key: cord-0907351-la0zgh6i
authors: Price, David R.; Benedetti, Elisa; Hoffman, Katherine L.; Gomez-Escobar, Luis; Alvarez-Mulett, Sergio; Capili, Allyson; Sarwath, Hina; Parkhurst, Christopher N.; Lafond, Elyse; Weidman, Karissa; Ravishankar, Arjun; Cheong, Jin Gyu; Batra, Richa; Büyüközkan, Mustafa; Chetnik, Kelsey; Easthausen, Imaani; Schenck, Edward J.; Racanelli, Alexandra C.; Reed, Hasina Outtz; Laurence, Jeffrey; Josefowicz, Steven Z.; Lief, Lindsay; Choi, Mary E.; Schmidt, Frank; Borczuk, Alain C.; Choi, Augustine M.K.; Krumsiek, Jan; Rafii, Shahin
title: Angiopoietin 2 is associated with vascular necroptosis induction in COVID-19 acute respiratory distress syndrome
date: 2022-04-22
journal: Am J Pathol
DOI: 10.1016/j.ajpath.2022.04.002
sha: bbd8be82248e9f6a3edbd5196ab95ec309dc53e6
doc_id: 907351
cord_uid: la0zgh6i

Vascular injury is a well-established, disease modifying factor in acute respiratory distress syndrome (ARDS) pathogenesis. Recently, COVID-19-induced injury to the vascular compartment has been linked to complement activation, microvascular thrombosis, and dysregulated immune responses. We sought to assess whether aberrant vascular activation in this prothrombotic context was associated with the induction of necroptotic vascular cell death. To achieve this, proteomic analysis was performed on blood samples from COVID-19 subjects at distinct timepoints during ARDS pathogenesis (hospitalized “at risk”, N=59, “ARDS”, N=31, and “recovery”, N=12). Assessment of circulating endothelial markers in the “at risk” cohort revealed a signature of low vascular protein abundance that tracked with low platelet levels and increased mortality. This signature was replicated in the “ARDS” cohort and correlated with increased plasma angiopoietin 2 (ANGPT2) levels. COVID-19 ARDS lung autopsy immunostaining confirmed a link between vascular injury (ANGPT2) and platelet-rich microthrombi (CD61) and induction of necrotic cell death (phosphorylated mixed lineage kinase domain-like, pMLKL). Among recovery subjects, the vascular signature identified patients with poor functional outcomes. Taken together, this vascular injury signature was associated with low platelet levels and increased mortality and could be used to identify ARDS patients most likely to benefit from vascular targeted therapies.

Vascular injury has been recognized for decades as a key element in the pathogenesis of acute respiratory distress syndrome (ARDS) 1 , but this has not translated into vascular targeted therapies for ARDS. This may in part be related to heterogeneity in the vascular response to injury among ARDS subjects, as well as to difficulty in selecting patients most at risk for ARDS vascular injury. Blood proteomics has been proposed as a novel translational approach to better match patients to precision therapies for ARDS 2 . A better understanding of the blood proteomic changes associated with ARDS vascular injury could therefore help identify patients likely to benefit from vascular therapies.

Previous targeted studies of circulating vascular proteins have greatly enhanced the understanding of ARDS vascular injury. For example, measurement of the plasma angiocrine factor angiopoietin 2 (ANGPT2) in patients at the early stages of ARDS demonstrate that vascular injury likely precedes mechanical ventilation 3 and is associated with ARDS disease mortality 4 .

However, these ANGPT2 mediated vascular disruptions can be countered. In mice, systemic administration of platelet derived pericyte chemokines such as angiopoietin-1 (ANGPT1) and platelet derived growth factor B (PDGFB) counter ANGPT2 mediated vascular disruption, demonstrating the homeostatic potential of the blood vascular proteome 5 . Improved understanding of the blood proteomic changes in ARDS subjects with high and low vascular injury could build on these prior observations and shed further light onto disease pathogenesis and identify protein targets for further investigation.

