Joshua N. Adkins

The Human Plasma Proteome A Nonredundant List Developed by Combination of Four Separate Sources

We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The “nonproteomic” literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.

Toward a Human Blood Serum Proteome Analysis By Multidimensional Separation Coupled With Mass Spectrometry

Blood serum is a complex body fluid that contains various proteins ranging in concentration over at least 9 orders of magnitude. Using a combination of mass spectrometry technologies with improvements in sample preparation, we have performed a proteomic analysis with submilliliter quantities of serum and increased the measurable concentration range for proteins in blood serum beyond previous reports. We have detected 490 proteins in serum by on-line reversed-phase microcapillary liquid chromatography coupled with ion trap mass spectrometry. To perform this analysis, immunoglobulins were removed from serum using protein A/G, and the remaining proteins were digested with trypsin. Resulting peptides were separated by strong cation exchange chromatography into distinct fractions prior to analysis. This separation resulted in a 3–5-fold increase in the number of proteins detected in an individual serum sample. With this increase in the number of proteins identified we have detected some lower abundance serum proteins (ng/ml range) including human growth hormone, interleukin-12, and prostate-specific antigen. We also used SEQUEST to compare different protein databases with and without filtering. This comparison is plotted to allow for a quick visual assessment of different databases as a subjective measure of analytical quality. With this study, we have performed the most extensive analysis of serum proteins to date and laid the foundation for future refinements in the identification of novel protein biomarkers of disease.

Activated ClpP kills persisters and eradicates a chronic biofilm infection

Chronic infections are difficult to treat with antibiotics but are caused primarily by drug-sensitive pathogens. Dormant persister cells that are tolerant to killing by antibiotics are responsible for this apparent paradox. Persisters are phenotypic variants of normal cells and pathways leading to dormancy are redundant, making it challenging to develop anti-persister compounds. Biofilms shield persisters from the immune system, suggesting that an antibiotic for treating a chronic infection should be able to eradicate the infection on its own. We reasoned that a compound capable of corrupting a target in dormant cells will kill persisters. The acyldepsipeptide antibiotic (ADEP4) has been shown to activate the ClpP protease, resulting in death of growing cells. Here we show that ADEP4-activated ClpP becomes a fairly nonspecific protease and kills persisters by degrading over 400 proteins, forcing cells to self-digest. Null mutants of clpP arise with high probability, but combining ADEP4 with rifampicin produced complete eradication of Staphylococcus aureus biofilms in vitro and in a mouse model of a chronic infection. Our findings indicate a general principle for killing dormant cells—activation and corruption of a target, rather than conventional inhibition. Eradication of a biofilm in an animal model by activating a protease suggests a realistic path towards developing therapies to treat chronic infections.

DAnTE: a statistical tool for quantitative analysis of -omics data

UNLABELLED Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly. AVAILABILITY DAnTE may be downloaded free of charge at http://omics.pnl.gov/software/. SUPPLEMENTARY INFORMATION An example dataset with instructions on how to perform a series of analysis steps is available at http://omics.pnl.gov/software/

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

BackgroundData generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls.ResultsWith a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample.ConclusionDecon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs.Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/.

VIPER: an advanced software package to support high-throughput LC-MS peptide identification

SUMMARY The accurate mass and time (AMT) tag approach is used for analysis of large scale experiments by combining information generated over multiple datasets and instrument types. The VIPER software package is one of the key components of the data processing pipeline and implements automated algorithms to discover LC-MS features, align and match these LC-MS features to a database of peptides previously identified in LC-MS/MS analyses, and identify and quantify pairs of isotopically labeled peptides. AVAILABILITY VIPER may be downloaded free of charge at http://ncrr.pnl.gov/software/

Proteogenomics: needs and roles to be filled by proteomics in genome annotation

While genome sequencing efforts reveal the basic building blocks of life, a genome sequence alone is insufficient for elucidating biological function. Genome annotation--the process of identifying genes and assigning function to each gene in a genome sequence--provides the means to elucidate biological function from sequence. Current state-of-the-art high-throughput genome annotation uses a combination of comparative (sequence similarity data) and non-comparative (ab initio gene prediction algorithms) methods to identify protein-coding genes in genome sequences. Because approaches used to validate the presence of predicted protein-coding genes are typically based on expressed RNA sequences, they cannot independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein. With the ability to directly measure peptides arising from expressed proteins, high-throughput liquid chromatography-tandem mass spectrometry-based proteomics approaches can be used to verify coding regions of a genomic sequence. Here, we highlight several ways in which high-throughput tandem mass spectrometry-based proteomics can improve the quality of genome annotations and suggest that it could be efficiently applied during the gene calling process so that the improvements are propagated through the subsequent functional annotation process.

