Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae

Abstract

The microbiome has a strong impact on human health and disease and is, therefore, increasingly studied in a clinical context. Metaproteomics is also attracting considerable attention, and such data can be efficiently generated today owing to improvements in mass spectrometry-based proteomics. As we will discuss in this study, there are still major challenges notably in data analysis that need to be overcome. Here, we analyzed 212 fecal samples from 56 hospitalized acute leukemia patients with multidrug-resistant Enterobactericeae (MRE) gut colonization using metagenomics and metaproteomics. This is one of the largest clinical metaproteomic studies to date, and the first metaproteomic study addressing the gut microbiome in MRE colonized acute leukemia patients. Based on this substantial data set, we discuss major current limitations in clinical metaproteomic data analysis to provide guidance to researchers in the field. Notably, the results show that public metagenome databases are incomplete and that sample-specific metagenomes improve results. Furthermore, biological variation is tremendous which challenges clinical study designs and argues that longitudinal measurements of individual patients are a valuable future addition to the analysis of patient cohorts.

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

“…who used a combination of direct precipitation methods using trichloroacetic acid‐deoxycholate (TCA‐DOC)/acetone and phenol/methanol extraction/precipitation methods to assist in concentrating those proteins . Further, gel electrophoresis and in‐gel digestion of supernatant and microbial pellets from fecal samples prior to MS analysis was shown to be effective in removing sample contaminants such as detergents and insoluble particles …”

“…Bioinformatics data processing is an important aspect of gut metaproteomics due to the complexity of the microbiome samples. Extensive reviews and perspectives have been published on database searching strategies for metaproteomic analysis; therefore, a general overview will be presented here. In the classical bottom‐up proteomics approach, a target‐decoy database search strategy followed by false discovery rate (FDR) filtering is used to confidently identify peptides and proteins .…”

Metaproteomics insights into traditional fermented foods and beverages

“…Metaproteomics has also made great progress in terms of protein isolation and identification, but it remains relatively underutilized, as compared with metagenomics and metatranscriptomics. Metaproteomics has undergone considerable development in terms of protein isolation and identification, but it remains relatively underutilized, mainly because several challenges remain to be addressed; these challenges result from the high complexity of the microbial community, the low coverage of the complete metaproteome, the high sequence similarity between many proteins (Haange & Jehmlich, 2016), the incompleteness of public databases (Rechenberger et al., 2019), and the high costs (Jansson & Baker, 2016; Schiebenhoefer et al., 2019; Xu et al., 2017). Research in metaproteomics has mainly focused on fields such as intestinal microecology (Zhang et al., 2017), marine biology (Williams & Cavicchioli, 2014), and soil biology (Starke, Jehmlich, & Bastida, 2019).…”

Deciphering the functioning of microbial communities: shedding light on the critical steps in metaproteomics

“…To what extend the methodology affects the metaproteome interpretation has already been studied in artificial microbial communities [17] and gut microbiomes [24,38] but its impact on marine samples still remains poorly documented [18]. In this study, we used a robust experimental design comparing the combined effect of protein search DB choice and protein fractionation approach on the same sea surface sample.…”

“…To overcome this issue, protein identification tools such as Pro Group algorithm (Protein Pilot) [31], Prophane [32] or MetaProteomeAnalyzer [33] automatically group homologous protein sequences. In our study, we used mPies program [34], which computes taxonomic consensus annotation on protein groups using LCA [13, 35] and provides a novel consensus functional annotation based on Uniprot DB giving more accurate insights into the diversity of protein functions compared to former strategies mapping proteins on broader functional categories, such as KEGG [36] or COGs [37].To what extend the methodology affects the metaproteome interpretation has already been studied in artificial microbial communities [17] and gut microbiomes [24,38] but its impact on marine samples still remains poorly documented [18]. In this study, we used a robust experimental design comparing the combined effect of protein search DB choice and protein fractionation approach on the same sea surface sample.…”