(2020) Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy,

Graphical abstract

More and more patients suffered from Coronavirus disease 2019 (COVID-19) have got recovery gradually due to suitable intervention. Increasing data mainly studies the clinical characteristics of recovered COVID-19 patients, and their molecular changes especially proteome changes also play the same important role in understanding of biological characteristics of recovered COVID-19 patients as clinical characteristics do. In our study, we reported the whole lung-ground glass-CT value-average of mild/severe recovered patients 3 months after discharge without underlying diseases was significantly lower than that of healthy subjects. Then we isolated the extracellular vesicles (EVs) of plasma from 19 healthy subjects and 67 recovered COVID-19 patients. Mass Spectrometry was used to catalogue the proteins of these EVs compared to a defined group of controls. Identified 174 proteins were differentially expressed in the EVs of COVID-19 patients compared with healthy subjects, which involved in lipid metabolic process, response to cellular, and response to stress oxygen-containing compound. Besides, we identified several protein of plasma EVs in recovered patients associated with coagulation activity, inflammatory reaction, immune response, and low organ function. In addition, proteins correlating with clinical index such as alkaline phosphatase (ALP) and alanine aminotransferase (ALT) were also detected. Moreover, we also identified many unique or characteristic associations found in the recovered COVID-19 patients, which especially involved the kidney, serum electrolyte levels, and inflammation functions. This finding suggests that monitoring the situation of recovered patients might be useful, especially the indexes of coagulation, inflammation, immunity, and organ function, which can prevent bleeding, reinfection and organ dysfunction.

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

No abstract

It has been reported that melatonin can relieve the symptoms of chronic obstructive pulmonary disease (COPD) by improving sleep quality, that is to say, the pineal secreted hormone melatonin has a protective effect in the pathogenesis of COPD, but its underlying mechanism remains unclear. In this study, we recruited 73 people into control (n = 22), stable COPD (n = 20), and acute exacerbation of COPD (n = 31) groups to detect the serum melatonin levels. Then, through the mouse model, we employed a systematic study based on the metabolomic and transcriptomic analyses to investigate the molecular mechanisms involved in the progression of the disease. Circulating melatonin in acute exacerbation of COPD patients was decreased compared with that in healthy donors and stable COPD patients. The serum melatonin level was positively correlated with lung function parameters, such as FEV1, FEV1/FVC, and FEV1% predicted in acute exacerbation of COPD patients. Animal experiments showed that melatonin can not only alleviate chronic lipopolysaccharide (LPS)-induced mouse lung destruction and chronic lung inflammation but also reduce necroptosis (RIP1/RIP3/MLKL), a programmed cell death process in bronchial epithelial cells. The protective effect of melatonin on chronic lung inflammation was further suggested to be dependent on targeting its membrane receptor MT1/MT2. In addition, transcriptomic and metabolomic profiling in the lungs of mice indicated that LPS can induce perturbations of the mainstream metabolites associated with amino acid and energy metabolism. Melatonin may reduce the necroptosis by modifying the disordered pathways of alanine, aspartate, and glutamate metabolism caused by LPS. This study suggests that melatonin may act as a potential therapeutic agent for alleviating the chronic inflammation associated with COPD.

– Acute lung injury (ALI) is a type of serious clinical syndrome leading to morbidity and mortality. However, the precise pathogenesis of ALI remains elusive. Here, we implemented an integrative meta-analysis of six GEO microarray studies with 76 samples in the ALI mouse model. A total of 958 differentially expressed genes (DEGs) were identified in LPS relative to normal samples. Then, a network-based meta-analysis was used to mine core DEGs and to unfold the interactions among these genes. We found that Ebi3 was the top upregulated genes in the LPS-induced ALI. GO, KEGG, and GSEA analyses were performed for functional annotation. qRT-PCR revealed augmented expression of six candidate genes ( Stat1 , Syk , Jak3 , Rac2 , Ripk1 , and Traf6 ) in the established ALI mouse model with LPS exposure. Taken together, our study investigated comprehensively hub DEGs and their networks for LPS-stimulated ALI, which might afford an additional approach to determine biomarkers and therapeutic targets and explore the molecular pathophysiology toward ALI. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10753-021-01518-8.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are life-threatening clinical conditions predominantly arising from uncontrolled inflammatory reactions. It has been found that the administration of astaxanthin (AST) can exert protective effects against lipopolysaccharide (LPS)-induced ALI; however, the robust genetic signatures underlying LPS induction and AST treatment remain obscure. Here we performed a statistical meta-analysis of five publicly available gene expression datasets from LPS-induced ALI mouse models, conducted RNA-sequencing (RNA-seq) to screen differentially expressed genes (DEGs) in response to LPS administration and AST treatment, and integrative analysis to determine robust genetic signatures associated with LPS-induced ALI onset and AST administration. Both the meta-analyses and our experimental data identified a total of 198 DEGs in response to LPS administration, and 11 core DEGs ( Timp1, Ly6i, Cxcl13, Irf7, Cxcl5, Ccl7, Isg15, Saa3, Saa1, Tgtp1, and Gbp11 ) were identified to be associated with AST therapeutic effects. Further, the 11 core DEGs were verified by quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC), and functional enrichment analysis revealed that these genes are primarily associated with neutrophils and chemokines. Collectively, these findings unearthed the robust genetic signatures underlying LPS administration and the molecular targets of AST for ameliorating ALI/ARDS which provide directions for further research.