosis markers from those of neutrophil activation or other types of cell death (33, 34). cfDNA or nucleosomes (DNA formations with histones or nuclear proteins, such as H3) can also originate from necrotic processes. MPO and NE frequently originate from neutrophil degranulation; however, their binding to DNA (NE-DNA and MPO-DNA complexes used in our study) likely makes them the most specific for NETs, because they are less likely to be formed incidentally by molecular interactions in plasma (21, 33). Citrullinated histones, such as CitH3, although considered specific, represent only one NETosis pathway (via PAD4) (35); their levels may be similar in children with COVID-19 as in healthy children (36), and can vary depending on stimulus (37). A recent study found a much stronger relationship between COVID-19 severity (and long COVID) and increased NETosis-induction capacity measured ex vivo, than that of circulating NET-specific marker levels (MPO-DNA complex) (38).

The lack of correlations observed between some markers considered specific, such as CitH3 with MPO-DNA and NE-DNA, has been reported in COVID-19 between CitH3 and MPO-DNA (2), and between CitH4-DNA and NE-DNA, which showed opposite trends in COVID-19 severity assessment (5). In another study, CitH3-NE complexes did not differ from those of controls (31). The simultaneous involvement of alternative non-PDA4 pathway-dependent NETosis in COVID-19 (2, 39) and the existence of sequestered NET fragments in damaged organs could explain the discrepancies between measurements of circulating citrullinated histones and other markers and with those of tissues (31).

Also, neutrophils can generate markers such as cfDNA and CitH3 by mechanisms other than NETosis (2, 12). Therefore, the use of citrullinated histones (CitH3) as specific NET markers, despite frequent employment, is controversial (33, 35). Lastly, markers with the potential to detect NETs have not been designed, and it is unclear whether NET markers are drivers of disease severity, a simple consequence of acute inflammation (2), or both.

The clear association of cfDNA, neutrophil counts, and IL-8 with severity and mortality, and the (much poorer) relationship of the more specific markers of NETosis suggest an important role for neutrophils in severe COVID-19 cases (30, 40); however, it is difficult to establish a clear relationship between circulating NET markers and these outcomes. The strong correlation of the 2 markers considered the more specific for NETs, MPO-DNA and NE-DNA, in all severity groups and throughout hospitalization, and the very limited correlation of the remaining biomarkers with each other, suggest that MPO-DNA and NE-DNA are the best circulating NETosis markers. Overall, the results of our study suggest, like others (2, 12, 41, 42), that cfDNA is not a marker with high specificity for NETosis, because it is also released by various hematopoietic cells and from a wide range of tissues after cell destruction, as has been demonstrated in COVID-19 (19, 32, 41). Also, cfDNA is a DAMP, capable of amplifying the inflammatory response (43, 44) via toll-like receptor 9 (TLR-9) (44). NET formation is likely better reflected by more neutrophil-specific markers, such as MPO-DNA and NE-DNA, whereas cfDNA better reflects cellular damage in a broader sense, correlating with disease severity parameters and better predicting disease outcomes (2, 12). The widespread inclusion of cfDNA in previous studies could have overestimated the role of NETs in COVID-19 severity.

On days 10–16, when viral control appears to be achieved in those with a good prognosis, the significantly higher blood levels of MPO-DNA and NE-DNA complexes in those who died suggest increased NET production, concurrent with a lack of viral clearance in the second week of evolution (45), associated with poor prognoses (46). Our results suggest that NET status varies at different time points in the evolution of COVID-19; that an increase in NET release during the second week of disease progression, probably associated with a higher viral load, can contribute to poorer outcomes; and that NET detection during this period could predict its evolution.

An association between NETs and COVID-19 severity could encourage new treatment approaches, such as DNases25, IL-8 receptor antagonists (30), certain monoclonal antibodies, PAD4 or nicotinamide adenine dinucleotide phosphate (NADPH) inhibitors (47), Fostamatinib (48), Resolvin T-series (49), and current and novel histone inhibitors (34).

Our study has limitations. Our cohort was small, from a single hospital. We were unable to measure viral load and thus demonstrate a parallel association of increased and sustained viral load and NET formation on days 10–16 in patients with poor prognosis. Quantification problems for NETosis and cytokines have been mentioned. The strengths of our study include that there is scarce information on NETosis markers on patients with COVID-19 progression. As far as we know, ours is the first study systematically including 3 evolutive phases, including the NET biomarker most used in previous studies and 3 markers of those considered more specific combined. Our controls were not healthy, as in most studies, but had comorbidities similar to those of our patients, which gives greater value to comparisons of their parameters with those of the patients.

