Visual Abstract

The COVID-19 pandemic has affected more than 20 million people worldwide, with mortality exceeding 800,000 patients. Risk factors associated with severe disease and mortality include advanced age, hypertension, diabetes, and obesity. Each of these risk factors pathologically disrupts the lipidome, including immunomodulatory eicosanoid and docosanoid lipid mediators (LMs). We hypothesized that dysregulation of LMs may be a defining feature of the severity of COVID-19. By examining LMs and polyunsaturated fatty acid precursor lipids in serum from hospitalized COVID-19 patients, we demonstrate that moderate and severe disease are separated by specific differences in abundance of immune-regulatory and proinflammatory LMs. This difference in LM balance corresponded with decreased LM products of ALOX12 and COX2 and an increase LMs products of ALOX5 and cytochrome p450. Given the important immune-regulatory role of LMs, these data provide mechanistic insight into an immuno-lipidomic imbalance in severe COVID-19.

Lipids function in disease to rearrange cellular signaling structures, modify metabolic processes, absorb reactive species, and act directly as both autocrine and endocrine ligands in the regulation of the immune system. In the context of an immune insult, eicosanoid and docosanoid polyunsaturated fatty acids (PUFA) are liberated from glycerolipids and converted via enzymatic hydroxylation to immune regulating lipid mediators (LMs) (17). LMs function as inflammatory, immune-regulatory, or proresolving immune signals during chronic and acute immune responses (1, 8). Previous studies have demonstrated that comorbidities associated with severe COVID-19 including obesity, hypertension, diabetes and heart disease feature pathological disruption of the lipidome including altered baseline levels of LMs and the PUFA-containing LM precursors in absence of infection (913). Several studies have examined systemic metabolic correlates of COVID-19, including shifts in the lipidome (1417) (J. Troisi et al., Research Square, 2020, https://doi.org/10.21203/rs.3.rs-34085/v1). Collectively, these studies demonstrated that severe COVID-19 is marked by a systemic dysregulation of metabolism and widespread changes in the lipidome suggesting the potential for dysregulation of LMs. However, the specific behavior of LMs and their implications for disease severity remains unexplored in the context of COVID-19.

Based on these previous results, we hypothesized that severe COVID-19 is associated with a disrupted balance of LMs potentially as a result of the associated metabolic comorbidities. In this study, we characterize a dysregulation of the LM lipidome, including the PUFA-containing LM precursors, in severe COVID-19 and identify new potential targets for modulating the aberrant immune response that results in morbidity.

This study was approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, Protocol ID. 2000027690). Informed consents were obtained from all enrolled patients. Samples from healthy patients were from frozen banked donations which were obtained under the protocol (HIC 0901004619; CPIRT) before the onset of COVID-19 outbreak.

COVID-19 patients were recruited among those who were admitted to the Yale-New Haven Hospital between March 18, 2020, and May 9, 2020 and were positive for SARS-CoV-2 by RT-PCR from nasopharyngeal and/or oropharyngeal swabs and displayed respiratory symptoms. Patients in this study were enrolled through the IMPACT biorepository study after obtaining informed consent. Samples were collected an average of 3 ± 1 and 4 ± 2 d postadmission for moderate and severe groups, respectively (Supplemental Table I). Control biospecimens are from healthy subjects without known inflammatory or lung disease or comorbidities from our Yale CPIRT biorepository. Basic demographics and clinical information of study participants were obtained and shown in Supplemental Table I. Following collection, all samples were immediately frozen at −80°C. Prior to thawing, all samples including healthy controls were γ-irradiated (2 Mrad) to inactivate potential infectious virus.

Liquid chromatography mass spectrometry grade solvents were used for all liquid chromatography mass spectrometry experiments. Sample order was randomized for each extraction. For aqueous and organic metabolites samples were extracted using a modified Bligh and Dyer extractions with details in B. Schwarz et al., medRxiv, 2020 (https://doi.org/10.1101/2020.07.09.20149849) (18).

LMs were extracted from patient serum as previously described with modifications detailed in B. Schwarz et al., medRxiv, 2020 (https://doi.org/10.1101/2020.07.09.20149849) (19).

