Biomedical subjects

Charles C Kim

Publications and source records attributed to Charles C Kim.

4 recordsLinked to original sources

PubMed2003

Significance analysis of lexical bias in microarray data.

BACKGROUND: Genes that are determined to be significantly differentially regulated in microarray analyses often appear to have functional commonalities, such as being components of the same biochemical pathway. This results in certain words being under- or overrepresented in the list of genes. Distinguishing between biologically meaningful trends and artifacts of annotation and analysis procedures is of the utmost importance, as only true biological trends are of interest for further experimentation. A number of sophisticated methods for identification of significant lexical trends are currently available, but these methods are generally too cumbersome for practical use by most microarray users. RESULTS: We have developed a tool, LACK, for calculating the statistical significance of apparent lexical bias in microarray datasets. The frequency of a user-specified list of search terms in a list of genes which are differentially regulated is assessed for statistical significance by comparison to randomly generated datasets. The simplicity of the input files and user interface targets the average microarray user who wishes to have a statistical measure of apparent lexical trends in analyzed datasets without the need for bioinformatics skills. The software is available as Perl source or a Windows executable. CONCLUSION: We have used LACK in our laboratory to generate biological hypotheses based on our microarray data. We demonstrate the program's utility using an example in which we confirm significant upregulation of SPI-2 pathogenicity island of Salmonella enterica serovar Typhimurium by the cation chelator dipyridyl.

2,2'-Dipyridyl↗

PubMed2003

Modulation of virulence by two acidified nitrite-responsive loci of Salmonella enterica serovar Typhimurium.

Two acidified nitrite-inducible genes of Salmonella enterica serovar Typhimurium were identified with a green fluorescent protein-based promoter-trap screen. The nitrite-inducible promoters were located upstream of loci that we designated nipAB and nipC, which correspond to hcp-hcr (hybrid cluster protein) of Escherichia coli and norA of Alcaligenes eutrophus, respectively. Maximal induction of the promoters by nitrite was dependent on pH. The nipAB promoter was regulated by oxygen in an Fnr-dependent manner. The nipC promoter was also regulated by oxygen but in an Fnr-independent manner. The promoters were upregulated in activated RAW264.7 macrophage-like cells, which produce NO via the inducible nitric oxide synthase (iNOS), and the induction was inhibited by aminoguanidine, an inhibitor of iNOS. Although the nipAB and nipC mutants displayed no defects under a variety of in vitro conditions or in tissue culture infections, they exhibited lower oral 50% lethal doses (LD(50)s) than did the wild type in C57BL/6J mouse infections. The lower LD(50)s reflected an unexpected increased ability of small inoculating doses of the mutant bacteria to cause lethal infection 2 to 3 weeks after challenge, compared to a similar challenge dose of wild-type bacteria. We conclude that these genes are regulated by physiological nitrogen oxides and that the absence of these bacterial genes in some way diminishes the ability of mice to clear a low dose infection.

Animals↗

PubMed2003

Genomic comparison of Salmonella enterica serovars and Salmonella bongori by use of an S. enterica serovar typhimurium DNA microarray.

The genus Salmonella consists of over 2,200 serovars that differ in their host range and ability to cause disease despite their close genetic relatedness. The genetic factors that influence each serovar's level of host adaptation, how they evolved or were acquired, their influence on the evolution of each serovar, and the phylogenic relationships between the serovars are of great interest as they provide insight into the mechanisms behind these differences in host range and disease progression. We have used an Salmonella enterica serovar Typhimurium spotted DNA microarray to perform genomic hybridizations of various serovars and strains of both S. enterica (subspecies I and IIIa) and Salmonella bongori to gain insight into the genetic organization of the serovars. Our results are generally consistent with previously published DNA association and multilocus enzyme electrophoresis data. Our findings also reveal novel information. We observe a more distant relationship of serovar Arizona (subspecies IIIa) from the subspecies I serovars than previously measured. We also observe variability in the Arizona SPI-2 pathogenicity island, indicating that it has evolved in a manner distinct from the other serovars. In addition, we identify shared genetic features of S. enterica serovars Typhi, Paratyphi A, and Sendai that parallel their unique ability to cause enteric fever in humans. Therefore, whereas the taxonomic organization of Salmonella into serogroups provides a good first approximation of genetic relatedness, we show that it does not account for genomic changes that contribute to a serovar's degree of host adaptation.

Bacterial Proteins↗

PubMed2002

Improved analytical methods for microarray-based genome-composition analysis.

BACKGROUND: Whereas genome sequencing has given us high-resolution pictures of many different species of bacteria, microarrays provide a means of obtaining information on genome composition for many strains of a given species. Genome-composition analysis using microarrays, or 'genomotyping', can be used to categorize genes into 'present' and 'divergent' categories based on the level of hybridization signal. This typically involves selecting a signal value that is used as a cutoff to discriminate present (high signal) and divergent (low signal) genes. Current methodology uses empirical determination of cutoffs for classification into these categories, but this methodology is subject to several problems that can result in the misclassification of many genes. RESULTS: We describe a method that depends on the shape of the signal-ratio distribution and does not require empirical determination of a cutoff. Moreover, the cutoff is determined on an array-to-array basis, accounting for variation in strain composition and hybridization quality. The algorithm also provides an estimate of the probability that any given gene is present, which provides a measure of confidence in the categorical assignments. CONCLUSIONS: Many genes previously classified as present using static methods are in fact divergent on the basis of microarray signal; this is corrected by our algorithm. We have reassigned hundreds of genes from previous genomotyping studies of Helicobacter pylori and Campylobacter jejuni strains, and expect that the algorithm should be widely applicable to genomotyping data.

Algorithms↗