Biomedical subjects

Samuel Purvine

Publications and source records attributed to Samuel Purvine.

9 recordsLinked to original sources

PubMed2005

Global profiling of Shewanella oneidensis MR-1: expression of hypothetical genes and improved functional annotations.

The gamma-proteobacterium Shewanella oneidensis strain MR-1 is a metabolically versatile organism that can reduce a wide range of organic compounds, metal ions, and radionuclides. Similar to most other sequenced organisms, approximately 40% of the predicted ORFs in the S. oneidensis genome were annotated as uncharacterized "hypothetical" genes. We implemented an integrative approach by using experimental and computational analyses to provide more detailed insight into gene function. Global expression profiles were determined for cells after UV irradiation and under aerobic and suboxic growth conditions. Transcriptomic and proteomic analyses confidently identified 538 hypothetical genes as expressed in S. oneidensis cells both as mRNAs and proteins (33% of all predicted hypothetical proteins). Publicly available analysis tools and databases and the expression data were applied to improve the annotation of these genes. The annotation results were scored by using a seven-category schema that ranked both confidence and precision of the functional assignment. We were able to identify homologs for nearly all of these hypothetical proteins (97%), but could confidently assign exact biochemical functions for only 16 proteins (category 1; 3%). Altogether, computational and experimental evidence provided functional assignments or insights for 240 more genes (categories 2-5; 45%). These functional annotations advance our understanding of genes involved in vital cellular processes, including energy conversion, ion transport, secondary metabolism, and signal transduction. We propose that this integrative approach offers a valuable means to undertake the enormous challenge of characterizing the rapidly growing number of hypothetical proteins with each newly sequenced genome.

Gene Expression Profiling↗

PubMed2004

Identification and functional analysis of 'hypothetical' genes expressed in Haemophilus influenzae.

The progress in genome sequencing has led to a rapid accumulation in GenBank submissions of uncharacterized 'hypothetical' genes. These genes, which have not been experimentally characterized and whose functions cannot be deduced from simple sequence comparisons alone, now comprise a significant fraction of the public databases. Expression analyses of Haemophilus influenzae cells using a combination of transcriptomic and proteomic approaches resulted in confident identification of 54 'hypothetical' genes that were expressed in cells under normal growth conditions. In an attempt to understand the functions of these proteins, we used a variety of publicly available analysis tools. Close homologs in other species were detected for each of the 54 'hypothetical' genes. For 16 of them, exact functional assignments could be found in one or more public databases. Additionally, we were able to suggest general functional characterization for 27 more genes (comprising approximately 80% total). Findings from this analysis include the identification of a pyruvate-formate lyase-like operon, likely to be expressed not only in H.influenzae but also in several other bacteria. Further, we also observed three genes that are likely to participate in the transport and/or metabolism of sialic acid, an important component of the H.influenzae lipo-oligosaccharide. Accurate functional annotation of uncharacterized genes calls for an integrative approach, combining expression studies with extensive computational analysis and curation, followed by eventual experimental verification of the computational predictions.

Amino Acid Sequence↗

PubMed2004

In Silico Metabolic Model and Protein Expression of Haemophilus influenzae Strain Rd KW20 in Rich Medium.

The intermediary metabolism of Haemophilus influenzae strain Rd KW20 was studied by a combination of protein expression analysis using a recently developed direct proteomics approach, mutational analysis, and mathematical modeling. Special emphasis was placed on carbon utilization, sugar fermentation, TCA cycle, and electron transport of H. influenzae cells grown microaerobically and anaerobically in a rich medium. The data indicate that several H. influenzae metabolic proteins similar to Escherichia coli proteins, known to be regulated by low concentrations of oxygen, were well expressed in both growth conditions in H. influenzae. An in silico model of the H. influenzae metabolic network was used to study the effects of selective deletion of certain enzymatic steps. This allowed us to define proteins predicted to be essential or non-essential for cell growth and to address numerous unresolved questions about intermediary metabolism of H. influenzae. Comparison of data from in vivo protein expression with the protein list associated with a genome-scale metabolic model showed significant coverage of the known metabolic proteome. This study demonstrates the significance of an integrated approach to the characterization of H. influenzae metabolism.

Biochemistry↗

PubMed2004

Standard mixtures for proteome studies.

Mixtures of moderate complexity were formed from 23 peptides and 12 proteins digested with trypsin, all individually characterized. These mixtures were analyzed with replicates in full and windowed m/z ranges using online high-performance reverse phase liquid chromatography coupled via electrospray ionization to an ion trap mass spectrometer. The resulting spectra were searched using SEQUEST against databases of different sizes and contents and confidences of the observed identifications were evaluated by our earlier statistical model. These data were then combined with biologically derived spectral data, searched, and further evaluated. All peptides but one and all proteins were identified with high confidence. Additionally, the presence and behavior of quadruply charged peptides was analyzed. The properties of the proposed peptide and protein mixtures as well as the performance of the statistical model were carefully investigated. These mixtures mimic the complexity seen in large-scale proteomics experiments, and are proposed to serve as quality assessment standards for future proteome studies.