Aqueous metabolite, lipid, and LM samples were analyzed using a series of targeted multiple-reaction monitoring methods. All samples were separated using a Sciex ExionLC AC system and analyzed using a Sciex 5500 QTRAP mass spectrometer. All methods were derived from previously published methodology with modifications detailed in B. Schwarz et al., medRxiv, 2020 (https://doi.org/10.1101/2020.07.09.20149849) (19, 20).

The published single-cell RNA sequencing dataset from Wilk et al. (21) was interrogated using Seurat v3.0.

Demographic data are presented as either counts and percentages (for categorical data) or means and standard deviations (for continuous data). To investigate the difference in the control, moderate and severe groups, GraphPad Prism (version 8.4.2) was used. The results were compared using the χ2 test or Fisher exact test for categorical variables and one-way ANOVA or unpaired t test was used for continuous variables with significance set at p ≤ 0.05.

Univariate and multivariate analysis was performed in MarkerView Software 1.3.1. The aqueous dataset and the combined LMs/cytokine dataset data were autoscaled prior to multivariate analysis to visualize the contribution of low ionization efficiency species. Lipid datasets were pareto scaled to avoid overrepresenting low abundance signals within each lipid class. For all datasets, a missing value cut-off of 50% and a CV of 30% were used as filters. For all univariate analyses, an unpaired t test was used, and a Benjamini–Hochberg correction with a false discovery of 10% was used on the combined polar metabolite and lipidomic datasets to correct for multiple comparisons. Adjusted p value cutoff levels were moderate versus healthy, 0.050; severe versus healthy, 0.032; and severe versus moderate, 0.017.

To measure differences in LM profiles between moderate and severe cases of COVID-19 and gain mechanistic insights into how these changes may drive disease severity, we used serum draws from 19 healthy patients (healthy), 18 hospitalized COVID-19 patients with respiratory symptoms who did not require ICU admission (moderate), and 20 patients with respiratory symptoms of a severity that required ICU admission (severe). Serum draws from healthy patients without known comorbidities were collected prior to the COVID-19 pandemic, to avoid any contribution of asymptomatic or convalescent cases in this dataset. The demographics, preexisting conditions, and treatment details of these patients are indicated in Supplemental Table I. To ensure recovery of both lipids and polar metabolites a modified chloroform extraction was used and measurements were made using a series of targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) methods providing high-confidence feature identification (20, 22, 23).

Changes in primarily polar metabolites, including methanol-soluble polar lipids, among COVID-19 patient cohorts from China, Italy, and France have been reported (1417). In agreement with those studies, we observed a dysregulation of amino acids and lactate and dysregulation of nucleotide catabolic products such as xanthine, hypoxanthine, and urate (Supplemental Fig. 1A) (14). Among lipid species, separation of disease from healthy and severe from moderate disease was marked by changes in sphingomyelins, lysophospholipids, cholesterol esters and multiple classes of glycerophospholipids in agreement with previous studies (Fig. 1A, 1B, Supplemental Fig. 1B–M) (1417).

FIGURE 1.

Mobilization of plasmalogen-derived PUFAs correlates with the disease severity in COVID-19. (A) Supervised partial least-squares discriminant analysis (PLSDA) of the healthy, moderate, and severe disease groups and (B) the corresponding feature loading plot. (CF) Heatmap of the autoscaled mean intensity of each patient group for significantly varied lipids containing C20:4 (C), C20:5 (D), C22:5 (E), and C22:6 (F) between one or more groups adjusted for a false discovery rate (FDR) of 10% because of multiple comparisons. Color scale is consistent for (C)–(F).

FIGURE 1.

Mobilization of plasmalogen-derived PUFAs correlates with the disease severity in COVID-19. (A) Supervised partial least-squares discriminant analysis (PLSDA) of the healthy, moderate, and severe disease groups and (B) the corresponding feature loading plot. (CF) Heatmap of the autoscaled mean intensity of each patient group for significantly varied lipids containing C20:4 (C), C20:5 (D), C22:5 (E), and C22:6 (F) between one or more groups adjusted for a false discovery rate (FDR) of 10% because of multiple comparisons. Color scale is consistent for (C)–(F).

