Mediators of monocyte chemotaxis and matrix remodeling are associated with mortality and pulmonary fibroproliferation in patients with severe COVID-19

Sarah E Holton; Mallorie Mitchem; Hamid Chalian; Sudhakar Pipavath; Eric D Morrell; Pavan K Bhatraju; Jessica A Hamerman; Cate Speake; Uma Malhotra; Mark M Wurfel; Steven E Ziegler; Carmen Mikacenic

doi:10.1371/journal.pone.0285638

. 2024 Aug 6;19(8):e0285638. doi: 10.1371/journal.pone.0285638

Mediators of monocyte chemotaxis and matrix remodeling are associated with mortality and pulmonary fibroproliferation in patients with severe COVID-19

Sarah E Holton ^1,^2,^*, Mallorie Mitchem ², Hamid Chalian ³, Sudhakar Pipavath ^1,³, Eric D Morrell ¹, Pavan K Bhatraju ¹, Jessica A Hamerman ², Cate Speake ², Uma Malhotra ^3,^4,⁵, Mark M Wurfel ¹, Steven E Ziegler ², Carmen Mikacenic ^1,^2,⁵

Editor: Gernot Zissel⁶

PMCID: PMC11302896 PMID: 39106254

Abstract

Acute respiratory distress syndrome (ARDS) has a fibroproliferative phase that may be followed by pulmonary fibrosis. Pulmonary fibrosis following COVID-19 pneumonia has been described at autopsy and following lung transplantation. We hypothesized that protein mediators of tissue remodeling and monocyte chemotaxis are elevated in the plasma and endotracheal aspirates of critically ill patients with COVID-19 who subsequently develop features of pulmonary fibroproliferation. We enrolled COVID-19 patients admitted to the ICU with hypoxemic respiratory failure. (n = 195). Plasma was collected within 24h of ICU admission and at 7d. In mechanically ventilated patients, endotracheal aspirates (ETA) were collected. Protein concentrations were measured by immunoassay. We tested for associations between protein concentrations and respiratory outcomes using logistic regression adjusting for age, sex, treatment with steroids, and APACHE III score. In a subset of patients who had CT scans during hospitalization (n = 75), we tested for associations between protein concentrations and radiographic features of fibroproliferation. Among the entire cohort, plasma IL-6, TNF-α, CCL2, and Amphiregulin levels were significantly associated with in-hospital mortality. In addition, higher plasma concentrations of CCL2, IL-6, TNF-α, Amphiregulin, and CXCL12 were associated with fewer ventilator-free days. We identified 20/75 patients (26%) with features of fibroproliferation. Within 24h of ICU admission, no measured plasma proteins were associated with a fibroproliferative response. However, when measured 96h-128h after admission, Amphiregulin was elevated in those that developed fibroproliferation. ETAs were not correlated with plasma measurements and did not show any association with mortality, ventilator-free days (VFDs), or fibroproliferative response. This cohort study identifies proteins of tissue remodeling and monocyte recruitment are associated with in-hospital mortality, fewer VFDs, and radiographic fibroproliferative response. Measuring changes in these proteins over time may allow for early identification of patients with severe COVID-19 at risk for fibroproliferation.

Introduction

The acute respiratory distress syndrome (ARDS) is a highly morbid, often fatal syndrome that affects approximately 23% of patients requiring mechanical ventilation. Pulmonary fibrosis following fibroproliferative ARDS has been described previously [1, 2], and was reported following the SARS epidemic of 2003 [3, 4] and MERS outbreak of 2012 [5]. The COVID-19 pandemic has caused severe respiratory failure in many patients [6–9], with longitudinal studies showing that a proportion of survivors of COVID-19 have ongoing respiratory symptoms [10] and decreased lung function [11], while others have persistent radiographic abnormalities including fibrosis [12–14]. Further investigation is needed to understand why this syndrome has such heterogeneous effects and to identify potential therapies that can be used during future viral pandemics. Patients with COVID-19 have a three-fold longer duration of mechanical ventilation compared to patients with influenza [15], which may be due in part to the development of a fibroproliferative phase after viral pneumonia and respiratory failure in patients with severe disease [16–19].

While an associative link between viral ARDS and fibrotic lung remodeling has been established, the underlying mechanism in humans remains unclear. Unlike other studies of post-ARDS fibrosis in which patients have had multiple etiologies for ARDS, studying patients with COVID-19 ARDS provides a unique opportunity to understand the timeline of lung injury and remodeling. Further, with growing numbers of survivors of COVID-19 we are beginning to understand the long-term sequelae both in the lungs and elsewhere in the body. Recent studies have identified pro-fibrotic macrophage populations in the lungs of patients with COVID-19, potentially from recruited circulating monocytes, consistent with prior studies showing monocyte recruitment is linked to the development of pulmonary fibrosis following injury [16, 20]. Other groups have analyzed transcriptional changes in lungs from patients undergoing lung transplant or at autopsy and identified pro-fibrotic programs that are similar to those in idiopathic pulmonary fibrosis [17–19]. This important work establishes a link between fibrotic remodeling during fibroproliferative ARDS and other fibrotic lung diseases, including post-ARDS fibrosis.

Prior studies have established some proteins identified in the circulation or alveolar space that are associated with fibroproliferative ARDS [21–26]. We hypothesized that proteins previously associated with fibrotic lung disease, including idiopathic pulmonary fibrosis, would be elevated in blood and lung fluid from critically ill patients with SARS-CoV-2 who go on to develop fibroproliferative ARDS determined by chest imaging. We measured these proteins in the plasma and endotracheal aspirates of patients at two points during hospitalization to determine associations with mortality, ventilator-free days (VFDs), and development of fibroproliferation. Some of the results of this study have been previously presented in the form of an abstract [27].

Results

Plasma cytokines and markers of matrix remodeling are elevated and associated with poor ICU outcomes

Patients were enrolled from three University of Washington ICUs with suspected COVID-19 as previously described [28–30] (n = 366) (Fig 1). Only those patients with confirmed COVID-19 (based on a positive SARS-CoV-2 PCR test) were included (n = 243). Patients were excluded if they were not hypoxemic at the time of enrollment, as defined by the use of supplemental oxygen delivered via nasal cannula, high flow nasal cannula, noninvasive ventilation, or invasive mechanical ventilation. A total of 198 patients had plasma collected for protein analysis and three were excluded for having pre-existing interstitial lung disease. Demographics and admission characteristics are shown in Table 1. Because the patients were recruited over a long time period, clinical practice regarding vaccination and immune-targeted therapies varied in the cohort. Only 7 patients received a vaccine directed towards SARS-CoV-2. Approximately 80% of the cohort received dexamethasone per protocol as they were recruited after publication of the RECOVERY trial [31].

Table 1. Clinical characteristics and selected outcomes of patients within cohort (n = 195) and subset of patients who had CT scans done (n = 75).

Characteristics	Cohort (n = 195)	Patients with CT scans (n = 75)
Age, average (std dev), range, yr	55.5 (15.4), 20–92	54 (13.4), 20–88
Male Sex, n (%)	133 (68%)	52 (69%)
Ethnicity (% Hispanic)	126 (65%)	45 (60%)
Race
• Native American	5 (2.5%)	3 (4%)
• Pacific Islander	4 (2%)	3 (4%)
• Asian	29 (9.8%)	9 (12%)
• Black	29 (9.8%)	10 (13%)
• White	115 (59%)	44 (59%)
• Multiracial	2 (1%)	2 (2.6%)
• Unknown	11 (5.6%)	4 (5%)
BMI, average, kg/m²	31.8	32.5
OSH Transfer, n (%)	101 (52%)	46 (61%)
ARDS at enrollment, n (%)	74 (38%)	38 (51%)
NIV/HFNC at enrollment, n (%)	55 (28%)	21 (28%)
Mechanical ventilation at enrollment, n (%)	100 (51%)	50 (67%)
ECLS at enrollment, n (%)	16 (8%)	9 (12%)
Received dexamethasone	147 (75%)	64 (85%)
Received any dose of COVID vaccination, n (%)	7 (3.5%)	4 (5%)
Admission P:F, median (IQR)	60 (40–90)	60 (50–90)
APACHE, median (IQR)	70.5 (52–93.3)	76 (54–95)
RALE score, day 1, median (IQR)	22 (12–28)	24 (14–32)
COVID test to proximal sample date, median (IQR), days	4 (1–11)	7 (1–14)
Outcomes
In-hospital death, n (%)	83 (43%)	24 (32%)
VFDs <14, n (%)	118 (60%)	45 (60%)
Treatment for refractory hypoxemia, n (%)	101 (52%)	41 (55%)

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BMI–body measurement index, OSH = outside hospital, ARDS–acute respiratory distress syndrome, NIV- non-invasive mechanical ventilation, HFNC–high flow nasal cannula, ECLS- extracorporeal life support, COVID- coronavirus disease 2019, P:F–Ratio of the PaO₂ to the delivered FiO₂, APACHE–Acute Physiology, Age, Chronic Health Evaluation III, RALE–Radiographic Assessment of Lung Edema

We hypothesized that proteins previously associated with pulmonary fibrosis would be elevated in the plasma of patients with poor ICU outcomes including increased mortality and fewer ventilator free days (VFDs). We measured proteins listed in S1 Table in S1 File and included those that met quality control markers. The cohort had an overall mortality of 43% (Table 1). In unadjusted analyses, higher concentrations of IL-6, TNFα, CCL2, Amphiregulin, and CXCL12 measured within 24hrs of ICU admission were associated with in hospital mortality (S2 and S3 Tables in S1 File). In models including age, sex, admission APACHE score, and steroid treatment as covariates, doubling of CCL2 (OR 1.3 [CI 1.02–1.73, p = 0.04), IL-6 (OR 1.3 [CI 1.12–1.56], p = 0.0009), TNF-α (OR 1.75 [CI 1.24–2.6], p = 0.003), and Amphiregulin (OR 1.6, [CI 1.1–2.4], p = 0.019) remained significantly associated with increased odds of in-hospital mortality (Fig 2). When measured in plasma collected at 7 days, only higher concentrations of IL-6 (OR = 2.0, [CI 1.14–5], p = 0.045) and Amphiregulin (OR 1.67, [CI 1.1–2.6], p = 0.02) were associated with mortality (S3 Table in S1 File).

Fig 2 — Plasma protein levels were log₂-transformed and adjusted for age, sex, treatment with steroids, and enrollment APACHE III score. Plasma was collected within 24h of enrollment. p-values reflect association of adjusted protein concentrations with in-hospital mortality using logistic regression.

We next studied associations with VFDs. We dichotomized patients to high vs low VFDs based on a threshold of 14 (high ≥14 VFDs, low <14 VFDs) given 60% of the cohort (n = 118) had fewer than 14 VFDs (Table 1). In unadjusted models, higher concentrations of CCL-2, IL-6, TNF-α, and Amphiregulin were significantly associated in patients who had few VFDs (S4 Table in S1 File). In addition, matrix remodeling proteins MMP-7 and MMP-9 and chemokines P-Selectin and CXCL12 were also elevated in those with fewer VFDs (S4 Table in S1 File). When we included these proteins in a model and adjusted for age, sex, admission APACHE score, and steroid treatment, CCL2 (OR .56, [CI 0.38-.78] p = 0.001), IL-6 (OR 0.75, [CI 0.6–0.9], p = 0.0045), TNF-α (OR 0.67, [CI 0.45–0.91], p = 0.03), CXCL12 (OR 0.59, [CI 0.41–0.82], p = 0.0026), and Amphiregulin (OR 0.53, [CI 0.33–0.81], p = 0.005) remained significantly associated with VFDs (Fig 3). At the second timepoint, higher plasma concentrations of IL-6, CXCL12, Amphiregulin, MMP7, and P-Selectin remain associated with VFDs only in unadjusted models (S5 Table in S1 File).

Fig 3 — Patients were dichotomized into high (VFD≥14) or low (VFD<14) groups. Plasma protein levels were log₂-transformed and adjusted for age, sex, treatment with steroids, and enrollment APACHE III score. Plasma was collected within 24h of enrollment. p-values reflect association of adjusted protein concentrations with in-hospital mortality using logistic regression.

