The enormous challenges of mass spectrometry-based metaproteomics are primarily related to the analysis and interpretation of the acquired data. This includes reliable identification of mass spectra and the meaningful integration of taxonomic and functional meta-information from samples containing hundreds of unknown species. To ease these difficulties, we developed a dedicated software suite, the MetaProteomeAnalyzer, an intuitive open-source tool for metaproteomics data analysis and interpretation, which includes multiple search engines and the feature to decrease data redundancy by grouping protein hits to so-called meta-proteins. We also designed a graph database back-end for the MetaProteomeAnalyzer to allow seamless analysis of results. The functionality of the MetaProteomeAnalyzer is demonstrated using a sample of a microbial community taken from a biogas plant.

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The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de .

Among the human milk oligosaccharides (HMOS), the galactosyllactoses (GLs) are only limitedly studied. This study aims to describe the presence and relative levels of HMOS, including GLs, in human milk (HM) according to maternal Secretor and Lewis (SeLe) phenotype and lactation stage. Relative levels of 19 HMOS were measured in 715 HM samples collected in the first 4 months postpartum from 371 donors participating in the PreventCD study. From a subset of 24 Dutch women (171 HM samples), samples were collected monthly up to 12 months postpartum and were additionally analyzed for relative and absolute levels of β6′-GL, β3′-GL and α3′-GL. Maternal SeLe phenotype or HM group was assigned based on the presence of specific fucosylated HMOS. Most HMOS, including β6′- and β3′-GL, were present in the vast majority (≥75%) of HM samples, whereas others (e.g., LNDFH II, 2′-F-LNH and α3′-GL) only occurred in a low number (<25%) of samples. Clear differences were observed between the presence and relative levels of the HMOS according to the maternal phenotype and lactation stage. Absolute concentrations of β6′-GL and β3′-GL were higher in HM group IV samples compared to samples of the other three HM groups. β3′-GL was also higher in HM group II samples compared to HM group I samples. β3′-GL and β6′-GL were stable over lactation stages. In conclusion, presence and levels of HMOS vary according to HM group and lactation stage. Not all HMOS behave similarly: some HMOS depend strongly on maternal phenotype and/or lactation stage, whereas others do not. β3′-GL and β6′-GL were present in low concentrations in over 75% of the analyzed HM samples and showed differences between HM groups, but not between the lactation stages.

Glycomics has become a rapidly emerging field and monitoring of protein glycosylation is needed to ensure quality and consistency during production processes of biologicals such as therapeutic antibodies or vaccines. Glycoanalysis via multiplexed CGE with LIF detection (xCGE-LIF) represents a powerful technique featuring high resolution, high sensitivity as well as high-throughput performance. However, sample data retrieved from this method exhibit challenges for downstream computational analysis due to intersample migration time shifts as well as stretching and compression of electropherograms. Here, we present glyXalign, a freely available and easy-to-use software package to automatically correct for distortions in xCGE-LIF based glycan data. We demonstrate its ability to outperform conventional algorithms such as dynamic time warping and correlation optimized warping in terms of processing time and alignment accuracy for high-resolution datasets. Built upon a set of rapid algorithms, the tool includes an intuitive graphical user interface and allows full control over all parameters. Additionally, it visualizes the alignment process and enables the user to readjust misaligned results. Software and documentation are available at http://www.glyxera.com.