Protein quaternary structure determination using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and chemical crosslinking

A means of analyzing protein quaternary structure using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI MS) and chemical crosslinking was evaluated. Proteins of known oligomeric structure, as well as monomeric proteins, were analyzed to evaluate the method. The quaternary structure of proteins of unknown or uncertain structure was investigated using this technique. The stoichiometry of recombinant E. coli carbamoyl phosphate synthetase and recombinant human farnesyl protein transferase were determined to be heterodimers using glutaraldehyde crosslinking, agreeing with the stoichiometry found for the wild type proteins. The stoichiometry of the gamma subunit of E. coli DNA polymerase III holoenzyme was determined in solution without the presence of other subunits to be a homotetramer using glutaraldehyde crosslinking and MALDI MS analysis. Chi and psi subunits of E. coli DNA polymerase III subunits appeared to form a heterodimer when crosslinked with heterobifunctional photoreactive crosslinkers.^ Comparison of relative % peak areas obtained from MALDI MS analysis of crosslinked proteins and densitometric scanning of silver stained sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gels showed excellent qualitative agreement for the two techniques, but the quantitative analyses differed, sometimes significantly. This difference in quantitation could be due to SDS-PAGE conditions (differential staining, loss of sample) or to MALDI MS conditions (differences in ionization and/or detection). Investigation of pre-purified crosslinked monomers and dimers recombined in a specific ratio revealed the presence of mass discrimination in the MALDI MS process. The calculation of mass discrimination for two different MALDI time-of-flight instruments showed the loss of a factor of approximately 2.6 in relative peak area as the m/z value doubles over the m/z range from 30,000 to 145,000 daltons.^ Indirect symmetry was determined for tetramers using glutaraldehyde crosslinking with MALDI MS analysis. Mathematical modelling and simple graphing allowed the determination of the symmetry for several tetramers known to possess isologous D2 symmetry. These methods also distinguished tetramers that did not fit D2 symmetry such as apo-avidin. The gamma tetramer of E. coli DNA polymerase III appears to have isologous D2 symmetry. ^

Using small area estimation and geographic information systems technology to target health services for the uninsured

The three articles that comprise this dissertation describe how small area estimation and geographic information systems (GIS) technologies can be integrated to provide useful information about the number of uninsured and where they are located. Comprehensive data about the numbers and characteristics of the uninsured are typically only available from surveys. Utilization and administrative data are poor proxies from which to develop this information. Those who cannot access services are unlikely to be fully captured, either by health care provider utilization data or by state and local administrative data. In the absence of direct measures, a well-developed estimation of the local uninsured count or rate can prove valuable when assessing the unmet health service needs of this population. However, the fact that these are “estimates” increases the chances that results will be rejected or, at best, treated with suspicion. The visual impact and spatial analysis capabilities afforded by geographic information systems (GIS) technology can strengthen the likelihood of acceptance of area estimates by those most likely to benefit from the information, including health planners and policy makers. ^ The first article describes how uninsured estimates are currently being performed in the Houston metropolitan region. It details the synthetic model used to calculate numbers and percentages of uninsured, and how the resulting estimates are integrated into a GIS. The second article compares the estimation method of the first article with one currently used by the Texas State Data Center to estimate numbers of uninsured for all Texas counties. Estimates are developed for census tracts in Harris County, using both models with the same data sets. The results are statistically compared. The third article describes a new, revised synthetic method that is being tested to provide uninsured estimates at sub-county levels for eight counties in the Houston metropolitan area. It is being designed to replicate the same categorical results provided by a current U.S. Census Bureau estimation method. The estimates calculated by this revised model are compared to the most recent U.S. Census Bureau estimates, using the same areas and population categories. ^