Because of the nature of our study, patients were enrolled into the ICU at varying points following initial positive SARS-CoV-2 PCR test. To address whether changes in plasma proteins were due to patients being in different states of their disease course, we assessed the association between log₂-transformed plasma protein concentration and the time interval between a patient’s initial COVID+ PCR and their first sample collection (within 24h of ICU admission/study enrollment). When we looked at each individual marker, we only found that CCL13 (r² = 0.05, p = 0.003) and CXCL12 (r² = 0.04, p = 0.01) were associated with time (S1 Fig in S1 File), suggesting that changes in measured plasma proteins are not entirely due to differences in disease course among our cohort.

Patients in the ICU with COVID-19 developed radiographic features of fibroproliferation during hospitalization

We then aimed to understand the association of these same proteins with the development of radiographic fibroproliferation. In a subset of patients who had a CT scan performed after day 3 of enrollment (n = 75), CTs were evaluated by two independent chest radiologists for features of fibroproliferation including traction bronchiectasis/bronchiolectasis and peripheral reticulation (S2 Fig in S1 File). Of these patients, 20 were found to have fibroproliferation, 5 were indeterminate, and 41 had no evidence of fibroproliferation. There were 9 scans which were reviewed and discordant between reviewers; these were not included in the analysis. The CT scan used to make this distinction was that most proximal to discharge or death, with a median of 22.5 days following a positive COVID test. Demographics including age, race and ethnicity were similar between the two groups (Table 2). There were more patients who were mechanically ventilated at the time of enrollment in the group with fibroproliferation, whereas patients without fibrosis were more likely to be admitted on high flow nasal cannula or with noninvasive ventilation. Patients with fibrosis tended to have higher APACHE scores but similar P:F ratios at the time of enrollment, although this was not significant. Patients with fibroproliferation had a longer time interval between their first positive SARS-CoV-2 PCR test and the first day of sampling and included a higher percentage of patients transferred from an outside hospital (70% compared to 56%), although this was not significant (Table 3). Patients with fibroproliferation tended to have a longer time between their COVID+ test and enrollment sample collection, and a longer time interval between COVID+ test and CT scan (S3 Fig in S1 File).

Table 2. Clinical characteristics of patients evaluated for fibroproliferation within cohort (n = 75).

P value reflects two-tailed z-test for proportions and Welch’s two-tailed t-test for unequal variance for continuous variables.

Characteristics	Patients with CT scans
Characteristics	Fibrosis (n = 20)	No Fibrosis (n = 41)	p-value
Age, average (std dev), range, yr	51 (13), 20–74	55 (13), 28–75
Male Sex, n (%)	11 (55%)	29 (71%)	0.23
Ethnicity (% Hispanic)	10 (50%)	26 (63%)	0.32
Race (% Black)	2 (10%)	6 (15%)	0.62
BMI, average, kg/m²	31.6	33.7	0.37
OSH Transfer, n (%)	14 (70%)	23 (56%)	0.3
ARDS at enrollment, n (%)	12 (60%)	18 (44%)	0.24
NIV/HFNC at enrollment, n (%)	2 (10%)	15 (37%)	0.03 (*)
Mechanical ventilation at enrollment, n (%)	17 (85%)	24 (59%)	0.04 (*)
ECLS at enrollment, n (%)	4 (20%)	3 (7%)	0.14
Received dexamethasone, n (%)	18 (90%)	35 (85%)	0.62
Received any dose of COVID vaccination, n (%)	1 (5%)	3 (7%)	0.73
Admission P:F, median (IQR)	50 (45–85)	65 (50–82.5)	0.4
APACHE, median (IQR)	87.5 (60.3–103.5)	75 (55–90.5)	0.18
RALE score, max in first 24h, median (IQR)	24.5 (13.75–33)	24 (14–32)	0.64
COVID⁺ test to proximal sample date, median (IQR), d	11 (2–17.5)	5 (1–9.75)	0.066
COVID+ test to CT scan, median (IQR), d	40 (23–57.25)	18 (5–22)	0.016 (*)
ICU enrollment to CT scan, median (IQR), d	23.5 (17–45.75)	11 (0–19)	0.026 (*)

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Table 3. Outcomes of patients evaluated for fibroproliferation.

P value reflects two-tailed z-test for proportions and Welch’s two-tailed t-test for unequal variance for continuous variables.

Outcome	Patients with CT scans (n = 75)
Outcome	Fibrosis (n = 20)	No Fibrosis (n = 41)	p-value
In-hospital death, n (%)	8 (40%)	16 (40%)	0.94
VFDs, median (IQR), days	0 (0–3.25)	7 (0–24)	0.007(*)
VFDs <14, n (%)	18 (90%)	27 (66%)	0.04 (*)
Treatment for refractory hypoxemia	15 (75%)	26 (63%)	0.37
Lowest S:F in 7 days, median (IQR)	198.8 (114.4–223.8)	146.7 (94–200.7)	0.6
Lowest P:F in 7 days, median (IQR)	71 (60.25–126)	85 (72.5–95.5)	0.8

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VFDs- Ventilator-free days, S:F—Ratio of the SpO₂ to the delivered FiO₂

Putative markers of pulmonary fibrosis are elevated in plasma later in ICU course in patients with radiographic features of fibroproliferation

We next tested for associations between our plasma proteins of inflammation and matrix remodeling and the development of fibroproliferation. Our primary hypothesis was that markers of monocyte chemotaxis and matrix remodeling would be elevated in patients with a radiographic fibroproliferative response. When measured at 24 hours, there were no significant associations between these proteins and development of fibroproliferation (S6 Table in S1 File). However, in plasma collected 96-128h after enrollment, higher concentrations of Amphiregulin (OR 2.15, [CI 1.09–4.83], p = 0.038) adjusted for age, sex, APACHE score, and steroids was associated with increased odds of development of fibroproliferation (Fig 4a, S7 Table in S1 File). When we evaluated whether the change over time in plasma protein concentration between the two timepoints, we found that an increasing concentration of MMP-7 over time was associated with fibroproliferation (OR 2.14, [CI 1.12–4.46], p = 0.028) (Fig 4B and S8 Table in S1 File).

Fig 4 — a) Increased levels of amphiregulin are associated with fibroproliferation on CT scan. Plasma protein levels were log₂-transformed and adjusted for age, sex, treatment with steroids, and enrollment APACHE III score. Plasma was collected 96-168h after enrollment. p-values reflect association of adjusted protein concentration with fibroproliferation using logistic regression. B) An increase in MMP-7 concentration over time (between plasma collected at 24h and at 96-128h) is associated with fibroproliferation. Plasma protein level was log₂-transformed. p-value reflects association of transformed protein concentration adjusted for age, sex, treatment with steroids, and enrollment APACHE III score with fibroproliferation seen on CT scan.

Endotracheal aspirate measurements are not associated with mortality, VFDs, or fibroproliferation and are not correlated with plasma measurements

A subset of mechanically ventilated had endotracheal aspirate measurements (ETA) collected within 24h of ICU admission and again approximately 72 hours later. Again, we measured the same proteins (S1 Table in S1 File) via immunoassay in the ETAs. We did not find any association between any individual protein measurement and any of our specified outcomes of mortality, VFDs, or development of fibroproliferation.

We performed a correlation analysis between plasma and endotracheal aspirate measurements among patients who had paired samples at 24h (n = 30) (S4 Fig in S1 File). We found that in general, plasma and endotracheal aspirate measurements of individual proteins were not positively correlated.

Discussion

In this cohort study of patients admitted to the ICU with severe COVID-19 pneumonia, we identified that plasma proteins previously associated with pulmonary fibrosis are elevated in patients with poor ICU outcomes including in-hospital mortality and low VFDs. A distinguishing feature of our study is the systematic review of chest imaging during hospitalization and the measurement of protein mediators in plasma at multiple timepoints. We hypothesized that proteins previously shown to be elevated in patients with pulmonary fibrosis (Amphiregulin [32, 33], MMP-9 [34, 35], MMP-7 [35], P-selectin [36], S100A12 [35], and CXCL12 [37]) would be elevated in patients who develop fibroproliferative ARDS.

We evaluated patients for fibroproliferative ARDS and identified that approximately 26% of the patients evaluated with CT scans during hospitalization had evidence of fibroproliferation, which is similar to previously described rates of post-ARDS fibrosis [38–40]. The patients who developed evidence of fibroproliferation were more likely to require mechanical ventilation at the time of enrollment and had fewer VFDs, which is consistent with documented risk factors and outcomes in other ICU cohorts [24, 38–40]. We found that these patients also had elevated levels of EGFR ligand amphiregulin in their plasma.

While it has been previously documented that TNF-α and IFN-γ are associated with mortality during COVID-19 [41–44], a novel finding from our study is the association of amphiregulin with VFDs and development of fibroproliferation. Our findings support a prior study that identified amphiregulin as a cytokine associated with disease severity in COVID-19 in a proteomics screen [45]. Amphiregulin most likely has both pro-inflammatory and reparative functions. A separate study reported lower serum levels of amphiregulin in patients with severe COVID-19 [46]. The authors showed that amphiregulin may play a role in T-cell mediated tissue repair via Notch4 signaling. Amphiregulin has also been implicated in fibrosis pathogenesis in multiple tissues including the lungs [47–50]. It is secreted by T-lymphocytes and CD11c+ dendritic cells with primary activity on epithelial cells [47, 50]. Epithelial cells stimulated by amphiregulin stimulate fibroblasts, and amphiregulin can also act directly on fibroblasts to produce extracellular matrix [47, 51]. Although we describe amphiregulin in a small cohort of patients with severe COVID-19, our findings add to the growing body of evidence that amphiregulin is important in both appropriate and aberrant tissue repair mechanisms in viral ARDS.

Further work is necessary to validate these findings in the context of other clinical patient cohorts. Many published ICU COVID-19 cohorts similar to ours do not have in-depth radiographic reports, which limits the ability to validate our findings [52–56]. These markers should also be measured in patients convalescing from COVID-19, in particular those who have prolonged respiratory symptoms to understand how the levels of these proteins change in the plasma over time. More recently, cohort studies are being published describing the effects of COVID-19 on longer term outcomes, including prolonged respiratory symptoms and post-COVID-19 fibrosis [57–60].

There are several limitations to our study. Our study was performed within one geographic region, but patients were enrolled in three area hospitals with different patient demographics and ICU practice patterns which can improve the generalizability of these findings. Our cohort was recruited over a time period where clinical practice patterns regarding the use of immunosuppression varied, and plasma and ETA proteins could have been affected by treatment with dexamethasone, tocilizumab, remdesivir, or convalescent plasma. We have outlined the treatment of patients in our cohort with these in S9 Table in S1 File. Only 75.4% of our cohort received treatment with dexamethasone. We aimed to address this source of variability by including steroid treatment as a covariate in our adjusted model. In addition, co-infection with another organism could alter the cytokine profile of patients over time. We do not have complete records of which patients had documented ICU infections, but 99/195 were on IV antibiotics at the time of enrollment (and sample collection at the first timepoint). This does not necessarily indicate presence of a secondary infection, only clinical suspicion. We expect that the fraction of patients with a true secondary infection would be much lower. 82/195 patients had culture data analyzed. Of these, 27/82 (33%) had positive cultures from blood, stool, throat swab, pleural fluid, or endotracheal aspirates within 1 week of enrollment.

When we evaluated patients for fibroproliferative response, we identified a significant difference in the interval of time between SARS-CoV-2 test positivity and the CT scan used to classify the patient into fibrosis/no fibrosis (S3B Fig in S1 File). The most proximal CT scan to discharge/death was used to make this classification, and so this time interval reflects that patients with fibroproliferation had longer hospital stays. There was a nonsignificant increase in the length of time between COVID+ test to study enrollment among patients with fibroproliferation as well. A higher number of these patients were transferred from other hospitals to our institution for specialized ARDS care, although this was not statistically significant (Table 3). It is possible that the differences we observed in plasma proteins in patients who developed fibroproliferation is due in part to a difference in time of disease course. However, when we measured the association between COVID+ test to sample collection against the concentration of proteins measured, we found an association only with MCP-4 and CXCL12. These proteins were not associated with the fibroproliferative response, suggesting that it is not only timing of disease course that leads to this association. Finally, it is unclear how many of the patients with radiographic features of fibrosis during hospitalization will go on to have persistent fibrotic changes. This is an active area of investigation in the field.

