Proteomic Analysis of Human Plasma during Intermittent Fasting

Abstract

Intermittent fasting (IF) increases lifespan and decreases metabolic disease phenotypes and cancer risk in model organisms, but the health benefits of IF in humans are less clear. Human plasma derived from clinical trials is one of the most difficult sample sets to analyze using mass spectrometry-based proteomics due to the extensive sample preparation required and the need to process many samples to achieve statistical significance. Here, we describe an optimized and accessible device (Spin96) to accommodate up to 96 StageTips, a widely used sample preparation medium enabling efficient and consistent processing of samples prior to LC-MS/MS. We have applied this device to the analysis of human plasma from a clinical trial of IF. In this longitudinal study employing 8-weeks IF, we identified significant abundance differences induced by the IF intervention, including increased apolipoprotein A4 (APOA4) and decreased apolipoprotein C2 (APOC2) and C3 (APOC3). These changes correlated with a significant decrease in plasma triglycerides after the IF intervention. Given that these proteins have a role in regulating apolipoprotein particle metabolism, we propose that IF had a positive effect on lipid metabolism through modulation of HDL particle size and function. In addition, we applied a novel human protein variant database to detect common protein variants across the participants. We show that consistent detection of clinically relevant peptides derived from both alleles of many proteins is possible, including some that are associated with human metabolic phenotypes. Together, these findings illustrate the power of accessible workflows for proteomics analysis of clinical samples to yield significant biological insight.

Keywords: 3D-printing; 96-well; human; intermittent fasting; liquid chromatography; mass spectrometry (MS); plasma; sample cleanup; solid-phase extraction (SPE).

Figure 1

PREFER clinical trial plasma quality control. Plasma from participants before and after IF were analyzed using our Spin96 proteomics workflow. Label-free quantitation (LFQ) intensity was plotted for (a) proteins indicative of blood clotting and hemolysis for each participant, before and after fasting, and (b) LFQ intensity plot for example proteins of very similar abundance before and after the IF intervention, which showed participant-specific abundance patterns. Each color represents a different protein. (c) Plot for each plasma protein of the adjusted LFQ (Adj. LFQ) intensity versus the Adj. LFQ percentage coefficient of variation (% CV). Green circles indicate <20% CV, orange circles indicate 20–40% CV and red circles indicate >40% CV.

Figure 2

Intermittent fasting induces changes in protein abundance in human plasma. (a) Heat map showing each protein ranked by its total log10 adjusted Label-Free Quantitation (Adj. LFQ) intensity (y-axis) versus the data for each participant from both before and after the IF intervention. Blue colors represent low abundance and red colors high abundance. Only complete cases were used; any protein with a missing value in any sample was excluded. (b) Volcano plot of plasma protein abundance changes after IF were plotted with the y-axis showing the −log₁₀ (p-value, paired) and the x-axis showing the log₂ fold-change of protein abundance (after IF/before IF) calculated from the LFQ intensity values. The gray area denotes significant (p < 0.05) changes with IF and the pink area denotes nonsignificant (p > 0.05) changes. The size of each circle represents the minimum number of razor and unique peptides across all participants, larger size indicates more razor and unique peptides. (c) Line graphs for the two most significant up and down regulated proteins after the IF intervention were plotted with the y-axis showing the Adj. LFQ intensity and each line representing one participant’s response.

Figure 3

Correlation analysis of plasma protein abundance versus clinical parameters monitored in the PREFER trial. Data derived from all detected proteins and all clinical parameters were correlated against each other across all 44 participant samples, disregarding any grouping information. Correlations were calculated using the Pearson’s correlation coefficient and the resulting p-values were subjected to Benjamini–Hochberg correction. The y-axis of each plot shows the −log₁₀ (corrected p-value) versus the Pearson correlation. Values in blue are insignificant (FDR > 5%) and values in orange are significant (FDR < 5%). Each measure starting with Clin. (Clinical) represents an individual clinical measurement. Proteins are indicated by their corresponding gene name.

Figure 4

Detection of clinically relevant natural protein variants in the PREFER plasma proteomes. Mass spectrometry-based proteomics data was reanalyzed using a modified plasma-specific protein database containing all known forms of naturally occurring clinically-relevant variants. (a) Bar plot for each protein variant where the y-axis shows the summed intensity of peptides derived from each allele as a percentage of total, and the x-axis shows the 44 participant samples with one bar per sample. The gene name for each variant and its position in the protein are indicated in bold to the right of each plot. Also shown are the associated human phenotypes. Peptides from each allele are shown in different colors on the same plot and correspond to each legend shown on the right. (b) Bar plot showing either the frequency of the genotype in the European population sampled by the 1000Genomes study, or the frequency of the corresponding peptide variants in the PREFER plasma proteome samples. The y-axis shows the frequency in percentage of total, and the x-axis shows the corresponding genomic allele and peptide variant combinations. A comparison of the frequencies for each allele was made using Fisher’s Exact Test, which is shown at the top of the plot.

Figure 5

Model of intermittent fasting modulation of apolipoprotein plasma abundance and metabolism. Proteins in green are up-regulated and proteins in red down-regulated by IF in human plasma. Proteins shown in black do not change significantly.