A burns and COVID-19 shared stress responding gene network deciphers CD1C-CD141- DCs as the key cellular components in septic prognosis (preprint)

Differential body responses to various stresses, infectious or noninfectious, govern clinical outcomes ranging from asymptoma to death. However, the common molecular and cellular nature of the stress responsome across different stimuli is not described. In this study, we compared the expression behaviors between burns and COVID-19 infection by choosing the transcriptome of peripheral blood from related patients as the analytic target since the blood cells reflect the systemic landscape of immune homeostasis. We identified an immune co-stimulator (CD86)-centered network, named stress-response core (SRC), which coordinated multiple immune processes and was robust in membership and highly related to the clinical traits in both burns and COVID-19. An independent whole blood single-cell RNA sequencing of COVID-19 patients demonstrated that the monocyte-dendritic cell (Mono-DC) wing was the major cellular source of the SRC, among which the higher expression of the SRC in the monocyte was associated with the asymptomatic COVID-19 patients, while the quantity-restricted and function-defected CD1C-CD141- DCs were recognized as the key signature which linked to bad consequences in COVID-19. Specifically, the proportion of the CD1C-CD141- DCs and their SRC expression levels were step-wise reduced along with worse clinic conditions while the sub-cluster of CD1C-CD141- DCs of the critical COVID-19 patients was characterized of IFN signaling quiescence, high mitochondrial metabolism and immune-communication inactivation. Thus, our study identified an expression-synchronized and function-focused gene network which was decreased under burns and COVID-19 stress and argued the CD1C-CD141- DC as the prognosis-related cell population which might serve as a new target of diagnosis and therapy.

Serum peptidome profiles immune response of COVID-19 Vaccine administration.

Background: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused significant loss of life and property. In response to the serious pandemic, recently developed vaccines against SARS-CoV-2 have been administrated to the public. Nevertheless, the research on human immunization response against COVID-19 vaccines is insufficient. Although much information associated with vaccine efficacy, safety and immunogenicity has been reported by pharmaceutical companies based on laboratory studies and clinical trials, vaccine evaluation needs to be extended further to better understand the effect of COVID-19 vaccines on human beings. Methods: We performed a comparative peptidome analysis on serum samples from 95 participants collected at four time points before and after receiving CoronaVac. The collected serum samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile the serum peptides, and also subjected to humoral and cellular immune response analyses to obtain typical immunogenicity information. Results: Significant difference in serum peptidome profiles by MALDI-TOF MS was observed after vaccination. By supervised statistical analysis, a total of 13 serum MALDI-TOF MS feature peaks were obtained on day 28 and day 42 of vaccination. The feature peaks were identified as component C1q receptor, CD59 glycoprotein, mannose-binding protein C, platelet basic protein, CD99 antigen, Leucine-rich alpha-2-glycoprotein, integral membrane protein 2B, platelet factor 4 and hemoglobin subunits. Combining with immunogenicity analysis, the study provided evidence for the humoral and cellular immune responses activated by CoronaVac. Furthermore, we found that it is possible to distinguish neutralizing antibody (NAbs)-positive from NAbs-negative individuals after complete vaccination using the serum peptidome profiles by MALDI-TOF MS together with machine learning methods, including random forest (RF), partial least squares-discriminant analysis (PLS-DA), linear support vector machine (SVM) and logistic regression (LR). Conclusions: The study shows the promise of MALDI-TOF MS-based serum peptidome analysis for the assessment of immune responses activated by COVID-19 vaccination, and discovered a panel of serum peptides biomarkers for COVID-19 vaccination and for NAbs generation. The method developed in this study can help not only in the development of new vaccines, but also in the post-marketing evaluation of developed vaccines.

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control.

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

Isothermal gene amplification coupled MALDI-TOF MS for SARS-CoV-2 detection.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for more than a year and has undergone several mutations and evolutions. Due to the lack of effective therapeutics and long-active vaccines, accurate and large-scale screening and early diagnosis of infected individuals are crucial to control the pandemic. Nevertheless, the current widely used RT-qPCR-based methods suffer from complicated temperature control, long processing time and the risk of false-negative results. Herein, we present a three-way junction induced exponential rolling circle amplification (3WJ-eRCA) combined MALDI-TOF MS assay for SARS-CoV-2 detection. The assay can detect simultaneously the target nucleocapsid (N) and open reading frame 1 ab (orf1ab) genes of SARS-CoV-2 in a single test within 30 min, with an isothermal process (55 °C). High specificity to discriminate SARS-CoV-2 from other coronaviruses, like SARS-CoV, MERS-CoV and bat SARS-like coronavirus (bat-SL-CoVZC45), was observed. We have further used the method to detect pseudovirus of SARS-CoV-2 in various matrices, e.g. water, saliva and urine. The results demonstrated a great potential of the method for large scale screening of COVID-19, which is an important part of the pandemic control.

Prediction of Disease Progression of COVID-19 Based upon Machine Learning.

BACKGROUND: Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression. METHODS: In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models. RESULTS: A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k-nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector-machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed. CONCLUSION: The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

A Lightweight Internet Sharing Scheme for Sectional Medical Images according to Existing Hospital Network Facilities and Basic Information Security Rules.

Background: With the outbreak of COVID-19, large-scale telemedicine applications can play an important role in the epidemic areas or less developed areas. However, the transmission of hundreds of megabytes of Sectional Medical Images (SMIs) from hospital's Intranet to the Internet has the problems of efficiency, cost, and security. This article proposes a novel lightweight sharing scheme for permitting Internet users to quickly and safely access the SMIs from a hospital using an Internet computer anywhere but without relying on a virtual private network or another complex deployment. Methods: A four-level endpoint network penetration scheme based on the existing hospital network facilities and information security rules was proposed to realize the secure and lightweight sharing of SMIs over the Internet. A "Master-Slave" interaction to the interactive characteristics of multiplanar reconstruction and maximum/minimum/average intensity projection was designed to enhance the user experience. Finally, a prototype system was established. Results: When accessing SMIs with a data size ranging from 251.6 to 307.04 MB with 200 kBps client bandwidth (extreme test), the network response time to each interactive request remained at approximately 1 s, the original SMIs were kept in the hospital, and the deployment did not require a complex process; the imaging quality and interactive experience were recognized by radiologists. Conclusions: This solution could serve Internet medicine at a low cost and may promote the diversified development of mobile medical technology. Under the current COVID-19 epidemic situation, we expect that it could play a low-cost and high-efficiency role in remote emergency support.

Stem cell therapy: a potential approach for treatment of influenza virus and coronavirus-induced acute lung injury.

Acute lung injury (ALI), an increasingly devastating human disorder, is characterized by a multitude of lung changes arising from a wide variety of lung injuries. Viral infection is the main cause of morbidity and mortality in ALI and acute respiratory distress syndrome (ARDS) patients. In particular, influenza virus, coronavirus, and other respiratory viruses circulate in nature in various animal species and can cause severe and rapidly spread human infections. Although scientific advancements have allowed for rapid progress to be made to understand the pathogenesis and develop therapeutics after each viral pandemic, few effective methods to treat virus-induced ALI have been described. Recently, stem cell therapy has been widely used in the treatment of various diseases, including ALI. In this review, we detail the present stem cell-based therapeutics for lung injury caused by influenza virus and the outlook for the future state of stem cell therapy to deal with emerging influenza and coronaviruses.