Biomedical subjects

Xueya Cai

Publications and source records attributed to Xueya Cai.

4 recordsLinked to original sources

PubMed2006

Haemophilus haemolyticus: a human respiratory tract commensal to be distinguished from Haemophilus influenzae.

BACKGROUND: Haemophilus influenzae is a common pathogen in adults with chronic obstructive pulmonary disease (COPD). In a prospective study, selected isolates of apparent H. influenzae had an altered phenotype. We tested the hypothesis that these variant strains were genetically different from typical H. influenzae. METHODS: A prospective study of adults with COPD was conducted. Strains of apparent H. influenzae obtained from a range of clinical sources were evaluated by ribosomal DNA sequence analysis, multilocus sequence analysis, DNA-DNA hybridization, and sequencing of the conserved P6 gene. RESULTS: Variant strains were determined to be Haemophilus haemolyticus by means of 4 independent methods. Analysis of 490 apparent H. influenzae strains, identified by standard methods, revealed that 39.5% of sputum isolates and 27.3% of nasopharyngeal isolates were H. haemolyticus. Isolates obtained from normally sterile sites were all H. influenzae. In a prospective study, acquisitions of new strains of H. haemolyticus were not associated with exacerbations of COPD, whereas 45% of acquisitions of new strains of H. influenzae were associated with exacerbations. CONCLUSIONS: Standard methods do not reliably distinguish H. haemolyticus from H. influenzae. H. haemolyticus is a respiratory tract commensal. The recognition that some strains of apparent H. influenzae are H. haemolyticus substantially strengthens the association of true H. influenzae with clinical infection.

Haemophilus↗

PubMed2006

Characterization of igaB, a second immunoglobulin A1 protease gene in nontypeable Haemophilus influenzae.

Nontypeable Haemophilus influenzae is an important respiratory pathogen, causing otitis media in children and lower respiratory tract infection in adults with chronic obstructive pulmonary disease (COPD). Immunoglobulin A1 (IgA1) protease is a well-described protein and potential virulence factor in this organism as well as other respiratory pathogens. IgA1 proteases cleave human IgA1, are involved in invasion, and display immunomodulatory effects. We have identified a second IgA1 protease gene, igaB, in H. influenzae that is present in addition to the previously described IgA1 protease gene, iga. Reverse transcriptase PCR and IgA1 protease assays indicated that the gene is transcribed, expressed, and enzymatically active in H. influenzae. The product of this gene is a type 2 IgA1 protease with homology to the iga gene of Neisseria species. Mutants that were deficient in iga, igaB, and both genes were constructed in H. influenzae strain 11P6H, a strain isolated from a patient with COPD who was experiencing an exacerbation. Analysis of these mutants indicated that igaB is the primary mediator of IgA1 protease activity in this strain. IgA1 protease activity assays on 20 clinical isolates indicated that the igaB gene is associated with increased levels of IgA1 protease activity. Approximately one-third of 297 strains of H. influenzae of diverse clinical and geographic origin contained igaB. Significant differences in the prevalence of igaB were observed among isolates from different sites of isolation (sputum > middle ear > nasopharynx). These data support the hypothesis that the newly discovered igaB gene is a potential virulence factor in nontypeable H. influenzae.

DNA, Bacterial↗

PubMed2006

Differential genome contents of nontypeable Haemophilus influenzae strains from adults with chronic obstructive pulmonary disease.

Haemophilus influenzae is an important cause of otitis media in children and lower respiratory infection in adults with chronic obstructive pulmonary disease (COPD). Patients with COPD experience periodic exacerbations that are associated with acquisition of new bacterial strains. However, not every strain acquisition is associated with exacerbation. To test the hypothesis that genetic differences among strains account for differences in pathogenic potential, a microarray consisting of 4,992 random 1.5- to 3-kb genomic fragments of an exacerbation strain was constructed. Competitive hybridization was performed using six strains associated with exacerbation as well as five strains associated with asymptomatic colonization. Seven sequences that were absent in all five colonization strains and present in at least two exacerbation strains were identified. One such sequence was a previously unreported gene with high homology to the meningococcal immunoglobulin A (IgA) protease gene, which is distinct from the previously described H. influenzae IgA protease. To assess the distribution of the seven sequences among well-characterized strains of H. influenzae, 59 exacerbation strains and 73 asymptomatic colonization strains were screened by PCR for the presence of these sequences. The presence or absence of any single sequence was not significantly associated with exacerbations of COPD. However, logistic regression and subgroup analysis identified combinations of the presence and absence of genes that are associated with exacerbations. These results indicate that patterns of genes are associated with the ability of strains of H. influenzae to cause exacerbations of COPD, supporting the concept that differences in pathogenic potential are based in part on genomic differences among infecting strains, not merely host factors.

Adult↗

PubMed2005

Differential and trajectory methods for time course gene expression data.

MOTIVATION: The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differentiation approaches can reduce the dimensions of data, help to remove redundant genes and noises, and highlight the most relevant genes that are major players in the development of certain diseases or the effect of drug treatment. The purpose of this study is to investigate the efficiency of parametric (including Bayesian and non-Bayesian, linear and non-linear), non-parametric and semi-parametric gene filtering methods through the application of time course microarray data from multiple sclerosis patients being treated with interferon-beta-1a. The analysis of variance with bootstrapping (parametric), class dispersion (semi-parametric) and Pareto (non-parametric) with permutation methods are presented and compared for filtering and finding differentially expressed genes. The Bayesian linear correlated model, the Bayesian non-linear model the and non-Bayesian mixed effects model with bootstrap were also developed to characterize the differential expression patterns. Furthermore, trajectory-clustering approaches were developed in order to investigate the dynamic patterns and inter-dependency of drug treatment effects on gene expression. RESULTS: Results show that the presented methods performed significant differently but all were adequate in capturing a small number of the potentially relevant genes to the disease. The parametric method, such as the mixed model and two Bayesian approaches proved to be more conservative. This may because these methods are based on overall variation in expression across all time points. The semi-parametric (class dispersion) and non-parametric (Pareto) methods were appropriate in capturing variation in expression from time point to time point, thereby making them more suitable for investigating significant monotonic changes and trajectories of changes in gene expressions in time course microarray data. Also, the non-linear Bayesian model proved to be less conservative than linear Bayesian correlated growth models to filter out the redundant genes, although the linear model showed better fit than non-linear model (smaller DIC). We also report the trajectories of significant genes-since we have been able to isolate trajectories of genes whose regulations appear to be inter-dependent.

Computer Simulation↗