Our study has shown the presence of NETs and neutrophil activation and recruitment at all COVID-19 severity levels, from admission throughout the evolution of hospitalised patients, but suggests differences in specificity and quality of some NETosis markers. Although previous studies have reported that these marker levels could be related to disease severity, at present there is no evidence that SARS-CoV-2 can directly induce NET production, and more information is needed on the possible mechanism by which NETs would promote COVID-19 progression (2, 29). Our results contradict in some ways some of the other previous results. However, in our previous study with the same samples, AUCs for other markers well known for their association with outcomes in COVID-19, such as IL-6, were validated (19). It seems clear that, with our current limitations in accurately measuring NETs by circulating markers, it is difficult to reach definitive conclusions about the proper role of NETs in the pathogenesis of COVID-19.

CCDC score: Chinese Center for Disease Control and Prevention classification

cfDNA: cell-free DNA

CI: Critically ill

CitH3: citrullinated histone 3

CitH3-NE: citrullinated histone 3-neutrophil elastase complexes

CitH4-DNA: citrullinated histone 4-DNA complexes

DAMPs: damage-associated molecular patterns

GCSF: granulocyte colony-stimulating factor

H3: histone 3

IL-1: interleukin 1

IL-8: interleukin 8

MI: moderately ill

MPO-DNA: myeloperoxidase-DNA complexes

NADPH: nicotinamide adenine dinucleotide phosphate

NE-DNA: neutrophilic elastase-DNA complexes

NETs: neutrophil extracellular traps

PAD4: peptidylarginine deiminase

ROS: reactive oxygen species

SI: severely ill

TLR-9: toll like receptor 9

WHO OS: World Health Organization ordinal scale

Ethics approval and consent to participate

The study was approved (01/04/2020) by IRB Aragón (Aragon Research Ethics Committee [CEICA]) (Record Nº 07/2020). All patients and controls signed the informed consent form to participate in the study.

Consent for publication

Not applicable

Availability of data and materials

The datasets supporting the conclusions of this article are available in the Zenodo repository, in https://zenodo.org/record/8205103 (50)

Competing interests

The authors report no conflicts of interest related to the submitted work.

Funding source

This work was supported with funds from the Pneumoaragon Foundation of the Aragonese Society of Pneumology (SADAR).

Author contributions

Conception and design: SB, ABL, CDD
Acquisition of data: LLV, CDD, LT, PRG, ATE, RL, CA, CB
Analysis and interpretation of data: JG, AC, BJ, ABL, SB, CDD
Writing of the manuscript: SB, ABL, CDD
Drafting or revising of the article: SB, ABL, CDD
Final approval of the manuscript: SB, ABL, LLV, CDD, LT, PRG, RL, CA, JG, AC, BJ, CB, ATE.

Acknowledgements

We thank Mar Gonzalez Cantalejo, head of the library of the Miguel Servet University Hospital and all its staff for their invaluable help in the availability of data and materials.

