RESUMEN
BACKGROUND: Divergence between deterioration to life-threatening COVID-19 or clinical improvement occurs for most within the first 14 days of symptoms. Life-threatening COVID-19 shares clinical similarities with Macrophage Activation Syndrome, which can be driven by elevated Free Interleukin-18 (IL-18) due to failure of negative-feedback release of IL-18 binding protein (IL-18bp). We, therefore, designed a prospective, longitudinal cohort study to examine IL-18 negative-feedback control in relation to COVID-19 severity and mortality from symptom day 15 onwards. METHODS: 662 blood samples, matched to time from symptom onset, from 206 COVID-19 patients were analysed by enzyme-linked immunosorbent assay for IL-18 and IL-18bp, enabling calculation of free IL-18 (fIL-18) using the updated dissociation constant (Kd) of 0.05 nmol. Adjusted multivariate regression analysis was used to assess the relationship between highest fIL-18 and outcome measures of COVID-19 severity and mortality. Re-calculated fIL-18 values from a previously studied healthy cohort are also presented. RESULTS: Range of fIL-18 in COVID-19 cohort was 10.05-1157.7 pg/ml. Up to symptom day 14, mean fIL-18 levels increased in all patients. Levels in survivors declined thereafter, but remained elevated in non-survivors. Adjusted regression analysis from symptom day 15 onwards showed a 100 mmHg decrease in PaO2/FiO2 (primary outcome) for each 37.7 pg/ml increase in highest fIL-18 (p < 0.03). Per 50 pg/ml increase in highest fIL-18, adjusted logistic regression gave an odds-ratio (OR) for crude 60-day mortality of 1.41 (1.1-2.0) (p < 0.03), and an OR for death with hypoxaemic respiratory failure of 1.90 [1.3-3.1] (p < 0.01). Highest fIL-18 was associated also with organ failure in patients with hypoxaemic respiratory failure, with an increase of 63.67 pg/ml for every additional organ supported (p < 0.01). CONCLUSIONS: Elevated free IL-18 levels from symptom day 15 onwards are associated with COVID-19 severity and mortality. ISRCTN: #13450549; registration date: 30/12/2020.
RESUMEN
Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.