3-(4-Hydroxy-3-methoxyphenyl) propionic acid contributes to improved hepatic lipid metabolism via GPR41
Data files
Nov 30, 2023 version files 37.96 GB
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README.md
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TR_2358_001_1.fastq.gz
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TR_2358_001_2.fastq.gz
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TR_2358_002_1.fastq.gz
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TR_2358_006_1.fastq.gz
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Abstract
3-(4-hydroxy-3-methoxyphenyl) propionic acid (HMPA) is a metabolite produced by the gut microbiota through the conversion of 4-hydroxy-3-methoxycinnamic acid (HMCA), which is a widely distributed hydroxycinnamic acid-derived metabolite found abundantly in plants. Several beneficial effects of HMPA have been suggested, such as antidiabetic properties, anticancer activities, and cognitive function improvement, in animal models and human studies. However, the intricate molecular mechanisms underlying the bioaccessibility and bioavailability profile following HMPA intake and the substantial modulation of metabolic homeostasis by HMPA require further elucidation. In this study, we effectively identified and characterized HMPA-specific GPR41 receptor, with greater affinity than HMCA. The activation of this receptor plays a crucial role in the anti-obesity effects and improvement of hepatic steatosis by stimulating the lipid catabolism pathway. For the improvement of metabolic disorders, our results provide insights into the development of functional foods, including HMPA, and preventive pharmaceuticals targeting GPR41.
README: 3-(4-Hydroxy-3-methoxyphenyl) propionic acid contributes to improved hepatic lipid metabolism via GPR41
https://doi.org/10.5061/dryad.z612jm6hd
RNA was extracted from the liver of HMPA (2.0 g/kg bodyweight)-treated mice using an RNAiso Plus reagent (Takara Bio) and RNeasy mini kit (Qiagen, Germany). RNA-seq libraries were generated with the TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, USA) and sequenced on NovaSeq 6000.Approximately 4-Gb paired-end reads of 100-bp length per sample were obtained.
Description of the data and file structure
Liver from HMPA-administered mice (n = 4 per group)
【Vehicle control group (n = 4)】
●TR_2358_001
TR_2358_001_1.fastq.gz
TR_2358_001_2.fastq.gz
●TR_2358_002
TR_2358_002_1.fastq.gz
TR_2358_002_2.fastq.gz
●TR_2358_003
TR_2358_003_1.fastq.gz
TR_2358_003_2.fastq.gz
●TR_2358_004
TR_2358_004_1.fastq.gz
TR_2358_004_2.fastq.gz
【HMPA group (n = 4)】
●TR_2358_006
TR_2358_006_1.fastq.gz
TR_2358_006_2.fastq.gz
●TR_2358_007
TR_2358_007_1.fastq.gz
TR_2358_007_2.fastq.gz
●TR_2358_009
TR_2358_009_1.fastq.gz
TR_2358_009_2.fastq.gz
●TR_2358_010
TR_2358_010_1.fastq.gz
TR_2358_010_2.fastq.gz
Methods
RNA was extracted from the liver of HMPA (2.0 g/kg bodyweight)-treated mice using an RNAiso Plus reagent (Takara Bio) and RNeasy mini kit (Qiagen, Germany). RNA-seq libraries were generated with the TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, USA) and sequenced on NovaSeq 6000. Approximately 4-Gb paired-end reads of 100-bp length per sample were obtained. RNA-seq data were pre-processed using Trimmomatic to remove adapters or poor-quality reads, and trimmed sequences were assessed using FastQC. The reads were aligned to the Illumina iGenomes NCBI GRCm38. To obtain DEGs from all comparisons, raw read counts were subjected to relative log expression normalization. Data expressed as fold change of DEGs were identified based on the following criteria: false discovery rate (FDR)-adjusted p-value < 0.05 (using the Benjamini–Hochberg procedure) and |log2 (fold change)| > 0.5. A gene set enrichment analysis was performed using Bioconductor version 3.0.