Abstract

Faecal proteomics targeting biomarkers of immunity and inflammation have demonstrated clinical application for the identification of changes in gastrointestinal function. However, there are limited comprehensive analyses of the host faecal proteome and how it may be influenced by dietary factors. To examine this, the Homo sapiens post-diet proteome of older males was analysed at the completion of a 10-week dietary intervention, either meeting the minimum dietary protein recommendations (RDA; n = 9) or twice the recommended dietary allowance (2RDA, n = 10). The host faecal proteome differed markedly between individuals, with only a small subset of proteins present in ≥ 60% of subjects (14 and 44 proteins, RDA and 2RDA, respectively, with only 7 common to both groups). No differences were observed between the diet groups on the profiles of host faecal proteins. Faecal proteins were detected from a wide range of protein classes, with high inter-individual variation and absence of obvious impact in response to diets with markedly different protein intake. This suggests that well-matched whole food diets with two-fold variation in protein intake maintained for 10 weeks have minimal impact on human faecal host proteins.

Highlights

  • The gastrointestinal tract (GIT) coordinates the complex tasks of digestion and nutrient absorption [1, 2]

  • We hypothesised that the faecal host proteome would include proteins secreted into the gastrointestinal tract, including enzymes, mucus proteins, secretory immune proteins and potentially proteins from epithelial cells dislodged along the GIT [4]

  • We did observe that the human host faecal proteome is variable in a set of individuals matched to age and sex

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Summary

INTRODUCTION

The gastrointestinal tract (GIT) coordinates the complex tasks of digestion and nutrient absorption [1, 2]. A more complete proteome can be obtained using fractionation prior to LC, which reduces the complexity of each LC separation and allows for more peptides to be fragmented and identified when comparing to analysing the whole sample in one run [15]. These fractions can be combined prior to searching to identify all compounds at once. PANTHER terms were associated with the related accessions using the R-package PANTHER.db

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