An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome
The intestinal microbiota plays a key role in shaping host homeostasis by regulating metabolism, immune responses and behaviour. Its dysregulation has been associated with metabolic, immune and neuropsychiatric disorders and is accompanied by changes in bacterial metabolic regulation. Although prote...
| Autores: | , , , , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/175918 |
| Acesso em linha: | https://hdl.handle.net/2445/175918 |
| Access Level: | acceso abierto |
| Palavra-chave: | Microbiota intestinal Gastrointestinal microbiome |
| Resumo: | The intestinal microbiota plays a key role in shaping host homeostasis by regulating metabolism, immune responses and behaviour. Its dysregulation has been associated with metabolic, immune and neuropsychiatric disorders and is accompanied by changes in bacterial metabolic regulation. Although proteomics is well suited for analysis of individual microbes, metaproteomics of faecal samples is challenging due to the physical structure of the sample, presence of contaminating host proteins and coexistence of hundreds of species. Furthermore, there is a lack of consensus regarding preparation of faecal samples, as well as downstream bioinformatic analyses following metaproteomic data acquisition. Here we assess sample preparation and data analysis strategies applied to mouse faeces in a typical LC-MS/MS metaproteomic experiment. We show that low speed centrifugation (LSC) of faecal samples leads to high protein identification rates and a balanced taxonomic representation. During database search, protein sequence databases derived from matched mouse faecal metagenomes provided up to four times more MS/MS identifications compared to other database construction strategies, while a two-step database search strategy led to accumulation of false positive protein identifications. Comparison of matching metaproteome and metagenome data revealed a positive correlation between protein and gene abundances, as well as significant overlap and correlation in taxonomic representation. Notably, nearly all functional categories of detected protein groups were differentially abundant in the metaproteome compared to what would be expected from the metagenome, highlighting the need to perform metaproteomics when studying complex microbiome samples. |
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