Kenneth J. Auberry

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.

Identification of Proteins in Human Cytomegalovirus (HCMV) Particles: the HCMV Proteome

ABSTRACT Human cytomegalovirus (HCMV), a member of the herpesvirus family, is a large complex enveloped virus composed of both viral and cellular gene products. While the sequence of the HCMV genome has been known for over a decade, the full set of viral and cellular proteins that compose the HCMV virion are unknown. To approach this problem we have utilized gel-free two-dimensional capillary liquid chromatography-tandem mass spectrometry (MS/MS) and Fourier transform ion cyclotron resonance MS to identify and determine the relative abundances of viral and cellular proteins in purified HCMV AD169 virions and dense bodies. Analysis of the proteins from purified HCMV virion preparations has indicated that the particle contains significantly more viral proteins than previously known. In this study, we identified 71 HCMV-encoded proteins that included 12 proteins encoded by known viral open reading frames (ORFs) previously not associated with virions and 12 proteins from novel viral ORFs. Analysis of the relative abundance of HCMV proteins indicated that the predominant virion protein was the pp65 tegument protein and that gM rather than gB was the most abundant glycoprotein. We have also identified over 70 host cellular proteins in HCMV virions, which include cellular structural proteins, enzymes, and chaperones. In addition, analysis of HCMV dense bodies indicated that these viral particles are composed of 29 viral proteins with a reduced quantity of cellular proteins in comparison to HCMV virions. This study provides the first comprehensive quantitative analysis of the viral and cellular proteins that compose infectious particles of a large complex virus.

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/.

“Spatial Mapping of the Neurite and Soma Proteomes Reveals a Functional Cdc42/Rac Regulatory Network”

Neurite extension and growth cone navigation are guided by extracellular cues that control cytoskeletal rearrangements. However, understanding the complex signaling mechanisms that mediate neuritogenesis has been limited by the inability to biochemically separate the neurite and soma for spatial proteomic and bioinformatic analyses. Here, we apply global proteome profiling in combination with a neurite purification methodology for comparative analysis of the soma and neurite proteomes of neuroblastoma cells. The spatial relationship of 4,855 proteins were mapped, revealing networks of signaling proteins that control integrins, the actin cytoskeleton, and axonal guidance in the extending neurite. Bioinformatics and functional analyses revealed a spatially compartmentalized Rac/Cdc42 signaling network that operates in conjunction with multiple guanine-nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) to control neurite formation. Interestingly, RNA interference experiments revealed that the different GEFs and GAPs regulate specialized functions during neurite formation, including neurite growth and retraction kinetics, cytoskeletal organization, and cell polarity. Our findings provide insight into the spatial organization of signaling networks that enable neuritogenesis and provide a comprehensive system-wide profile of proteins that mediate this process, including those that control Rac and Cdc42 signaling.

Comparative Bacterial Proteomics: Analysis of the Core Genome Concept

While comparative bacterial genomic studies commonly predict a set of genes indicative of common ancestry, experimental validation of the existence of this core genome requires extensive measurement and is typically not undertaken. Enabled by an extensive proteome database developed over six years, we have experimentally verified the expression of proteins predicted from genomic ortholog comparisons among 17 environmental and pathogenic bacteria. More exclusive relationships were observed among the expressed protein content of phenotypically related bacteria, which is indicative of the specific lifestyles associated with these organisms. Although genomic studies can establish relative orthologous relationships among a set of bacteria and propose a set of ancestral genes, our proteomics study establishes expressed lifestyle differences among conserved genes and proposes a set of expressed ancestral traits.

Electrospray ionization-Fourier transform ion cyclotron mass spectrometry using ion preselection and external accumulation for ultrahigh sensitivity.

