Deep spatial proteomic exploration of severe COVID-19-related pulmonary injury in post-mortem specimens (preprint)

The lung, as a primary target of SARS-CoV-2, exhibits heterogeneous microenvironment accompanied by various histopathological changes following virus infection. However, comprehensive insight into the protein basis of COVID-19-related pulmonary injury with spatial resolution is currently deficient. Here, we generated a region-resolved quantitative proteomic atlas of seven major pathological structures within the lungs of COVID-19 victims by integrating histological examination, laser microdissection, and ultrasensitive proteomic technologies. Over 10,000 proteins were quantified across 71 dissected FFPE post-mortem specimens. By comparison with control samples, we identified a spectrum of COVID-19-induced protein and pathway dysregulations in alveolar epithelium, bronchial epithelium, and pulmonary blood vessels, providing evidence for the proliferation of transitional-state pneumocytes. Additionally, we profiled the region-specific proteomes of hallmark COVID-19 pulmonary injuries, including bronchiole mucus plug, pulmonary fibrosis, airspace inflammation, and hyperplastic alveolar type 2 cells. Bioinformatic analysis revealed the enrichment of cell-type and functional markers in these regions (e.g. enriched TGFBI in fibrotic region). Furthermore, we identified the up-regulation of proteins associated with viral entry, host restriction, and inflammatory response in COVID-19 lungs, such as FURIN and HGF. Collectively, this study provides spatial proteomic insights for understanding COVID-19-caused pulmonary injury, and may serve as a valuable reference for improving therapeutic intervention for severe pneumonia.

Alveolar regeneration following viral infection is independent of tuft cells (preprint)

Severe injuries following viral infection cause lung epithelial destruction with the presence of ectopic basal progenitor cells (EBCs), although the exact function of EBCs remains controversial. We and others previously showed the presence of ectopic tuft cells in the disrupted alveolar region following severe influenza infection. Here, we further revealed that the ectopic tuft cells are derived from EBCs. This process is amplified by Wnt signaling inhibition but suppressed by Notch inhibition. Further analysis revealed that p63-CreER labeled population de novo arising during regeneration includes alveolar epithelial cells when Tamoxifen was administrated after viral infection. The generation of the p63-CreER labeled alveolar cells is independent of tuft cells, demonstrating segregated differentiation paths of EBCs in lung repair. EBCs and ectopic tuft cells can also be found in the lung parenchyma post SARS-CoV-2 infection, suggesting a similar response to severe injuries in humans.

Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China (preprint)

Background: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). Methods: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. Results: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. Conclusion: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Discussion on the Management of Fever Clinic during the Epidemic Period of Corona Virus Disease 2019 / 中华急诊医学杂志

Objective@#To investigate the role of epidemiological history in the screening of Corona Virus Disease 2019 (COVID-19) in fever clinic, to improve the efficiency in fever clinic and reduce the incidence of cross infection.@*Methods@#This is a retrospective study. Patients who were admitted to the fever clinic in West China Hospital of Sichuan University from January 23th, 2020 to February 11th, 2020 included the study. According to epidemiological history, the patients were divided into epidemiological history group (the experimental group) and no epidemiological history group (the control group). The two groups of patients were admitted and treated separately. The clinical data, NEWS score, etiology results, viral pneumonia showed on CT, time of visit, COVID-19 patient ratio, and admission composition ratio were compared between the two groups. The measurement data were presented as the mean ± standard deviation (SD), and the numeration data were expressed as ratio or constituent ratio. The measurement data of normal distribution between the two groups were compared by independent sample t test. The measurement data of skewed distribution are expressed by the median (interquartile range), and the comparison between the two groups is tested by non-parameter. The differences between enumeration data were assessed by chi-square test. A P<0.05 was considered statistically significant.@*Results@#A total of 2423 patients were included, including 927 patients in the experimental group and 1296 patients in the control group. There were no significant differences in gender, NEWS score and clinical symptoms between the two groups (P> 0.05). The age (35.00 ± 12.80 vs 38.13 ± 15.57 years) , the proportion of fever patients (28.80% vs 32.75%) and waiting time (31.72 vs 58.08 min) of the experimental group were lower than the control group, the difference was statistically significant (P <0.05). The CT examination ratio (37.54% vs 20.39%), viral pneumonia ratio showed on CT (9.77% vs 2.95%), ratio of examined COVID-19 nucleic acid test (85.44% vs 56.75%), and the admission ratio (16.72% vs 9.63%) of the experimental group were higher than the control group, and the differences were statistically significant (P <0.05); There was no significant difference in the positive rates of influenza virus and rhinovirus between the two groups (P> 0.05).@*Conclusion@#It is necessary to adjust the management mode of fever clinic during the Corona Virus Disease 2019, and to manage the patients according to the epidemiological history which can improve the screening efficiency and reduce the risk of cross infection.