Our study defines a cohort of patients with COVID-19 who develop radiographic features of fibroproliferation during illness and compares them to patients who did not develop these features despite critical illness and severe hypoxemic respiratory failure. There may be individual immune host factors that increase the likelihood that a patient will develop fibrotic remodeling after injury. Further studies are necessary to identify the pathways that are involved. Our findings have an implication for the development of biomarkers that may predict patients at risk for developing pulmonary fibroproliferation following hypoxemic respiratory failure, as well as elucidating potential pathways that could be targeted to improve outcomes of patients with viral induced ARDS.

Methods

Study design

This is a prospective cohort study nested within a larger study that has been previously described [28, 61]. We describe a cohort of patients admitted to the ICU from three hospitals in Seattle, WA between March 16, 2020 and May 16, 2021 with clinical suspicion for COVID-19. Patients were included if they tested positive for COVID-19 by PCR nasal swab and had hypoxemic respiratory failure requiring organ support as defined by need for oxygen supplementation of high flow nasal cannula, noninvasive ventilation, or invasive mechanical ventilation at the time of enrollment. Patients were excluded if they tested negative for COVID-19, had pre-existing interstitial lung disease, age ≤ 18, pregnancy, or current incarceration. Some patients were admitted to the ICU for monitoring but did not have hypoxemic respiratory failure, and so only patients who were on supplemental oxygen, high flow nasal cannula, non-invasive ventilation, or invasive mechanical ventilation were included in the analysis (n = 206). A total of 195 patients had plasma collected for analysis and met the inclusion/exclusion criteria for the study.

All subjects were enrolled and had plasma collected within 24 hours of enrollment. A subset of patients had plasma collected 96-128h after ICU admission. Patients who were intubated had endotracheal aspirates collected within 24h of ICU admission. SARS-CoV-2 positive subjects were classified based on a positive SARS-CoV-2 RT-PCR nasal swab clinical test at the time of enrollment. Subjects did not have multiple SARS-CoV-2 RT-PCR nasal swab tests done during the study and so we cannot comment on whether they remained PCR positive at the second timepoint. However, a study done in a similar cohort suggests that the majority of critically ill patients will remain PCR positive a median of 13 days after initial test positivity [62]. Subjects were enrolled under a waiver of consent which was approved and supervised by the University of Washington IRB (Human Subjects Division Study: 9763). ETAs were also collected from critically ill patients with SARS-CoV-2 supported on invasive mechanical ventilation from Virginia Mason Franciscan Health Hospital (Benaroya Research Institute IRB number: 20–036). Patients/legal representatives were consented for data usage; if consent was withdrawn, patient data and samples were removed from the study. Some authors had access to information that could identify individual participants during data collection; this data was stored in a secure online database (RedCap).

Analysis of CT scans

A subset of patients had chest CTs done between day 3 and discharge/death. These images were ordered as part of routine clinical care and were not pre-specified. Images obtained closest to discharge or death were reviewed by a blinded chest radiologist for the presence of fibrosis. The chest CT scans were analyzed according to predominant pattern (ground glass opacities, consolidation, linear densities, reticulation, honeycombing, traction bronchiectasis, cysts, pneumatoceles), distribution (craniocaudal, axial, anterior/posterior), and overall disease extent (none, <5%, >5%). Finally, the pattern was identified as either fibrotic or non-fibrotic as determined by two independent chest radiologists using the same criteria.

Plasma and endotracheal aspirate cytokine/chemokine measurements

Cytokines and chemokines were measured from blood collected in cell preparation tubes (CPT) containing buffered sodium citrate or EDTA and a density gradient solution for the isolation of plasma and mononuclear cells simultaneously. The tubes containing whole blood were centrifuged at 1500g for 20 minutes, at which point the tube contained layers of plasma, mononuclear cells, and platelets, followed by granulocytes and erythrocytes separated by a gel layer. The plasma was aliquoted in 0.5 mL aliquots and frozen down, carefully avoiding the mononuclear cell layer. The aliquots were stored at -80C until further analysis. The proteins were measured using electrochemiluminescent immunoassays per the manufacturer’s instructions (V-Plex Proinflammatory Panel 1 (K15049D); V-Plex Chemokine Panel 1 (K15047D); V-Plex Cytokine Panel 1 (K15050D)). Mesoscale discovery U-Plex kits were used for total MMP-9, MMP-7, SDF-1a, TGF-β1, S100A12, and P-selectin. These plasma samples were collected in a sodium citrate tube but otherwise were processed the same way as those previously described. All plasma samples underwent two freeze-thaw cycles prior to analysis. Analytes that did not meet any of the following quality control parameters were excluded from subsequent analysis: 1) intraplate % CV > 25%; 2) interplate % CV > 25%; or 3) > 10% of samples with a measurement below the lower limit of detection. For values that were below the lower limit of detection per assay (S1 Table in S1 File), concentrations were imputed by using one-half of the lower limit of detection.

ETAs were obtained by suctioning the endotracheal tube after instilling 10 mL of normal saline. The collected aspirate fluid was immediately mixed with an equal volume of 0.1% dithiothreitol and then placed on ice for 15 minutes to promote sample homogenization. Samples were then filtered through a 70 μm cell-strainer by gravity and flow-through was centrifuged at 400 x g for 10 minutes. The flow-through was immediately aliquoted and stored at -80°C until use. The samples underwent two freeze-thaw cycles prior to immunoassay analysis. We applied the V-Plex Pro-inflammatory (K15049D), V-Plex chemokine (K15047D), and U-Plex immunoassays on ETA samples as described above.

Quantification and statistical analyses

Outcome definitions

We abstracted clinical data from the electronic medical record into standardized case report forms. ARDS was defined by the 2012 Berlin definition and chest x-rays were adjudicated for ARDS by a board-certified radiologist blinded to the primary data. APACHE III score was calculated based on the original instrument [63]. VFDs were defined as the total number of days alive and free of invasive mechanical ventilation in the 28 days following ICU admission [64]. Patients who died prior to day 28 were considered to have zero VFDs. RALE score was calculated as previously described [65].

Statistical analyses

Our primary analysis tested for association between plasma or ETA protein concentrations and in-hospital mortality, VFDs≥14 (“high”) and the development of fibroproliferation based on CT scan. We used multivariable logistic regression and adjusted the log₂-transformed protein concentration for age, sex, treatment with dexamethasone, and enrollment APACHE III score. This regression analysis was performed in R using the glm function with the “binomial” family to specify a logistic regression where the dependent variable was binary (death, fibrosis, or high/low VFDs).

All analyses were performed in R version 4.1.1 and graphs were created in GraphPad Prism version 8.4.3.

Supporting information

S1 File

(DOCX)

pone.0285638.s001.docx^{(951.3KB, docx)}

Acknowledgments

The authors thank Sharon Sahi, Carolyn Brager, Sana S. Sakr, Neall Koetje, Ashley Garay, Brian Lee, Leslie Lazar, Sonya Homami, Grigory Loginov, Jana Zahlan, Hana Morris, Jada Roth and the Benaroya Research Institute COVID-19 Research Team for sample collection and processing. The authors also thank the patients, families, surrogates, and hospital clinical staff who contributed to this work during the COVID-19 pandemic.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was sponsored by the National Institutes of Health via the following grant mechanisms: NIH NHLBI T32 HL007287-42 (SEH) NIH U19 AI42733 (CM) NIH NHLBI K23 HL144916 (EDM) NIH NIAID 3R01AI150178-01S1 (JAH).