More recently, vascular injury has been associated with COVID-19 acute respiratory distress syndrome (ARDS) 6, 7 , including the vascular complications of inflammation and thrombosis. In this context, COVID-19-induced injury to the vascular compartment has been J o u r n a l P r e -p r o o f associated with complement activation and microvascular thrombosis [8] [9] [10] , systemic thrombosis 9, 11 , and with dysregulated immune responses 12, 13 . However, this focus on inflammation and thrombosis limits our insights into other disruptions associated with aberrant vascular activation, including angiogenesis, junctional barrier integrity, the role of activated platelets in vascular injury, and induction of vascular cell death, including specialized RIPK3-mediated necrotic cell death. Specifically, while ANGPT2 mediated vascular disruption has been documented in COVID- 19 14 the association between ANGPT2 and induction of vascular cell death remains largely unexplored in ARDS investigations.

The purpose of this study was to assess whether aberrant vascular activation in was associated with the induction of necroptotic vascular cell death. To this aim, blood proteomics was performed in three independent COVID-19 cohorts, which enrolled patients at distinct timepoint in disease pathogenesis and included non-COVID-19 ARDS control samples as well.

Protein expression was linked to relevant clinical outcomes and vascular injury and cell death markers in COVID-19 autopsy lung tissue.

This study is an exploratory analysis of three cohorts that independently enrolled COVID-19 controls. The three COVID-19 cohorts were identified according to ARDS status at enrollment, yielding an early hospitalization at risk cohort, termed "at risk", an intensive care unit cohort with ARDS, termed "ARDS", and a recovery cohort in early convalescence outside the ICU, termed "recovery".

The at risk cohort included 59 adult (>18) non-pregnant COVID-19 subjects admitted to the general wards of WCM with serum available and who did not meet ARDS criteria at study enrollment. The ARDS cohort included adult (>18) non-pregnant COVID-19 (N=31) and historic non-COVID ARDS (N=29) subjects admitted to the intensive care unit (ICU) at WCM. For the ARDS cohort, only study subjects meeting ARDS criteria and with blood sampling within 10 days of ICU admission were considered for analysis. The recovery cohort included 12 adult (>18) nonpregnant COVID-19 ARDS subjects with plasma samples available from both the time of ICU care and the subsequent recovery period to allow for longitudinal analyses.

In the at risk cohort, between 1 and 3 consecutive daily samples were obtained from the central lab after routine processing to obtain serum. To obtain serum, blood collected in serum separator tubes (SST) was processed within 2 hours of venipuncture. Whole blood was centrifuged at 1,500 g for 7 minutes. The serum layer was aliquoted and stored at -80°C. These samples were obtained with a waiver of informed consent. In this cohort, samples collected after patient intubation were excluded from the analysis. In the ARDS and recovery cohorts, plasma was isolated from study subjects according to existing plasma isolation protocols [15] [16] [17] [18] . To obtain J o u r n a l P r e -p r o o f plasma, blood collected in EDTA tubes was processed within 6 hours of venipuncture. Whole blood was centrifuged at 490 g for 10 minutes. The plasma layer was removed in 200 uL aliquots and stored at -80. ARDS samples were obtained from patients in the intensive care unit while Recovery blood samples were obtained from patients convalescing in the hospital rehabilitation floors, as well as from the New York Presbyterian Weill Cornell Medicine Post-ICU recovery clinic.

Baseline clinical parameters and outcomes were extracted from the electronic medical record (EMR) as described previously 19, 20 . Baseline comorbidities were manually extracted from the EMR. Baseline clinical data (labs, severity of illness, ventilator data) were measured at time of blood sampling in both the at risk cohort and ARDS cohort. In the recovery cohort, baseline clinical data was measured from the ICU timepoint to allow for direct comparison with the ARDS cohort.