Pedersen SF, Ho YC. SARS-CoV-2: a storm is raging. J Clin Invest 2020; 130(5):2202–2205. doi: 10.1172/JCI137647. PMID: 32217834; PMCID: PMC7190904.
Zuo Y, Yalavarthi S, Shi H, et al. Neutrophil extracellular traps in COVID-19. JCI Insight 2020;5(11):e138999. doi: 10.1172/jci.insight.138999.
Fox SE, Akmatbekov A, Harbert JL, Li G, Brown JQ, Vander Heide RS. Pulmonary and cardiac pathology in COVID-19: the first autopsy series from New Orleans. Lancet Respir Med 2020; 8:681–6. doi: 10.1016/S2213-2600(20)30243-5
Barnes BJ, Adrover JM, Baxter-Stoltzfus A, et al. Targeting potential drivers of COVID-19: neutrophil extracellular traps. J Exp Med 2020; 217:e20200652. doi: 10.1084/jem.20200652.
Carmona-Rivera C, Zhang Y, Dobbs K, et al. Multicenter analysis of neutrophil extracellular trap dysregulation in adult and pediatric COVID-19. JCI Insight 2022;7(16):e160332. doi: 10.1172/jci.insight.160332.
Brinkmann V, Reichard U, Goosmann C, et al. Neutrophil extracellular traps kill bacteria. Science 2004;303(5663): 1532–1535. doi: 10.1126/science.1092385. PMID: 15001782.
Papayannopoulos V. Neutrophil extracellular traps in immunity and disease. Nat Rev Immunol 2018;18(2):134–147. doi: 10.1038/nri.2017.105. Epub 2017 Oct 9. PMID: 28990587.
Johansson C, Kirsebom FCM. Neutrophils in respiratory viral infections. Mucosal Immunol 2021;14(4):815-827. doi: 10.1038/s41385-021-00397-4.
Nathan C. Neutrophils and COVID-19: Nots, NETs, and knots. J Exp Med. 2020; 217(9):e20201439. doi: 10.1084/jem.20201439.
Ackermann M, Anders HJ, Bilyy R, et al. Patients with COVID-19: in the dark-NETs of neutrophils. Cell Death Differ 2021; 28(11):3125-3139. doi: 10.1038/s41418-021-00805-z.
Jo A, Kim DW. Neutrophil Extracellular Traps in Airway Diseases: Pathological Roles and Therapeutic Implications. Int J Mol Sci 2023; 24(5):5034. doi: 10.3390/ijms24055034.
Huckriede J, Anderberg SB, Morales A, et al. Evolution of NETosis markers and DAMPs have prognostic value in critically ill COVID-19 patients. Sci Rep 2021; 11(1):15701. doi: 10.1038/s41598-021-95209-x.
Ouwendijk WJD, Raadsen MP, van Kampen JJA, et al. High Levels of Neutrophil Extracellular Traps Persist in the Lower Respiratory Tract of Critically Ill Patients With Coronavirus Disease 2019. J Infect Dis 2021; 223(9):1512-1521. doi: 10.1093/infdis/jiab050.
Borges L, Pithon-Curi TC, Curi R, Hatanaka E. COVID-19 and Neutrophils: The Relationship between Hyperinflammation and Neutrophil Extracellular Traps. Mediators Inflamm 2020; 2020:8829674. doi: 10.1155/2020/8829674.
Veras FP, Pontelli MC, Silva CM, et al. SARS-CoV-2-triggered neutrophil extracellular traps mediate COVID-19 pathology. J Exp Med 2020; 217(12):e20201129. doi: 10.1084/jem.20201129.
Middleton EA, He XY, Denorme F, et al. Neutrophil extracellular traps contribute to immunothrombosis in COVID-19 acute respiratory distress syndrome. Blood 2020; 136:1169–79. doi: 10.1182/blood.2020007008. PMID: 32597954; PMCID: PMC7472714.
Radermecker C, Detrembleur N, Guiot J, et al. Neutrophil extracellular traps infiltrate the lung airway, interstitial, and vascular compartments in severe COVID-19. J Exp Med 2020; 217(12):e20201012. doi: 10.1084/jem.20201012.
Rowley AH. Understanding SARS-CoV-2-related multisystem inflammatory syndrome in children. Nat Rev Immunol 2020; 20(8):453-454. doi: 10.1038/s41577-020-0367-5. PMID: 32546853; PMCID: PMC7296515.
Bello S, Lasierra AB, López-Vergara L, et al. IL-6 and cfDNA monitoring throughout COVID-19 hospitalization are accurate markers of its outcomes. Respir Res 2023; 24(1):125. doi: 10.1186/s12931-023-02426-1.
Durán-Méndez A, Aguilar-Arroyo AD, Vivanco-Gómez E, et al. Tocilizumab reduces COVID-19 mortality and pathology in a dose and timing-dependent fashion: a multi-centric study. Sci Rep 2021; 11(1): 19728. doi: 10.1038/s41598-021-99291-z.
Hayden H, Ibrahim N, Klopf J, et al. ELISA detection of MPO-DNA complexes in human plasma is error-prone and yields limited information on neutrophil extracellular traps formed in vivo. PLoS ONE 2021;16(4): e0250265. https://doi.org/10.1371/journal.pone.0250265
Wu Z, McGoogan JM. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239–42. doi: 10.1001/jama.2020.2648. PMID: 32091533.
Marshall JC, Murthy S, Diaz J, et al. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis. 2020;20(8):e192–e197. doi: 10.1016/S1473-3099(20)30483-7. Epub 2020 Jun 12. Erratum in: Lancet Infect Dis. 2020 Oct;20(10):e250. PMID: 32539990; PMCID: PMC7292605.
Kano H, Huq MA, Tsuda M, Noguchi H, Takeyama N. Sandwich ELISA for Circulating Myeloperoxidase- and Neutrophil Elastase-DNA Complexes Released from Neutrophil Extracellular Traps. Adv Tech Biol Med 2016; 5: 196. doi: 10.4172/2379-1764.1000196.
Veras FP, Gomes GF, Silva BMS, et al. Targeting neutrophils extracellular traps (NETs) reduces multiple organ injury in a COVID-19 mouse model. Respir Res 2023;24(1):66. doi: 10.1186/s12931-023-02336-2.
Wang J, Li Q, Yin Y, et al. Excessive Neutrophils and Neutrophil Extracellular Traps in COVID-19. Front Immunol 2020; 11:2063. doi: 10.3389/fimmu.2020.02063.