References

1.Wilcox M. E. et al., “Radiologic outcomes at 5 years after severe ARDS,” Chest, vol. 143, no. 4, pp. 920–926, Apr. 2013, doi: 10.1378/chest.12-0685 [DOI] [PubMed] [Google Scholar]
2.Burnham E. L. et al. , “Chest CT features are associated with poorer quality of life in acute lung injury survivors,” Crit. Care Med., vol. 41, no. 2, pp. 445–456, Feb. 2013, doi: 10.1097/CCM.0b013e31826a5062 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Hui D. S. et al. , “Impact of severe acute respiratory syndrome (SARS) on pulmonary function, functional capacity and quality of life in a cohort of survivors,” Thorax, vol. 60, no. 5, pp. 401–409, May 2005, doi: 10.1136/thx.2004.030205 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Zhang P. et al. , “Long-term bone and lung consequences associated with hospital-acquired severe acute respiratory syndrome: a 15-year follow-up from a prospective cohort study,” Bone Res., vol. 8, no. 1, Art. no. 1, Feb. 2020, doi: 10.1038/s41413-020-0084-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Das K. M. et al. , “Acute Middle East Respiratory Syndrome Coronavirus: Temporal Lung Changes Observed on the Chest Radiographs of 55 Patients,” AJR Am. J. Roentgenol., vol. 205, no. 3, pp. W267–274, Sep. 2015, doi: 10.2214/AJR.15.14445 [DOI] [PubMed] [Google Scholar]
6.Grasselli G. et al. , “Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy,” JAMA, vol. 323, no. 16, pp. 1574–1581, Apr. 2020, doi: 10.1001/jama.2020.5394 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Richardson S. et al. , “Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area,” JAMA, vol. 323, no. 20, pp. 2052–2059, May 2020, doi: 10.1001/jama.2020.6775 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Huang C. et al. , “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” Lancet Lond. Engl., vol. 395, no. 10223, pp. 497–506, 2020, doi: 10.1016/S0140-6736(20)30183-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Kurtz P. et al. , “Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months,” Intensive Care Med., vol. 47, no. 5, pp. 538–548, 2021, doi: 10.1007/s00134-021-06388-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Chopra V., Flanders S. A., O’Malley M., Malani A. N., and Prescott H. C., “Sixty-Day Outcomes Among Patients Hospitalized With COVID-19,” Ann. Intern. Med., vol. 174, no. 4, pp. 576–578, Apr. 2021, doi: 10.7326/M20-5661 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Torres-Castro R. et al. , “Respiratory function in patients post-infection by COVID-19: a systematic review and meta-analysis,” Pulmonology, vol. 27, no. 4, pp. 328–337, Aug. 2021, doi: 10.1016/j.pulmoe.2020.10.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Zhou S., Wang Y., Zhu T., and Xia L., “CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China,” AJR Am. J. Roentgenol., vol. 214, no. 6, pp. 1287–1294, Jun. 2020, doi: 10.2214/AJR.20.22975 [DOI] [PubMed] [Google Scholar]
13.Writing Committee for the COMEBAC Study Group et al. , “Four-Month Clinical Status of a Cohort of Patients After Hospitalization for COVID-19,” JAMA, vol. 325, no. 15, pp. 1525–1534, Apr. 2021, doi: 10.1001/jama.2021.3331 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Caruso D. et al. , “Post-Acute Sequelae of COVID-19 Pneumonia: Six-month Chest CT Follow-up,” Radiology, vol. 301, no. 2, pp. E396–E405, Nov. 2021, doi: 10.1148/radiol.2021210834 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Cobb N. L. et al. , “Comparison of Clinical Features and Outcomes in Critically Ill Patients Hospitalized with COVID-19 versus Influenza,” Ann. Am. Thorac. Soc., vol. 18, no. 4, pp. 632–640, Apr. 2021, doi: 10.1513/AnnalsATS.202007-805OC [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Wendisch D. et al. , “SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis,” Cell, vol. 184, no. 26, pp. 6243–6261.e27, Dec. 2021, doi: 10.1016/j.cell.2021.11.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Bharat A. et al. , “Early outcomes after lung transplantation for severe COVID-19: a series of the first consecutive cases from four countries,” Lancet Respir. Med., vol. 9, no. 5, pp. 487–497, May 2021, doi: 10.1016/S2213-2600(21)00077-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Bharat A. et al. , “Lung transplantation for patients with severe COVID-19,” Sci. Transl. Med., vol. 12, no. 574, p. eabe4282, Dec. 2020, doi: 10.1126/scitranslmed.abe4282 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Thille A. W. et al. , “Chronology of histological lesions in acute respiratory distress syndrome with diffuse alveolar damage: a prospective cohort study of clinical autopsies,” Lancet Respir. Med., vol. 1, no. 5, pp. 395–401, Jul. 2013, doi: 10.1016/S2213-2600(13)70053-5 [DOI] [PubMed] [Google Scholar]
20.Misharin A. V. et al. , “Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span,” J. Exp. Med., vol. 214, no. 8, pp. 2387–2404, Aug. 2017, doi: 10.1084/jem.20162152 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.McGroder C. F. et al. , “Pulmonary fibrosis 4 months after COVID-19 is associated with severity of illness and blood leucocyte telomere length,” Thorax, vol. 76, no. 12, pp. 1242–1245, Dec. 2021, doi: 10.1136/thoraxjnl-2021-217031 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Arnold D. T. et al. , “Krebs von den Lungen 6 (KL-6) as a marker for disease severity and persistent radiological abnormalities following COVID-19 infection at 12 weeks,” PloS One, vol. 16, no. 4, p. e0249607, 2021, doi: 10.1371/journal.pone.0249607 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Hj C. et al. , “Immunofibrotic drivers of impaired lung function in postacute sequelae of SARS-CoV-2 infection,” JCI Insight, vol. 6, no. 14, Jul. 2021, doi: 10.1172/jci.insight.148476 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Forel J.-M. et al. , “Type III procollagen is a reliable marker of ARDS-associated lung fibroproliferation,” Intensive Care Med., vol. 41, no. 1, pp. 1–11, Jan. 2015, doi: 10.1007/s00134-014-3524-0 [DOI] [PubMed] [Google Scholar]
25.Madtes D. K. et al. , “Elevated transforming growth factor-alpha levels in bronchoalveolar lavage fluid of patients with acute respiratory distress syndrome,” Am. J. Respir. Crit. Care Med., vol. 158, no. 2, pp. 424–430, Aug. 1998, doi: 10.1164/ajrccm.158.2.9711112 [DOI] [PubMed] [Google Scholar]
26.Capelozzi V. L. et al. , “Molecular and Immune Biomarkers in Acute Respiratory Distress Syndrome: A Perspective From Members of the Pulmonary Pathology Society,” Arch. Pathol. Lab. Med., vol. 141, no. 12, pp. 1719–1727, Dec. 2017, doi: 10.5858/arpa.2017-0115-SA [DOI] [PubMed] [Google Scholar]
27.Holton S. E., Morrell E. D., Pipavath S., Bhatraju P. K., Wurfel M. M., and Mikacenic C., “Plasma Markers of Lung Injury and Remodeling are Associated with Radiographic Features of Fibrosis in Patients with COVID-19 Acute Respiratory Distress Syndrome,” presented at the American Thoracic Society 2022 International Conference, San Francisco, CA, May 15, 2022. [Online]. Available: https://doi-org.offcampus.lib.washington.edu/10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A1287 [Google Scholar]
28.Morrell E. D. et al. , “Chemokines, soluble PD-L1, and immune cell hyporesponsiveness are distinct features of SARS-CoV-2 critical illness,” Am. J. Physiol. Lung Cell. Mol. Physiol., vol. 323, no. 1, pp. L14–L26, Jul. 2022, doi: 10.1152/ajplung.00049.2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Bhatraju P. K. et al. , “Comparison of host endothelial, epithelial and inflammatory response in ICU patients with and without COVID-19: a prospective observational cohort study,” Crit. Care Lond. Engl., vol. 25, no. 1, p. 148, Apr. 2021, doi: 10.1186/s13054-021-03547-z [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Mabrey F. L. et al. , “Presence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus-2 on Admission Is Associated With Decreased Mortality in COVID-19 Critical Illness,” Crit. Care Explor., vol. 4, no. 9, p. e0754, Sep. 2022, doi: 10.1097/CCE.0000000000000754 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.RECOVERY Collaborative Group et al. , “Dexamethasone in Hospitalized Patients with Covid-19,” N. Engl. J. Med., vol. 384, no. 8, pp. 693–704, Feb. 2021, doi: 10.1056/NEJMoa2021436 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Meng C., Liu G., Mu H., Zhou M., Zhang S., and Xu Y., “Amphiregulin may be a new biomarker of classically activated macrophages,” Biochem. Biophys. Res. Commun., vol. 466, no. 3, pp. 393–399, Oct. 2015, doi: 10.1016/j.bbrc.2015.09.037 [DOI] [PubMed] [Google Scholar]
33.Venkataraman T., Coleman C. M., and Frieman M. B., “Overactive Epidermal Growth Factor Receptor Signaling Leads to Increased Fibrosis after Severe Acute Respiratory Syndrome Coronavirus Infection,” J. Virol., vol. 91, no. 12, Jun. 2017, doi: 10.1128/JVI.00182-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Espindola M. S. et al. , “Differential Responses to Targeting Matrix Metalloproteinase 9 in Idiopathic Pulmonary Fibrosis,” Am. J. Respir. Crit. Care Med., vol. 203, no. 4, pp. 458–470, Feb. 2021, doi: 10.1164/rccm.201910-1977OC [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Richards T. J. et al. , “Peripheral blood proteins predict mortality in idiopathic pulmonary fibrosis,” Am. J. Respir. Crit. Care Med., vol. 185, no. 1, pp. 67–76, Jan. 2012, doi: 10.1164/rccm.201101-0058OC [DOI] [PMC free article] [PubMed] [Google Scholar]
36.McGroder C. F. et al. , “Circulating adhesion molecules and subclinical interstitial lung disease: the Multi-Ethnic Study of Atherosclerosis,” Eur. Respir. J., vol. 54, no. 3, p. 1900295, Sep. 2019, doi: 10.1183/13993003.00295-2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Xu J., Mora A., Shim H., Stecenko A., Brigham K. L., and Rojas M., “Role of the SDF-1/CXCR4 Axis in the Pathogenesis of Lung Injury and Fibrosis,” Am. J. Respir. Cell Mol. Biol., vol. 37, no. 3, pp. 291–299, Sep. 2007, doi: 10.1165/rcmb.2006-0187OC [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Burnham E. L. et al. , “Detection of fibroproliferation by chest high-resolution CT scan in resolving ARDS,” Chest, vol. 146, no. 5, pp. 1196–1204, Nov. 2014, doi: 10.1378/chest.13-2708 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Stewart I. et al. , “Residual Lung Abnormalities after COVID-19 Hospitalization: Interim Analysis of the UKILD Post–COVID-19 Study,” Am. J. Respir. Crit. Care Med., vol. 207, no. 6, pp. 693–703, Mar. 2023, doi: 10.1164/rccm.202203-0564OC [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Zeng Z. et al. , “Early fibroproliferative signs on high-resolution CT are associated with mortality in COVID-19 pneumonia patients with ARDS: a retrospective study,” Ther. Adv. Chronic Dis., vol. 12, p. 2040622320982171, Feb. 2021, doi: 10.1177/2040622320982171 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Chen L.-F., Yang C.-D., and Cheng X.-B., “Anti-Interferon Autoantibodies in Adult-Onset Immunodeficiency Syndrome and Severe COVID-19 Infection,” Front. Immunol., vol. 12, p. 788368, 2021, doi: 10.3389/fimmu.2021.788368 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Engel J. J. et al. , “Dexamethasone attenuates interferon-related cytokine hyperresponsiveness in COVID-19 patients,” Front. Immunol., vol. 14, p. 1233318, 2023, doi: 10.3389/fimmu.2023.1233318 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Montalvo Villalba M. C. et al. , “Interferon gamma, TGF-β1 and RANTES expression in upper airway samples from SARS-CoV-2 infected patients,” Clin. Immunol. Orlando Fla, vol. 220, p. 108576, Nov. 2020, doi: 10.1016/j.clim.2020.108576 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Mangioni D. et al. , “Prognostic Value of Mid-Region Proadrenomedullin and In Vitro Interferon Gamma Production for In-Hospital Mortality in Patients with COVID-19 Pneumonia and Respiratory Failure: An Observational Prospective Study,” Viruses, vol. 14, no. 8, p. 1683, Jul. 2022, doi: 10.3390/v14081683 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Ebihara T. et al. , “Cytokine Elevation in Severe COVID-19 From Longitudinal Proteomics Analysis: Comparison With Sepsis,” Front. Immunol., vol. 12, p. 798338, 2021, doi: 10.3389/fimmu.2021.798338 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Harb H. et al. , “Notch4 signaling limits regulatory T-cell-mediated tissue repair and promotes severe lung inflammation in viral infections,” Immunity, vol. 54, no. 6, pp. 1186–1199.e7, Jun. 2021, doi: 10.1016/j.immuni.2021.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Ding L. et al. , “Bone Marrow CD11c+ Cell-Derived Amphiregulin Promotes Pulmonary Fibrosis,” J. Immunol. Baltim. Md 1950, vol. 197, no. 1, pp. 303–312, Jul. 2016, doi: 10.4049/jimmunol.1502479 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Morimoto Y. et al. , “Amphiregulin-Producing Pathogenic Memory T Helper 2 Cells Instruct Eosinophils to Secrete Osteopontin and Facilitate Airway Fibrosis,” Immunity, vol. 49, no. 1, pp. 134–150.e6, Jul. 2018, doi: 10.1016/j.immuni.2018.04.023 [DOI] [PubMed] [Google Scholar]
49.Wang Q., Wang F., Li X., Ma Z., and Jiang D., “Quercetin inhibits the amphiregulin/EGFR signaling-mediated renal tubular epithelial-mesenchymal transition and renal fibrosis in obstructive nephropathy,” Phytother. Res. PTR, vol. 37, no. 1, pp. 111–123, Jan. 2023, doi: 10.1002/ptr.7599 [DOI] [PubMed] [Google Scholar]
50.Zhao X. et al. , “Th17 Cell-Derived Amphiregulin Promotes Colitis-Associated Intestinal Fibrosis Through Activation of mTOR and MEK in Intestinal Myofibroblasts,” Gastroenterology, vol. 164, no. 1, pp. 89–102, Jan. 2023, doi: 10.1053/j.gastro.2022.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Cheng W.-H. et al. , “Amphiregulin induces CCN2 and fibronectin expression by TGF-β through EGFR-dependent pathway in lung epithelial cells,” Respir. Res., vol. 23, no. 1, p. 381, Dec. 2022, doi: 10.1186/s12931-022-02285-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Filbin M. R. et al. , “Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions,” Cell Rep. Med., vol. 2, no. 5, May 2021, doi: 10.1016/j.xcrm.2021.100287 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Demichev V. et al. , “A proteomic survival predictor for COVID-19 patients in intensive care,” PLOS Digit. Health, vol. 1, no. 1, p. e0000007, Jan. 2022, doi: 10.1371/journal.pdig.0000007 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Arthur L. et al. , “Cellular and plasma proteomic determinants of COVID-19 and non-COVID-19 pulmonary diseases relative to healthy aging,” Nat. Aging, vol. 1, no. 6, Art. no. 6, Jun. 2021, doi: 10.1038/s43587-021-00067-x [DOI] [PubMed] [Google Scholar]
55.Demichev V. et al. , “A time-resolved proteomic and prognostic map of COVID-19,” Cell Syst., vol. 12, no. 8, pp. 780–794.e7, Aug. 2021, doi: 10.1016/j.cels.2021.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Szabo P. A. et al. , “Longitudinal profiling of respiratory and systemic immune responses reveals myeloid cell-driven lung inflammation in severe COVID-19,” Immunity, vol. 54, no. 4, pp. 797–814.e6, Apr. 2021, doi: 10.1016/j.immuni.2021.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Gu X. et al. , “Probing long COVID through a proteomic lens: a comprehensive two-year longitudinal cohort study of hospitalised survivors,” EBioMedicine, vol. 98, p. 104851, Nov. 2023, doi: 10.1016/j.ebiom.2023.104851 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Sanhueza S. et al. , “Clinical and pulmonary function analysis in long-COVID revealed that long-term pulmonary dysfunction is associated with vascular inflammation pathways and metabolic syndrome,” Front. Med., vol. 10, p. 1271863, 2023, doi: 10.3389/fmed.2023.1271863 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Talla A. et al. , “Persistent serum protein signatures define an inflammatory subcategory of long COVID,” Nat. Commun., vol. 14, no. 1, p. 3417, Jun. 2023, doi: 10.1038/s41467-023-38682-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Zhang S. et al. , “The alveolar fibroproliferative response in moderate to severe COVID-19-related acute respiratory distress syndrome and one-year follow up,” Am. J. Physiol. Lung Cell. Mol. Physiol., Nov. 2023, doi: 10.1152/ajplung.00156.2023 [DOI] [PubMed] [Google Scholar]
61.“Comparison of host endothelial, epithelial and inflammatory response in ICU patients with and without COVID-19: a prospective observational cohort study—PubMed.” Accessed: Sep. 06, 2022. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/33874973/ [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Funk D. J. et al. , “Persistence of live virus in critically ill patients infected with SARS-COV-2: a prospective observational study,” Crit. Care Lond. Engl., vol. 26, no. 1, p. 10, Jan. 2022, doi: 10.1186/s13054-021-03884-z [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Knaus W. A. et al. , “The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults,” Chest, vol. 100, no. 6, pp. 1619–1636, Dec. 1991, doi: 10.1378/chest.100.6.1619 [DOI] [PubMed] [Google Scholar]
64.Acute Respiratory Distress Syndrome Network et al. , “Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome,” N. Engl. J. Med., vol. 342, no. 18, pp. 1301–1308, May 2000, doi: 10.1056/NEJM200005043421801 [DOI] [PubMed] [Google Scholar]
65.Warren M. A. et al. , “Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS,” Thorax, vol. 73, no. 9, pp. 840–846, Sep. 2018, doi: 10.1136/thoraxjnl-2017-211280 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0285638.r001