Close modal

To pursue our hypothesis that dysregulation of PUFA-containing lipids and immune-regulatory LMs may contribute to the inflammatory progression of severe COVID-19, we specifically interrogated patterns of LM precursors among the portion of lipids that significantly varied between any two groups in the cohort. Disease groups contained lowered levels of PUFA-containing phosphatidylcholine, PUFA-containing phosphatidylserine, and PUFA-containing phosphatidylethanolamine-plasmalogen and increased levels of PUFA free fatty acids as well as PUFA-containing phosphatidylethanolamine, PUFA-containing lysophospholipids, and PUFA-containing triacylglycerols compared with healthy controls (Fig. 1C–F). These PUFA patterns were exacerbated in severe patients compared with moderate patients. Of these PUFA-containing lipids, plasmalogen was of particular interest as a primary pool of PUFAs for the generation of LMs in both immune and structural cells (24, 25). This shift in PUFA pools strongly suggested differential mobilization of PUFAs for the generation of LMs between moderate and severe COVID-19.

To assess LMs directly, we targeted 67 LMs species using LC-MS/MS with comparison with standards and available spectral libraries (https://serhanlab.bwh.harvard.edu/). Principle component analysis of the LM lipidome showed separation of infected and healthy cohorts and between moderate and severe patients (Fig. 2A). Nearly all LMs measured were positively correlated with infection and moderate and severe disease were characterized by unique profiles of LMs (Fig. 2B, Supplemental Fig. 1N–P). Of particular interest were LMs present at lower levels in the severe group compared with the moderate group as these molecules behave counter to the pattern of increased immune activity expected in the severe disease. Specifically, moderate disease was characterized by significantly higher levels of the proresolving LM resolvin (Rv) E3 and a trend toward increased presence of the PG family members, particularly PGE2 (p = 0.051), PGD2 (p = 0.220) and PGF2a (p = 0.242). In contrast, severe disease was characterized by a significant increase in free PUFAs levels, monohydroxylated species, and AA-derived dihydroxylated species (Supplemental Fig. 1P). Multiple DHA- and AA-derived proresolving LMs also trend upward in severe disease (RvD3, p = 0.063) but do not reach significance.

FIGURE 2.

A unique milieu of LMs defines moderate and severe COVID-19 disease. (A) Unsupervised principle component analysis (PCA) of autoscaled LMs and (B) corresponding feature loading plot. (CG) Heatmaps of the autoscaled mean for each patient group across molecules synthesized by ALOX5 (C), ALOX12 (D), ALOX15 (E), COX (F), or CYP (G). Color scale is consistent across (C)–(G).

FIGURE 2.

A unique milieu of LMs defines moderate and severe COVID-19 disease. (A) Unsupervised principle component analysis (PCA) of autoscaled LMs and (B) corresponding feature loading plot. (CG) Heatmaps of the autoscaled mean for each patient group across molecules synthesized by ALOX5 (C), ALOX12 (D), ALOX15 (E), COX (F), or CYP (G). Color scale is consistent across (C)–(G).

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LMs are generated by a single or a series of oxygenase mediated conversions of the parent PUFA. To examine the potential contribution of each oxygenase enzyme to the severe disease phenotype, we grouped LMs according to synthesis pathway (Fig. 2C–G). Several LMs are shared between multiple enzyme groups as they require sequential stereospecific hydroxylations by multiple enzymes. This grouping revealed that moderate disease was characterized by higher levels of LMs that require cyclooxygenase (COX) activity as well as certain EPA products of ALOX12, whereas severe disease was characterized by LMs that require activity of ALOX5 and cytochrome p450 (CYP) enzymes. This is in good agreement with previous observations from influenza, which associated symptom severity with ALOX5 activity (26).

Elevation of ALOX5- and CYP-dependent LMs in severe COVID-19 patient sera suggested upregulation of these pathways but provided no data as to the origin of these LMs. To begin to examine potential cellular origins of these enzymes in COVID-19 patients, we interrogated a published single-cell RNA sequencing dataset of severe COVID-19 patient PBMCs (Fig. 3A) for expression of ALOX and CYP genes (21). CYP gene expression was not detected (data not shown), whereas ALOX5 expression was detected in multiple cell types (Fig. 3B). ALOX5 was significantly increased in neutrophils and trended upward in CD14 monocytes, CD16 monocytes, and developing neutrophils (a population found almost exclusively in diseased individuals) from severe COVID-19 patients compared with healthy controls (Fig. 3C). Although expression is important, ALOX enzymes are primarily regulated by subcellular location and pathways involving LM production are intercellular highlighting the importance of ALOX expressing cell numbers and not just expression levels in these pathways (27, 28). Interestingly, severe COVID-19 is characterized by elevated ALOX5 expressing monocyte/macrophage population and depletion of lymphocyte populations (21, 29, 30). The absence of CYP gene expression in peripheral blood is consistent with the primarily hepatic localization of these enzymes (31). Taken together, these data support a systemic increase in ALOX5 activity in severe COVID-19 and provide a plausible foundation for the data in this study wherein ALOX5 products separate moderate and severe disease (32).