Masso-Silva JA, Moshensky A, Lam MTY, et al. Increased Peripheral Blood Neutrophil Activation Phenotypes and Neutrophil Extracellular Trap Formation in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients: A Case Series and Review of the Literature. Clin Infect Dis 2022;74(3):479-489. doi: 10.1093/cid/ciab437. Erratum in: Clin Infect Dis. 2022;74(10):1889-1890.
Silvestre-Roig C, Braster Q, Wichapong K, et al. Externalized histone H4 orchestrates chronic inflammation by inducing lytic cell death. Nature 2019; 569(7755), 236–240. doi: 10.1038/s41586-019-1167-6. Epub 2019 May 1. PMID: 31043745; PMCID: PMC6716525.
Zhao X, Zhou L, Kou Y, Kou J. Activated neutrophils in the initiation and progression of COVID-19: hyperinflammation and immunothrombosis in COVID-19. Am J Transl Res 2022;14(3):1454-1468. PMID: 35422922; PMCID: PMC8991139.
Melero I, Villalba-Esparza M, Recalde-Zamacona B, et al. Neutrophil Extracellular Traps, Local IL-8 Expression, and Cytotoxic T-Lymphocyte Response in the Lungs of Patients With Fatal COVID-19. Chest 2022;162(5):1006-1016. doi: 10.1016/j.chest.2022.06.007.
Kinnare N, Hook JS, Patel PA, Monson NL, Moreland JG. Neutrophil Extracellular Trap Formation Potential Correlates with Lung Disease Severity in COVID-19 Patients. Inflammation 2022 ; 45(2):800-811. doi: 10.1007/s10753-021-01585-x.
Chen Z, Zhang H, Qu M, et al. Review: The Emerging Role of Neutrophil Extracellular Traps in Sepsis and Sepsis-Associated Thrombosis. Front Cell Infect Microbiol 2021;11:653228. doi: 10.3389/fcimb.2021.653228.
de Buhr N, von Köckritz-Blickwede M. Detection, Visualization, and Quantification of Neutrophil Extracellular Traps (NETs) and NET Markers. Methods Mol Biol 2020;2087:425-442. doi: 10.1007/978-1-0716-0154-9_25.
de Vries F, Huckriede J, Wichapong K, Reutelingsperger C, Nicolaes GAF. The role of extracellular histones in COVID-19. J Intern Med 2023. 293(3):275-292. doi: 10.1111/joim.13585.
Kenny EF, Herzig A, Kruger R, et al. Diverse stimuli engage different neutrophil extracellular trap pathways. Elife 2017; 6:e24437. doi: 10.7554/eLife.24437. PMID: 28574339; PMCID: PMC5496738.
Seery V, Raiden SC, Algieri SC, et al. Blood neutrophils from children with COVID-19 exhibit both inflammatory and anti-inflammatory markers. EBioMedicine 2021;67:103357. doi: 10.1016/j.ebiom.2021.103357.
Jorch SK, Kubes P. An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med 2017;23(3):279–87; doi: 10.1038/nm.4294. PMID: 28267716.
Krinsky N, Sizikov S, Nissim S, et al. NETosis induction reflects COVID-19 severity and Long COVID: insights from a two-center patient cohort study in Israel. J Thromb Haemost 2023; 11:S1538-7836(23)00274-X. doi: 10.1016/j.jtha.2023.02.033.
Boeltz S, Amini P, Anders H-J, et al. To NET or not to NET:Current opinions and state of the science regarding the formation of neutrophil extracellular traps. Cell Death Differ 2019; 26 (3): 395–408. https:// doi. org/ 10. 1038/ s41418- 018- 0261-x
Cesta MC, Zippoli M, Marsiglia C, et al. Neutrophil activation and neutrophil extracellular traps (NETs) in COVID-19 ARDS and immunothrombosis. Eur J Immunol 2023;53(1):e2250010. doi: 10.1002/eji.202250010.
Andargie TE, Tsuji N, Seifuddin F, et al. Cell-free DNA maps COVID-19 tissue injury and risk of death and can cause tissue injury. JCI Insight 2021; 6(7):e147610 https://doi.org/10.1172/jci.insight.147610
Hammad R, Eldosoky MAER, Fouad SH, et al. Circulating cell-free DNA, peripheral lymphocyte subsets alterations and neutrophil lymphocyte ratio in assessment of COVID-19 severity. Innate Immun 2021; 27(3):240-250. doi: 10.1177/1753425921995577.
Tsuji N, Tsuji T, Ohashi N, et al. Role of mitochondrial DNA in septic AKI via toll-like receptor 9. J Am Soc Nephrol 2016;27(7):2009–2020. doi: 10.1681/ASN.2015040376. Epub 2015 Nov 16. PMID: 26574043; PMCID: PMC4926971.
Hauser C, Otterbein L. Danger signals from mitochondrial DAMPS in trauma and post-injury sepsis. Eur J Trauma Emerg Surg 2018;44(3):317–324. doi: 10.1007/s00068-018-0963-2. Epub 2018 May 24. PMID: 29797026.
Munker D, Osterman A, Stubbe H, et al. Dynamics of SARS-CoV-2 shedding in the respiratory tract depends on the severity of disease in COVID-19 patients. Eur Respir J 2021;58(1):2002724. doi: 10.1183/13993003.02724-2020.
Li H, Gu X, Li H, et al. Risk Factors of Viral RNAaemia and Its Association With Clinical Prognosis Among Patients With Severe COVID-19. Chest 2021 ;159(4):1382-1386. doi: 10.1016/j.chest.2020.11.071.
Jiménez-Alcázar M, Rangaswamy C, Panda R, et al. Host DNases prevent vascular occlusion by neutrophil extracellular traps. Science 2017;358(6367):1202-1206. doi: 10.1126/science.aam8897.
Strich JR, Ramos-Benitez MJ, Randazzo D, et al. Fostamatinib Inhibits Neutrophils Extracellular Traps Induced by COVID-19 Patient Plasma: A Potential Therapeutic. J Infect Dis 2021;223(6):981-984. doi: 10.1093/infdis/jiaa789.
Chiang N, Sakuma M, Rodriguez AR, Spur BW, Irimia D, Serhan CN. Resolvin T-series reduce neutrophil extracellular traps. Blood 2022;139(8):1222-1233. doi: 10.1182/blood.2021013422.
de Diego C, Lasierra AB, López-Vergara L, Torralba L, Ruiz de Gopegui P, Lahoz R, Abadía C, Godino J, Cebollada A, Jimeno B, Bello C, Tejada A, Bello S. What is the actual relationship between neutrophil extracellular traps and COVID-19 severity? A longitudinal study. 2023. https://zenodo.org/record/8205103