Severity of illness was defined by the sequential organ failure assessment score (SOFA) 21 . ARDS was determined according to the Berlin definition with ARDS severity capped at mild for subjects on non-invasive ventilation 22 . Two critical care investigators independently adjudicated the ARDS diagnosis. In all study subjects, COVID-19 was diagnosed if a subject had a syndrome compatible with COVID-19 and a nasopharyngeal (NP) swab positive for SARS-CoV-2 by reverse transcriptase polymerase chain reaction (RT-PCR).

The EQ-5D-3L was used to assess recovery at 12 months from ICU admission. The EQ-5D-3L is a self-assessment of the patient recovery, and considers 5 distinct domains, namely J o u r n a l P r e -p r o o f mobility, self-care, usual activities, pain or discomfort, and anxiety or depression 23 . Each domain was scored 0, 1, or 2 depending on whether the patient reported no, some, or extensive limitations in each respective domain. For each patient, a final score was defined as the sum of the scores across the five domains and treated as an ordinal variable in the statistical analysis. Maximal functional limitation would have a score of (2*5=)10 while an optimal recovery would be scored 0.

Twenty autopsies performed between March 19 and June 30, 2020 with pre-mortem nasopharyngeal swabs positive for SARS-CoV-2 were considered for lung tissue staining.

Hematoxylin and eosin (H&E), ANGPT2, CD61, and phosphorylated mixed lineage kinase domain-like (pMLKL) staining were performed in all autopsy subjects. Additionally, CD31 and ANGPT2/ERG co-staining were performed on the four autopsy subjects highlighted in the manuscript.

All autopsies were performed in a negative pressure ventilation room with full personal protective equipment including N-95 masks. No bone saw was used to prevent aerosolized dusts and, as such, brain examination was not performed. All tissues were immediately fixed in formalin for a minimum of 24 hours. All tissues for RNA studies were immediately immersed in Trizol for its viricidal effects. To minimize exposure, only two individuals were allowed in the suite during the autopsy and the room was disinfected before and after each case. Lung tissue specimens were fixed in 10% formalin for 48-72 hours. Hematoxylin and eosin staining were performed for all cases. Immunohistochemistry was carried out for angiopoietin-2 (sc-74403, Santa Cruz, TX, 1:100), CD-61 (CD61 clone 2F2, Leica Biosystems, IL), ERG (ab92513, Abcam, Cambridge, UK.

J o u r n a l P r e -p r o o f 1:100), CD31 (PA0250, Leica Biosystems, IL, ready to use), and phosphorylated mixed lineage kinase domain-like (pMLKL, MAB91871, NOVUS Biologicals, CO, 1:750 with casein for background reduction). Specimens were scanned by whole-slide image technique using an Aperio slide scanner with a resolution of 0.24 μm/pixel. Quantification of ANGPT2 and CD61 was performed on four random 20X images selected using a random overlay of points and excluding fields with large vessels or airway. All twenty autopsies were analyzed using Immunohistochemistry profiler 24 as a plugin for Image J (https://imagej.nih.gov Version 1.52a, National Institutes of Health, USA, last accessed 3/10/2022). After deconvolution of the 20X images, both area of expression (e.g., number of pixels) and intensity of expression (e.g., intensity of pixel) were measured and combined into a single score according to the equation score=[(number of pixels in a zone x score of the zone)]/total number of pixels in image. High, intermediate, low, and overall percent positive was averaged over the four measurements. The median ANGPT2 quantification was used to define the high (>median) and low (<median) ANGPT2 staining. The association between CD61 and ANGPT2 was then calculated based on CD61 quantification in the low and high ANGPT2 groups using Mann-Whitney U test.

To quantify the circulating vascular proteome, plasma and serum samples from the at risk, ARDS and recovery cohorts were profiled using O-Link through the Proteomics Core of Weill The at risk cohort was profiled in two separate runs. The second run included a total of 11 samples, among which 5 bridge samples were used to scale this batch toward the first one, as recommended by O-link. First, for each bridge sample, the pairwise difference between the first and second batch was computed. An overall batch adjustment factor was then derived as the median of these pairwise differences and subtracted to the values in the second batch.