FIGURE 3.

Human PBMCs from COVID-19 patients are enriched for ALOX5 expressing cells and express higher levels of ALOX5. (A) UMAP dimensionality reduction plot of a published human PBMC single-cell RNA Seq dataset (21) identifying 20 cell types. (B) UMAP depicting ALOX5 expressing cells in blue. (C) Violin plots indicating ALOX5 expression levels within specific cellular populations in healthy (blue) or COVID (red) PBMCs. *p < 0.05, **p < 0.01, determined by a Mann–Whitney U test.

FIGURE 3.

Human PBMCs from COVID-19 patients are enriched for ALOX5 expressing cells and express higher levels of ALOX5. (A) UMAP dimensionality reduction plot of a published human PBMC single-cell RNA Seq dataset (21) identifying 20 cell types. (B) UMAP depicting ALOX5 expressing cells in blue. (C) Violin plots indicating ALOX5 expression levels within specific cellular populations in healthy (blue) or COVID (red) PBMCs. *p < 0.05, **p < 0.01, determined by a Mann–Whitney U test.

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COVID-19 comorbidities are known to pathologically disrupt the homeostatic lipidome and may predispose patients to the different signatures of LMs seen in this study. To examine this potential association further, we correlated the LM dataset with the severity of disease, comorbidities, and demographics (Fig. 4). Thirteen LMs were significantly (p ≤ 0.05) correlated with disease severity and several families of LMs group together in the positive and negative direction including COX2 and ALOX12 products in the negative direction and ALOX15 and CYP in the positive direction despite not all members reaching significance. Among comorbidities, body mass index (BMI) most closely reflected the LM pattern of disease severity with all but three LMs sharing a similar coefficient direction and magnitude as disease severity (Fig. 4A). Hypertension and heart disease reflected the pattern of disease severity but not to the degree of BMI. Of note, PGs (PGE2, PGD2 and PGF2a) and RvE3 were negatively correlated with disease severity, BMI, hypertension, and heart disease. Neither age, gender, nor presence of diabetes correlated well with the pattern of LM dysregulation observed in severe cases of COVID-19. This correlative analysis supports the association of the specific LM dysregulation in severe disease with known comorbidities, in particular high BMI.

FIGURE 4.

Correlation of comorbidities with LMs and severe COVID-19. (A) Correlation plot of spearman (BMI and age) or point biserial Pearson (disease severity, hypertension, heart disease, diabetes, and gender) correlation coefficient of each LM with disease severity or patient demographics and comorbidities. LMs are ordered by the strength of correlation with disease severity. For gender, a positive correlation is correlated with male. (B) Corresponding p values of each LM patient condition correlation displayed as −log(p).

FIGURE 4.

Correlation of comorbidities with LMs and severe COVID-19. (A) Correlation plot of spearman (BMI and age) or point biserial Pearson (disease severity, hypertension, heart disease, diabetes, and gender) correlation coefficient of each LM with disease severity or patient demographics and comorbidities. LMs are ordered by the strength of correlation with disease severity. For gender, a positive correlation is correlated with male. (B) Corresponding p values of each LM patient condition correlation displayed as −log(p).

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The downward trend of PGs in severe disease and the strong negative correlation of this family of LMs with COVID-19 comorbidities suggested a role for these molecules in the differential immune response between moderate and severe disease. Products of COX, including the PGs, have been shown to play complex roles in regulating initiation and suppression of elements of inflammation in viral infection (33). In the context of COVID-19, the potentially negative effects of both inhibitors of COX and the COX product thromboxane (TXB2) in severe disease and death have been discussed, albeit inconclusively, further highlighting the complexity and multifunctional nature of this pathway (34, 35). In this study, lower levels of PGs, in the context of hospitalized COVID-19 patients, were associated with higher disease severity, but further investigation is needed to understand the mechanistic role PGs play in COVID-19 progression.

