Data from: Puerarin alleviates atherosclerosis via the inhibition of Prevotella copri and its trimethylamine production
Data files
May 21, 2024 version files 6.63 MB
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README.md
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Result.zip
Abstract
Objective Puerarin (PU) is a natural compound that exhibits limited oral bioavailability but has shown promise in the treatment of atherosclerosis (AS). However, the precise mechanisms underlying its therapeutic effects remain incompletely understood. This study aimed to investigate the effects of PU and its mechanisms in mitigating AS in both mice and humans.
Design The impact of PU on AS was examined in ApoE-/- mice fed a high-fat diet (HFD) and in human patients with carotid artery plaque. To explore the causal link between PU-associated gut microbiota and AS, faecal microbiota transplantation (FMT) and mono-colonization of mice with Prevotella copri (P. copri) were employed.
Results PU alleviated AS by modulating the gut microbiota, as evidenced by alterations in gut microbiota composition and the amelioration of AS following FMT from PU-treated mice into ApoE-/- mice fed HFD. Specifically, PU reduced the abundance of P. copri, which exacerbated AS by producing trimethylamine (TMA). Prolonged mono-colonization of P. copri undermines the beneficial effects of PU on AS. In clinical, the plaque scores of AS patients were positively correlated with the abundance of P. copri and plasma trimethylamine-N-oxide (TMAO) levels. A 1-week oral intervention with PU effectively decreased P. copri levels and reduced TMAO concentrations in patients with carotid artery plaque.
Conclusion PU may provide therapeutic benefits in combating AS by targeting P. copri and its production of TMA.
README: Data from: Puerarin Alleviates Atherosclerosis via the Inhibition of Prevotella copri and its Trimethylamine Production
https://doi.org/10.5061/dryad.sf7m0cgfg
Description of the Data and file structure
The file contains the results of RNA-seq analysis from the study titled "Puerarin Alleviates Atherosclerosis via the Inhibition of Prevotella copri and Its Trimethylamine Production." The samples include normal Prevotella copri cultures (Control) and Prevotella copri cultures treated with Puerarin (Treament). RNA was extracted and sequenced using next-generation sequencing. The raw data was analyzed using bioinformatics tools.
RNA seq data from 5 normal Prevotella copri cultures and 5 Prevotella copri cultures treated with Puerarin. The comparison report contains the following information:
Result
|——1_Quality_Control\ Data Quality Control
|————data_stat.xls Data Quality Statistics Table
|——2_Alignment\ Alignment Statistics
|————rRNA_alignment_stat.xls Reads Alignment Statistics for Samples Compared to Ribosomal References
|————ref_alignment_stat.xls Reads Alignment Statistics for Samples Compared to Reference Genome
|——3_Sample_statistics\ Sample Correlation Analysis
|————count.xls Reads Count Results Statistics Table
|————fpkm.xls FPKM Results Statistics Table
|——4_Diff_statistics\ Differential Expression Analysis
|————Diff_exp\
|——————Treament.vs.Control\
|————————Treament.vs.Control.xls Differential Expression Genes Statistics Table Between Samples
|————————Treament.vs.Control.fil.xls Significant Differential Genes Statistics Table Between Samples
|————Heatmap\
|——————Treament.vs.Control\Gene list
|————GO\
|——————GO_annotation.xl Functional Annotation Results of Differential Genes
|——————Control.vs.Treament\
|————————DEGs_in_all_level.xls GO Term Distribution Statistics of Differential Genes
|————————DEGs_in_level2.xls GO Term (Level 2) Statistics of Differential Genes
|————————enrichment\ Gene Ontology (GO) enrichment analysis results
|——————————*.GO_enrichment.xls BP, CC, MF Enrichment Analysis Statistics Table
|——————————*.GO_enrichment.fil.xls Filtered BP, CC, MF Enrichment Analysis Statistics Table
|——————————*.gse.xls GSEA Statistics Table for BP, CC, MF
|——————————*.gse.fil.xls Filtered GSEA Statistics Table for BP, CC, MF
|————KEGG\
|——————ko_annotation.xls KEGG Pathway Annotation Results of Differential Genes
|——————Control.vs.Treament\
|————————DEGs_in_A_Class.xls Statistics Table of Differential Genes in KEGG A Class
|————————DEGs_in_ko.xls Statistics Table of Differential Genes in KEGG Pathways
|————————enrichment\ Pathway enrichment analysis using KEGG pathways
|——————————kegg_enrichment.xls KEGG Enrichment Statistics Table of Differential Genes
|——————————kegg_enrichment.fil.xls Filtered KEGG Enrichment Statistics Table of Differential Genes
|——————————KEGG_gse.xls GSEA Statistics Table
|——————————KEGG_gse.fil.xls Filtered GSEA Statistics Table
|————————KEGG_Graph\* KEGG Pathway Maps of Differential Genes
|——5_Advanced_analysis\
|————operons.xls Operon Structure Prediction Results
|————transcripts.xls Transcript Structure Prediction Results
Usage notes
The file is compatible with Microsoft Excel.
Methods
The RNA extraction, sequencing, and library construction were performed according to previously published methods with minor modifications.[1,2] In brief, total RNA was extracted from samples using commercial kits (Guangdong Magigene Biotechnology Co., Ltd, China), and RNA quantity was measured using Qubit 3.0 (Thermo Fisher Scientific, MA, USA) and Nanodrop One (Thermo Fisher Scientific, MA, USA) instruments. Whole mRNAseq libraries were generated using the NEB Next® Ultra™ Nondirectional RNA Library Prep Kit for Illumina® (New England Biolabs, USA), following the manufacturer’s recommended protocol. The library was then sequenced using an Illumina NovaSeq 6000 platform. For mRNA analysis, raw data underwent quality control and the reads were mapped to a reference genome. After sequence alignment, gene expression levels were quantified and analyzed, and differentially expressed genes between two conditions/groups were identified using edgeR (version 3.16.5). The resulting p-values were adjusted using Benjamini and Hochberg’s approach to control the false discovery rate. Candidate genes were defined as those with FDR≤0.05 and |log2(fold change)|≥1, and these genes were subjected to enrichment analysis. Finally, differentially expressed genes were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis using the cluster Profiler (version 3.4.4) package. Enrichment analysis was considered significant when FDR≤0.05.
References
[1] Hao Y, Liao X, Wang X, Lao S, Liao W. The biological regulatory activities of Flammulina velutipes polysaccharide in mice intestinal microbiota, immune repertoire and heart transcriptome. Int J Biol Macromol. 2021;185:582-591.
[2] Lavelle A, Hoffmann TW, Pham HP, Langella P, Guédon E, Sokol H. Baseline microbiota composition modulates antibiotic-mediated effects on the gut microbiota and host. Microbiome. 2019;7(1):111.