Biomedical subjects

Joel G Pounds

Publications and source records attributed to Joel G Pounds.

9 recordsLinked to original sources

PubMed2006

Particokinetics in vitro: dosimetry considerations for in vitro nanoparticle toxicity assessments.

The rapid growth in the use of in vitro methods for nanoparticle toxicity assessment has proceeded with limited consideration of the unique kinetics of these materials in solution. Particles in general and nanoparticles specifically, diffuse, settle, and agglomerate in cell culture media as a function of systemic and particle properties: media density and viscosity and particle size, shape, charge and density, for example. Cellular dose then is also a function of these factors as they determine the rate of transport of nanoparticles to cells in culture. Here we develop and apply the principles of dosimetry in vitro and outline an approach for simulation of nanoparticle particokinetics in cell culture systems. We illustrate that where equal mass concentrations (mug/ml) imply equal doses for dissimilar materials, the corresponding particle number or surface area concentration doses differ by orders of magnitude. More importantly, when rates of diffusional and gravitational particle delivery are accounted for, trends and magnitude of the cellular dose as a function of particle size and density differ significantly from those implied by "concentration" doses. For example, 15-nm silver nanoparticles appear approximately 4000 times more potent than micron-sized cadmium oxide particles on a cm(2)/ml media basis, but are only approximately 50 times more potent when differences in delivery to adherent cells are considered. We conclude that simple surrogates of dose can cause significant misinterpretation of response and uptake data for nanoparticles in vitro. Incorporating particokinetics and principles of dosimetry would significantly improve the basis for nanoparticle toxicity assessment, increasing the predictive power and scalability of such assays.

Cells, Cultured↗

PubMed2006

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.

Animals↗

PubMed2005

A study of spectral integration and normalization in NMR-based metabonomic analyses.

Metabonomics involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification [J.K. Nicholson, J.C. Lindon, E. Holmes, Xenobiotica 29 (1999) 1181-1189]. The analysis of these data involves the use of appropriate multivariate statistical methods; Principal Component Analysis (PCA) has been documented as a valuable pattern recognition technique for 1H NMR spectral data [J.T. Brindle, H. Antti, E. Holmes, G. Tranter, J.K. Nicholson, H.W. Bethell, S. Clarke, P.M. Schofield, E. McKilligin, D.E. Mosedale, D.J. Grainger, Nat. Med. 8 (2002) 1439-1444; B.C. Potts, A.J. Deese, G.J. Stevens, M.D. Reily, D.G. Robertson, J. Theiss, J. Pharm. Biomed. Anal. 26 (2001) 463-476; D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; L.C. Robosky, D.G. Robertson, J.D. Baker, S. Rane, M.D. Reily, Comb. Chem. High Throughput Screen. 5 (2002) 651-662]. Prior to PCA the raw data is typically processed through four steps; (1) baseline correction, (2) endogenous peak removal, (3) integration over spectral regions to reduce the number of variables, and (4) normalization. The effect of the size of spectral integration regions and normalization has not been well studied. The variability structure and classification accuracy on two distinctly different datasets are assessed via PCA and a leave-one-out cross-validation approach under two normalization approaches and an array of spectral integration regions. The first dataset consists of urine from 15 male Wistar-Hannover rats dosed with ANIT measured at five time points, mimicking drug-induced cholangiolitic hepatitis [D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; J.P. Shockcor, E. Holmes, Curr. Top. Med. Chem. 2 (2002) 35-51; N.J. Waters, E. Holmes, A. Williams, C.J. Waterfield, R.D. Farrant, J.K. Nicholson, Chem. Res. Toxicol. 14 (2001) 1401-1412]. The second data is serum samples from young male C57BL/6 mice subjected to instillation of pancreatic elastase producing emphysema type symptoms [C. Kuhn, S.Y. Yu, M. Chraplyvy, H.E. Linder, R.M. Senior, Lab. Invest. 34 (1976) 372-380; C. Kuhn, R.M. Senior, Lung 155 (1978) 185-197]. This study indicates that independent of the normalization method the classification accuracy achieved from metabonomic studies is not highly sensitive to the size of the spectral integration region. Additionally, both datasets scaled to mean zero and unity variance (auto-scaled) have higher variability within classification accuracy over spectral integration window widths than data scaled to the total intensity of the spectrum. Of the top 10 latent variables for the ANIT dataset the auto-scale normalization has standard deviations larger than the total-scale in seven cases. In the case of the elastase all standard deviations are larger for the auto-scaling.