Decision Letter 0

Gernot Zissel

22 Jun 2023

PONE-D-23-11100

Mediators of monocyte chemotaxis and matrix remodeling are associated with the development of fibrosis in patients with COVID-19

PLOS ONE

Dear Dr. Holton,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected. This is based on the reviewers' concerns regarding the timing of the sample acquisition and imaging, the high rate of plain film chest radiographs and others. Please refer to the suggestions by the reviewers for detailed information.

This is an "OPEN REJECTON". For major revisions PLOS offeres a time frame of 45 days which might be a short time for the additional analyses requested by by the reviewers. If you think that a reanalysis of your data according with the suggestions of the reviewers is possible, a resubmission of the revised manuscript is welcome. I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Kind regards,

Gernot Zissel, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you very much for giving me the opportunity to review this manuscript.

In this work, the authors monitored chemokine and fibrotic markers in the blood of patients that developed, or not, early fibrotic features on the CT scan within 24 h after ICU admission.

While ,the statistical methodology seems robust several points dampened my enthusiasm.

1)Description of the population:

It would be important for the reader to get more information regarding the treatment or the vaccination status of the patients. These factors could definitely affect the cytokines quantification

Did the patients continue to receive dexamethasone during plasma cytokine quantification

How many patients received other immunosuppressive drugs etc.….

I was surprised to see that only 50 and 75 % of the patient’s received dexamethasone. Could the authors explain this result.

2) Could the authors detail the infection status of these patients?

-COVID status (PCR negative or positive)

- Other ICU acquired infection

3) I’m not complete sure that the section about the trachea aspiration is really useful considering all the limitation related to this kind of measurement.

MMP7, MMP9, CCL2 appear to be associated with fibrotic features onset.

Did the authors run any analysis to assess the relationship between the cytokines course or the and some outcomes like mortality or time to extubating etc……

Reviewer #2: In the manuscript entitled “Mediators of monocyte chemotaxis and matrix remodelling are associated with the development of fibrosis in patients with COVID-19” Holton and colleagues analysed the plasma and endotracheal aspirates of COVID-19 patients admitted to ICU. Using chest imaging they evaluated each patient for the presence of lung fibrotic features, and subsequently measured markers of matrix remodelling and monocyte migration. They identified CCL-2/MCP-1, CCL13/MCP-4, Amphiregulin, MMP-7 and MMP-9 increased in the plasma of patients with evidence of fibrosis and CCL2/MCP-1 in the endotracheal aspirates. The authors conclude these are relevant biomarkers to tissue remodelling and monocyte recruitment occurring early during the development of lung fibrosis induced by COVID-19.

This work is an important topic to address in the field. However, there are several flaws with the paper that need a large effort to address. Of particular concern is the difference in the timing between testing, sampling and imaging the study groups, the reliance on non-CT imaging for fibrotic diagnosis, and the presenting of this data as early, predictive markers of fibrosis. This makes it difficult to be sure the conclusions of the study are supported by the data presented. From this prospective, I cannot recommend the paper for publication in its current form. I would encourage the authors to reanalyse their data, taking account of the concerns listed below.

Major concerns

1. The timing of the sample acquisition and imaging is an issue.

First, the time between a positive COVID-19 test and sampling is significantly longer in the non-survivor group (Supp Fig 2a). Why is the data in Supp Fig 2 stratified based on survivability? It does not appear the data in Figs 3 and 4 are segregated in this way, so why are they stratified here? Could the data in Supp Fig 2 be graphed with both survivor and non-survivor data combined, to see whether COVID+ test to enrolment (sampling) is significantly different overall?

In table 1, timing between positive test and sample date is median 5 days vs 12.5 days for non-fibrotic vs fibrotic. This makes it difficult to exclude the possibility that changes in plasma proteins are not a fibrotic difference but rather due to the fibrotic group being later in COVID-19 disease course. This is a concern for the study conclusion, that these proteins are early markers of COVID-19 induced lung fibrosis. Fibroproliferation happens at exactly the time-point seen in table 1 (days 10-21), with a higher percentage of mechanically ventilated (intubated) patients here. The protein changes are probably more so markers of where the patient is in the ARDS pathogenesis disease time-course- a CT scan performed at the time of sampling could also likely give you this information. Although the authors seek to address this through the explanation of hospital transfers, this does not adequately address the concerns raised.

Secondly, the difference between a positive test date and date of imaging is significantly longer in the fibrotic groups (Supp Fig 2). Patients may have had a CT scan as close as 3 days to the initial sample and no patient had any imaging after discharge. i.e. the imaging is very close to time of sampling, and there has been no evidence of “post-ARDS fibrosis”, which would be the clinical end point that matters most for long-term survivors. This should be measured several weeks into convalescent to give time for matrix remodelling and lung repair to have occurred. i.e. it needs to be established fibrosis. There are no firm guidelines but in ILD centres we are generally assessing for post-COVID fibrosis 6 months after infection. We tend to assess for post-ARDS fibrosis at least 6 weeks after discharge and then again at 6 months to determine whether this is established fibrosis- COVID-19 and ARDS fibrosis can improve surprising amounts.

2. The use of plain film chest radiographs for the determination of fibrosis is a problem, as it has extremely poor sensitivity-a CT scan is the gold standard diagnosis tool and only 32/119 patients had CT scans. While there are many good reasons CT scans cannot be performed, the authors should ensure that the decision to include non-CT imaging does not confound the study conclusion.

Despite the statement in lines 133-136, examination of Supp Table 3 demonstrates patients diagnosed without CT scans did not show the same changes as CT scans alone, (e.g. CCL13 p=0.04 with CT scan compared to p=0.62 for without CT scans). For CCL2, the results, while significant overall, are not significant for CT (p=0.1) or without CT (p=0.95)- I am unsure how this could have occurred? Further, the bias towards diagnosis of fibrosis in CT vs non-CT described in the discussion (lines 238-242) advocates that non-CT analysed imaging should be dropped from the study altogether. I suggest including only CT scan patient data in the results to be sure of the diagnosis and hence the conclusion of the study.

3. Primarily the authors discuss their findings as early biomarkers of fibrosis that are present soon after ICU admission (e.g. Lines 21-23 of abstract and line 201-202 and 261-263 of discussion). However, as samples were taken many days after a COVID+ test (see point 1) it is likely that many of these patients could have ARDS fibroproliferation already at ICU admission (which is why patients have deteriorated to needing ICU admission). This is a pathological process within lots of patients who develop worsening ARDS and can start from 7 days and continue up to 3 weeks after injury. Therefore, I would argue that the biomarker measurements are not predictive biomarkers of fibrosis but markers of severity of ARDS fibroproliferation at that time point. We already know that worse ARDS= worse outcome, and there are ways of measuring ARDS severity clinically, which table 1 shows are higher in the fibrosis group at time of sampling (Berlin, P/F ratio, APACHE II score- as a side note, can statistical tests be performed here to determine whether any are statistically significant?).

To demonstrate any prognostic utility the authors would need to show that these biomarkers identify mortality or ventilator free days better than clinical tools criteria. Ideally sampling should be done on hospital admission to give opportunity to therapeutically immunomodulate. Failing that, the authors should reword the text throughout to make clear these results are not predictive of fibrosis but markers of severity.

Can the authors provide supplementary data/sub analysis from the patients who had a CT scan within 72 hours of ICU admission and how many had elevated biomarkers and radiographic fibrosis at this timepoint (i.e. were sampled in the first few days)? This would be important to explore whether they are just diagnostic markers of fibrosis (of which there are several already known).

4. The authors make their fibrotic diagnosis using a single, blinded specialist thoracic radiologist. Given this is the primary outcome of the study, you would usually want two people to independently arrive at the same conclusion, as these are holistic judgement interpretations essentially based upon a variety of radiological features.

In addition, 17 patients did not have their images reviewed, just the original radiologist report provided (therefore the decision was not completely blinded). I would suggest these samples need removing from the analysis.

Other comments

-The authors assert that these proteins are monocyte/macrophage derived. While a very plausible theory, no data has been shown on these cell types directly in this study. Can these factors expression be shown from PBMC isolations from patients? It would be understandable that these samples don’t exist and are not acquirable, in which case the text should be altered to address this.

-Lines 114-117- when looking at supp table 2 normalising for steroids (right hand column) there is no significant difference for IL-6 and TNFa as described. In fact, in contrast to the text, there is a difference for CCL-2, CCl13, Amphiregulin, MMP9 and MMP7. The data points used for IL-6 and TNFa in this case appear to come from the unadjusted data.

-Lines 74-75 and Table 1- Can you perform statistical tests here to demonstrate whether the characteristics, particularly APACHE III, P:F ratios, and time to COVID+ test proximal sample date, are statistically significant between groups?

-The referencing of monocyte and macrophage evidence in COVID-19, fibrosis and ARDS could be improved. There are a number of strong studies on this that could be included.

-Supp Table 5 and Supp Table 6 is misquoted in the text (lines 145-146 and 148-149). Lines 155-157 should quote Supp Table 6 instead of Supp Table 4.

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PLoS One. 2024 Aug 6;19(8):e0285638. doi: 10.1371/journal.pone.0285638.r002

Author response to Decision Letter 0

19 Jan 2024

Reviewer 1 General Comments: Thank you very much for giving me the opportunity to review this manuscript.

In this work, the authors monitored chemokine and fibrotic markers in the blood of patients that developed, or not, early fibrotic features on the CT scan within 24 h after ICU admission.

While ,the statistical methodology seems robust several points dampened my enthusiasm.

Response to Reviewer 1: Thank you for taking the time to extensively review our manuscript and for providing your thoughtful comments. We address each of your specific comments below.

Reviewer Comment #1: Description of the population:

It would be important for the reader to get more information regarding the treatment or the vaccination status of the patients. These factors could definitely affect the cytokines quantification. Did the patients continue to receive dexamethasone during plasma cytokine quantification

How many patients received other immunosuppressive drugs etc.….

I was surprised to see that only 50 and 75 % of the patient’s received dexamethasone. Could the authors explain this result.

Response to Comment #1: Thank you for bringing up this important point regarding the vaccination status of the patients. Only 7 patients had received one dose of any COVID vaccination at the time of sample collection and we have clarified this in the text (Lines 71-73) as well as Table 1. This very low number reflects two characteristics of our cohort. First, the cohort was enrolled between March 2020 and May 2021 which is prior to wide release of the COVID vaccination in December 2020. Second, many of our patients came from vaccine-hesitant populations in regions of Washington, Wyoming, Montana, Idaho, and Alaska.

The administration of dexamethasone or other immunosuppressive medication can certainly affect cytokine production as you have noted. The majority of patients who did not receive dexamethasone in our cohort were enrolled prior to the publication of the RECOVERY trial in February 2021. We have extracted other immunosuppressive treatments that could affect cytokine production from patients records including tocilizumab, methylprednisolone, prednisone, hydroxychloroquine, and remdesivir for each and the dosing relative to timing of sample collection in our supplementary table 9.