Proteomic Analysis of Salmonella enterica Serovar Typhimurium Isolated from RAW 264.7 Macrophages IDENTIFICATION OF A NOVEL PROTEIN THAT CONTRIBUTES TO THE REPLICATION OF SEROVAR TYPHIMURIUM INSIDE MACROPHAGES

To evade host resistance mechanisms, Salmonella enterica serovar Typhimurium (STM), a facultative intracellular pathogen, must alter its proteome following macrophage infection. To identify new colonization and virulence factors that mediate STM pathogenesis, we have isolated STM cells from RAW 264.7 macrophages at various time points following infection and used a liquid chromatography-mass spectrometry-based proteomic approach to detect the changes in STM protein abundance. Because host resistance to STM infection is strongly modulated by the expression of a functional host-resistant regulator, i.e. natural resistance-associated macrophage protein 1 (Nramp1, also called Slc11a1), we have also examined the effects of Nramp1 activity on the changes of STM protein abundances. A total of 315 STM proteins have been identified from isolated STM cells, which are largely housekeeping proteins whose abundances remain relatively constant during the time course of infection. However, 39 STM proteins are strongly induced after infection, suggesting their involvement in modulating colonization and infection. Of the 39 induced proteins, 6 proteins are specifically modulated by Nramp1 activity, including STM3117, as well as STM3118-3119 whose time-dependent abundance changes were confirmed using Western blot analysis. Deletion of the gene encoding STM3117 resulted in a dramatic reduction in the ability of STM to colonize wild-type RAW 264.7 macrophages, demonstrating a critical involvement of STM3117 in promoting the replication of STM inside macrophages. The predicted function common for STM3117-3119 is biosynthesis and modification of the peptidoglycan layer of the STM cell wall.

DeconMSn: A Software Tool for accurate parent ion monoisotopic mass determination for tandem mass spectra

UNLABELLED DeconMSn accurately determines the monoisotopic mass and charge state of parent ions from high-resolution tandem mass spectrometry data, offering significant improvement for LTQ_FT and LTQ_Orbitrap instruments over the commercially delivered Thermo Fisher Scientifics extract_msn tool. Optimal parent ion mass tolerance values can be determined using accurate mass information, thus improving peptide identifications for high-mass measurement accuracy experiments. For low-resolution data from LCQ and LTQ instruments, DeconMSn incorporates a support-vector-machine-based charge detection algorithm that identifies the most likely charge of a parent species through peak characteristics of its fragmentation pattern. AVAILABILITY http://ncrr.pnl.gov/software/ or http://www.proteomicsresource.org/.

Analysis of the Salmonella typhimurium Proteome through Environmental Response toward Infectious Conditions

Salmonella enterica serovar Typhimurium (also known as Salmonella typhimurium) is a facultative intracellular pathogen that causes ∼8,000 reported cases of acute gastroenteritis and diarrhea each year in the United States. Although many successful physiological, biochemical, and genetic approaches have been taken to determine the key virulence determinants encoded by this organism, the sheer number of uncharacterized reading frames observed within the S. enterica genome suggests that many more virulence factors remain to be discovered. We used a liquid chromatography-mass spectrometry-based “bottom-up” proteomic approach to generate a more complete picture of the gene products that S. typhimurium synthesizes under typical laboratory conditions as well as in culture media that are known to induce expression of virulence genes. When grown to logarithmic phase in rich medium, S. typhimurium is known to express many genes that are required for invasion of epithelial cells. Conversely stationary phase cultures of S. typhimurium express genes that are needed for both systemic infection and growth within infected macrophages. Lastly bacteria grown in an acidic, magnesium-depleted minimal medium (MgM) designed to mimic the phagocytic vacuole have been shown to up-regulate virulence gene expression. Initial comparisons of protein abundances from bacteria grown under each of these conditions indicated that the majority of proteins do not change significantly. However, we observed subsets of proteins whose expression was largely restricted to one of the three culture conditions. For example, cells grown in MgM had a higher abundance of Mg2+ transport proteins than found in other growth conditions. A second more virulent S. typhimurium strain (14028) was also cultured under these same growth conditions, and the results were directly compared with those obtained for strain LT2. This comparison offered a unique opportunity to contrast protein populations in these closely related bacteria. Among a number of proteins displaying a higher abundance in strain 14028 were the products of the pdu operon, which encodes enzymes required for propanediol utilization. These pdu operon proteins were validated in culture and during macrophage infection. Our work provides further support for earlier observations that suggest pdu gene expression contributes to S. typhimurium pathogenesis.