Nocturnal transcutaneous carbon dioxide tension in postmenopausal estrogen users and non-users.

OBJECTIVE: The effect of menopause on breathing is not fully understood. We have previously shown that postmenopausal women have a higher sleep-induced increase in transcutaneously measured carbon dioxide tension (TcCO(2)) than premenopausal women. Therefore, we hypothesized that estrogen therapy (ET) would normalize this sleep-induced TcCO(2) increase. METHODS: Nine postmenopausal ET users and nine non-users went through an overnight polygraphic sleep study including continuous monitoring of TcCO(2). RESULTS: TcCO(2) levels were higher during sleep than evening wakefulness (awake median 6.55 kPa versus sleep median 6.90 kPa, P = 0.001). ET users had a greater sleep-induced increase in TcCO(2) than non-users when comparing the difference between wakefulness and slow-wave sleep (0.85 kPa versus 0.28 kPa, P = 0.004). Lower sleep efficiency was associated with higher sleep-induced increase in TcCO(2). CONCLUSIONS: In contrast to our initial hypothesis, postmenopausal ET users have a higher sleep-induced increase in TcCO(2) than women without ET. Thus, TcCO(2) may be sensitive in measuring the peripheral estrogen effect. These findings warrant placebo-controlled intervention studies to confirm the effects of ET on TcCO(2) measurements.

Resampling reveals sample-level differential expression in clinical genome-wide studies.

Genome-scale molecular profiling of clinical sample material often results in heterogeneous datasets beyond the capability of standard statistical procedures. Statistical tests for differential expression, in particular, rely upon the assumption that the sample groups being compared are relatively homogeneous. Such assumption rarely holds in clinical materials, which leads to detection of secondary findings (false positives) or loss of significant targets (false negatives). Here, we introduce a resampling-based procedure, named ReScore, which aggregates individual changes across all the samples while preserving their clinical classes, and thereby provides multiple sets of markers that can effectively characterize distinct sample subsets. When applied to a public leukemia microarray study, the procedure could accurately reveal hidden subgroup structures associated with underlying genotypic abnormalities. The procedure improved both the sensitivity and specificity of the findings, as well as helped us to identify several disease subtype-specific genes that have remained undetected in the conventional analyses. In our endometriosis study, we were able to accurately distinguish between various sources of systematic variation, linked, for example, to tissue-specificity and disease-related factors, many of which would have been missed with standard approaches. The generic procedure should benefit also other global profiling experiments such as those based on mass spectrometry-based proteomic assays.

Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets.

Identification of reliable molecular markers that show differential expression between distinct groups of samples has remained a fundamental research problem in many large-scale profiling studies, such as those based on DNA microarray or mass-spectrometry technologies. Despite the availability of a wide spectrum of statistical procedures, the users of the high-throughput platforms are still facing the crucial challenge of deciding which test statistic is best adapted to the intrinsic properties of their own datasets. To meet this challenge, we recently introduced an adaptive procedure, named ROTS (Reproducibility-Optimized Test Statistic), which learns an optimal statistic directly from the given data, and whose relative benefits have previously been shown in comparison with state-of-the-art procedures for detecting differential expression. Using gene expression microarray and mass-spectrometry (MS)-based protein expression datasets as case studies, we illustrate here the practical usage and advantages of ROTS toward detecting reliable marker lists in clinical transcriptomic and proteomic studies. In a public leukemia microarray dataset, the procedure could improve the sensitivity of the gene marker lists detected with high specificity. When applied to a recent LC-MS dataset, involving plasma samples from severe burn patients, the procedure could identify several peptide markers that remained undetected in the conventional analysis, thus demonstrating the effectiveness of ROTS also for global quantitative proteomic studies. To promote its widespread usage, we have made freely available efficient implementations of ROTS, which are easily accessible either as a stand-alone R-package or as integrated in the open-source data analysis software Chipster.

Predicting quantitative genetic interactions by means of sequential matrix approximation.

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well.

Overnight variability in transcutaneous carbon dioxide predicts vascular impairment in women.

While there are a number of studies demonstrating association between arterial oxyhaemoglobin saturation events during sleep and markers of vascular impairment, the contribution of peripheral carbon dioxide to the development of atherosclerosis is poorly understood. We used ultrasound imaging to measure carotid artery intima-media thickness (IMT), as well as flow-mediated dilatation (FMD) and nitroglycerin-mediated dilatation (NMD) of brachial artery, in 103 generally healthy 46-year-old (+/-2 years) women. Characteristic event patterns were extracted from their overnight recordings of arterial oxyhaemoglobin saturation , end-tidal partial pressure of carbon dioxide and transcutaneous partial pressure of carbon dioxide . Importance of the event patterns was evaluated through predictive modelling of classes of the ultrasound measurements while controlling for potential confounders. Prediction accuracy was assessed with cross-validation and reported as the area under the receiver operating characteristic curve (AUC). Overnight patterns predicted each of the ultrasound measurements with high accuracy (IMT, AUC = 0.70; FMD, AUC = 0.75; and NMD, AUC = 0.81; all with P < 0.001). Adding the or patterns into the models did not significantly increase their predictive powers (AUC = 0.72, AUC = 0.77 and AUC = 0.83, respectively). The most important patterns reflected overnight variability in . These results suggest a novel link between overnight carbon dioxide events and early signs of vascular impairment in middle-aged women. Non-invasive measurements combined with non-linear modelling techniques could be used to reveal potential markers of vascular impairment present in relatively healthy subjects.