Reviewer Comment #2: Could the authors detail the infection status of these patients?

-COVID status (PCR negative or positive)

- Other ICU acquired infection

Response to Comment #2: Thank you for bringing up this important point. All of the patients contained within our cohort were COVID PCR positive at the time of sample collection at the first timepoint and this has been clarified in the methods section. The patients were not tested repeatedly while in the ICU due to limited capacity of clinical testing during the height of the pandemic so we do not know the persistence of SARS-CoV2 PCR positivity of the patients at the second timepoint. We suspect that the patients were still PCR positive at the second timepoint (96-128h after enrollment) given their severe critical illness. We have noted this in the methods section (lines 258-263) and cited an article that reports 74.1% of patients (n=108) remaining RT-PCR positive a median of 13 days after initial positive PCR test in a similar critically-ill cohort (Funk DJ et al Persistence of live virus in critically ill patients infected with SARS-CoV-2: a prospective observational study, Crit Care 2022 26(1):10).

We do not have complete records of which patients had documented ICU infections for the entire cohort. However, 99/195 were on IV antibiotics at the time of enrollment (sample collection V1). This does not necessarily indicate presence of a secondary infection, only clinical suspicion. In 82 patients who had culture data analyzed, only 27 had positive cultures from blood, stool, throat swab, pleural fluid, or endotracheal aspirates. This rate of 33% when projected over our entire cohort would suggest only 66 patients had a secondary infection at the time of sampling. We have similarly included this in the discussion (lines 207-214).

Reviewer Comment #3: I’m not complete sure that the section about the trachea aspiration is really useful considering all the limitation related to this kind of measurement.

MMP7, MMP9, CCL2 appear to be associated with fibrotic features onset.

Did the authors run any analysis to assess the relationship between the cytokines course or the and some outcomes like mortality or time to extubating etc……

Response to Comment #3: Thank you again for this excellent point. We included the endotracheal aspirate data in part to show the discordance between plasma and endotracheal aspirate measurements. We also wanted to include these negative results as there is significant debate in the field as to the value of these measurements (Mikacenic et al 2023 Reply: Research Bronchoscopy Standards and the need for Non-invasive sampling of the Failing Lungs, Annals ATS PMID 37776284). Also, due to limitations on personnel and PPE at our hospitals during the COVID pandemic, bronchoalveolar lavage samples were not able to be collected on patients with COVID-19. We have removed the endotracheal aspirate data and incorporated this into our discussion section (lines 152-160). We have included the correlation analysis between plasma and ETA measurements in Supplemental Figure 4.

We have reframed our manuscript around clinical endpoints associated with prolonged respiratory failure and mortality in addition to radiographic features of fibroproliferation. We have assessed the relationship between cytokine measurements in the plasma and in-hospital mortality and high/low ventilator-free days. These analyses are shown in Figures 2 and 3.

Reviewer 2 General Comments: In the manuscript entitled “Mediators of monocyte chemotaxis and matrix remodelling are associated with the development of fibrosis in patients with COVID-19” Holton and colleagues analysed the plasma and endotracheal aspirates of COVID-19 patients admitted to ICU. Using chest imaging they evaluated each patient for the presence of lung fibrotic features, and subsequently measured markers of matrix remodelling and monocyte migration. They identified CCL-2/MCP-1, CCL13/MCP-4, Amphiregulin, MMP-7 and MMP-9 increased in the plasma of patients with evidence of fibrosis and CCL2/MCP-1 in the endotracheal aspirates. The authors conclude these are relevant biomarkers to tissue remodelling and monocyte recruitment occurring early during the development of lung fibrosis induced by COVID-19.

Response to Reviewer 2:

Thank you for your thorough review and comments on our manuscript. We have addressed your comments and made extensive changes both in the analysis of our data and the framing of our conclusions. We now discuss radiographic fibroproliferation or fibroproliferative response rather than “fibrosis” to help clarify that we are not making generalizations regarding the development of post-COVID fibrosis. The major concern of the timing between testing, sampling, and imaging of the study groups is one that is difficult to overcome given the retrospective nature of the study. All CT scans were clinically indicated rather than predetermined, and physician practice varied during different times of the pandemic. Nevertheless, there is scant literature in the field as to biomarkers associated with fibroproliferation in ARDS and we believe that despite these limitations this is an important contribution. In addition to further address these concerns, we have performed considerable new analyses and rewritten the majority of the manuscript to focus on other clinical outcomes that reflect the development of pulmonary remodeling/fibrosis such as in-hospital mortality and prolonged ventilator-free days. We have specifically addressed your major and minor concerns individually below.

Reviewer 2 Major Comment 1: The timing of the sample acquisition and imaging is an issue.

Response to Major Comment 1:

We agree that the timing discrepancies between sample collection and imaging is the biggest issue with our dataset. As such, we have reframed our manuscript such that our primary outcomes are not dependent on radiographic definitions.

We have re-graphed the data in Supplemental Figure 3 so that the data are not stratified based on survivability.

It is very possible that changes in plasma proteins are due to differences in the COVID-19 disease course and are reflective of a different phase of ARDS. We have adjusted our discussion of this in the text. We have reviewed all CT scans performed during hospitalization in the cohort and found that at the time of sample collection, no patient had evidence of a fibrotic pattern on their CT scan (this is evaluated now by two independent chest radiologists). This makes it more likely that the patients who develop fibrosis are different in some way but we can only speculate rather than make a conclusion based on our study design and results.

We used the term “fibrosis” too loosely in our original manuscript. We did not have the ability to follow patients or make sample collections after discharge from the hospital which prevented us from understanding who had true post-ARDS/post-COVID fibrosis. We used a radiographic definition during hospitalization which is more reflective of the fibroproliferative phase of ARDS. We have adjusted our definitions in the manuscript and highlighted this important discrepancy in the discussion. A significant number of patients within our cohort died from overwhelming ARDS/respiratory failure and other associated complications. We think that a strength of our manuscript is that these patients are included whereas in many studies looking at post-ARDS fibrosis they would be excluded. We feel it is important to study this group of patients to determine what modifiable factors may lead to death from fibrotic ARDS.

To address whether the proteins measured reflect where the patients are in the disease course, we have plotted the log2-transformed plasma concentrations of each marker against the interval between the patient’s initial covid+ PCR and the V1/enrollment sample collection. Then, simple linear regression was used to determine if there was any linear association. We found that only MCP-4 (p=0.003) and CXCL12 (p=0.01) had a linear association with time. This suggests that the protein mediators discussed in this article (with the exception of MCP-4 and CXCL12) are not entirely dependent on disease course. We have discussed this in the text (Lines 102-110) and included it in Supplemental Figure 1. To be as thorough as possible, the date of COVID+ test was extracted from the patient’s chart and included results collected outside of our system including at primary care clinics and urgent care.

Reviewer 2 Major Comment 2: The use of plain film chest radiographs for the determination of fibrosis is a problem, as it has extremely poor sensitivity-a CT scan is the gold standard diagnosis tool and only 32/119 patients had CT scans. While there are many good reasons CT scans cannot be performed, the authors should ensure that the decision to include non-CT imaging does not confound the study conclusion.

Response to Major Comment 2: We agree that the use of plain film chest radiographs is not the gold standard for assessing fibrosis. As such, we have excluded patients who did not have CT scans for our fibrosis outcome and redone our graphs, calculations, and statistical tests.

Reviewer 2 Major Comment 3: Primarily the authors discuss their findings as early biomarkers of fibrosis that are present soon after ICU admission (e.g. Lines 21-23 of abstract and line 201-202 and 261-263 of discussion). However, as samples were taken many days after a COVID+ test (see point 1) it is likely that many of these patients could have ARDS fibroproliferation already at ICU admission (which is why patients have deteriorated to needing ICU admission). This is a pathological process within lots of patients who develop worsening ARDS and can start from 7 days and continue up to 3 weeks after injury. Therefore, I would argue that the biomarker measurements are not predictive biomarkers of fibrosis but markers of severity of ARDS fibroproliferation at that time point. We already know that worse ARDS= worse outcome, and there are ways of measuring ARDS severity clinically, which table 1 shows are higher in the fibrosis group at time of sampling (Berlin, P/F ratio, APACHE II score- as a side note, can statistical tests be performed here t

Attachment

Submitted filename: Response_to_Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0285638.r003

Decision Letter 1

Gernot Zissel

11 Apr 2024

PONE-D-23-11100R1Mediators of monocyte chemotaxis and matrix remodeling are associated with mortality and pulmonary fibroproliferation in patients with severe COVID-19PLOS ONE

Dear Dr. Holton,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #2: I thank the authors for addressing the review comments rigorously. I am happy to recommend publication of this manuscript.

Reviewer #3: The manuscript describes the analysis of a COVID cohort subjected to ICU with regard to various proteins in plasma and endotracheal aspirates and specifically addresses their significance as biomarkers for the course of the infection and the development of fibrosis.

This work examines an important topic. However, important information is missing that needs to be added to make the study comprehensible:

1. the methods section lacks a precise description of how the plasma was purified after blood collection (in EDTA or nitrate tubes). Please add.

2. why were the plasma samples thawed twice before analysis? Please add. Normally plasma is aliquoted and used without thawing for protein analysis, as proteins may be degraded due to multiple thawing cycles.

3. the statistical analysis is not comprehensible to me. As I understand it, the aim was to analyze whether there is a difference in the amount of cytokines in the plasma of different proteins in different groups of the cohort. For example, the amount of CCL2 in patients who survived or died was compared. Why was a logistic regression analysis carried out here and not multiple t-tests with a correction for false discovery rate?

4. A detailed description for how the adjustments regarding age, sex, treatment with dexamethasone, and enrollment APACHE III score were performed, is missing. Please add.

5. Please add the detection limit of the proteins in the electrochemiluminescent immunoassays. Were they around 0.001 pg/ml? Otherwise a negative value of log2=-10 is not possible (TFN-a Figure2).

5. line 353: "Figure 4" is missing

6. Figure S4: CRP, MCP-4 and CXCL12 are missing, please add and remove CCL-13, as this protein is not shown in an other figure/table.

**********

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Reviewer #2: Yes: Nicholas A Scott

Reviewer #3: No

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PLoS One. 2024 Aug 6;19(8):e0285638. doi: 10.1371/journal.pone.0285638.r004

Author response to Decision Letter 1

26 Apr 2024

Reviewer 2 General Comments: I thank the authors for addressing the review comments rigorously. I am happy to recommend publication of this manuscript.

Response to Reviewer 2: Thank you for taking the time to extensively review our manuscript and provide your comments, which we feel improved our manuscript.

Reviewer 3 General Comments: The manuscript describes the analysis of a COVID cohort subjected to ICU with regard to various proteins in plasma and endotracheal aspirates and specifically addresses their significance as biomarkers for the course of the infection and the development of fibrosis.

This work examines an important topic. However, important information is missing that needs to be added to make the study comprehensible.

Response to Reviewer 3: Thank you for thoroughly reviewing our manuscript. We have responded to your comments and made edits to our manuscript/statistical approaches as described below.

Reviewer 3 Comment 1: the methods section lacks a precise description of how the plasma was purified after blood collection (in EDTA or nitrate tubes). Please add

Response to Comment 1: The blood was collected in cell preparation tubes (CPT) containing buffered sodium citrate and a density gradient solution for the isolation of plasma and mononuclear cells simultaneously. The tubes containing whole blood were centrifuged at 1500g for 20 minutes, at which point the tube contained layers of plasma, mononuclear cells and platelets, followed by granulocytes and erythrocytes separated by a gel layer. The plasma was then aliquoted in 0.5 mL aliquots and frozen down, carefully avoiding the mononuclear cell layer. The aliquots were then frozen down and stored at -80C until further aliquoting/analysis. These methods have been added to our methods section in the text (lines 283-289).

Reviewer 3 Comment 2: why were the plasma samples thawed twice before analysis? Please add. Normally plasma is aliquoted and used without thawing for protein analysis, as proteins may be degraded due to multiple thawing cycles.

