Immunometabolic features of natural killer cells are associated with infection outcomes in critical illness

Abstract

Immunosuppression increases the risk of nosocomial infection in patients with chronic critical illness. This exploratory study aimed to determine the immunometabolic signature associated with nosocomial infection during chronic critical illness. We prospectively recruited patients who were admitted to the respiratory care center and who had received mechanical ventilator support for more than 10 days in the intensive care unit. The study subjects were followed for the occurrence of nosocomial infection until 6 weeks after admission, hospital discharge, or death. The cytokine levels in the plasma samples were measured. Single-cell immunometabolic regulome profiling by mass cytometry, which analyzed 16 metabolic regulators in 21 immune subsets, was performed to identify immunometabolic features associated with the risk of nosocomial infection. During the study period, 37 patients were enrolled, and 16 patients (43.2%) developed nosocomial infection. Unsupervised immunologic clustering using multidimensional scaling and logistic regression analyses revealed that expression of nuclear respiratory factor 1 (NRF1) and carnitine palmitoyltransferase 1a (CPT1a), key regulators of mitochondrial biogenesis and fatty acid transport, respectively, in natural killer (NK) cells was significantly associated with nosocomial infection. Downregulated NRF1 and upregulated CPT1a were found in all subsets of NK cells from patients who developed a nosocomial infection. The risk of nosocomial infection is significantly correlated with the predictive score developed by selecting NK cell-specific features using an elastic net algorithm. Findings were further examined in an independent cohort of COVID-19-infected patients, and the results confirm that COVID-19-related mortality is significantly associated with mitochondria biogenesis and fatty acid oxidation pathways in NK cells. In conclusion, this study uncovers that NK cell-specific immunometabolic features are significantly associated with the occurrence and fatal outcomes of infection in critically ill population, and provides mechanistic insights into NK cell-specific immunity against microbial invasion in critical illness.

Keywords: CPT1a; NRF1; chronic critical illness; metabolism; natural killer cells; nosocomial infection.

Figure 1

Overview of study and the single-cell immunometabolic regulomic profiling (scMEP) process. Patients with chronic critical illness, defined as intensive care unit (ICU) hospitalization with mechanical ventilation support for more than 10 days, are followed to determine the clinical outcome, the occurrence of nosocomial infection. Samples are analyzed through scMEP, which employs cytometry by time of flight (CyTOF), automated data processing, and manual gating to determine immune cell abundances and to evaluate the immunometabolic regulome. (TEMRA CD4/CD8, terminally differentiated effector memory CD4+/CD8+ T lymphocyte; Treg, regulatory T lymphocyte; γδT, γδT lymphocyte; MAIT, mucosal-associated invariant T lymphocyte; NKT, natural killer T lymphocyte; NK, natural killer cells; GLUT1, glucose transporter 1; HK2, hexokinase 2; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LDH, lactate dehydrogenase; HADHA, hydroxyacyl-CoA dehydrogenase trifunctional multi-enzyme complex subunit α; ACADM, acyl-CoA dehydrogenase medium chain; CPT1a, carnitine pamitoyltransferase 1a; DRP1, dynamin-related protein 1; OPA1, optic atrophy type 1; PGC1α, peroxisome proliferator-activated receptor γ coactivator 1α; NRF1, nuclear respiratory factor 1; CS, citrate synthase; OGDH, oxoglutarate dehydrogenase; OXPHOS, oxidative phosphorylation; CytC, cytochrome C; ATP5a, ATP synthase F1 subunit alpha; VDAC, voltage-dependent anion channel).

Figure 2

Manual gating algorithm to identify various immune cell subsets by mass cytometric analyses.

Figure 3

Unsupervised immunologic clustering of the study population. (A) Stress versus dimension plot is generated to evaluate the ideal number of the dimensions for multidimensional scaling (MDS) analysis. (B) Area under receiver operating characteristics curve (AUROC) and associated 95% confidence interval are calculated for assessing the performance of MDS coordinate 3 in predicting the risk of nosocomial infection. (C) Three-dimensional MDS plot is generated to visualize the clustering of the study population based on the development of nosocomial infection. (D, E) Twenty-seven immunometabolic features are significantly correlated with coordinate 3 of the MDS plot (also see Table 3 ). AUROC (D) and odds ratio (E) of nosocomial infection are calculated for these 23 features. The odds ratio and the p value are determined by logistic regression analyses.

Figure 4

Levels of NRF1 and CPT1a expression in NK cells are correlated with occurrence of nosocomial infection (NI). (A, B) Plots of NRF1 (A), and CPT1a (B) levels in indicated NK cell populations. The lines indicated mean ± standard deviation, and the p values are determined by Mann-Whitney U tests (** p< 0.01, * p< 0.05). (C, D) Uniform manifold approximation and projection (UMAP) plots visualizing indicated marker expression in all cells from patients with and those without NI. The major cell groups are annotated (C), and the expression levels of (D) CPT1a and NRF1 in different immune cell subsets are demonstrated.

Figure 5

Elastic net logistic regression is applied to identify NK cell-specific features predictive of nosocomial infection risk. (A) Three of 11 NK cell-related features, which are significantly associated with coordinate 3 of the multidimensional scaling plot, are selected using elastic net algorithm, and are included to generate the predictive score for assessing the risk of nosocomial infection. (B) Predictive scores based on elastic net models for patients who develop nosocomial infection and those who do not. Horizontal line indicates median, boxes indicate interquartile range, and the upper and lower whiskers extended to the largest and the smallest value at most 1.5 interquartile range from the upper and the lower hinges, respectively. Outliers beyond the ends of the whiskers are plotted individually. The p value is calculated by the Mann-Whitney U test. (C) Area under receiver operating characteristics curve (AUROC) is calculated for assessing the performance of the predictive score in evaluating the risk of nosocomial infection.

Figure 6

Correlation of NK cell-specific immunometabolic features with the disease severity and clinical outcome in critical COVID-19 infection. (A) Diagram outlining the method for validation utilizing publicly available single-cell RNA sequencing (scRNA-seq) data from fourteen COVID-19 patients including three from the moderate group, six from the severe group, and five from the deceased group. (B) t-distributed stochastic neighbor embedding (tSNE) plots of scRNA-seq data showing the major cell types, each labeled with a distinct color. (C) The lineage specific marker genes for each cell types are shown. The expression levels of indicated genes are color coded. (D, E) Relative expression of the mitochondrial fatty acid β-oxidation pathway (D) and the mitochondrial biogenesis pathway (E) across patient groups are exhibited through violin plots of z-scores for genes involved in specific pathways. The lines indicate median and interquartile range. The p values are calculated using Kruskal-Wallis test and adjusted for multiple comparisons using the Dunn’s method. (**** p< 0.0001).