Expression and Regulation of Human Cytochrome P450 4F Isoforms in Tissue Samples and Under TNF-Alpha Challenges

CYP4F (Cytochrome P4504F) enzymes metabolize endogenous molecules including leukotrienes, prostaglandins and arachidonic acid. The involvement of these endogenous compounds in inflammation has led to the hypothesis that changes in the inflamed tissue environment may affect the expression of CYP4Fs during the pro-inflammatory state, which in turn may modulate inflammatory conditions during the anti-inflammatory state. We demonstrated that inflamed tissues have different levels of CYP4F isoform expression profiles in a number of human samples when compared to the average population. The CYP4F isoform expression levels change with the degree of inflammation present in tissue. Further investigation in cell culture studies revealed that inflammatory cytokines, in particular TNF-α, play a role in regulating the expression of the CYP4F family. One of the isoforms, CYP4F11, had different characteristics than that of the other five CYP4F family members. CYP4F11 metabolizes xenobiotics while the other isoforms metabolize endogenous compounds with higher affinity. CYP4F11 also was expressed at high quantities in the brain, and was up-regulated by TNF-α, while the other isoforms were not expressed at high quantities in the brain and were down-regulated by TNF-α. We identified the AP-1 protein of the JNK pathway as the signaling protein that causes significant increase in CYP4F11 expression. Since TNF-α stimulation causes a simultaneous activation of both JNK pathway and NF-κB signaling, we investigated further the role that NF-κB plays on expression of the CYP4F11 gene. We concluded that although there is a significant increase in CYP4F11 expression in the presence of TNF-α, the activation of NF-κB signaling inhibits CYP4F11 expression in a time dependent manner. The expression of CYP4F11 is only significantly increased after 24 hours of treatment with TNF-α; at shorter time points NF-κB signaling overpowers the JNK pathway activation. We believe that these findings may in the future lead to improved drug design for modulating inflammation.

Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Comprehensive environmental study in high risk areas for cancer patients comparing air and water Aspergillus species samples

Background. Nosocomial invasive aspergillosis (a highly fatal disease) is an increasing problem for immunocompromised patients. Aspergillus spp. can be transmitted via air (most commonly) and by water. ^ The hypothesis for this prospective study was that there is an association between patient occupancy, housekeeping practices, patients, visitors, and Aspergillus spp. loading. Rooms were sampled as not terminally cleaned (dirty) and terminally cleaned (clean). The secondary hypothesis was that Aspergillus spp. positive samples collected from more than one sampling location within the same patient room represent the same isolate. ^ Methods. Between April and October 2004, 2873 environmental samples (713 air, 607 water, 1256 surface and 297 spore traps) were collected in and around 209 “clean” and “dirty” patient rooms in a large cancer center hospital. Water sources included aerosolized water from patient room showerheads, sinks, drains, and toilets. Bioaerosol samples were from the patient room and from the running shower, flushing toilet, and outside the building. The surface samples included sink and shower drains, showerheads, and air grills. Aspergillus spp. positive samples were also sent for PCR, molecular typing (n = 89). ^ Results. All water samples were negative for Aspergillus spp. There were a total of 130 positive culturable samples (5.1%). The predominant species found was Aspergillus niger. Of the positive culturable samples, 106 (14.9%) were air and 24 (3.8%) were surface. There were 147 spore trap samples, and 49.5% were positive for Aspergillus/Penicillum spp. Of the culturable positive samples sent for PCR, 16 were indistinguishable matches. There was no significant relationship between air and water samples and positive samples from the same room. ^ Conclusion. Primarily patients, visitors and staff bring the Aspergillus spp. into the hospital. The high number of A. niger samples suggests the spores are entering the hospital from outdoors. Eliminating the materials brought to the patient floors from the outside, requiring employees, staff, and visitors to wear cover up over their street clothes, and improved cleaning procedures could further reduce positive samples. Mold strains change frequently; it is probably more significant to understand pathogenicity of viable spores than to commit resources on molecular strain testing on environmental samples alone. ^