Response to Comment 2: We recognize that the proteins may be degraded during multiple freeze-thaw cycles, which is why we ensured that the plasma underwent the same number of freeze-thaw cycles prior to analysis. Plasma was processed from whole blood on the same day of sample collection and frozen down (1 freeze). 0.5 mL aliquots were subsequently sub-aliquoted onto plates for our multiplexed analysis (1 thaw followed by 1 freeze). Then, the samples were thawed on the day of analysis (1 thaw), in total resulting in 2 freeze-thaw cycles. This process allowed us to measure several hundred samples simultaneously while minimizing assay batch effects.

Reviewer 3 Comment 3: the statistical analysis is not comprehensible to me. As I understand it, the aim was to analyze whether there is a difference in the amount of cytokines in the plasma of different proteins in different groups of the cohort. For example, the amount of CCL2 in patients who survived or died was compared. Why was a logistic regression analysis carried out here and not multiple t-tests with a correction for false discovery rate?

Response to Comment 3: Our hypothesis was that there would be differences in the amount of cytokines detected in patients who 1) had higher overall in-hospital mortality, 2) fewer ventilator free days, or 3) development of radiographic fibroproliferation. Our pre-determined covariates that could affect cytokine production were age, sex, APACHE III score at the time of enrollment, and treatment with dexamethasone. We used a logistic regression analysis in order to address/control these covariates.

To demonstrate that the statistical approach does not change our conclusions, please see below a table that shows the p-values based on Mann-Whitney U test for each of our comparisons with the Benjamini-Hochberg correction for false discovery rate. The significantly different cytokines are the same whether the Mann-Whitney test is used, or an unadjusted logistic regression model is used (Supplemental Tables 2-7) A Mann-Whitney test was used rather than a t-test because the cytokine concentrations are not normally distributed. Please see the Response to Reviewers document for statistical tables.

Reviewer 3 Comment 4: A detailed description for how the adjustments regarding age, sex, treatment with dexamethasone, and enrollment APACHE III score were performed, is missing. Please add.

Response to Comment 4: We performed the logistic regression analysis in R studio. Adjustments regarding age, sex, treatment with dexamethasone, and enrollment APACHE III score were performed by including these as covariates in our model. We used the glm function in R with the “binomial” family to specify a logistic regression where the dependent variable was the binary variable of death, fibrosis, or VFDs (high vs low).

This is also described in the methods section, lines 323-326.

Reviewer 3 Comment 5: Please add the detection limit of the proteins in the electrochemiluminescent immunoassays. Were they around 0.001 pg/ml? Otherwise a negative value of log2=-10 is not possible (TFN-a Figure2).

Response to Comment 5: The lower limit of detection for TNF-a was 0.028 pg/mL. The data point you are referring to was below the lower limit of detection and we have imputed a value of 0.014, resulting in the datapoint shown. This has been clarified in the text (Lines 298-300). We have added the limits of detection for each analyte to Supplemental Table 1.

Reviewer 3 Comment 6: line 353: "Figure 4" is missing

Response to Comment 6: Thank you for catching this error, we have added this in the text (line 365 )

Reviewer 3 Comment 7: Figure S4: CRP, MCP-4 and CXCL12 are missing, please add and remove CCL-13, as this protein is not shown in another figure/table

Response to Comment 7: Thank you for this point. MCP-4 is another name for CCL13. The graph has been adjusted to reflect that label. CXCL12 was not included as the CXCL12 endotracheal aspirate test characteristics did not meet our quality control standards. We did not discuss CRP in the manuscript and it has been removed from all of the tables.

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Submitted filename: Response_to_Reviewers_Revision2_final.docx

pone.0285638.s003.docx^{(165.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0285638.r005

Decision Letter 2

Gernot Zissel

17 May 2024

Mediators of monocyte chemotaxis and matrix remodeling are associated with mortality and pulmonary fibroproliferation in patients with severe COVID-19

PONE-D-23-11100R2

Dear Dr. Holton,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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[pone.0285638.ref001] 1.Wilcox M. E. et al., “Radiologic outcomes at 5 years after severe ARDS,” Chest, vol. 143, no. 4, pp. 920–926, Apr. 2013, doi: 10.1378/chest.12-0685 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref002] 2.Burnham E. L. et al. , “Chest CT features are associated with poorer quality of life in acute lung injury survivors,” Crit. Care Med., vol. 41, no. 2, pp. 445–456, Feb. 2013, doi: 10.1097/CCM.0b013e31826a5062 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref003] 3.Hui D. S. et al. , “Impact of severe acute respiratory syndrome (SARS) on pulmonary function, functional capacity and quality of life in a cohort of survivors,” Thorax, vol. 60, no. 5, pp. 401–409, May 2005, doi: 10.1136/thx.2004.030205 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref004] 4.Zhang P. et al. , “Long-term bone and lung consequences associated with hospital-acquired severe acute respiratory syndrome: a 15-year follow-up from a prospective cohort study,” Bone Res., vol. 8, no. 1, Art. no. 1, Feb. 2020, doi: 10.1038/s41413-020-0084-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref005] 5.Das K. M. et al. , “Acute Middle East Respiratory Syndrome Coronavirus: Temporal Lung Changes Observed on the Chest Radiographs of 55 Patients,” AJR Am. J. Roentgenol., vol. 205, no. 3, pp. W267–274, Sep. 2015, doi: 10.2214/AJR.15.14445 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref006] 6.Grasselli G. et al. , “Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy,” JAMA, vol. 323, no. 16, pp. 1574–1581, Apr. 2020, doi: 10.1001/jama.2020.5394 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref007] 7.Richardson S. et al. , “Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area,” JAMA, vol. 323, no. 20, pp. 2052–2059, May 2020, doi: 10.1001/jama.2020.6775 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref008] 8.Huang C. et al. , “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” Lancet Lond. Engl., vol. 395, no. 10223, pp. 497–506, 2020, doi: 10.1016/S0140-6736(20)30183-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref009] 9.Kurtz P. et al. , “Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months,” Intensive Care Med., vol. 47, no. 5, pp. 538–548, 2021, doi: 10.1007/s00134-021-06388-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref010] 10.Chopra V., Flanders S. A., O’Malley M., Malani A. N., and Prescott H. C., “Sixty-Day Outcomes Among Patients Hospitalized With COVID-19,” Ann. Intern. Med., vol. 174, no. 4, pp. 576–578, Apr. 2021, doi: 10.7326/M20-5661 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref011] 11.Torres-Castro R. et al. , “Respiratory function in patients post-infection by COVID-19: a systematic review and meta-analysis,” Pulmonology, vol. 27, no. 4, pp. 328–337, Aug. 2021, doi: 10.1016/j.pulmoe.2020.10.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref012] 12.Zhou S., Wang Y., Zhu T., and Xia L., “CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China,” AJR Am. J. Roentgenol., vol. 214, no. 6, pp. 1287–1294, Jun. 2020, doi: 10.2214/AJR.20.22975 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref013] 13.Writing Committee for the COMEBAC Study Group et al. , “Four-Month Clinical Status of a Cohort of Patients After Hospitalization for COVID-19,” JAMA, vol. 325, no. 15, pp. 1525–1534, Apr. 2021, doi: 10.1001/jama.2021.3331 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref014] 14.Caruso D. et al. , “Post-Acute Sequelae of COVID-19 Pneumonia: Six-month Chest CT Follow-up,” Radiology, vol. 301, no. 2, pp. E396–E405, Nov. 2021, doi: 10.1148/radiol.2021210834 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref015] 15.Cobb N. L. et al. , “Comparison of Clinical Features and Outcomes in Critically Ill Patients Hospitalized with COVID-19 versus Influenza,” Ann. Am. Thorac. Soc., vol. 18, no. 4, pp. 632–640, Apr. 2021, doi: 10.1513/AnnalsATS.202007-805OC [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref016] 16.Wendisch D. et al. , “SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis,” Cell, vol. 184, no. 26, pp. 6243–6261.e27, Dec. 2021, doi: 10.1016/j.cell.2021.11.033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref017] 17.Bharat A. et al. , “Early outcomes after lung transplantation for severe COVID-19: a series of the first consecutive cases from four countries,” Lancet Respir. Med., vol. 9, no. 5, pp. 487–497, May 2021, doi: 10.1016/S2213-2600(21)00077-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref018] 18.Bharat A. et al. , “Lung transplantation for patients with severe COVID-19,” Sci. Transl. Med., vol. 12, no. 574, p. eabe4282, Dec. 2020, doi: 10.1126/scitranslmed.abe4282 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref019] 19.Thille A. W. et al. , “Chronology of histological lesions in acute respiratory distress syndrome with diffuse alveolar damage: a prospective cohort study of clinical autopsies,” Lancet Respir. Med., vol. 1, no. 5, pp. 395–401, Jul. 2013, doi: 10.1016/S2213-2600(13)70053-5 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref020] 20.Misharin A. V. et al. , “Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span,” J. Exp. Med., vol. 214, no. 8, pp. 2387–2404, Aug. 2017, doi: 10.1084/jem.20162152 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref021] 21.McGroder C. F. et al. , “Pulmonary fibrosis 4 months after COVID-19 is associated with severity of illness and blood leucocyte telomere length,” Thorax, vol. 76, no. 12, pp. 1242–1245, Dec. 2021, doi: 10.1136/thoraxjnl-2021-217031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref022] 22.Arnold D. T. et al. , “Krebs von den Lungen 6 (KL-6) as a marker for disease severity and persistent radiological abnormalities following COVID-19 infection at 12 weeks,” PloS One, vol. 16, no. 4, p. e0249607, 2021, doi: 10.1371/journal.pone.0249607 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref023] 23.Hj C. et al. , “Immunofibrotic drivers of impaired lung function in postacute sequelae of SARS-CoV-2 infection,” JCI Insight, vol. 6, no. 14, Jul. 2021, doi: 10.1172/jci.insight.148476 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref024] 24.Forel J.-M. et al. , “Type III procollagen is a reliable marker of ARDS-associated lung fibroproliferation,” Intensive Care Med., vol. 41, no. 1, pp. 1–11, Jan. 2015, doi: 10.1007/s00134-014-3524-0 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref025] 25.Madtes D. K. et al. , “Elevated transforming growth factor-alpha levels in bronchoalveolar lavage fluid of patients with acute respiratory distress syndrome,” Am. J. Respir. Crit. Care Med., vol. 158, no. 2, pp. 424–430, Aug. 1998, doi: 10.1164/ajrccm.158.2.9711112 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref026] 26.Capelozzi V. L. et al. , “Molecular and Immune Biomarkers in Acute Respiratory Distress Syndrome: A Perspective From Members of the Pulmonary Pathology Society,” Arch. Pathol. Lab. Med., vol. 141, no. 12, pp. 1719–1727, Dec. 2017, doi: 10.5858/arpa.2017-0115-SA [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref027] 27.Holton S. E., Morrell E. D., Pipavath S., Bhatraju P. K., Wurfel M. M., and Mikacenic C., “Plasma Markers of Lung Injury and Remodeling are Associated with Radiographic Features of Fibrosis in Patients with COVID-19 Acute Respiratory Distress Syndrome,” presented at the American Thoracic Society 2022 International Conference, San Francisco, CA, May 15, 2022. [Online]. Available: https://doi-org.offcampus.lib.washington.edu/10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A1287 [Google Scholar]

