Suppression of innate and acquired immunity in severe hand foot and mouth disease caused by EV71 infections in children.

Hand, foot, and mouth disease (HFMD) is a common children infectious disease caused by human enteroviruses. Most of the cases have minimal symptoms, however, some patients may develop serious neurological, cardiac complications, or even death. The pathological mechanism leading to severe HFMD is not clearly understood, and the immunological status of the individual patient may play an important role. Transcriptomes of peripheral blood mononuclear cells from EV71-infected patients (n = 45) and healthy controls (n = 36) were examined. Immune pathways were up-regulated in patients with mild disease symptoms (n = 11, M) compared to the healthy controls (n = 36, H), demonstrating an effective anti-viral response upon EV71 infection. However, in patients with severe symptoms (n = 23, S) as well as severe patients following treatment (n = 11, A), their innate and acquired immune pathways were down-regulated, indicating a global immunity suppression. Such immune suppression characteristics could thus provide an opportunity for early EV-71 infection prognosis prediction. Based on our cohort, an SVM model using RNA-seq expression levels of five genes (MCL1, ZBTB37, PLEKHM1P, IFNAR2 and YEATS2) was developed and achieved a high ROC-AUC (91·3%) in predicting severe HFMD. Meanwhile, qPCR fold-changes method was performed based three genes (MCL1, IFNAR2 and YEATS2) on additional cohort. This qPCR method achieved a ROC-AUC of 78.6% in predicting severe HFMD, which the patients could be distinguished in 2-3 h. Therefore, our models demonstrate the possibility of HFMD severity prediction based on the selected biomarkers that predict severe HFMD effectively.

Clinical characteristics and microbial signatures in the lower airways of diabetic and nondiabetic patients with pneumonia.

Background: The microbial signatures in diabetes with pneumonia and the risk factors of severe pneumonia (SP) in diabetic patients are not clear. Our study explored microbial signatures and the association between clinical characteristics and SP then constructed a risk model to find effective biomarkers for predicting pneumonia severity. Methods: Our study was conducted among 273 patients with pneumonia diagnosed and treated in our hospital from January 2018 to May 2021. Bronchoalveolar lavage fluid (BALF) samples and clinical data were collected. Metagenomic sequencing was applied after extracting the DNA from samples. Appropriate statistical methods were used to compare the microbial signatures and clinical characteristics in patients with or without diabetes mellitus (DM). Results: In total, sixty-one pneumonia patients with diabetes and 212 pneumonia patients without diabetes were included. Sixty-six differential microorganisms were found to be associated with SP in diabetic patients. Some microbes correlated with clinical indicators of SP. The prediction model for SP was established and the receiver operating characteristic (ROC) curve demonstrated its accuracy, with the sensitivity and specificity of 0.82 and 0.91, respectively. Conclusions: Some microorganisms affect the severity of pneumonia. We identified the microbial signatures in the lower airways and the association between clinical characteristics and SP. The predictive model was more accurate in predicting SP by combining microbiological indicators and clinical characteristics, which might be beneficial to the early identification and management of patients with SP.

A multicenter prospective study of comprehensive metagenomic and transcriptomic signatures for predicting outcomes of patients with severe community-acquired pneumonia.

BACKGROUND: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients. METHODS: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study. FINDINGS: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98). INTERPRETATION: In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP. FUNDING: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).