The challenge of metaproteomic analysis in human samples

Targeted Metagenomics of Retting in Flax: The Beginning of the Quest to Harness the Secret Powers of the Microbiota

“…. ), successful shotgun metaproteomics still faces a number of conceptual and technological hurdles that need to be overcome (Blackburn and Martens, 2016;Heyer et al, 2017;Knight et al, 2018;Matallana-Surget et al, 2018;Abiraami et al, 2020). Currently, a major limitation for metaproteomics in such systems is the lack of effective and reproducible protein extraction protocols and standardized data analyses resulting from i) the tremendous heterogeneity of samples (i.e., plant, soil and litter), ii) the low protein yield that can be obtained from soil and litter matrices and iii) the wide range of protein abundance levels, Becher et al (2013), Keiblinger et al (2016), Keiblinger and Riedel (2018).…”

Chancen und Risiken von Metaproteomik in der Mikrobiomforschung

“…Um dieses Problem in gewisser Weise zu begegnen, ohne auf das Vorhandensein eines teuren Metagenoms angewiesen zu sein, kann alternativ eine 16 S rRNA Genanalyse verwendet werden. 16 S rRNA Gensequenzierung ergibt eine deutlich präzisere und kürzere Liste von Bakterientaxa der Probe, für die dann die entsprechenden Proteinsequenzen wie oben beschrieben heruntergeladen werden kann [35]. Bei der Analyse sollte aber immer klar sein, dass viele Stämme bisher nur teilweise oder überhaupt nicht sequenziert wurden.…”

MetaNovo: a probabilistic approach to peptide discovery in complex metaproteomic datasets

“…Classically, the identification of peptides (and by inference, the parent proteins) from complex mass spectrometry datasets has been dependent on the availability of focussed, representative and relatively small sequence databases or spectral libraries against which to search tandem mass spectra; as a result, the field of proteomics has been primarily directed to the analysis of single organism proteomes to date. However, analysis of complex, multi-species samples -such as human microbiome samples, where the total number of microbial genes may vastly exceed the number of human genes -is far less straightforward for a variety of reasons, including: the vast majority of specific organisms in any given microbiome are likely to not have been cultured, identified, or characterised in the laboratory, so appropriate reference genomes may not exist to underpin proteomic data analyses; and the fact that massive expansion of proteomic sequence databases used in target-decoy-based peptide-spectrum matching has been shown to lead to dramatically reduced identification rates due to reduced sensitivity and false discovery mis-estimation problems (Heyer et al , 2017;Blackburn & Martens, 2016).…”

“…De novo sequencing of peptides from mass spectrometry data has long been used for database filtration, allowing for rapid searches of very large search spaces with sequence tags prior to peptide-spectral matching (Frank et al , 2007). Particularly considering significant inter-subject variation and the polymorphism of clinical strains, de novo sequencing has been suggested as a viable strategy for peptide identification in gut metaproteomics (Blackburn & Martens, 2016). However, scalable and robust de novo sequencing-based pipelines need to be able to process the rapidly expanding amount of proteomics and genomics information available.…”