Joseph N. Brown

DanteR: an extensible R-based tool for quantitative analysis of -omics data.

MOTIVATION The size and complex nature of mass spectrometry-based proteomics datasets motivate development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab. AVAILABILITY DanteR and its associated user guide are available for download free of charge at http://omics.pnl.gov/software/. We have an updated binary source for the DanteR package up on our website together with a vignettes document. For Windows, a single click automatically installs DanteR along with the R programming environment. For Linux and Mac OS X, users must install R and then follow instructions on the DanteR website for package installation. CONTACT rds@pnnl.gov.

Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics

In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.

A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments.

Liquid chromatography coupled with mass spectrometry (LC‐MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label‐free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label‐free LC‐MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label‐free LC‐MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.

A Multi-Omic View of Host-Pathogen-Commensal Interplay in Salmonella-Mediated Intestinal Infection

The potential for commensal microorganisms indigenous to a host (the ‘microbiome’ or ‘microbiota’) to alter infection outcome by influencing host-pathogen interplay is largely unknown. We used a multi-omics “systems” approach, incorporating proteomics, metabolomics, glycomics, and metagenomics, to explore the molecular interplay between the murine host, the pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium), and commensal gut microorganisms during intestinal infection with S. Typhimurium. We find proteomic evidence that S. Typhimurium thrives within the infected 129/SvJ mouse gut without antibiotic pre-treatment, inducing inflammation and disrupting the intestinal microbiome (e.g., suppressing Bacteroidetes and Firmicutes while promoting growth of Salmonella and Enterococcus). Alteration of the host microbiome population structure was highly correlated with gut environmental changes, including the accumulation of metabolites normally consumed by commensal microbiota. Finally, the less characterized phase of S. Typhimurium’s lifecycle was investigated, and both proteomic and glycomic evidence suggests S. Typhimurium may take advantage of increased fucose moieties to metabolize fucose while growing in the gut. The application of multiple omics measurements to Salmonella-induced intestinal inflammation provides insights into complex molecular strategies employed during pathogenesis between host, pathogen, and the microbiome.

Suite of activity-based probes for cellulose-degrading enzymes.

Microbial glycoside hydrolases play a dominant role in the biochemical conversion of cellulosic biomass to high-value biofuels. Anaerobic cellulolytic bacteria are capable of producing multicomplex catalytic subunits containing cell-adherent cellulases, hemicellulases, xylanases, and other glycoside hydrolases to facilitate the degradation of highly recalcitrant cellulose and other related plant cell wall polysaccharides. Clostridium thermocellum is a cellulosome-producing bacterium that couples rapid reproduction rates to highly efficient degradation of crystalline cellulose. Herein, we have developed and applied a suite of difluoromethylphenyl aglycone, N-halogenated glycosylamine, and 2-deoxy-2-fluoroglycoside activity-based protein profiling (ABPP) probes to the direct labeling of the C. thermocellum cellulosomal secretome. These activity-based probes (ABPs) were synthesized with alkynes to harness the utility and multimodal possibilities of click chemistry and to increase enzyme active site inclusion for liquid chromatography-mass spectrometry (LC-MS) analysis. We directly analyzed ABP-labeled and unlabeled global MS data, revealing ABP selectivity for glycoside hydrolase (GH) enzymes, in addition to a large collection of integral cellulosome-containing proteins. By identifying reactivity and selectivity profiles for each ABP, we demonstrate our ability to widely profile the functional cellulose-degrading machinery of the bacterium. Derivatization of the ABPs, including reactive groups, acetylation of the glycoside binding groups, and mono- and disaccharide binding groups, resulted in considerable variability in protein labeling. Our probe suite is applicable to aerobic and anaerobic microbial cellulose-degrading systems and facilitates a greater understanding of the organismal role associated with biofuel development.

Approach to cerebrospinal fluid (CSF) biomarker discovery and evaluation in HIV infection

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previously-defined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

Quantitative analysis of human salivary gland-derived intact proteome using top-down mass spectrometry

There are several notable challenges inherent for fully characterizing the entirety of the human saliva proteome using bottom‐up approaches, including polymorphic isoforms, PTMs, unique splice variants, deletions, and truncations. To address these challenges, we have developed a top‐down based LC‐MS/MS approach, which cataloged 20 major human salivary proteins with a total of 83 proteoforms, containing a broad range of PTMs. Among these proteins, several previously reported disease biomarker proteins were identified at the intact protein level, such as beta‐2 microglobulin. In addition, intact glycosylated proteoforms of several saliva proteins were also characterized, including intact N‐glycosylated protein prolactin inducible protein and O‐glycosylated acidic protein rich protein. These characterized proteoforms constitute an intact saliva proteoform database, which was used for quantitative comparison of intact salivary proteoforms among six healthy individuals. Human parotid and submandibular/sublingual gland secretion samples (2 μg of protein each) from six healthy individuals were compared using RPLC coupled with the 12T FT‐ICR mass spectrometer. Significantly different proteoform profiles were resolved with high reproducibility between parotid secretion and submandibular/sublingual glands. The results from this study provide further insight into the potential mechanisms of PTM pathways in oral glandular secretion, expanding our knowledge of this complex yet easily accessible fluid. Intact protein LC‐MS approach presented herein can potentially be applied for rapid and accurate identification of biomarkers from only a few microliters of human glandular saliva.

