Triflic Acid Treatment Enables LC-MS/MS Analysis of Insoluble Bacterial Biomass

Quantitative metaproteomics and activity-based protein profiling of patient fecal microbiome identifies host and microbial serine-type endopeptidase activity associated with ulcerative colitis

The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC.

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Identified 176 significantly altered protein groups between healthy and UC patients.

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Serine-type endopeptidase activity is overrepresented in UC patients.

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Fluorophosphonate ABPP shows that endopeptidases are active in fecal samples.

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ABPP enrichment helps identify additional putative serine hydrolases in samples.

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De novo sequencing used to estimate number of MS2 spectra unidentified by ComPIL.

The Landscape of Pseudomonas aeruginosa Membrane-Associated Proteins

Background: Pseudomonas aeruginosa cell envelope-associated proteins play a relevant role in infection mechanisms. They can contribute to the antibiotic resistance of the bacterial cells and be involved in the interaction with host cells. Thus, studies contributing to elucidating these key molecular elements are of great importance to find alternative therapeutics. Methods: Proteins and peptides were extracted by different methods and analyzed by Multidimensional Protein Identification Technology (MudPIT) approach. Proteomic data were processed by Discoverer2.1 software and multivariate statistics, i.e., Linear Discriminant Analysis (LDA), while the Immune Epitope Database (IEDB) resources were used to predict antigenicity and immunogenicity of experimental identified peptides and proteins. Results: The combination of 29 MudPIT runs allowed the identification of 10,611 peptides and 2539 distinct proteins. Following application of extraction methods enriching specific protein domains, about 15% of total identified peptides were classified in trans inner-membrane, inner-membrane exposed, trans outer-membrane and outer-membrane exposed. In this scenario, nine outer membrane proteins (OprE, OprI, OprF, OprD, PagL, OprG, PA1053, PAL and PA0833) were predicted to be highly antigenic. Thus, they were further processed and epitopes target of T cells (MHC Class I and Class II) and B cells were predicted. Conclusion: The present study represents one of the widest characterizations of the P. aeruginosa membrane-associated proteome. The feasibility of our method may facilitates the investigation of other bacterial species whose envelope exposed protein domains are still unknown. Besides, the stepwise prioritization of proteome, by combining experimental proteomic data and reverse vaccinology, may be useful for reducing the number of proteins to be tested in vaccine development.

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics Based on Acid Extraction

The main challenge of bottom-up proteomic sample preparation is to extract proteomes in a manner that enables efficient protein digestion for subsequent mass spectrometric analysis. Today's sample preparation strategies are commonly conceptualized around the removal of detergents, which are essential for extraction but strongly interfere with digestion and LC-MS. These multi-step preparations contribute to a lack of reproducibility as they are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which consists of three mandatory steps, acidification, neutralization and digestion. SPEED is a universal method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction by complete sample dissolution. The protocol is highly reproducible, virtually loss-less, enables very rapid sample processing and is superior to the detergent/chaotropic agent-based methods FASP, ISD-Urea and SP3 for quantitative proteomics. SPEED holds the potential to dramatically simplify and standardize sample preparation while improving the depth of proteome coverage especially for challenging samples.

ComPIL 2.0: An Updated Comprehensive Metaproteomics Database

We designed a metaproteomic analysis method (ComPIL) to accommodate the ever-increasing number of sequences against which experimental shotgun proteomics spectra could be accurately and rapidly queried. Our objective was to create these large databases for the analysis of complex metasamples with unknown composition, including those derived from human, animal, and environmental microbiomes. The amount of high-throughput sequencing data has substantially increased since our original database was assembled in 2014. Here, we present a rebuild of the ComPIL libraries comprised of updated publicly disseminated sequence data as well as a modified version of the search engine ProLuCID-ComPIL optimized for querying experimental spectra. ComPIL 2.0 consists of 113 million protein records and roughly 4.8 billion unique tryptic peptide sequences and is 2.3 times the size of our original version. We searched a data set collected on a healthy human gut microbiome proteomic sample and compared the results to demonstrate that ComPIL 2.0 showed a substantial increase in the number of unique identified peptides and proteins compared to the first ComPIL version. The high confidence of protein identification and accuracy demonstrated by the use of ComPIL 2.0 may encourage the method's application for large-scale proteomic annotation of complex protein systems.