Subsequently, protein levels were exponentiated, normalized using probabilistic quotient normalization 25 

In the at risk cohort proteomic analysis, protein associations with death (i.e. whether the patient ended up dying) and platelet count (minimum value across the sampling days) were J o u r n a l P r e -p r o o f computed using a mixed linear effect model, which allows to properly account for the multiple samples collected per patient. The model was formulated as follows: protein ~ outcome + replicate + batch + (1|patient), where outcome is either death or platelet count, replicate indicated the day of blood sample draw (first, second or third since hospital admission), and batch indicated whether the sample was measured in the first or second run. Association pvalues were corrected for multiple testing using the Benjamini-Hochberg method for controlling the false discovery rate 27 . Adjusted p-values less than 0.1 were considered significant.

The protein vascular signature was selected from proteins significantly associated with both outcomes, and proteins significantly associated with either mortality or platelet count and with known, well characterized links to vascular function. TIE2 was additionally included as it is the receptor for ANGPT2.

For all cohorts, patient hierarchical clustering based on the standardized proteomics value was performed using Ward linkage and Euclidean distance. The differential analysis between patient clusters was performed using Mann-Whitney U tests for continuous variables, Kendall's rank correlation for ordinal variables, and log-rank tests for survival times. The correlation between ANGPT2 and RIPK3 was estimated using Pearson correlation. For these analyses, a pvalue less than 0.05 was considered significant.

In the recovery cohort, patients were first divided into two groups based on unsupervised hierarchical clustering (Ward linkage, Euclidean distance) performed on the recovery timepoint.

Then, for each patient, a protein abundance difference (delta) was calculated between the ICU and recovery timepoints. Finally, for each protein it was determined whether the protein delta was different across the two patient groups using the linear model delta ~ group. P-values were corrected for multiple tests using the Benjamini-Hochberg method. Given the small sample size J o u r n a l P r e -p r o o f and validation of protein set in two prior cohort, an adjusted p-value less than 0.25 was considered significant.

All statistical analyses were performed in R 4.0.1 using the maplet package 28 . The R code used to generate the statistical findings presented in this paper is publicly available at https://github.com/krumsieklab/covid-vascular-injury (accessed 3/10/2022).

The study was approved by the institutional review board (IRB) at WCM (20-05022072, 20-03021681, and 1811019771). Written informed consent was received prior to participation by all patients, except when the IRB approved a waiver of informed consent (e.g., for the use of discarded samples and deidentified patient data in the at risk cohort).

The hospitalized at risk cohort blood proteome identifies a signature of vascular limitation preceding critical illness. A total of 1,384 subjects were admitted to the medical floors of New York Presbyterian Weill Cornell Medical Center (WCM) during the study period. Fifty nine of the 1,384 hospitalized COVID-19 subjects were profiled (Figure 1) . Profiled subjects were more likely to have cancer (15% versus 7.5%, P=0.044) but otherwise were similar to unprofiled subjects (Supplementary Table 1 Table 4 ).

There were no baseline differences between profiled and unprofiled non-COVID-19 ARDS control subjects (Supplementary Table 4 ). Among all profiled ARDS subjects (N=60), there were no significant age, sex or race differences between COVID-19 ARDS (N=31) and non-COVID-19 ARDS subjects (N=29) in the cohort (Supplementary Table 2 Table 3 ) were more frequent in ARDS subject in the low mean vascular protein abundance cluster. Importantly, this low abundance signature did not reflect the relative expression of these proteins compared to healthy controls. As an example, CD40LG and ANGPT1 had opposite expression patterns in ARDS compared to healthy controls, with CD40LG being increased in ARDS compared to control while ANGPT1 expression was similar to healthy control (Supplementary Figure 1B) . Despite the opposite expression patterns of the ARDS biomarkers compared to healthy controls, lower expression of both CD40LG and ANGPT1 was associated with the low platelet, worse clinical outcomes cluster.