It remains unclear whether these LM patterns represent drivers of disease, arise from preexisting conditions, or COVID-19 related treatment. LMs operate as part of an expansive immune signaling network and broadly deconvoluting their role from other immune ligands will require controlled experimentation. Baseline PUFA and LM levels are known to be dysregulated in several of the comorbidities examined in this study (1113). Thus, although the LM levels in this study reflect the COVID-19 disease state, the differences between disease severity groups is likely to be affected by different starting levels of both LMs and LM precursors. Differences in LM levels could also arise from treatment differences. Unfortunately, in this cohort, therapies were either overrepresented or underrepresented to an extent that prevented correlative analysis and these therapies remain potentially confounding factors.

Together, the results presented in this study provide the first, to our knowledge, detailed description of the behavior of LMs in COVID-19 disease progression (26, 36). We provide evidence that a systemic lipid network consisting of liberation of PUFAs from plasmalogen and their subsequent conversion to LMs, capable of modulating inflammatory responses, characterizes both the onset and severity of COVID-19. Specifically, the loss of the immune-regulatory PGs and RvE3 and the increased products of ALOX5 and CYP provides both a measure of disease severity and a potentially mechanistic understanding of the immune balance allowing for patient recovery (37). Importantly, these pathways are directly targetable with drugs previously approved for use in other inflammatory conditions and, thus, provide new therapeutic opportunities to control severe COVID-19 (2, 5).

We are deeply indebted to the patients and families of patients for contribution to this study. Prof. Charles N. Serhan and his group including, K. Boyle, A. Shay, C. Jouvene, X. de la Rosa, S. Libreros, and N. Chiang generously provided methodology, consultation, and extensive training for the assessment of LMs by LC-MS/MS. AB Sciex, in particular, M. Pearson, P. Norris, and P. Baker (currently Avanti Polar Lipids), provided LC-MS/MS consultation and methods.

This work was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health (U19 supplement AI089992-09S2 to A.I.K. and C.S.D.C.). This work was supported by the Department of Internal Medicine at the Yale School of Medicine, the Yale School of Public Health, and the Beatrice Kleinberg Neuwirth Fund. L.S. is supported by Parker B. Francis Fellowship 0000. C.S.D.C. is supported by Veterans Affairs Merit Grant BX004661 and U.S. Department of Defense Grant PR181442.

The online version of this article contains supplemental material.

Abbreviations used in this article:

BMI

body mass index

COX

cyclooxygenase

CYP

cytochrome p450

LC-MS/MS

liquid chromatography tandem mass spectrometry

LM

lipid mediator

PUFA

polyunsaturated fatty acid

Rv

resolvin.

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Kelly Anastasio, Michael H. Askenase, Maria Batsu, Sean Bickerton, Kristina Brower, Molly L. Bucklin, Staci Cahill, Yiyun Cao, Edward Courchaine, Giuseppe DeIuliis, Bertie Geng, Laura Glick, Akiko Iwasaki, Nathan Grubaugh, Chaney Kalinich, William Khoury-Hanold, Daniel Kim, Lynda Knaggs, Maxine Kuang, Eriko Kudo, Joseph Lim, Melissa Linehan, Alice Lu-Culligan, Anjelica Martin, Irene Matos, David McDonald, Maksym Minasyan, M. Catherine Muenker, Nida Naushad, Allison Nelson, Jessica Nouws, Abeer Obaid, Camilla Odio, Saad Omer, Isabel Ott, Annsea Park, Hong-Jai Park, Mary Petrone, Sarah Prophet, Harold Rahming, Tyler Rice, Kadi-Ann Rose, Lorenzo Sewanan, Denise Shepard, Erin Silva, Michael Simonov, Mikhail Smolgovsky, Nicole Sonnert, Yvette Strong, Codruta Todeasa, Jordan Valdez, Sofia Velazquez, Pavithra Vijayakumar, Annie Watkins, Elizabeth B. White, Yexin Yang

The authors have no financial conflicts of interest.

Supplementary data