[pone.0285638.ref028] 28.Morrell E. D. et al. , “Chemokines, soluble PD-L1, and immune cell hyporesponsiveness are distinct features of SARS-CoV-2 critical illness,” Am. J. Physiol. Lung Cell. Mol. Physiol., vol. 323, no. 1, pp. L14–L26, Jul. 2022, doi: 10.1152/ajplung.00049.2022 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref029] 29.Bhatraju P. K. et al. , “Comparison of host endothelial, epithelial and inflammatory response in ICU patients with and without COVID-19: a prospective observational cohort study,” Crit. Care Lond. Engl., vol. 25, no. 1, p. 148, Apr. 2021, doi: 10.1186/s13054-021-03547-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref030] 30.Mabrey F. L. et al. , “Presence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus-2 on Admission Is Associated With Decreased Mortality in COVID-19 Critical Illness,” Crit. Care Explor., vol. 4, no. 9, p. e0754, Sep. 2022, doi: 10.1097/CCE.0000000000000754 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref031] 31.RECOVERY Collaborative Group et al. , “Dexamethasone in Hospitalized Patients with Covid-19,” N. Engl. J. Med., vol. 384, no. 8, pp. 693–704, Feb. 2021, doi: 10.1056/NEJMoa2021436 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref032] 32.Meng C., Liu G., Mu H., Zhou M., Zhang S., and Xu Y., “Amphiregulin may be a new biomarker of classically activated macrophages,” Biochem. Biophys. Res. Commun., vol. 466, no. 3, pp. 393–399, Oct. 2015, doi: 10.1016/j.bbrc.2015.09.037 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref033] 33.Venkataraman T., Coleman C. M., and Frieman M. B., “Overactive Epidermal Growth Factor Receptor Signaling Leads to Increased Fibrosis after Severe Acute Respiratory Syndrome Coronavirus Infection,” J. Virol., vol. 91, no. 12, Jun. 2017, doi: 10.1128/JVI.00182-17 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref034] 34.Espindola M. S. et al. , “Differential Responses to Targeting Matrix Metalloproteinase 9 in Idiopathic Pulmonary Fibrosis,” Am. J. Respir. Crit. Care Med., vol. 203, no. 4, pp. 458–470, Feb. 2021, doi: 10.1164/rccm.201910-1977OC [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref035] 35.Richards T. J. et al. , “Peripheral blood proteins predict mortality in idiopathic pulmonary fibrosis,” Am. J. Respir. Crit. Care Med., vol. 185, no. 1, pp. 67–76, Jan. 2012, doi: 10.1164/rccm.201101-0058OC [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref036] 36.McGroder C. F. et al. , “Circulating adhesion molecules and subclinical interstitial lung disease: the Multi-Ethnic Study of Atherosclerosis,” Eur. Respir. J., vol. 54, no. 3, p. 1900295, Sep. 2019, doi: 10.1183/13993003.00295-2019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref037] 37.Xu J., Mora A., Shim H., Stecenko A., Brigham K. L., and Rojas M., “Role of the SDF-1/CXCR4 Axis in the Pathogenesis of Lung Injury and Fibrosis,” Am. J. Respir. Cell Mol. Biol., vol. 37, no. 3, pp. 291–299, Sep. 2007, doi: 10.1165/rcmb.2006-0187OC [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref038] 38.Burnham E. L. et al. , “Detection of fibroproliferation by chest high-resolution CT scan in resolving ARDS,” Chest, vol. 146, no. 5, pp. 1196–1204, Nov. 2014, doi: 10.1378/chest.13-2708 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref039] 39.Stewart I. et al. , “Residual Lung Abnormalities after COVID-19 Hospitalization: Interim Analysis of the UKILD Post–COVID-19 Study,” Am. J. Respir. Crit. Care Med., vol. 207, no. 6, pp. 693–703, Mar. 2023, doi: 10.1164/rccm.202203-0564OC [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref040] 40.Zeng Z. et al. , “Early fibroproliferative signs on high-resolution CT are associated with mortality in COVID-19 pneumonia patients with ARDS: a retrospective study,” Ther. Adv. Chronic Dis., vol. 12, p. 2040622320982171, Feb. 2021, doi: 10.1177/2040622320982171 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref041] 41.Chen L.-F., Yang C.-D., and Cheng X.-B., “Anti-Interferon Autoantibodies in Adult-Onset Immunodeficiency Syndrome and Severe COVID-19 Infection,” Front. Immunol., vol. 12, p. 788368, 2021, doi: 10.3389/fimmu.2021.788368 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref042] 42.Engel J. J. et al. , “Dexamethasone attenuates interferon-related cytokine hyperresponsiveness in COVID-19 patients,” Front. Immunol., vol. 14, p. 1233318, 2023, doi: 10.3389/fimmu.2023.1233318 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref043] 43.Montalvo Villalba M. C. et al. , “Interferon gamma, TGF-β1 and RANTES expression in upper airway samples from SARS-CoV-2 infected patients,” Clin. Immunol. Orlando Fla, vol. 220, p. 108576, Nov. 2020, doi: 10.1016/j.clim.2020.108576 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref044] 44.Mangioni D. et al. , “Prognostic Value of Mid-Region Proadrenomedullin and In Vitro Interferon Gamma Production for In-Hospital Mortality in Patients with COVID-19 Pneumonia and Respiratory Failure: An Observational Prospective Study,” Viruses, vol. 14, no. 8, p. 1683, Jul. 2022, doi: 10.3390/v14081683 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref045] 45.Ebihara T. et al. , “Cytokine Elevation in Severe COVID-19 From Longitudinal Proteomics Analysis: Comparison With Sepsis,” Front. Immunol., vol. 12, p. 798338, 2021, doi: 10.3389/fimmu.2021.798338 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref046] 46.Harb H. et al. , “Notch4 signaling limits regulatory T-cell-mediated tissue repair and promotes severe lung inflammation in viral infections,” Immunity, vol. 54, no. 6, pp. 1186–1199.e7, Jun. 2021, doi: 10.1016/j.immuni.2021.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref047] 47.Ding L. et al. , “Bone Marrow CD11c+ Cell-Derived Amphiregulin Promotes Pulmonary Fibrosis,” J. Immunol. Baltim. Md 1950, vol. 197, no. 1, pp. 303–312, Jul. 2016, doi: 10.4049/jimmunol.1502479 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref048] 48.Morimoto Y. et al. , “Amphiregulin-Producing Pathogenic Memory T Helper 2 Cells Instruct Eosinophils to Secrete Osteopontin and Facilitate Airway Fibrosis,” Immunity, vol. 49, no. 1, pp. 134–150.e6, Jul. 2018, doi: 10.1016/j.immuni.2018.04.023 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref049] 49.Wang Q., Wang F., Li X., Ma Z., and Jiang D., “Quercetin inhibits the amphiregulin/EGFR signaling-mediated renal tubular epithelial-mesenchymal transition and renal fibrosis in obstructive nephropathy,” Phytother. Res. PTR, vol. 37, no. 1, pp. 111–123, Jan. 2023, doi: 10.1002/ptr.7599 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref050] 50.Zhao X. et al. , “Th17 Cell-Derived Amphiregulin Promotes Colitis-Associated Intestinal Fibrosis Through Activation of mTOR and MEK in Intestinal Myofibroblasts,” Gastroenterology, vol. 164, no. 1, pp. 89–102, Jan. 2023, doi: 10.1053/j.gastro.2022.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref051] 51.Cheng W.-H. et al. , “Amphiregulin induces CCN2 and fibronectin expression by TGF-β through EGFR-dependent pathway in lung epithelial cells,” Respir. Res., vol. 23, no. 1, p. 381, Dec. 2022, doi: 10.1186/s12931-022-02285-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref052] 52.Filbin M. R. et al. , “Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions,” Cell Rep. Med., vol. 2, no. 5, May 2021, doi: 10.1016/j.xcrm.2021.100287 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref053] 53.Demichev V. et al. , “A proteomic survival predictor for COVID-19 patients in intensive care,” PLOS Digit. Health, vol. 1, no. 1, p. e0000007, Jan. 2022, doi: 10.1371/journal.pdig.0000007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref054] 54.Arthur L. et al. , “Cellular and plasma proteomic determinants of COVID-19 and non-COVID-19 pulmonary diseases relative to healthy aging,” Nat. Aging, vol. 1, no. 6, Art. no. 6, Jun. 2021, doi: 10.1038/s43587-021-00067-x [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref055] 55.Demichev V. et al. , “A time-resolved proteomic and prognostic map of COVID-19,” Cell Syst., vol. 12, no. 8, pp. 780–794.e7, Aug. 2021, doi: 10.1016/j.cels.2021.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref056] 56.Szabo P. A. et al. , “Longitudinal profiling of respiratory and systemic immune responses reveals myeloid cell-driven lung inflammation in severe COVID-19,” Immunity, vol. 54, no. 4, pp. 797–814.e6, Apr. 2021, doi: 10.1016/j.immuni.2021.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref057] 57.Gu X. et al. , “Probing long COVID through a proteomic lens: a comprehensive two-year longitudinal cohort study of hospitalised survivors,” EBioMedicine, vol. 98, p. 104851, Nov. 2023, doi: 10.1016/j.ebiom.2023.104851 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref058] 58.Sanhueza S. et al. , “Clinical and pulmonary function analysis in long-COVID revealed that long-term pulmonary dysfunction is associated with vascular inflammation pathways and metabolic syndrome,” Front. Med., vol. 10, p. 1271863, 2023, doi: 10.3389/fmed.2023.1271863 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref059] 59.Talla A. et al. , “Persistent serum protein signatures define an inflammatory subcategory of long COVID,” Nat. Commun., vol. 14, no. 1, p. 3417, Jun. 2023, doi: 10.1038/s41467-023-38682-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref060] 60.Zhang S. et al. , “The alveolar fibroproliferative response in moderate to severe COVID-19-related acute respiratory distress syndrome and one-year follow up,” Am. J. Physiol. Lung Cell. Mol. Physiol., Nov. 2023, doi: 10.1152/ajplung.00156.2023 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref061] 61.“Comparison of host endothelial, epithelial and inflammatory response in ICU patients with and without COVID-19: a prospective observational cohort study—PubMed.” Accessed: Sep. 06, 2022. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/33874973/ [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref062] 62.Funk D. J. et al. , “Persistence of live virus in critically ill patients infected with SARS-COV-2: a prospective observational study,” Crit. Care Lond. Engl., vol. 26, no. 1, p. 10, Jan. 2022, doi: 10.1186/s13054-021-03884-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285638.ref063] 63.Knaus W. A. et al. , “The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults,” Chest, vol. 100, no. 6, pp. 1619–1636, Dec. 1991, doi: 10.1378/chest.100.6.1619 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref064] 64.Acute Respiratory Distress Syndrome Network et al. , “Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome,” N. Engl. J. Med., vol. 342, no. 18, pp. 1301–1308, May 2000, doi: 10.1056/NEJM200005043421801 [DOI] [PubMed] [Google Scholar]

[pone.0285638.ref065] 65.Warren M. A. et al. , “Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS,” Thorax, vol. 73, no. 9, pp. 840–846, Sep. 2018, doi: 10.1136/thoraxjnl-2017-211280 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Mediators of monocyte chemotaxis and matrix remodeling are associated with mortality and pulmonary fibroproliferation in patients with severe COVID-19

Sarah E Holton

Mallorie Mitchem

Hamid Chalian

Sudhakar Pipavath

Eric D Morrell

Pavan K Bhatraju

Jessica A Hamerman

Cate Speake

Uma Malhotra

Mark M Wurfel

Steven E Ziegler

Carmen Mikacenic

Roles

Abstract

Introduction

Results

Plasma cytokines and markers of matrix remodeling are elevated and associated with poor ICU outcomes

Fig 1. Development of cohort.

Table 1. Clinical characteristics and selected outcomes of patients within cohort (n = 195) and subset of patients who had CT scans done (n = 75).

Fig 2. Plasma proteins associated with in-hospital mortality.

Fig 3. Plasma proteins associated with high/low ventilator free days (VFD).

Patients in the ICU with COVID-19 developed radiographic features of fibroproliferation during hospitalization

Table 2. Clinical characteristics of patients evaluated for fibroproliferation within cohort (n = 75).

Table 3. Outcomes of patients evaluated for fibroproliferation.

Putative markers of pulmonary fibrosis are elevated in plasma later in ICU course in patients with radiographic features of fibroproliferation

Fig 4. Plasma proteins associated with fibroproliferation on CT scan.

Endotracheal aspirate measurements are not associated with mortality, VFDs, or fibroproliferation and are not correlated with plasma measurements

Discussion

Methods

Study design

Analysis of CT scans

Plasma and endotracheal aspirate cytokine/chemokine measurements

Quantification and statistical analyses

Outcome definitions

Statistical analyses

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Gernot Zissel

Roles

Author response to Decision Letter 0

Decision Letter 1

Gernot Zissel

Roles

Author response to Decision Letter 1

Decision Letter 2

Gernot Zissel

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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