Similar to the at risk cohort, the junctional integrity proteins ANGPT1 and TIE2 had lower expression in the low platelet, high mortality cluster B ( Figure 3A) . As ANGPT2 is known to negatively regulate ANGPT1 and TIE2 we measured plasma ANGPT2 in the ARDS cohort.

Notably, plasma ANGPT2 was higher in the low mean protein abundance cluster (P=0.001, Figure   3C and Supplementary Figure 4E) , linking low vascular protein abundance and plasma ANGPT2 in diverse ARDS subjects.

Interestingly, when COVID-19 ARDS was considered alone (Supplementary Figure 4) , this higher vascular injury signature was present in 39% (12 of 31) of COVID-19 ARDS subjects, yet when all three infection types were considered (Figure 3 ANGPT2 and endothelial nuclear stain ERG co-staining confirmed endothelial origin of the ANGPT2 staining (Supplementary Figure 6A) . Importantly, staining for CD31 as a constitutive endothelial marker (Supplementary Figure 6B) showed that differences in ANGPT2 and CD61 staining in these subjects was not due to differences in preservation of endothelium between autopsy subjects. Quantification of ANGPT2 and CD61 staining showed that high ANGPT2 protein was associated with increased CD61 (P=0.005, Figure 4B ). Blood proteomics was performed in 3 of the autopsy subjects (Figure 4A and 4C ; subjects labelled as P1, P2, and P3).

Consistent with the ARDS cohort, low protein abundance of our protein set, including lower expression of angiopoietin axis proteins ANGPT1 and TIE2 (Figure 4C , red font), was associated with increased vascular ANGPT2 staining ( Figure 4C) . ARDS subjects (P=0.020, Figure 5A ). Plasma RIPK3 was also correlated with plasma ANGPT2 (r=0.40, P=0.003, Figure 5B) , supporting the existence of a link between circulating necroptosis proteins and ANGPT2-associated vascular injury. High ANGPT2 staining/low vascular protein abundance autopsy subjects (Figure 4A ; NA and P3) demonstrated diffuse microvascular staining for pMLKL, a terminal protein in necrotic cell death execution downstream of RIPK3 ( Figure   5C ). Conversely, the low ANGPT2 staining/high vascular protein abundance autopsy subjects Table 1 ). Among the profiled recovery subjects, the median age of this cohort was 47 years old and was majority male (67% versus 33% female,

Patient clustering based on the recovery plasma protein set revealed two distinct clusters ( Figure 6A) . Again, the low protein abundance cluster was associated with platelet level (P=0.048, Figure 7A ) and higher age (P=0.048, Supplementary Figure 7B) . One year J o u r n a l P r e -p r o o f follow up functional recovery data based on the EQ-5D-3L questionnaire was available on 11 of these 12 recovery individuals (top annotation in Figure 6A , Methods for details). Notably, the cluster of patients with lower abundance of the protein set (P=0.004, Supplementary Figure 7C) displayed worse functional recovery 12 months after admission from the ICU, while higher vascular protein abundance was associated with better functional recovery (P=0.027, Figure 6B ).

To test whether the protein trajectory from ICU to recovery was different between good and poor functional recovery subjects, the differences in protein abundances was compared between the two The role of activated platelets in vascular injury and repair is also apparent in the data.

