An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange

BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. CONCLUSIONS/SIGNIFICANCE: We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification

A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

Perfluorooctanoic Acid for Shotgun Proteomics

Here, we describe the novel use of a volatile surfactant, perfluorooctanoic acid (PFOA), for shotgun proteomics. PFOA was found to solubilize membrane proteins as effectively as sodium dodecyl sulfate (SDS). PFOA concentrations up to 0.5% (w/v) did not significantly inhibit trypsin activity. The unique features of PFOA allowed us to develop a single-tube shotgun proteomics method that used all volatile chemicals that could easily be removed by evaporation prior to mass spectrometry analysis. The experimental procedures involved: 1) extraction of proteins in 2% PFOA; 2) reduction of cystine residues with triethyl phosphine and their S-alkylation with iodoethanol; 3) trypsin digestion of proteins in 0.5% PFOA; 4) removal of PFOA by evaporation; and 5) LC-MS/MS analysis of the resulting peptides. The general applicability of the method was demonstrated with the membrane preparation of photoreceptor outer segments. We identified 75 proteins from 1 µg of the tryptic peptides in a single, 1-hour, LC-MS/MS run. About 67% of the proteins identified were classified as membrane proteins. We also demonstrate that a proteolytic 18O labeling procedure can be incorporated after the PFOA removal step for quantitative proteomic experiments. The present method does not require sample clean-up devices such as solid-phase extractions and membrane filters, so no proteins/peptides are lost in any experimental steps. Thus, this single-tube shotgun proteomics method overcomes the major drawbacks of surfactant use in proteomic experiments

Multidimensional separation prior to mass spectrometry: Getting closer to the bottom of the iceberg

While prefractionation has previously been shown to improve results in MS analysis, a novel combination provides an additional dimension of separation: protein fractionation by SDS-PAGE followed by IEF of tryptic peptides before separation by RP-LC [Atanassov and Urlaub, Proteomics 2013, 13, 2947-2955]. This three-step separation procedure prior to MS/MS substantially increases proteome coverage and represents a further step toward a more comprehensive analysis of complex proteomes

Exploring Information Technologies to Support Shotgun Proteomics

Shotgun proteomics refers to the direct analysis of complex protein mixtures to create a profile of the proteins present in the cell. These profiles can be used to study the underlying biological basis for cancer development. Closely studying the profiles as the cancer proliferates reveals the molecular interactions in the cell. They provide clues to researchers on potential drug targets to treat the disease. A little more than a decade old, shotgun proteomics is a relatively new form of discovery, one that is data intensive and requires complex data analysis. Early studies indicated a gap between the ability to analyze biological samples with a mass spectrometer and the information systems available to process and analyze this data. This thesis reflects on an automated proteomic information system at the University of Colorado Central Analytical Facility. Investigators there are using cutting edge proteomic techniques to analyze melanoma cell lines responsible for skin cancer in patients. The paper will provide insight on key design processes in the development of an Oracle relational database and automation system to support high-throughput shotgun proteomics in the facility. It will also discuss significant contributions, technologies, software, a data standard, and leaders in the field developing solutions and products in proteomics