An iterative workflow for mining the human intestinal metaproteome

Abstract

BackgroundPeptide spectrum matching (PSM) is the standard method in shotgun proteomics data analysis. It relies on the availability of an accurate and complete sample proteome that is used to make interpretation of the spectra feasible. Although this procedure has proven to be effective in many proteomics studies, the approach has limitations when applied on complex samples of microbial communities, such as those found in the human intestinal tract. Metagenome studies have indicated that the human intestinal microbiome contains over 100 times more genes than the human genome and it has been estimated that this ecosystem contains over 5000 bacterial species. The genomes of the vast majority of these species have not yet been sequenced and hence their proteomes remain unknown. To enable data analysis of shotgun proteomics data using PSM, and circumvent the lack of a defined matched metaproteome, an iterative workflow was developed that is based on a synthetic metaproteome and the developing metagenomic databases that are both representative for but not necessarily originating from the sample of interest.ResultsTwo human fecal samples for which metagenomic data had been collected, were analyzed for their metaproteome using liquid chromatography-mass spectrometry and used to benchmark the developed iterative workflow to other methods. The results show that the developed method is able to detect over 3,000 peptides per fecal sample from the spectral data by circumventing the lack of a defined proteome without naive translation of matched metagenomes and cross-species peptide identification.ConclusionsThe developed iterative workflow achieved an approximate two-fold increase in the amount of identified spectra at a false discovery rate of 1% and can be applied in metaproteomic studies of the human intestinal tract or other complex ecosystems.

Microbes inside—from diversity to function: the case of Akkermansia

“…Thus far, only few reports describe this technique to study the gut metaproteome (Klaassens et al, 2007;Verberkmoes et al, 2009;Rooijers et al, 2011). Results in these studies are in line with the predictions from the metatranscriptome and metagenome data.…”

“…Results in these studies are in line with the predictions from the metatranscriptome and metagenome data. Most bacterial proteins were matched to Bacteriodes, Bifidobacterium or Firmicutes species, emphasizing the dominance of these groups and their functional significance in the human distal intestine (Dicksved et al, 2008;Verberkmoes et al, 2009;Rooijers et al, 2011). The main discrepancy with the metagenome data has been the skewed distribution of proteins related to translation, energy production and carbohydrate metabolism that appeared more abundant in the metaproteome data (Verberkmoes et al, 2009).…”

“…Moreover, the availability of genome sequences of intestinal isolates (Nelson et al, 2010) as well as new bioinformatic tools (Rooijers et al, 2011) will contribute to further expanding these approaches (see Figure 2). Stable isotope probing is another technique to link metabolic conversions to phylogeny (Boschker et al, 1998;Radajewski et al, 2000;Kovatcheva-Datchary et al, 2009).…”

High-fat diet alters gut microbiota physiology in mice

“…Since the first gel-based metaproteome published in 2007 (Klaassens et al, 2007) and the state-of-the art LC-MS/MS work by Verberkmoes et al (2009), the size of genomic databases has been rising exponentially. Nevertheless, annotation of peptide spectra remains challenging and functional interpretation based on the occurrence of dominant proteins is limited, that is, taking into account the high degree of diversity of the intestinal microbial ecosystem, we still profit only from a narrow window of analysis at the proteome level (Rooijers et al, 2011;Haange et al, 2012;Kolmeder et al, 2012;Perez-Cobas et al, 2012). This challenge is reflected in the present study by the fact that only 5% of all tandem MS spectra acquired could be matched to protein sequences from the databases.…”

Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology

“…Cantarel et al (2011) used the same samples to test several computational strategies to improve protein identification. A new iterative workflow for protein identification leads to an increase of Clusters of Orthologous Groups (COG) categories (Rooijers et al, 2011). Special emphasis has been placed on the role of the gut microbiota for metabolic syndromes, inflammation and obesity.…”