No competing interests reported.

Additionalfile1.docx
Supplementary Table 1 (S1): Demographics and comorbidities of the patients and controls
Additionalfile2.docx
Supplementary Table 2 (S2): Detailed view of the multivariate model for severity, mortality, and need for ICU.
Additionalfile3.docx
Supplementary Table 3 (S3): Correlations between usual clinical parameters of severity, neutrophil counts, neutrophil associated cytokines (IL-8 and G-CSF), and NET markers (MPO-DNA and NE-DNA complex, cfDNA and CitH3) in all samples (n=201)

Download PDF

Journal Publication

published 19 Jan, 2024

Read the published version in Respiratory Research →

Editorial decision: Revision requested
04 Nov, 2023
Reviews received at journal
29 Oct, 2023
Reviews received at journal
05 Sep, 2023
Reviewers agreed at journal
16 Aug, 2023
Reviewers agreed at journal
15 Aug, 2023
Reviewers invited by journal
15 Aug, 2023
Editor assigned by journal
04 Aug, 2023
Submission checks completed at journal
03 Aug, 2023
First submitted to journal
02 Aug, 2023

You are reading this latest preprint version

What is the actual relationship between neutrophil extracellular traps and COVID-19 severity? A longitudinal study

Status:

Journal Publication

Version 1

Abstract

Summary at a Glance

Background

Methods

Patients and controls

Blood samples

IL-8 and G-CSF

NE-DNA and MPO-DNA complexes

Citrullinated histone H3

Cell-free DNA

Statistical analysis

Results

Patients and controls

NETs markers in patients and controls

NET markers, neutrophil count, IL-8, G-CSF, and severity and mortality

Evolution of NET markers throughout the disease course

Correlations

Discussion

Conclusions

Abbreviation

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1