Gut microbial metabolites reveal diet-dependent metabolic changes induced by nicotine administration
Data files
Jan 12, 2024 version files 2.42 GB
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README.md
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Abstract
The gut microbiota has emerged as an important factor that potentially influences various physiological functions and pathophysiological processes such as obesity and type 2 diabetes mellitus. Accumulating evidence from human and animal studies suggests that gut microbial metabolites play a critical role as integral molecules in host–microbe interactions. Notably, several dietary environment-dependent fatty acid metabolites have been recognized as potent modulators of host metabolic homeostasis. More recently, nicotine, the primary active molecule in tobacco, has been shown to potentially affect host metabolism through alterations in the gut microbiota and its metabolites. However, the mechanisms underlying the interplay between host nutritional status, diet-derived microbial metabolites, and metabolic homeostasis during nicotine exposure remain unclear. Our findings revealed that nicotine administration had potential effects on weight regulation and metabolic phenotype, independent of reduced caloric intake. Moreover, nicotine-induced body weight suppression is associated with specific changes in gut microbial composition, including Lactobacillus spp., and KetoB, a nicotine-sensitive gut microbiota metabolite, which could be linked to changes in host body weight, suggesting its potential role in modulating host metabolism. Our findings highlight the remarkable impact of the interplay between nutritional control and the gut environment on host metabolism during smoking and smoking cessation.
README: Gut microbial metabolites reveal diet-dependent metabolic changes induced by nicotine administration
https://doi.org/10.5061/dryad.rxwdbrvfh
- Analysis of gut microbiota by 16S rRNA gene sequencing following with nicotine administration by intraperitoneal injection. At 7 weeks of age, the male mice with comparable average body weight were divided into two groups: the normal diet (ND) group and the high-fat diet (HFD) group (D12492, 60% kcal fat; Research Diets, Inc., NJ, USA) and received twice daily injections of nicotine (0.75 mg/kg body weight, intraperitoneal administration) or saline for 4 weeks.
- Analysis of gut microbiota by 16S rRNA gene sequencing following with nicotine administration by drinking water. At 7 weeks of age, the male mice received a nicotine solution (200 mg/mL in 2% saccharin vehicle) in drinking water during the 4-week period of HFD exposure.
The V4 region of the 16S rRNA gene was amplified using dual-indexed primers. Sequencing of the amplicons was carried out on an Illumina MiSeq instrument with a MiSeq Reagent Kit V3 (Illumina, San Diego, CA, USA).
Description of the data and file structure
1) Analysis of gut microbiota by 16S rRNA gene sequencing followed with nicotine administration by intraperitoneal injection
1) Fecal DNA from mice fed normal diet (ND)
Vehicle control group (n = 8)
DRR493554
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DRR493555
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DRR493556
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DRR493558
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DRR493559
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DRR493560
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- DRR493560_2.fastq.bz2
DRR493561
- DRR493561_1.fastq.bz2
- DRR493561_2.fastq.bz2
Nicotine administered group (n = 8)
DRR493562
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DRR493563
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DRR493569
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2) Fecal DNA from mice fed high-fat diet (HFD)
Vehicle control group (n = 8)
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Nicotine administered group (n = 8)
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DRR493585
- DRR493585_1.fastq.bz2
- DRR493585_2.fastq.bz2
2) Analysis of gut microbiota by 16S rRNA gene sequencing following with nicotine administration by drinking water
1) Vehicle control group (n = 8)
DRR493586
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2) 2% Saccharin group (n = 8)
DRR493594
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DRR493595
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DRR493596
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DRR493598
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DRR493600
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DRR493601
- DRR493601_1.fastq.bz2
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3) 2% Saccharin with 200 μg/mL nicotine group (n = 8)
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DRR493603
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Methods
Fecal samples were collected from mice treated with nicotine under normal diet or high-fat diet feeding condition. Fecal DNA was extracted using the FastDNA® SPIN Kit for Feces. Sequencing of the amplicons was carried out on an Illumina MiSeq instrument with a MiSeq Reagent Kit V3, following established protocols. Read processing and quality filtering were performed using Quantitative Insights into Microbial Ecology (QIIME) version 1.9.1. and aligned with the SILVA database (http://www.arb-silva.de) at the Unclassified level.