Thomas McGowan

A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies

Large databases (>106 sequences) used in metaproteomic and proteogenomic studies present challenges in matching peptide sequences to MS/MS data using database‐search programs. Most notably, strict filtering to avoid false‐positive matches leads to more false negatives, thus constraining the number of peptide matches. To address this challenge, we developed a two‐step method wherein matches derived from a primary search against a large database were used to create a smaller subset database. The second search was performed against a target‐decoy version of this subset database merged with a host database. High confidence peptide sequence matches were then used to infer protein identities. Applying our two‐step method for both metaproteomic and proteogenomic analysis resulted in twice the number of high confidence peptide sequence matches in each case, as compared to the conventional one‐step method. The two‐step method captured almost all of the same peptides matched by the one‐step method, with a majority of the additional matches being false negatives from the one‐step method. Furthermore, the two‐step method improved results regardless of the database search program used. Our results show that our two‐step method maximizes the peptide matching sensitivity for applications requiring large databases, especially valuable for proteogenomics and metaproteomics studies.

Deep metaproteomic analysis of human salivary supernatant

The human salivary proteome is extremely complex, including proteins from salivary glands, serum, and oral microbes. Much has been learned about the host component, but little is known about the microbial component. Here we report a metaproteomic analysis of salivary supernatant pooled from six healthy subjects. For deep interrogation of the salivary proteome, we combined protein dynamic range compression (DRC), multidimensional peptide fractionation, and high‐mass accuracy MS/MS with a novel two‐step peptide identification method using a database of human proteins plus those translated from oral microbe genomes. Peptides were identified from 124 microbial species as well as uncultured phylotypes such as TM7. Streptococcus, Rothia, Actinomyces, Prevotella, Neisseria, Veilonella, Lactobacillus, Selenomonas, Pseudomonas, Staphylococcus, and Campylobacter were abundant among the 65 genera from 12 phyla represented. Taxonomic diversity in our study was broadly consistent with metagenomic studies of saliva. Proteins mapped to 20 KEGG pathways, with carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, membrane transport, and signal transduction most represented. The communities sampled appear to be actively engaged in glycolysis and protein synthesis. This first deep metaproteomic catalog from human salivary supernatant provides a baseline for future studies of shifts in microbial diversity and protein activities potentially associated with oral disease.

Large-scale phosphoproteomics analysis of whole saliva reveals a distinct phosphorylation pattern.

In-depth knowledge of bodily fluid phosphoproteomes, such as whole saliva, is limited. To better understand the whole saliva phosphoproteome, we generated a large-scale catalog of phosphorylated proteins. To circumvent the wide dynamic range of phosphoprotein abundance in whole saliva, we combined dynamic range compression using hexapeptide beads, strong cation exchange HPLC peptide fractionation, and immobilized metal affinity chromatography prior to mass spectrometry. In total, 217 unique phosphopeptides sites were identified representing 85 distinct phosphoproteins at 2.3% global FDR. From these peptides, 129 distinct phosphorylation sites were identified of which 57 were previously known, but only 11 of which had been previously identified in whole saliva. Cellular localization analysis revealed salivary phosphoproteins had a distribution similar to all known salivary proteins, but with less relative representation in extracellular and plasma membrane categories compared to salivary glycoproteins. Sequence alignment showed that phosphorylation occurred at acidic-directed kinase, proline-directed, and basophilic motifs. This differs from plasma phosphoproteins, which predominantly occur at Golgi casein kinase recognized sequences. Collectively, these results suggest diverse functions for salivary phosphoproteins and multiple kinases involved in their processing and secretion. In all, this study should lay groundwork for future elucidation of the functions of salivary protein phosphorylation.