Animals↗

PubMed2004

Spectral quality assessment for high-throughput tandem mass spectrometry proteomics.

Current techniques in tandem mass spectrometric analyses of cellular protein contents often produce thousands to tens of thousands of spectra per experiment. This study introduces a new algorithm, named SPEQUAL, which is aimed at automated tandem mass spectral quality assessment. The quality of a given spectrum can be evaluated from three basic components: (i) charge state differentiation, (ii) total signal intensity, and (iii) signal-to-noise estimates. The differentiation between single and multiple precursor charge states (i) provides a binary score for a given spectrum. Components (ii) and (iii) provide partial scores which are subsequently summarized and multiplied by the first score. SPEQUAL was applied to over 10,000 data files derived from almost 3,000 tandem mass spectra, and the results (final cumulative scores) were manually verified. SPEQUAL's performance was determined to have high sensitivity and specificity and low error rates for both spectral quality estimates in general and precursor charge state differentiation in particular. Each of the partial scores is controlled by adjustable thresholds to fine-tune SPEQUAL's performance for different analysis pipelines and instrumentation. This spectral quality assessment tool is intended to act in an advisory role to the researcher, assisting in filtration of thousands of spectra typically produced by high throughput tandem mass spectrometric proteome analyses. Lastly, SPEQUAL was implemented as Java GUI-based and command-line-based interfaces freely available for both academic and industrial researchers.

Mass Spectrometry↗

PubMed2003

Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer.

Parallel collision-induced dissociation (CID) of peptides rather than serial, as is customary, results in loss of the obvious parent-fragment ion lineage available from CID on a single ion. We report proof-of-principle results suggesting the feasibility of parallel peptide CID, referred to here as shotgun CID, for protein identification when using the measured mass accuracies available from a time of flight mass analyzer and currently available search routines such as SEQUEST. Additionally, we report that parent-fragment ion lineage may be reconstructed from information encoded in the chromatographic single ion current traces of peptides.

Feasibility Studies↗

PubMed2003

Initial proteome analysis of model microorganism Haemophilus influenzae strain Rd KW20.

The proteome of Haemophilus influenzae strain Rd KW20 was analyzed by liquid chromatography (LC) coupled with ion trap tandem mass spectrometry (MS/MS). This approach does not require a gel electrophoresis step and provides a rapidly developed snapshot of the proteome. In order to gain insight into the central metabolism of H. influenzae, cells were grown microaerobically and anaerobically in a rich medium and soluble and membrane proteins of strain Rd KW20 were proteolyzed with trypsin and directly examined by LC-MS/MS. Several different experimental and computational approaches were utilized to optimize the proteome coverage and to ensure statistically valid protein identification. Approximately 25% of all predicted proteins (open reading frames) of H. influenzae strain Rd KW20 were identified with high confidence, as their component peptides were unambiguously assigned to tandem mass spectra. Approximately 80% of the predicted ribosomal proteins were identified with high confidence, compared to the 33% of the predicted ribosomal proteins detected by previous two-dimensional gel electrophoresis studies. The results obtained in this study are generally consistent with those obtained from computational genome analysis, two-dimensional gel electrophoresis, and whole-genome transposon mutagenesis studies. At least 15 genes originally annotated as conserved hypothetical were found to encode expressed proteins. Two more proteins, previously annotated as predicted coding regions, were detected with high confidence; these proteins also have close homologs in related bacteria. The direct proteomics approach to studying protein expression in vivo reported here is a powerful method that is applicable to proteome analysis of any (micro)organism.

Aerobiosis↗

PubMed2002

H. influenzae Consortium: integrative study of H. influenzae-human interactions.

Developments in high-throughput analysis tools coupled with integrative computational techniques have enabled biological studies to reach new levels. The ability to correlate large volumes of diverse data types into cohesive models of organism function has spawned a new systematic approach to biological investigation. The creation of a new consortium has been proposed to investigate a single organism utilizing these comprehensive approaches. The Haemophilus influenzae Consortium (HIC) would be comprised of five laboratories, each providing separate and complementary areas of expertise in the study of Haemophilus influenzae (HI). The 5-year study proposes to develop coherent models of HI, both as a stand-alone organism, and more importantly, as a human pathogen. Studies in growth condition specificity followed by genomic, metabolic, and proteomic experimentation will be combined and integrated through computational and experimental analyses to form dynamic and predictive models of HI and its responses. Data from the HIC will allow greater understanding of cellular behavior, pathogen-host interactions, bacterial infection, and provide future scientific endeavors with a template for studies of other pathogens.

Bacterial Adhesion↗

PubMed2002

Experimental protein mixture for validating tandem mass spectral analysis.

Several methods have been used to identify peptides that correspond to tandem mass spectra. In this work, we describe a data set of low energy tandem mass spectra generated from a control mixture of known protein components that can be used to evaluate the accuracy of these methods. As an example, these spectra were searched by the SEQUEST application against a human peptide sequence database. The numbers of resulting correct and incorrect peptide assignments were then determined. We show how the sensitivity and error rate are affected by the use of various filtering criteria based upon SEQUEST scores and the number of tryptic termini of assigned peptides.

Amino Acid Sequence↗