Activated platelets amplify immune responses in early ARDS but also play an essential role in vascular repair. The consistently low platelet levels across the cohorts and the extensive microthrombi observed in the autopsy subjects implies a circulating milieu of platelet consumption. This milieu of platelet consumption is supported by a blood signature of ongoing thrombolysis (high UPA and low PAI) and low levels of platelet derived proteins (low SELP, and GP6) in the high ANGPT2-associated vascular injury subjects. Relative loss of ADAMTS13, linked to secondary microangiopathy in COVID-19 42 , is similarly deficient in the high ANGPT2 subjects, linking platelet consumption with microangiopathy in severe COVID-19. Low platelets J o u r n a l P r e -p r o o f have previously been linked to ARDS mortality 43 and the data suggest this may be related to depletion in platelet related angiogenic [44] [45] [46] and junctional barrier factors 47, 48 . Consistently low circulating angiogenic (low PDGFA and PDGFB) and barrier protein (low ANGPT1) in the higher ANGPT2-and low platelet subjects imply limitations in these essential reparative processes.

The validation of the vascular phenotype across diverse causes of ARDS broadens the relevance of the reported findings. In linking low platelets, vascular function, and mortality in COVID-19, bacterial sepsis, and influenza ARDS, we hint at a common final pathway of vascular injury that is more disease-(ARDS) than cause-(COVID-19) specific. While we acknowledge that matching COVID-19 and non-COVID-19 ARDS subjects would also have strengthened any comparison between these groups, the fact that there were similarities in protein expression despite stark differences in clinical parameters, including a marked difference in baseline cancer prevalence (14/29, 48% in non-COVID-19 ARDS vs 1/31, 3% COVID-19 ARDS), remains a strength of the analysis as it suggests that different causes of platelet depletion (e.g. failure of production is cancer patients with non-COVID-19 ARDS and consumption in COVID-19 ARDS patients) leads to the same protein expression pattern and worse clinical outcomes. Of note is that this vascular injury pattern may be related to a reduced baseline vascular resilience in the high ANGPT2-associated vascular injury subjects. Consistently, the high vascular injury subjects are older 49 , have worse baseline renal function 50, 51 , and are more likely to have cancer 52 , all variables know to be associated with vascular disease.

The identification of this severe vascular phenotype across infectious causes of ARDS also presents an opportunity for targeted vascular therapies in ARDS, including those that have shown promise in COVID-19 53 , ARDS generally 54 , and in exciting preclinical 55 57 . We demonstrate that a stable circulating vascular proteome is important for functional recovery. This association between vascular stability, platelet levels, and functional recovery could also support platelet levels as a novel biomarker in ARDS recovery. Larger studies will be needed to validate this observation.

We recognize that our study has limitations. While we describe a milieu of platelet consumption in ARDS subjects with increased vascular injury, we cannot rule out alternative mechanisms of platelet depletion in these subjects, including decreased bone marrow production, particularly in the ARDS subjects with malignancies. Platelet depletion also tracks with the vascular protein signature in the three cohorts, yet we do not establish cohort-specific platelet value that clinicians can use to identify these subjects. Also, while we suspect that endothelial cells, pericytes, and platelets represent the likely cellular origin of the vascular signature, the lack of spatial proteomic or transcriptomic data allows for the possibility that some identified proteins are from non-vascular sources, including immune cells 58 . Future murine cell-specific knockout studies J o u r n a l P r e -p r o o f will be needed to resolve the cellular origin of the signature. Finally, while we show an observed association between ANGPT2 and vascular cell death proteins RIPK3 and pMLKL, additional in vivo and in vitro studies will be needed to determine the critical crosstalk between vascular injury and vascular cell death proteins.

In summary, we identify a vascular injury signal in COVID-19 ARDS that has predictive value in early disease through to recovery and well as in bacterial sepsis and influenza ARDS and could improve patient selection and timing of vascular targeted therapies in ARDS. HO1  UPA  IL1RA  EGFR  TIE2  ADAMTS13  VEGFA  CXCL1  CXCL5  SELP  MMP9  GP6  CD84  SORT1  HBEGF  PAI  IL7  CD40LG  LAP TGFB1  PDGFA 

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The authors thank Ilias I. Siempos for critically reviewing the manuscript.J o u r n a l P r e -p r o o f