Animals↗

PubMed2005

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy.

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.

Blood Proteins↗

PubMed2004

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.

Biomarkers, Tumor↗

PubMed2003

Proteomic characterization of nipple aspirate fluid: identification of potential biomarkers of breast cancer.

Mammary ductal cells are the origin for 70-80% of breast cancers. Nipple aspirate fluid (NAF) contains proteins directly secreted by the ductal and lobular epithelium in non-lactating women. Proteomic approaches offer a largely unbiased way to evaluate NAF as a source of biomarkers and are sufficiently sensitive for analysis of small NAF volumes (10-50 microl). In this study, we initially evaluated a new process for obtaining NAF and discovered that this process resulted in a volume of NAF that was suitable for analysis in approximately 90% of subjects. Proteomic characterization of NAF identified 64 proteins. Although this list primarily includes abundant and moderately abundant NAF proteins, very few of these proteins have previously been reported in NAF. At least 15 of the NAF proteins identified have previously been reported to be altered in serum or tumor tissue from women with breast cancer, including cathepsin D and osteopontin. In summary, this study provides the first characterization of the NAF proteome and identifies several candidate proteins for future studies on breast cancer markers in NAF.

Adult↗

PubMed2002

Arsenite disrupts mitosis and induces apoptosis in SV40-transformed human skin fibroblasts.

Chronic ingestion of arsenite-contaminated drinking water causes skin, bladder, and liver cancer. The mechanism of arsenite-induced carcinogenesis is unknown. Arsenite is known to disrupt mitosis and to delay transit through M phase in normal diploid fibroblasts. SV40-transformed human fibroblasts were observed to be hypersensitive to the cytotoxic and cytostatic effects of NaAsO(2) compared with normal diploid fibroblasts in concentration-response experiments. Five to 20 microM NaAsO(2) induced cytostasis in cycling normal diploid fibroblasts but not overt lethality in quiescent normal diploid fibroblasts. High concentrations of arsenite were overtly lethal in both cycling and quiescent cells. The IC50 for cycling SV40-transformed fibroblasts was 3.8 and 4.8 microM for the SV40-transformed lines GM4429 and GM0637, respectively, whereas, in cycling normal diploid fibroblasts (GM0024), the IC50 was 24.7 microM. Microscopic examination of NaAsO(2)-treated SV40-transformed fibroblasts suggested a concentration-dependent accumulation of cells in mitosis undergoing apoptosis. Treatment of SV40-transformed fibroblasts with 0-10 microM NaAsO(2) caused a concentration-dependent inhibition of cell proliferation, accumulation of cells having G2/M DNA contents, and increases in the mitotic index. Phase microscopy, annexin V binding, and electron microscopy demonstrated that arrested mitotic cells underwent apoptosis. These results indicate that SV40-transformation sensitizes cells to arsenite-induced mitotic arrest and induction of apoptosis in the mitotic cells.

Apoptosis↗

PubMed2002

Protein kinase C does not mediate the inhibitory action of lead on vitamin D3-dependent production of osteocalcin in osteoblastic bone cells.

The level of osteocalcin in serum is lower in lead-intoxicated children than in their normal counterparts. To explain this clinical observation, we investigated the mechanism of action of lead on vitamin D3-dependent osteocalcin production. Lead (5-20 microM) blocked the stimulating effects of vitamin D3 on osteocalcin production in cultured rat osteosarcoma cells (ROS 17/2.8). It is often suggested that activation of protein kinase C (PKC) is a critical mediator of the toxic actions of lead. Treatment of ROS cells with Gö6976, an inhibitor of PKC alpha and beta isozymes, produced similar effects as lead on vitamin D3-dependent osteocalcin production, while activation of PKC by phorbol-12-myristate-13-acetate (TPA) did not reverse or mimic this effect of lead. Thus activation of PKC is not consistent with the actions of lead on vitamin D3-dependent osteocalcin production. Measurement of PKC enzyme activity showed that 10 microM lead treatment does not activate or inhibit the activity of PKC in ROS cells. Western blot analysis indicated that lead treatment does not translocate PKC alpha, beta, or zeta from cytosol to membrane. Therefore, we concluded that PKC does not mediate the cellular toxicity of lead on vitamin D3-dependent osteocalcin production.

Alkaline Phosphatase↗

PubMed2002

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.

Blood Proteins↗