 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Mar 04, 2021 |
| Title |
Very low protein diets lead to reduced food intake and weight loss, linked to inhibition of hypothalamic mTOR signaling, in mice |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
|
| Summary |
Purpose: To investigate graded levels of low protien dites effects on hypothalamic gene expression pattern, RNA-seq was performed on hypothalamus of mice treated with different levels of low protein and normal protein under both 60% fat and 20% fat conditions. Methods: There were 8 diets in total, the fat content of first 4 diets were fixed at 60% fat level by energy and protein was varied from 1% to 20% (1%, 2.5%, 5% and 20% respectively) by energy (D14121903, D14121904, D14071601,D14071604), another 4 kinds of diets contained the same graded levels of protein content as the first 4 diets but fat contents were fixed at 20% by energy (D14121905, D14121906, D14071607, D14071610). The remaining energy was compensated of carbohydrate which included corn starch and maltodextrose. Casein was used as the protein source in all diets. To mimic the typical western diet a mix of cocoa butter, menhaden oil, sunflower oil, palm oil and coconut oil was used as the fat source and was designed to generate a 47.5: 36.8: 15.8 proportion of saturated, mono-unsaturated and polyunsaturated fats and 14.7: 1 proportion of n-6 and n-3 fatty acids, the proportions of different fat compositions didn’t change under two different 60% and 20% fat conditions. Sucrose and cellulose were fixed at 5% level by energy and standard vitamin and mineral mix were also added to all diets as well (Hu et al., 2018). The two series were isocaloric within each series. Diets can be ordered direct from research diets (https://researchdiets.com) using the diet codes provided. The hypothalamus of 6 individuals were collected for each diet group, 3 of which were pooled together as one sample, resulting in each group having 2 pooled samples of 3 hypothalamus. Hypothalamic RNA was extracted using Trizol method described in star methods in paper and then sent to Beijing Genomic Institute (BGI) for RNA sequencing. All RNA sequencing was measured by using the BGI-seq 500 sequencer. To determine the sequencing quality of each sample, FASTQ raw data files were measured by using fastQC (www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) quality control tool, after confirmed sequencing quality of each sample, raw reads were aligned to the Mus musculus reference genome (GRCm38) using HISAT2-2.1.0 (Kim et al., 2015; Pertea et al., 2016) and Samtools-1.2 modules (Li et al., 2009), and then featureCounts (Liao et al., 2014) tool in Subread-5.0 (Liao et al., 2013) was used to get counts for each sample from BAM files which was obtained from alignment step. To exclude the low counts, only genes with counts per million (CPM) value > 1 were included in the further analysis. Counts were normalized by the trimmed means of M values (TMM normalization) (Robinson and Oshlack, 2010) method in the edgeR (Anders et al., 2013; Lund et al., 2012; McCarthy et al., 2012; Robinson et al., 2010; Robinson and Oshlack, 2010; Robinson and Smyth, 2007) package. To investigate different dietary protein contents under fixed two levels fat content effects on gene expression levels Generalized Linear Modelling (GLM) function in R-3.5.3 edgeR package was used, The GLM model used in this study were: ~P+F+P:F, which means regression against protein (P) and fat contents (F) of diets plus their interaction (P:F). If the interaction effect was not significant (p > 0.05), the interaction was not included in the analysis and a revised model (~P+F) was utilized (Hu et al., 2018). To explore significantly correlated genes with dietary protein content respectively under 60% fat and 20% fat conditions, Pearson correlation analysis was performed for normalized log counts of all genes by using Pearson correlation method in R-3.5.3. Then selected the significantly correlated genes with dietary protein content (GLM: p < 0.05) respectively in the GLM and Pearson correlation (Pearson: p < 0.05 for 60% fat and 20% fat conditions) analysis were loaded into the Ingenuity pathway analysis (IPA) program (Ingenuity Systems; http://www.ingenuity.com/) to observe the significantly affected pathways. Results: The most affected pathways by the dietary low protein included the mTOR signaling pathway, the hunger signaling pathway, eIF2a signaling pathway and regulation of eIF4 and p70S6K signaling pathway. In the hunger signal pathway, we found there were strong correlations for four key hunger signaling pathway genes Pomc, Cart, Npy and Agrp with the protein content in the diet across different protein content groups (from 1% protein to 20% protein group) with Pomc and Cart expression significantly reduced as protein level declined while Npy and Agrp expression increased as protein levels decreased.
|
| |
|
| Overall design |
There are 8 groups in total, the fat content of first 4 diets were fixed at 60% fat level by energy and protein was varied from 1% to 20% (1%, 2.5%, 5% and 20% respectively) by energy , another 4 kinds of diets contained the same graded levels of protein content as the first 4 diets but fat contents were fixed at 20% by energy. To investigate different dietary protein contents under fixed two levels fat content effects on gene expression levels Generalized Linear Modelling (GLM) function in R-3.5.3 edgeR package was used, The GLM model used in this study were: ~P+F+P:F, which means regression against protein (P) and fat contents (F) of diets plus their interaction (P:F). If the interaction effect was not significant (p > 0.05), the interaction was not included in the analysis and a revised model (~P+F) was utilized (Hu et al., 2018). To explore significantly correlated genes with dietary protein content respectively under 60% fat and 20% fat conditions, Pearson correlation analysis was did for normalized log counts of all genes by using Pearson correlation method
|
| |
|
| Contributor(s) |
Wu Y, Li B, Li L, Mitchell SE, Green CL, D'Agostino G, Wang G, Wang L, Li M, Li J, Niu C, Jin Z, Wang A, Douglas A, Zheng Y, Speakman JR |
| Citation missing |
Has this study been published? Please login to update or notify GEO. |
| Submission date |
Sep 18, 2020 |
| Last update date |
Mar 04, 2021 |
| Contact name |
Yingga Wu |
| E-mail(s) |
j.speakman@abdn.ac.uk
|
| Phone |
+8615600624781
|
| Organization name |
Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
|
| Street address |
No.1 West Beichen Road,Chaoyang District
|
| City |
BEIJING |
| State/province |
BEIJING |
| ZIP/Postal code |
100101 |
| Country |
China |
| |
|
| Platforms (1) |
|
| Samples (16)
|
|
| Relations |
| BioProject |
PRJNA664373 |
| SRA |
SRP283671 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE158215_LPD_hypo_counts.csv.gz |
1.2 Mb |
(ftp)(http) |
CSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
|
|
|
|
 |
Less...
More...