Immunometabolic analysis shows a distinct cyto-metabotype in Covid-19 compared to sepsis from other causes

Abstract

Background: In Covid-19, profound systemic inflammatory responses are accompanied by both metabolic risk factors for severity and, separately, metabolic mechanisms have been shown to underly disease progression. It is unknown whether this reflects similar situations in sepsis or is a unique characteristic of Covid-19.

Aims: Define the immunometabolic signature of Covid-19.

Methods: 65 patients with Covid-19,19 patients with sepsis and 14 healthy controls were recruited and sampled for plasma, serum and peripheral blood mononuclear cells (PBMCs) through 10 days of critical illness. Metabotyping was performed using the Biocrates p180 kit and multiplex cytokine profiling undertaken. PBMCs underwent phenotyping by flow cytometry. Immune and metabolic readouts were integrated and underwent pathway analysis.

Results: Phopsphatidylcholines (PC) are reduced in Covid-19 but greater than in sepsis. Compared to controls, tryptophan is reduced in Covid-19 and inversely correlated with the severity of the disease and IFN-ɣ concentrations, conversely the kyneurine and kyneurine/tryptophan ratio increased in the most severe cases. These metabolic changes were consistent through 2 pandemic waves in our centre. PD-L1 expression in CD8+ T cells, Tregs and CD14+ monocytes was increased in Covid-19 compared to controls.

Conclusions: In our cohort, Covid-19 is associated with monocytopenia, increased CD14+ and Treg PD-L1 expression correlating with IFN-ɣ plasma concentration and disease severity (SOFA score). The latter is also associated with metabolic derangements of Tryptophan, LPC 16:0 and PCs. Lipid metabolism, in particular phosphatidylcholines and lysophosphatidylcolines, seems strictly linked to immune response in Covid-19. Our results support the hypothesis that IFN-ɣ -PD-L1 axis might be involved in the cytokine release syndrome typical of severe Covid-19 and the phenomenon persisted through multiple pandemic waves despite use of immunomodulation.

Keywords: Covid-19; Immune checkpoint; Immunometabolism; Phosphatidylcholine; Tryptophan.

Figure 1

A) Gating strategy for CD14+ cells. B) PD-L1 expression in CD14+ cells of healthy controls, Covid -19 positive and septic patients. Results are presented as both Mean Fluorescence Intensity and % of CD14+ cells. C) Gating strategy for CD3+ cells, CD4+, CD8+ and CD25+ CD127-cells (Tregs). D) PD-L1 expression in CD3+ cells (CD4+, CD8+ and Tregs) of healthy controls, Covid -19 positive and septic patients. Results are presented as both Mean Fluorescence Intensity and % of CD3+ cells.

Figure 2

A) Pro inflammatory Cytokines are increased in sepsis and Covid-19 compared to healthy controls (HC). Interferon (IFN)-ɣ, Interleukin (IL)-10, IL-1 beta, IL-6, Tumor Necrosis Factor (TNF) alpha and IL-8. B) Interferon (IFN)-ɣ is the only proinflammatory cytokine that showed progressive decrease on sequential samples (day 1,3,7,10) in COVID-19 patients. Median and 95% CI. C) Osteopontin (OPN) is increased in septic patients both with radiological and clinical feature of COVID-19 (Supected = susp) and with positive PCR (Covid). D) Correlation Matrix showing Interferon-gamma is directly correlated to C-reacting protein (CRP), Sofa score, osteopontin and PD-L1 expression in Monocytes.

Figure 3

A) Multivariate analysis including 180 metabolites (Biocrates). I) PCA analysis. Covid-19 positive patients have distinct features compared to septic patients and healthy controls. II) OPLS-DA analysis including Covid -19 positive patients versus healthy control. III) S-plot identifying the panel of metabolites responsible for the greatest variance between Covid-19 positive patients and healthy controls. Covid-19 n = 65, Sepsis n = 19, healthy control n = 14. B) Enrichment Analysis showing the main classes of metabolites. Data analysed with Metaboanalyst, a publicly available platform dedicated for metabolomics data analysis, including several libraries containing about 9,000 metabolite sets from human studies. C) Univariariate analysis comparing Covid-19 positive, septic patients, and healthy controls. Phosphatidylcholines (PCs,) Lysophosphatidylcholine (LPC) 16.0, Tryptophan (Trp) are reduced in sepsis more than in Covid 19, comparing to HC. Glutamine (Gln), Leucine (Leu), Kynurenine and phenylalanine (Phe) are increased in Covid-19 patients. D) Phosphatidylcholines increase progressively during hospital admission (Day 1,3,7,10).

Figure 4

A) I) PLS analysis in Covid-19 positive patients using Sequential Organ Failure Assessment (SOFA) score as Y variable. II) PCA analysis failed to identify a panel of metabolites able to predict mortality at 90 days in patients affected by Covid-19. B) PCs are directly correlated with SOFA score, LPC is inversely correlated with the severity of the disease. C) Autotaxin catalyse the conversion of lysophosphatidylcholine (LPC) into Lysophosphatidylcholinic Acid (LPA) and is homogeneously expressed in healthy controls, Covid-19 positive patients (at admission Day 1 and one week after admission, Day 7) and septic patients. Excluding a Covid-19 specific role of the enzyme. Lysophosphatidylcholine (LPC) 16.0 is increased at one week after admission in Covid-19 positive patients. Both PLA1 and PLA 2 expression, measured by ELISA, is increased in Covid-19 compared to controls. D) Kyneurin (Kyn)/Tryptophan (Trp) ratio was significantly increased in Covid-19 and septic patients compared to controls. Trp is significantly reduced with the increased severity of the disease expressed by SOFA score, conversely Kyneurine is increased.

Figure 5

A) Univariate analysis comparing Phosphatidylcholines (PCs), Lysophosphatisylcholine (LPC)16.0, Glutamine (Gln), Tryptophan (Trp), Kyneurine (Kyn) in patients recovered from Covid-19 (alive) and patients dead at 90 days post admission. No one of the metabolites identified was significantly different between the two groups. B) No difference was found in phosphatidylchlines (PCs), Tryptophan (Trp), Kyneurine and Glutamine (Gln) between patients on steroids vs patients managed without steroid therapy. C) Correlation matrix showing numbers indicate Spearman r and intensity of colour indicate the strength of correlations. White blood cell count (WBC), Neutrophils count (Neutr), Lymphocyte count (Lymph), Monocytes coutn (Mono), C-reactive protein (CRP), Sequential Organ failure assessment score (SOFA), Interlerukin (IL) 10, 6 and 8, Tumour Necrosis factor (TNF) alpha, Glutamine (Gln), Tryptophan (Trp) Lysophosphatidylcholine (LPC) 16, Phosphatidylcholine (PC).