Journal of translational medicine · 2025
Changes of gastric microflora and metabolites in patients with chronic atrophic gastritis.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ma Yumei, Jiang Jianming, Yang Zhufeng, Li Yongzhang, Bai Haiyan, Liu Zongxiu, Zhang Shuo, Zhi Zheng, Yang Qian
Аннотация
BACKGROUND: Chronic atrophic gastritis (CAG) is related to the body's microbial and metabolic systems. Combined studies of microbiome and metabolomics can clarify the mechanisms of disease occurrence and progression. We used 16S rRNA sequencing, metagenomics sequencing and metabolomics sequencing to depict the landscapes of bacterium and metabolites, construct correlation networks of different bacterium and metabolites describe potential pathogenic mechanisms of chronic atrophic gastritis.
METHODS: The gastric juices of 30 non-atrophic gastritis (NAG) patients and 30 CAG patients were collected. Gastric microflora was analyzed by 16S rRNA sequencing and metagenomics sequencing. Gastric metabolites were analyzed by LC-MS analysis. Different bioinformatics methods were used to analyze the data of microbiome and metabolome, and to analyze the relationship between them.
RESULTS: In atrophic gastritis, bacteria diversity decreased. The genera with a mean decrease in Gini greater than 1.5 included peptostreptococcus, fusobacterium, prevotella, sphingomonas and bacteroides. KEGG pathway included renal cell carcinoma, proximal tubule bicarbonate reclamation, citrate cycle and aldosterone synthesis and secretion with significant enrichment of differential metabolites. Peptostreptococcus, fusobacterium, prevotella and sphingomonas were in pivot positions of the correlation network of differential metabolites and differential bacterium. Viral carcinogenesis, glycine serine and threonine metabolism, RNA polymerase, galactose metabolism and retinol metabolism were enriched in chronic atrophic gastritis based on the metagenomic sequencing data.
CONCLUSION: Peptostreptococcus, fusobacterium, prevotella, sphingomonas and bacteroides were the essential features that distinguish atrophic gastritis from non-atrophic gastritis, and caused disease by altering various metabolic pathways. Viral carcinogenesis, glycine serine and threonine metabolism, RNA polymerase, galactose metabolism and retinol metabolism may be related to the occurrence and progression of CAG.
METHODS: The gastric juices of 30 non-atrophic gastritis (NAG) patients and 30 CAG patients were collected. Gastric microflora was analyzed by 16S rRNA sequencing and metagenomics sequencing. Gastric metabolites were analyzed by LC-MS analysis. Different bioinformatics methods were used to analyze the data of microbiome and metabolome, and to analyze the relationship between them.
RESULTS: In atrophic gastritis, bacteria diversity decreased. The genera with a mean decrease in Gini greater than 1.5 included peptostreptococcus, fusobacterium, prevotella, sphingomonas and bacteroides. KEGG pathway included renal cell carcinoma, proximal tubule bicarbonate reclamation, citrate cycle and aldosterone synthesis and secretion with significant enrichment of differential metabolites. Peptostreptococcus, fusobacterium, prevotella and sphingomonas were in pivot positions of the correlation network of differential metabolites and differential bacterium. Viral carcinogenesis, glycine serine and threonine metabolism, RNA polymerase, galactose metabolism and retinol metabolism were enriched in chronic atrophic gastritis based on the metagenomic sequencing data.
CONCLUSION: Peptostreptococcus, fusobacterium, prevotella, sphingomonas and bacteroides were the essential features that distinguish atrophic gastritis from non-atrophic gastritis, and caused disease by altering various metabolic pathways. Viral carcinogenesis, glycine serine and threonine metabolism, RNA polymerase, galactose metabolism and retinol metabolism may be related to the occurrence and progression of CAG.