A combined test for feature selection on sparse metaproteomics data-an alternative to missing value imputation.

One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely "at random" or "not at random". To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential presence). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods.

Modern Metaproteomics: A Unique Tool to Characterize the Active Microbiome in Health and Diseases, and Pave the Road towards New Biomarkers-Example of Crohn's Disease and Ulcerative Colitis Flare-Ups.

Thanks to the latest developments in mass spectrometry, software and standards, metaproteomics is emerging as the vital complement of metagenomics, to make headway in understanding the actual functioning of living and active microbial communities. Modern metaproteomics offers new possibilities in the area of clinical diagnosis. This is illustrated here, for the still highly challenging diagnosis of intestinal bowel diseases (IBDs). Using bottom-up proteomics, we analyzed the gut metaproteomes of the same twenty faecal specimens processed either fresh or after a two-month freezing period. We focused on metaproteomes of microbial cell envelopes since it is an outstanding way of capturing host and host-microbe interaction signals. The protein profiles of pairs of fresh and frozen-thawed samples were closely related, making feasible deferred analysis in a distant diagnosis centre. The taxonomic and functional landscape of microbes in diverse IBD phenotypes-active ulcerative colitis, or active Crohn's disease either with ileo-colonic or exclusive colonic localization-differed from each other and from the controls. Based on their specific peptides, we could identify proteins that were either strictly overrepresented or underrepresented in all samples of one clinical group compared to all samples of another group, paving the road for promising additional diagnostic tool for IBDs.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows.

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Benefits of Iterative Searches of Large Databases to Interpret Large Human Gut Metaproteomic Data Sets.

The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.