Targeted 18O-labeling for improved proteomic analysis of carbonylated peptides by mass spectrometry

Proteomic characterization of carbonylated amino acid sites currently relies on confidently matching tandem mass spectra (MS) to peptides within a sequence database. Although effective to some degree, reliable proteomic characterization of carbonylated peptides using this approach remains a challenge needing new, complementary solutions. To this end, we developed a method based on partial 18O-labeling of reactive carbonyl modifications, which produces a unique isotope signature in mass spectra of carbonylated peptides and enables their detection without reliance on matching MS2 spectra to a peptide sequence. Key to our method were optimized measures for eliminating trypsin-catalyzed incorporation of 18O at peptide C-termini, and for stabilizing the incorporated O within the carbonyl modification to prevent its loss during liquid chromatography separation. Applying our method to a rat skeletal muscle homogenate treated with the carbonyl modification 4-hyroxynonenal (4-HNE), we demonstrated its compatibility with solid-phase hydrazide enrichment of carbonylated peptides from complex mixtures. Additionally, we demonstrated the value of 18O isotope signatures for confirming HNE-modified peptide sequences matched via sequence database searching, and identifying modified peptides missed by MS2 and/or sequence database searching. Combining our 18O-labeling method with a customized automated software script, we systematically evaluated for the first time the efficiency of MS2 and sequence database searching for identifying HNE-modified peptides. We estimated that less than half of the modified peptides selected for MS2 were successfully identified. Collectively, our method and software should provide valuable new tools for investigators studying protein carbonylation via mass spectrometry-based proteomics.

Novel In Situ Collection of Tumor Interstitial Fluid from a Head and Neck Squamous Carcinoma Reveals a Unique Proteome with Diagnostic Potential

IntroductionTumors lack normal drainage of secreted fluids and consequently build up tumor interstitial fluid (TIF). Unlike other bodily fluids, TIF likely contains a high proportion of tumor-specific proteins with potential as biomarkers.MethodsHere, we evaluated a novel technique using a unique ultrafiltration catheter for in situ collection of TIF and used it to generate the first catalog of TIF proteins from a head and neck squamous cell carcinoma (HNSCC). To maximize proteomic coverage, TIF was immunodepleted for high abundance proteins and digested with trypsin, and peptides were fractionated in three dimensions prior to mass spectrometry.ResultsWe identified 525 proteins with high confidence. The HNSCC TIF proteome was distinct compared to proteomes of other bodily fluids. It contained a relatively high proportion of proteins annotated by Gene Ontology as “extracellular” compared to other secreted fluid and cellular proteomes, indicating minimal cell lysis from our in situ collection technique. Several proteins identified are putative biomarkers of HNSCC, supporting our catalog’s value as a source of potential biomarkers.ConclusionsIn all, we demonstrate a reliable new technique for in situ TIF collection and provide the first HNSCC TIF protein catalog with value as a guide for others seeking to develop tumor biomarkers.

Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm

LTQ Orbitrap data analyzed with ProteinPilot can be further improved by MaxQuant raw data processing, which utilizes precursor‐level high mass accuracy data for peak processing and MGF creation. In particular, ProteinPilot results from MaxQuant‐processed peaklists for Orbitrap data sets resulted in improved spectral utilization due to an improved peaklist quality with higher precision and high precursor mass accuracy (HPMA). The output and postsearch analysis tools of both workflows were utilized for previously unexplored features of a three‐dimensional fractionated and hexapeptide library (ProteoMiner) treated whole saliva data set comprising 200 fractions. ProteinPilots ability to simultaneously predict multiple modifications showed an advantage from ProteoMiner treatment for modified peptide identification. We demonstrate that complementary approaches in the analysis pipeline provide comprehensive results for the whole saliva data set acquired on an LTQ Orbitrap. Overall our results establish a workflow for improved protein identification from high mass accuracy data.

Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics “Contribution Fest“ undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.

An Accessible Proteogenomics Informatics Resource for Cancer Researchers.

Proteogenomics has emerged as a valuable approach in cancer research, which integrates genomic and transcriptomic data with mass spectrometry-based proteomics data to directly identify expressed, variant protein sequences that may have functional roles in cancer. This approach is computationally intensive, requiring integration of disparate software tools into sophisticated workflows, challenging its adoption by nonexpert, bench scientists. To address this need, we have developed an extensible, Galaxy-based resource aimed at providing more researchers access to, and training in, proteogenomic informatics. Our resource brings together software from several leading research groups to address two foundational aspects of proteogenomics: (i) generation of customized, annotated protein sequence databases from RNA-Seq data; and (ii) accurate matching of tandem mass spectrometry data to putative variants, followed by filtering to confirm their novelty. Directions for accessing software tools and workflows, along with instructional documentation, can be found at z.umn.edu/canresgithub. Cancer Res; 77(21); e43-46. ©2017 AACR.