Role of EpCAM+ CD133+ extracellular vesicles in steatosis to steatohepatitis transition in NAFLD.

BACKGROUND AND AIMS: Extracellular vesicles (EVs) have emerged as a potential source of circulating biomarkers in liver disease. We evaluated circulating AV+ EpCAM+ CD133+ EVs as a potential biomarker of the transition from simple steatosis to steatohepatitis. METHODS: EpCAM and CD133 liver proteins and EpCAM+ CD133+ EVs levels were analysed in 31 C57BL/6J mice fed with a chow or high fat, high cholesterol and carbohydrates diet (HFHCC) for 52 weeks. The hepatic origin of MVs was addressed using AlbCrexmT/mG mice fed a Western (WD) or Dual diet for 23 weeks. Besides, we assessed plasma MVs in 130 biopsy-proven NAFLD patients. RESULTS: Hepatic expression of EpCAM and CD133 and EpCAM+ CD133+ EVs increased during disease progression in HFHCC mice. GFP+ MVs were higher in AlbCrexmT/mG mice fed a WD (5.2% vs 12.1%) or a Dual diet (0.5% vs 7.3%). Most GFP+ MVs were also positive for EpCAM and CD133 (98.3% and 92.9% respectively), suggesting their hepatic origin. In 71 biopsy-proven NAFLD patients, EpCAM+ CD133+ EVs were significantly higher in those with steatohepatitis compare to those with simple steatosis (286.4 ± 61.9 vs 758.4 ± 82.3; p < 0.001). Patients with ballooning 367 ± 40.6 vs 532.0 ± 45.1; p = 0.01 and lobular inflammation (321.1 ± 74.1 vs 721.4 ± 80.1; p = 0.001), showed higher levels of these EVs. These findings were replicated in an independent cohort. CONCLUSIONS: Circulating levels of EpCAM+ CD133+ MVs in clinical and experimental NAFLD were increased in the presence of steatohepatitis, showing high potential as a non-invasive biomarker for the evaluation and management of these patients.

Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission.

BACKGROUND: SARS-CoV-2 emerged as a new coronavirus causing COVID-19, and it has been responsible for more than 760 million cases and 6.8 million deaths worldwide until March 2023. Although infected individuals could be asymptomatic, other patients presented heterogeneity and a wide range of symptoms. Therefore, identifying those infected individuals and being able to classify them according to their expected severity could help target health efforts more effectively. METHODOLOGY/PRINCIPAL FINDINGS: Therefore, we wanted to develop a machine learning model to predict those who will develop severe disease at the moment of hospital admission. We recruited 75 individuals and analysed innate and adaptive immune system subsets by flow cytometry. Also, we collected clinical and biochemical information. The objective of the study was to leverage machine learning techniques to identify clinical features associated with disease severity progression. Additionally, the study sought to elucidate the specific cellular subsets involved in the disease following the onset of symptoms. Among the several machine learning models tested, we found that the Elastic Net model was the better to predict the severity score according to a modified WHO classification. This model was able to predict the severity score of 72 out of 75 individuals. Besides, all the machine learning models revealed that CD38+ Treg and CD16+ CD56neg HLA-DR+ NK cells were highly correlated with the severity. CONCLUSIONS/SIGNIFICANCE: The Elastic Net model could stratify the uninfected individuals and the COVID-19 patients from asymptomatic to severe COVID-19 patients. On the other hand, these cellular subsets presented here could help to understand better the induction and progression of the symptoms in COVID-19 individuals.