Publications

Heme oxygenase (HO) represents an intrinsic cytoprotective system based on its anti-oxidative and anti-inflammatory properties mediated via its products biliverdin/bilirubin and carbon monoxide (CO). We showed that deletion of HO-2 results in impaired corneal wound healing with associated chronic inflammatory complications. This study was undertaken to examine the role of HO activity and the contribution of HO-1 and HO-2 to corneal wound healing in an in vitro epithelial scratch injury model. A scratch wound model was established using human corneal epithelial (HCE) cells. These cells expressed both HO-1 and HO-2 proteins. Injury elicited a rapid and transient increase in HO-1 and HO activity; HO-2 expression was unchanged. Treatment with biliverdin or CORM-A1, a CO donor, accelerated wound closure by 10% at 24 h. Inhibition of HO activity impaired wound closure by more than 50%. However, addition of biliverdin or CORM-A1 reversed the effect of HO inhibition on wound healing. Moreover, knockdown of HO-2 expression, but not HO-1, significantly impaired wound healing. These results indicate that HO activity is required for corneal epithelial cell migration. Inhibition of HO activity impairs wound healing while amplification of its activity restores and accelerates healing. Importantly, HO-2, which is highly expressed in the corneal epithelium, appears to be critical for the wound healing process in the cornea. The mechanisms by which it contributes to cell migration in response to injury may reside in the cytoprotective properties of CO and biliverdin.

BACKGROUND

Pediatric Clostridium difficile infection (CDI)-related hospitalizations are increasing. We sought to describe the epidemiology of pediatric CDI at a quaternary care hospital.

METHODS

Nested case-control study within a cohort of children <18 years tested for C. difficile between January and August 2008. The study included patients who were ≥ 1 year with a positive test and diarrhea; those without diarrhea (ie, presumed colonization) were excluded. Two unmatched controls per case were randomly selected from patients ≥ 1 year with a negative test. Potential predictors of CDI included age, gender, comorbidities, prior hospitalization, receipt of C. difficile-active antibiotics in the prior 24 hours, and recent (≤ 4 weeks) exposure to antibiotics or acid-blocking medications. Multivariate logistic regression models were created to identify independent predictors of CDI.

RESULTS

Of 1891 tests performed, 263 (14%) were positive in 181 children. Ninety-five patients ≥ 1 year with CDI were compared with 238 controls. In multivariate analyses, predictors of CDI included solid organ transplant (odds ratio [OR], 8.09; 95% confidence interval [CI], 2.10-31.12), lack of prior hospitalization (OR, 8.43; 95% CI, 4.39-16.20), presence of gastrostomy or jejunostomy (G or J) tube (OR, 3.32; 95% CI 1.71-6.42), and receipt of fluoroquinolones (OR, 17.04; 95% CI, 5.86-49.54) or nonquinolone antibiotics (OR, 2.23; 95% CI, 1.18-4.20) in the past 4 weeks. Receipt of C. difficile-active antibiotics within 24 hours before testing was associated with a lower odds of CDI (OR, 0.22; 95% CI, 0.09-0.58).

CONCLUSIONS

Recent antibiotic exposure and certain comorbid conditions (solid organ transplant, presence of a gastrostomy or jejunostomy tube) were associated with CDI. Diagnostic testing has less utility in patients being treated with C. difficile-active antibiotics.

MOTIVATION

The analysis of gene coexpression is at the core of many types of genetic analysis. The coexpression between two genes can be calculated by using a traditional Pearson's correlation coefficient. However, unobserved confounding effects may cause inflation of the Pearson's correlation so that uncorrelated genes appear correlated. Many general methods have been suggested, which aim to remove the effects of confounding from gene expression data. However, the residual confounding which is not accounted for by these generic correction procedures has the potential to induce correlation between genes. Therefore, a method that specifically aims to calculate gene coexpression between gene expression arrays, while accounting for confounding effects, is desirable.

RESULTS

In this article, we present a statistical model for calculating gene coexpression called mixed model coexpression (MMC), which models coexpression within a mixed model framework. Confounding effects are expected to be encoded in the matrix representing the correlation between arrays, the inter-sample correlation matrix. By conditioning on the information in the inter-sample correlation matrix, MMC is able to produce gene coexpressions that are not influenced by global confounding effects and thus significantly reduce the number of spurious coexpressions observed. We applied MMC to both human and yeast datasets and show it is better able to effectively prioritize strong coexpressions when compared to a traditional Pearson's correlation and a Pearson's correlation applied to data corrected with surrogate variable analysis (SVA).

AVAILABILITY

The method is implemented in the R programming language and may be found at http://genetics.cs.ucla.edu/mmc.

CONTACT

[email protected]; [email protected].