The dynamic range of Fourier transform ion cyclotron mass spectrometry (FTICR) is typically limited by the useful charge capacity of an FTICR cell (to ∼106 to 107 elementary charges) and the minimum number of ions required to produce a useful signal (∼102 elementary charges). We show that the expansion of the dynamic range by 2 orders of magnitude can be achieved by preselecting lower abundance species in a quadrupole interface to an electrospray ionization (ESI) source. Ion preselection is then followed by ion accumulation in external to the FTICR cell a linear (2-D) quadrupole trap and subsequent transfer to the region of high magnetic field for gated trapping in the FTICR cell. Two modes of ion preselection, using either the quadrupole filtering mode or rf-only dipolar excitation, were studied and mass resolutions of 30 to 100 were achieved for selective external ion accumulation of peptides and proteins with molecular weights ranging from 500 to 17,000 Da. The ability to selectively eject the most abundant species before trapping in the FTICR has enormous practical benefits for increasing the sensitivity and dynamic range of measurements.

A method for investigating protein-protein interactions related to Salmonella Typhimurium pathogenesis

We successfully modified an existing method to investigate protein-protein interactions in the pathogenic bacterium Salmonella enterica serovar Typhimurium (Salmonella Typhimurium). This method includes (i) addition of a histidine-biotin-histidine tag to the bait proteins via recombinant DNA techniques, (ii) in vivo cross-linking with formaldehyde, (iii) tandem affinity purification of bait proteins under fully denaturing conditions, and (iv) identification of the proteins cross-linked to the bait proteins by liquid-chromatography in conjunction with tandem mass-spectrometry. In vivo cross-linking stabilized protein interactions and permitted the subsequent two-step purification step conducted under denaturing conditions. The two-step purification greatly reduced nonspecific binding of noncross-linked proteins to bait proteins. Two different negative controls were employed to eliminate the possibility of identifying background and nonspecific proteins as interacting partners, especially those caused by nonspecific binding to the stationary phase used for protein purification. In an initial demonstration of this approach, we tagged three Salmonella proteinsHimD, PduB and PhoPwith known binding partners that ranged from stable (e.g., HimD) to transient (i.e., PhoP). Distinct sets of interacting proteins were identified for each bait protein, including the known binding partners such as HimA for HimD, as well as unexpected binding partners. Our results suggest that novel protein-protein interactions identified may be critical to pathogenesis by Salmonella.

An analysis pipeline for the inference of protein-protein interaction networks

We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis.

SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction

The Software Environment for Biological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and testing of network inference algorithms that use high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein- protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. In this paper, we present a case study on the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein- protein interaction networks from sets of mass spectrometry bait-prey experiment data.This paper addresses the problem of understanding preservation and reconstruction requirements for computer- aided medical decision-making. With an increasing number of computer-aided decisions having a large impact on our society, the motivation for our work is not only to document these decision processes semi-automatically but also to understand the preservation cost and related computational requirements. Our objective is to support computer-assisted creation of medical records, to guarantee authenticity of records, as well as to allow managers of electronic medical records (EMR), archivists and other users to explore and evaluate computational costs (e.g., storage and processing time) depending on several key characteristics of appraised records. Our approach to this problem is based on designing an exploratory simulation framework for investigating preservation tradeoffs and assisting in appraisals of electronic records. We have a prototype simulation framework called image provenance to learn (IP2Learn) to support computer-aided medical decisions based on visual image inspection. The current software enables to explore some of the tradeoffs related to (1) information granularity (category and level of detail), (2) representation of provenance information, (3) compression, (4) encryption, (5) watermarking and steganography, (6) information gathering mechanism, and (7) final medical report content (level of detail) and its format. We illustrate the novelty of IP2Learn by performing example studies and the results of tradeoff analyses for a specific image inspection task.

An adaptable multiple power source for mass spectrometry and other scientific instruments

An Adaptable Multiple Power Source (AMPS) system has been designed and constructed. The AMPS system can provide up to 16 direct current (DC) (±400 V; 5 mA), 4 radio frequency (RF) (two 500 VPP sinusoidal signals each, 0.5-5 MHz) channels, 2 high voltage sources (±6 kV), and one ∼40 W, 250 °C temperature-regulated heater. The system is controlled by a microcontroller, capable of communicating with its front panel or a computer. It can assign not only pre-saved fixed DC and RF signals but also profiled DC voltages. The AMPS system is capable of driving many mass spectrometry components and ancillary devices and can be adapted to other instrumentation/engineering projects.