Ontology highlight
ABSTRACT:
PROVIDER: PRJNA974858 | ENA |
REPOSITORIES: ENA
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Project description:The role of gut microbiome dysbiosis in the pathogenesis of psoriasis has gained increasing attention in recent years. Secukinumab, targeting interleukin (IL)-17, has a promising efficacy in psoriasis treatment. However, it remains unclear the gut microbiota alteration and related functional changes caused by successful secukinumab therapy in psoriatic patients. In our study, we compared fecal microbiome profile between psoriatic patients after secukinumab successful treatment (AT) and the other two groups, psoriatic patients without therapy (BT) and healthy people (H), respectively by using next-generation sequencing targeting 16S ribosomal RNA. Then, shotgun metagenomic sequencing was firstly used to characterize bacterial gut microbial communities and related functional change in AT group. We found that the diversity and structure of the microbial community in AT group were significantly changed compared to that of BT group and H group. AT group showed a microbiota profile characterized by increased proportions of the phylum Firmicute, families Ruminococcaceae, and a reduction in the phylum Bacteroidota (elevated F/B ratio). To detect functional alteration, we discovered that secukinumab treatment may construct a more stable homeostasis of gut microbiome with functional alteration. There were different KEGG pathways such as downregulated cardiovascular diseases pathway and upregulated infectious diseases in AT group. By metagenomic analysis, metabolic functional pathway was changed after secukinumab therapy. It seems that gut microbiota investigation during biologic drug treatment is useful for predicting the efficacy and risks of drug treatment in disease.
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Project description:In this study, we performed a comparative analysis of gut microbiota composition and gut microbiome-derived bacterial extracellular vesicles (bEVs) isolated from patients with solid tumours and healthy controls. After isolating bEVs from the faeces of solid tumour patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of faeces from patientsand controls using 16S rRNA sequencing. Machine learning was used to classify the samples into patients and controls based on their bEVs and faecal microbiomes.
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