Very low protein diets lead to reduced food intake and weight loss, linked to inhibition of hypothalamic mTOR signaling, in mice.

Published: 20 September 2020| Version 1 | DOI: 10.17632/h96dcs23rr.1
Yingga Wu, John Speakman


To investigate graded levels of low protien dites effects on serum metabolite expression pattern, LC-MS was performed on serum of mice treated with different levels of low protein and normal protein under both 60% fat and 20% fat conditions. 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). Serum metabolites were extracted by mixing Chloroform : Methanol : Serum in 1:3:1 ratio and centrifuged at 1,3000 rpm for 3 minutes, supernatant was collected and did LC-MS using an OrbitrapTM ExactiveTM mass spectrometer at the Glasgow Polyomics facility. Each metabolite was expressed by raw peak intensity at last, and then these peaks were analyzed step by step using R packages called xcms (Smith et al., 2006), mscombine, missforest (Stekhoven and Buhlmann, 2012), and xmsannotator (Uppal et al., 2017) to match specific mass to charge ratio (m/z ratio) and retention times with HMDB and KEGG databases metabolites. Normalization was performed for raw peak intensities for each sample by using the Metabolomics package (De Livera et al., 2012; De Livera et al., 2013; Sysi-Aho et al., 2007), then log2-transformed. Then Generalized Linear Modelling (GLM) function in R-3.5.3 was used according to following design model: ~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. Pearson correlation analysis was performed for normalized log2-transformed intensities of all metabolites by using Pearson correlation method in R-3.5.3. Then selected the significantly correlated genes with dietary protein content respectively in the GLM (GLM: p < 0.05) and Pearson correlation (Pearson: p < 0.05 for 60% fat and 20% fat conditions) analysis were loaded into the Ingenuity pathway (IPA) program (Ingenuity Systems; to observe the significantly affected pathways. Normalized log2-transformed intensities were used to express the specific metabolite expression levels. The table contains significantly changed metabolites in GLM and Pearson correaltion analysis respectively under 60% fat and 20% fat conditions.



Protein, Obesity, Metabolomics, Macronutrient