Biomedical subjects

David G Camp

Publications and source records attributed to David G Camp.

41 records · Page 3Linked to original sources

PubMedDate not supplied

Application of peptide LC retention time information in a discriminant function for peptide identification by tandem mass spectrometry.

We describe the application of a peptide retention time reversed phase liquid chromatography (RPLC) prediction model previously reported (Petritis et al. Anal. Chem. 2003, 75, 1039) for improved peptide identification. The model uses peptide sequence information to generate a theoretical (predicted) elution time that can be compared with the observed elution time. Using data from a set of known proteins, the retention time parameter was incorporated into a discriminant function for use with tandem mass spectrometry (MS/MS) data analyzed with the peptide/protein identification program SEQUEST. For singly charged ions, the number of confident identifications increased by 12% when the elution time metric is included compared to when mass spectral data is the sole source of information in the context of a Drosophila melanogaster database. A 3-4% improvement was obtained for doubly and triply charged ions for the same biological system. Application to the larger Rattus norvegicus (rat) and human proteome databases resulted in an 8-9% overall increase in the number of confident identifications, when both the discriminant function and elution time are used. The effect of adding "runner-up" hits (peptide matches that are not the highest scoring for a spectra) from SEQUEST is also explored, and we find that the number of confident identifications is further increased by 1% when these hits are also considered. Finally, application of the discriminant functions derived in this work with approximately 2.2 million spectra from over three hundred LC-MS/MS analyses of peptides from human plasma protein resulted in a 16% increase in confident peptide identifications (9022 vs 7779) using elution time information. Further improvements from the use of elution time information can be expected as both the experimental control of elution time reproducibility and the predictive capability are improved.

Animals↗

PubMedDate not supplied

Utilizing human blood plasma for proteomic biomarker discovery.

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.

Biomarkers↗

PubMedDate not supplied

Comparison of normal and breast cancer cell lines using proteome, genome, and interactome data.

Normal and cancer cell line proteomes were profiled using high throughput mass spectrometry techniques. Application of protein-level and peptide-level sample fractionation combined with LC-MS/MS analysis enabled identification of 2235 unmodified proteins representing a broad range of functional and compartmental classes. An iterative multistep search strategy was used to identify post-translational modifications, revealing several proteins that are preferentially modified in cancer cells. Information regarding both unmodified and modified protein forms was combined with publicly available gene expression and protein-protein interaction data. The resulting integrated dataset revealed several functionally related proteins that are differentially regulated between normal and cancer cell lines.

Amino Acid Sequence↗

PubMedDate not supplied

Development and evaluation of a micro- and nanoscale proteomic sample preparation method.

Challenges associated with the efficient and effective preparation of micro- and nanoscale (micro- and nanogram) clinical specimens for proteomic applications include the unmitigated sample losses that occur during the processing steps. Herein, we describe a simple "single-tube" preparation protocol appropriate for small proteomic samples using the organic cosolvent, trifluoroethanol (TFE) that circumvents the loss of sample by facilitating both protein extraction and protein denaturation without requiring a separate cleanup step. The performance of the TFE-based method was initially evaluated by comparisons to traditional detergent-based methods on relatively large scale sample processing using human breast cancer cells and mouse brain tissue. The results demonstrated that the TFE-based protocol provided comparable results to the traditional detergent-based protocols for larger, conventionally sized proteomic samples (>100 microg protein content), based on both sample recovery and numbers of peptide/protein identifications. The effectiveness of this protocol for micro- and nanoscale sample processing was then evaluated for the extraction of proteins/peptides and shown effective for small mouse brain tissue samples (approximately 30 microg total protein content) and also for samples of approximately 5000 MCF-7 human breast cancer cells (approximately 500 ng total protein content), where the detergent-based methods were ineffective due to losses during cleanup and transfer steps.

Animals↗

PubMedDate not supplied

Human plasma N-glycoproteome analysis by immunoaffinity subtraction, hydrazide chemistry, and mass spectrometry.

The enormous complexity, wide dynamic range of relative protein abundances of interest (over 10 orders of magnitude), and tremendous heterogeneity (due to post-translational modifications, such as glycosylation) of the human blood plasma proteome severely challenge the capabilities of existing analytical methodologies. Here, we describe an approach for broad analysis of human plasma N-glycoproteins using a combination of immunoaffinity subtraction and glycoprotein capture to reduce both the protein concentration range and the overall sample complexity. Six high-abundance plasma proteins were simultaneously removed using a pre-packed, immobilized antibody column. N-linked glycoproteins were then captured from the depleted plasma using hydrazide resin and enzymatically digested, and the bound N-linked glycopeptides were released using peptide-N-glycosidase F (PNGase F). Following strong cation exchange (SCX) fractionation, the deglycosylated peptides were analyzed by reversed-phase capillary liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Using stringent criteria, a total of 2053 different N-glycopeptides were confidently identified, covering 303 nonredundant N-glycoproteins. This enrichment strategy significantly improved detection and enabled identification of a number of low-abundance proteins, exemplified by interleukin-1 receptor antagonist protein (approximately 200 pg/mL), cathepsin L (approximately 1 ng/mL), and transforming growth factor beta 1 (approximately 2 ng/mL). A total of 639 N-glycosylation sites were identified, and the overall high accuracy of these glycosylation site assignments as assessed by accurate mass measurement using high-resolution liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR) is initially demonstrated.

Amino Acid Motifs↗