Assessing the impact of protein extraction methods for human gut metaproteomics.

Metaproteomics is a promising methodology for the functional characterizations of the gut microbiome. However, the performance of metaproteomic analysis is affected by protein extraction protocols in terms of the amount of protein recovered and the relative abundance of different bacteria observed in microbiome. Currently, there is a lack of consistency on protein extraction methods in published metaproteomics studies. Here we evaluated the effects of different protein extraction methods on human fecal metaproteome characterizations. We found that sodium dodecyl sulfate (SDS)-based lysis buffer obtained higher protein yields and peptide/protein group identifications compared to urea and the non-ionic detergent-based B-Per buffer. The addition of bead beating to any of the extraction buffers increased both protein yields and protein identifications. As well, bead beating led to a significant increase of the relative abundances of Firmicutes and Actinobacteria. We also demonstrated that ultrasonication, another commonly used mechanical disruption approach, performed even better than bead beating for gut microbial protein extractions. Importantly, proteins of the basic metabolic pathways showed significantly higher relative abundances when using ultrasonication. Overall, these results demonstrate that protein extraction protocols markedly impact the metaproteomic results and recommend a protein extraction protocol with both SDS and ultrasonication for metaproteomic studies. BIOLOGICAL SIGNIFICANCE The gut microbiome is emerging as an important factor influencing human health. Metaproteomics is promising for advancing the understanding of the functional roles of the microbiome in disease. However, metaproteomics suffers from a lack of consistent sample preparation procedures. In the present study, protein extraction protocols for fecal microbiome samples were evaluated for their effects on protein yields, peptide identifications, protein group identifications, taxonomic compositions and functional category distributions. While different protocols favor different microbial taxa and protein functions, our results suggest that a protein extraction protocol using sodium dodecyl sulfate (SDS) and ultrasonication provides the best performance for general shotgun metaproteomics studies.

[1]  M. Pop,et al.  Metagenomic Analysis of the Human Distal Gut Microbiome , 2006, Science.

[2]  V. Thongboonkerd,et al.  Comparative analyses of cell disruption methods for mitochondrial isolation in high-throughput proteomics study. , 2009, Analytical biochemistry.

[3]  Peer Bork,et al.  Enterotypes of the human gut microbiome , 2011, Nature.

[4]  Justin L Sonnenburg,et al.  Monitoring host responses to the gut microbiota , 2015, The ISME Journal.

[5]  T. von der Haar,et al.  Optimized Protein Extraction for Quantitative Proteomics of Yeasts , 2007, PloS one.

[6]  Cristian Piras,et al.  Unravelling the effect of clostridia spores and lysozyme on microbiota dynamics in Grana Padano cheese: A metaproteomics approach. , 2016, Journal of proteomics.

[7]  Mirjam Kooistra-Smid,et al.  Improved detection of microbial DNA after bead-beating before DNA isolation. , 2010, Journal of microbiological methods.

[8]  José A. Dianes,et al.  2016 update of the PRIDE database and its related tools , 2016, Nucleic Acids Res..

[9]  Rachel M. Adams,et al.  Metaproteomics reveals functional shifts in microbial and human proteins during a preterm infant gut colonization case , 2015, Proteomics.

[10]  T. Haar Optimized Protein Extraction for Quantitative Proteomics of Yeasts , 2007 .

[11]  Dazhi Wang,et al.  Marine metaproteomics: current status and future directions. , 2014, Journal of proteomics.

[12]  J. Raes,et al.  Population-level analysis of gut microbiome variation , 2016, Science.

[13]  W. D. de Vos,et al.  Comparative Metaproteomics and Diversity Analysis of Human Intestinal Microbiota Testifies for Its Temporal Stability and Expression of Core Functions , 2012, PloS one.

[14]  Emanuel Schmid,et al.  Soil metaproteomics – Comparative evaluation of protein extraction protocols , 2012, Soil biology & biochemistry.

[15]  M. Mann,et al.  Universal sample preparation method for proteome analysis , 2009, Nature Methods.

[16]  James Butcher,et al.  MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota , 2016, Microbiome.

[17]  Marco Y. Hein,et al.  Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ * , 2014, Molecular & Cellular Proteomics.

[18]  Peter Dawyndt,et al.  The Unipept metaproteomics analysis pipeline , 2015, Proteomics.

[19]  A. Schmidt,et al.  Comparison of Different Sample Preparation Protocols Reveals Lysis Buffer-Specific Extraction Biases in Gram-Negative Bacteria and Human Cells. , 2015, Journal of proteome research.

[20]  A. Tanca,et al.  Enrichment or depletion? The impact of stool pretreatment on metaproteomic characterization of the human gut microbiota , 2015, Proteomics.

[21]  Thilo Muth,et al.  Colonic metaproteomic signatures of active bacteria and the host in obesity , 2015, Proteomics.

[22]  E. Martens,et al.  Development of an Integrated Pipeline for Profiling Microbial Proteins from Mouse Fecal Samples by LC-MS/MS. , 2016, Journal of proteome research.

[23]  M. Mann,et al.  Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins* , 2012, Molecular & Cellular Proteomics.

[24]  Joseph A. Loo,et al.  Enhanced FASP (eFASP) to Increase Proteome Coverage and Sample Recovery for Quantitative Proteomic Experiments , 2014, Journal of proteome research.

[25]  Peter Dawyndt,et al.  Unipept: tryptic peptide-based biodiversity analysis of metaproteome samples. , 2012, Journal of proteome research.

[26]  Janne Nikkilä,et al.  Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. , 2010, Journal of microbiological methods.

[27]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[28]  Massimo Deligios,et al.  A straightforward and efficient analytical pipeline for metaproteome characterization , 2014, Microbiome.

[29]  Jüergen Cox,et al.  The MaxQuant computational platform for mass spectrometry-based shotgun proteomics , 2016, Nature Protocols.

[30]  Lukas N. Mueller,et al.  Full Dynamic Range Proteome Analysis of S. cerevisiae by Targeted Proteomics , 2009, Cell.

[31]  J. Jansson,et al.  A multi-omic future for microbiome studies , 2016, Nature Microbiology.

[32]  A. Heintz‐Buschart,et al.  Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes , 2016, Nature Microbiology.

[33]  Davide Heller,et al.  eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences , 2015, Nucleic Acids Res..

[34]  Marco Y. Hein,et al.  The Perseus computational platform for comprehensive analysis of (prote)omics data , 2016, Nature Methods.

[35]  J. Clemente,et al.  The Impact of the Gut Microbiota on Human Health: An Integrative View , 2012, Cell.

[36]  Thilo Muth,et al.  Navigating through metaproteomics data: A logbook of database searching , 2015, Proteomics.

[37]  M. Mann,et al.  Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells , 2014, Nature Methods.

[38]  Jens Roat Kultima,et al.  An integrated catalog of reference genes in the human gut microbiome , 2014, Nature Biotechnology.

[39]  G. Stark,et al.  Reactions of the Cyanate Present in Aqueous Urea with Amino Acids and Proteins , 1960 .

[40]  A. Stensballe,et al.  Metaproteomics: Evaluation of protein extraction from activated sludge , 2014, Proteomics.

[41]  Bernard Henrissat,et al.  Effects of Diet on Resource Utilization by a Model Human Gut Microbiota Containing Bacteroides cellulosilyticus WH2, a Symbiont with an Extensive Glycobiome , 2013, PLoS biology.