Faculty Bibliography

OBJECTIVE:Postoperative atrial fibrillation complicating general thoracic surgery increases morbidity and stroke risk. We aimed to determine whether preoperative atrial dysfunction or other echocardiographic markers are associated with postoperative atrial fibrillation. METHODS:In 191 patients who had undergone anatomic lung or esophageal resection, preoperative clinical and echocardiographic data were compared between patients with and without postoperative atrial fibrillation. Presence of postoperative atrial fibrillation lasting more than 5 minutes during hospitalization was detected using continuous telemetry or 12-lead electrocardiography. Maximal left atrial volume and indices of left atrial function were assessed. RESULTS:Patients with postoperative atrial fibrillation (33/191, 17%) were older (71 ± 5 years vs 64 ± 12 years, P < .0001), were taking β-blockers more often, had greater left atrial volume, had decreased left atrial emptying fraction, and had lower E' and A' septal velocities compared with patients without postoperative atrial fibrillation. The incidence of postoperative atrial fibrillation in patients with left atrial volume 32 mL/m(2) or greater was 37% (11/30) and greater than in those with left atrial volume less than 32 mL/m(2) (14%, 22/160, P = .002). Length of hospital stay was significantly increased in patients with postoperative atrial fibrillation compared with patients without (P = .04). Older age was significantly associated with greater β-blocker use and left atrial volume and lower left atrial emptying fraction. On multivariate analysis, lower left atrial emptying fraction (odds ratio, 1.03 per unit decrement; 95% confidence interval, 1.002-1.065; P = .04) and preoperative use of β-blockers (odds ratio, 2.82; 95% confidence interval, 1.18-6.77; P = .02) were the only independent risk factors associated with postoperative atrial fibrillation. CONCLUSIONS:These data show that an echocardiogram before major thoracic surgery, increased use of preoperative β-blockers, and decreased left atrial emptying fraction were associated with postoperative atrial fibrillation. Echocardiographic predictors of left atrial mechanical dysfunction may prove clinically useful in risk stratifying patients in whom postoperative atrial fibrillation is more likely to develop and to benefit from prevention strategies aimed at mitigating atrial function before surgery.

BACKGROUND: The first changes associated with smoking are in the small airway epithelium (SAE). Given that smoking alters SAE gene expression, but only a fraction of smokers develop chronic obstructive pulmonary disease (COPD), we hypothesized that assessment of SAE genome-wide gene expression would permit biologic phenotyping of the smoking response, and that a subset of healthy smokers would have a "COPD-like" SAE transcriptome. METHODOLOGY/PRINCIPAL FINDINGS: SAE (10th-12th generation) was obtained via bronchoscopy of healthy nonsmokers, healthy smokers and COPD smokers and microarray analysis was used to identify differentially expressed genes. Individual responsiveness to smoking was quantified with an index representing the % of smoking-responsive genes abnormally expressed (I(SAE)), with healthy smokers grouped into "high" and "low" responders based on the proportion of smoking-responsive genes up- or down-regulated in each smoker. Smokers demonstrated significant variability in SAE transcriptome with I(SAE) ranging from 2.9 to 51.5%. While the SAE transcriptome of "low" responder healthy smokers differed from both "high" responders and smokers with COPD, the transcriptome of the "high" responder healthy smokers was indistinguishable from COPD smokers. CONCLUSION/SIGNIFICANCE: The SAE transcriptome can be used to classify clinically healthy smokers into subgroups with lesser and greater responses to cigarette smoking, even though these subgroups are indistinguishable by clinical criteria. This identifies a group of smokers with a "COPD-like" SAE transcriptome.

BACKGROUND: Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples (total n = 223) of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals and hybridized to Affymetrix microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) > or = 7.0; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets < or = 3.0; and (3) the multi-chip normalization scaling factor < or = 10.0. RESULTS: Of the 223 samples, all three criteria were assessed in 191; of these 184 (96.3%) passed all three criteria. For the remaining 32 samples, the RIN was not available, and only the other two criteria were used; of these 29 (90.6%) passed these two criteria. Correlation coefficients for pairwise comparisons of expression levels for 100 maintenance genes in which at least one array failed the QC criteria (average Pearson r = 0.90 +/- 0.04) were significantly lower (p < 0.0001) than correlation coefficients for pairwise comparisons between arrays that passed the QC criteria (average Pearson r = 0.97 +/- 0.01). Inter-array variability was significantly decreased (p < 0.0001) among samples passing the QC criteria compared with samples failing the QC criteria. CONCLUSION: Based on the aberrant maintenance gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data, and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation.

BACKGROUND: Despite overwhelming data that cigarette smoking causes COPD, only a minority of long-term smokers are affected, strongly suggesting that genetic factors modify susceptibility to this disease. We hypothesized that individual variations exist in the response to cigarette smoking, with variability among smokers in expression levels of protective/susceptibility genes. METHODS: Affymetrix arrays and quantitative polymerase chain reaction were used to assess the variability of gene expression in the small airway epithelium obtained by fiberoptic bronchoscopy of 18 normal nonsmokers, 18 normal smokers, and 18 smokers with COPD. RESULTS: We identified 201 probe sets representing 152 smoking-responsive genes that were significantly up-regulated or down-regulated, and assessed the coefficient of variation in expression levels among the study population. Variation was a reproducible property of each gene as assessed by different microarray probe sets and real-time polymerase chain reaction, and was observed in both normal smokers and smokers with COPD. Greater individual variability was found in smokers with COPD than in normal smokers. The majority of these highly variable smoking-responsive genes were in the functional categories of signal transduction, xenobiotic degradation, metabolism, transport, oxidant related, and transcription. A similar pattern of the same highly variable genes was observed in an independent data set. CONCLUSIONS: We propose that genetic diversity is likely within this subset of genes, with highly variable individual-to-individual responses of the small airway epithelium to smoking, and that this subset of genes represents putative candidates for assessment of susceptibility/protection from disease in future gene-based epidemiologic studies of smokers' risk for COPD.

To identify genes participating in human airway epithelial repair, we used bronchoscopy and brushing to denude the airway epithelium of healthy individuals, sequentially sampled the same region 7 and 14 days later, and assessed gene expression by Affymetrix microarrays with TaqMan RT-PCR confirmation. Histologically, the injured area was completely covered by a partially redifferentiated epithelial layer after 7 days; by 14 days the airway epithelium was very similar to the uninjured state. At day 7 compared with resting epithelium, there were substantial differences in gene expression pattern, with a distinctive airway epithelial "repair transcriptome" of actively proliferating cells in the process of redifferentiation. The repair transcriptome at 7 days was dominated by cell cycle, signal transduction, metabolism and transport, and transcription genes. Interestingly, the majority of differentially expressed cell cycle genes belonged to the G2 and M phases, suggesting that the proliferating cells were relatively synchronized 1 wk following injury. At 14 days postinjury, the expression profile was similar to that of resting airway epithelium. These observations provide a baseline of the functional gene categories participating in the process of normal human airway epithelial repair that can be used in future studies of injury and repair in airway epithelial diseases.