Morphine Produces Immunosuppressive Effects in Nonhuman Primates at the Proteomic and Cellular Levels

Morphine has long been known to have immunosuppressive properties in vivo, but the molecular and immunologic changes induced by it are incompletely understood. To explore how these changes interact with lentiviral infections in vivo, animals from two nonhuman primate species (African green monkeys and pigtailed macaques) were provided morphine and studied using a systems biology approach. Biological specimens were obtained from multiple sources (e.g. lymph node, colon, cerebrospinal fluid, and peripheral blood) before and after the administration of morphine (titrated up to a maximum dose of 5 mg/kg over a period of 20 days). Cellular immune, plasma cytokine, and proteome changes were measured and morphine-induced changes in these parameters were assessed on an interorgan, interindividual, and interspecies basis. In both species, morphine was associated with decreased levels of Ki-67+ T-cell activation but with only minimal changes in overall T-cell counts, neutrophil counts, and NK cell counts. Although changes in T-cell maturation were observed, these varied across the various tissue/fluid compartments studied. Proteomic analysis revealed a morphine-induced suppressive effect in lymph nodes, with decreased abundance of protein mediators involved in the functional categories of energy metabolism, signaling, and maintenance of cell structure. These findings have direct relevance for understanding the impact of heroin addiction and the opioids used to treat addiction as well as on the potential interplay between opioid abuse and the immunological response to an infective agent.

Macaque Proteome Response to Highly Pathogenic Avian Influenza and 1918 Reassortant Influenza Virus Infections

ABSTRACT The host proteome response and molecular mechanisms that drive disease in vivo during infection by a human isolate of the highly pathogenic avian influenza virus (HPAI) and 1918 pandemic influenza virus remain poorly understood. This study presents a comprehensive characterization of the proteome response in cynomolgus macaque (Macaca fascicularis) lung tissue over 7 days of infection with HPAI (the most virulent), a reassortant virus containing 1918 hemagglutinin and neuraminidase surface proteins (intermediate virulence), or a human seasonal strain (least virulent). A high-sensitivity two-dimensional liquid chromatography-tandem mass spectroscopy strategy and functional network analysis were implemented to gain insight into response pathways activated in macaques during influenza virus infection. A macaque protein database was assembled and used in the identification of 35,239 unique peptide sequences corresponding to approximately 4,259 proteins. Quantitative analysis identified an increase in expression of 400 proteins during viral infection. The abundance levels of a subset of these 400 proteins produced strong correlations with disease progression observed in the macaques, distinguishing a “core” response to viral infection from a “high” response specific to severe disease. Proteome expression profiles revealed distinct temporal response kinetics between viral strains, with HPAI inducing the most rapid response. While proteins involved in the immune response, metabolism, and transport were increased rapidly in the lung by HPAI, the other viruses produced a delayed response, characterized by an increase in proteins involved in oxidative phosphorylation, RNA processing, and translation. Proteomic results were integrated with previous genomic and pathological analysis to characterize the dynamic nature of the influenza virus infection process.

Expediting SRM assay development for large-scale targeted proteomics experiments.

Because of its high sensitivity and specificity, selected reaction monitoring (SRM)-based targeted proteomics has become increasingly popular for biological and translational applications. Selection of optimal transitions and optimization of collision energy (CE) are important assay development steps for achieving sensitive detection and accurate quantification; however, these steps can be labor-intensive, especially for large-scale applications. Herein, we explored several options for accelerating SRM assay development evaluated in the context of a relatively large set of 215 synthetic peptide targets. We first showed that HCD fragmentation is very similar to that of CID in triple quadrupole (QQQ) instrumentation and that by selection of the top 6 y fragment ions from HCD spectra, >86% of the top transitions optimized from direct infusion with QQQ instrumentation are covered. We also demonstrated that the CE calculated by existing prediction tools was less accurate for 3+ precursors and that a significant increase in intensity for transitions could be obtained using a new CE prediction equation constructed from the present experimental data. Overall, our study illustrated the feasibility of expediting the development of larger numbers of high-sensitivity SRM assays through automation of transition selection and accurate prediction of optimal CE to improve both SRM throughput and measurement quality.