CD4+ cytotoxic T lymphocytes (CTLs) were recently implicated in immune-mediated inflammation and fibrosis progression of Graves' orbitopathy (GO). However, little is known about therapeutic targeting CD4+ CTLs. Herein, we studied the effect of rapamycin, an approved mammalian target of rapamycin complex 1(mTORC1) inhibitor, in GO mouse model, in vitro and in refractory GO patients. In the adenovirus-induced model, rapamycin significantly decreased the incidence of GO. This was accompanied by reduction of both CD4+ CTLs, as well as reduction of orbital inflammation, adipogenesis and fibrosis. CD4+CTLs from active GO patients showed upregulation of mTOR pathway, while rapamycin decreased their proportions and cytotoxic function. Low-dose rapamycin treatment substantially improved diplopia and clinical activity score in steroid-refractory GO patients. Single-cell RNA sequencing revealed that eye motility improvement was closely related to suppression of inflammation and chemotaxis in CD4+ CTLs. In conclusion, rapamycin is a promising treatment for CD4+ CTL-mediated inflammation and fibrosis in GO.
IntroductionFrequent exacerbation phenotype of chronic obstructive pulmonary disease (COPD) represents a more concerning disease subgroup requiring better prevention and intervention, of which airway microbiome provides new perspective for further exploration.MethodsTo investigate whether frequent exacerbators of COPD have distinguishable sputum microbiome during clinical stability, COPD patients at high disease grades with or without frequent exacerbation were recruited for sputum microbiome analysis. Sputum samples were collected during clinical stability and underwent 16S rRNA sequencing, which was then subjected for amplicon sequence variants (ASVs)-based microbiome analysis.ResultsOur results revealed that compared with healthy controls and infrequent exacerbators, frequent COPD exacerbators have distinguishably dysbiotic sputum microbiome, as featured by fewer ASVs features, lower alpha diversity, distinct beta diversity patterns. Further taxonomic compositional analysis illustrated the structural distinctions between frequent COPD exacerbators and infrequent exacerbators at differential taxa levels and highlighted Stenotrephomonas due to its prominent elevation in frequent COPD exacerbators, providing a promising candidate for further exploration of microbiome biomarker. Moreover, we also demonstrated that frequent exacerbation phenotype is distinguishable from infrequent exacerbation phenotype with respect of functional implications.ConclusionOur study demonstrated the first positive correlation between the frequent exacerbation phenotype of COPD and the sputum microbiome during clinical stability in a single-center Chinese COPD cohort and provide potential diagnostic and therapeutic targets for further investigation.
Motivation Tumor purity is a fundamental property of each cancer sample and affects downstream investigations. Current tumor purity estimation methods either require matched normal sample or report moderately high tumor purity even on normal samples. It is critical to develop a novel computational approach to estimate tumor purity with sufficient precision based on tumor-only sample. Results In this study, we developed MEpurity, a beta mixture model-based algorithm, to estimate the tumor purity based on tumor-only Illumina Infinium 450k methylation microarray data. We applied MEpurity to both The Cancer Genome Atlas (TCGA) cancer data and cancer cell line data, demonstrating that MEpurity reports low tumor purity on normal samples and comparable results on tumor samples with other state-of-art methods. Availability and implementation MEpurity is a C++ program which is available at https://github.com/xjtu-omics/MEpurity. Supplementary information Supplementary data are available at Bioinformatics online.
Phylogenetic tree is essential to understand evolution and it is usually constructed through multiple sequence alignment, which suffers from heavy computational burdens and requires sophisticated parameter tuning. Recently, alignment free methods based on k-mer profiles or common substrings provide alternative ways to construct phylogenetic trees. However, most of these methods ignore the global similarities between sequences or some specific valuable features, e.g., frequent patterns overall datasets. To make further improvement, we propose an alignment free algorithm based on sequential pattern mining, where each sequence is converted into a binary representation of sequential patterns among sequences. The phylogenetic tree is further constructed via clustering distance matrix which is calculated from pattern vectors. To increase accuracy for highly divergent sequences, we consider pattern weight and filtering redundancy sub-patterns. Both simulated and real data demonstrates our method outperform other alignment free methods, especially for large sequence set with low similarity.
The microbiome is prevalent throughout human bodies, with profound health implications. However, it remains unclear whether it is present and active in human CSF, which has been long considered aseptic due to the blood-brain barrier.
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