PLX218448

GSE114960: Dietary fat, but not dietary protein or carbohydrate (sucrose), regulates energy intake and causes adiposity in mice

• Organsim mouse
• Type RNASEQ
• Target gene
• Project ARCHS4

Purpose: Obesity is a global health issue. To investigate if protein and fat contents of the diets had effects on energy balance via the canonical hunger signaling pathways in the hypothalamus, RNAseq was performed on RNA extracted from the hypothalami of mice exposed to the different diets. A suggested mechanism by which animals may avoid obesity is by burning off excess energy via upregulation of white adipose tissue (WAT) browning. To investigate if protein and fat content of the diet had effects on energy balance via the browning related signaling pathways in the WAT, RNAseq was performed on RNA extracted from the subcutaneous WAT (sWAT) and epididymal WAT (eWAT) of mice exposed to the different diets.; Methods: C57BL/6 male mice were used in this work. All mice were fed a standard diet with 10% fat and 20% protein (D12450B, Research Diets Ltd) for 2 weeks as the baseline period. Following 2 weeks of baseline monitoring (at age 12 weeks), all mice were randomly allocated to different groups and switched to the experimental diets for 12 weeks. After 12 weeks all mice were sacrificed and dissected.; Methods: In total, mice were fed on 4 diet series, each series consisting of 6 different diets (total = 24 diets). In the first two series (Series 1: D14071601D14071606 and series 2: D14071607 D14071612) we fixed the level of fat by energy, and varied the protein content. The protein source was casein. The balance was made up by carbohydrate (roughly equal mix of corn starch and maltodextrose). The source of fat was a mix of cocoa butter, coconut oil, menhaden oil, palm oil and sunflower oil. This mix was designed to match the balance of saturated, mono-unsaturated and polyunsaturated fats (ratio 47.5: 36.8: 15.8) and the n-6: n-3 ratio (14.7: 1) in the typical western diet. The proportions of the different fat constituents and hence fatty acid distributions did not change as the total fat content changed. Sucrose and cellulose were both fixed 5% by energy and weight respectively, and all diets were supplemented with a standard vitamin and mineral mix. In the second two series of diets (series 3: D14071613 D14071618 and series 4: D14071619 D14071624) we fixed the level of protein by energy and then allowed the fat content to vary. In these diets the sucrose, cellulose and vitamin and mineral contents were the same as the diets in series 1 and 2. All these diets can be ordered direct from research diets (www.researchdiets.com) using the diet codes provided.; Methods: From each diet group, the hypothalami of 8/20 individuals were collected. The left halves of two, and the right halves of another two, were pooled together as one sample, and the same was performed with the other 4 hypothalami, resulting in each diet group having 2 pooled samples of 4 hypothalami (n = 48 samples in total across 24 diets). From each diet group, the sWAT and eWAT of 12/20 individuals were also collected. A small piece from each of six sWAT collections were pooled together as one sample, and the same was performed with the other six eWAT collections. In this way each diet group had one pooled sWAT sample and one pooled eWAT sample (also n = 48 across 24 diets).; Methods: The total RNA of the hypothalamus and WAT was isolated using the RNeasy Mini Kit (QIAGEN, 74104) according to manufacturer's protocol. All sequencing was carried out using the Illumina NextSeq 500 sequencer. RNA fragments were sequenced by 75 bp long reads from paired ends (PE 2 x 75 bp, 150 bp per fragment). Quality control checks for raw data FASTQ files were done by using FASTQC (a quality control tool for high throughput sequence data; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Paired-end reads were mapped to the Mus musculus genome (GRCm38) using Bowtie 2-2.1.0, TopHat-2.0.10, and Samtools-0.1.19; uniquely mapped reads for each gene were counted against the GTF file of GRCm38 provided by Ensembl (release 83) using HTSeq-0.6.1p1 using the strand = reverse; after read count data were obtained from the TopHat-HTSeq pipeline, counts per million (CPM) value for each gene was calculated by using the R package edgeR (version 3.12.0, R version 3.2.2) to normalize the count data by the size of the library of each sample. Genes with the CPM value 1 in at least one of the 24 diets group were retained (Anders et al., 2013). Generalized Linear Modelling (GLM) was applied by R (version 3.2.2). The GLM model used here was: ~p+f+p:f, which regresses gene expression (CPM) against the protein (p) and fat contents (f) of diets, as well as their interaction (p:f). However, when the effect of the interaction was not significant (p value 0.05), the interaction term was dropped and a revised model (~p+f) was utilized.; Results: With TopHat-HTSeq pipeline, reads of each sample were mapped to 46,078 genes. In hypothalamus there were 15,371 genes with the counts per million (CPM) value 1 in at least one of the 24 diets group; in white adipose tissue there were 18,202 genes with the CPM value 1 in at least one of the 24 diets group. No major changes in hypothalamic gene expression levels were found in relation to different dietary protein levels at fixed fat contents, however hypothalamic gene expression showed increase in expression of genes in reward pathways in relation to dietary fat, while Agrp and Npy were both downregulated in relation to dietary fat levels. WAT gene expression showed decrease in expression of general thermogenic related genes and WAT browning related genes in relation to both dietary protein and dietary fat, while Tgfb1, Pdk4 and Fgf1 were all upregulated in relation to dietary fat levels.; Conclusions: Significant positive associations were evident between the fat levels of the diet and the main hedonic signaling systems linked to food intake. Significant negative associations were found between both protein and fat levels of the diet and WAT browning or general thermogenic signalings linked to energy expenditure. SOURCE: John Speakman (j.speakman@abdn.ac.uk) - Institute of Genetics and Developmental Biology, Chinese Academy of Sciences