Analysis and correction of crosstalk effects in pathway analysis

Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent. Here we show that this is not the case, and that many pathways can considerably affect each other''s P-values through a “crosstalk” phenomenon. Although it is intuitive that various pathways could influence each other, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. Here, we show that all three major categories of pathway analysis methods (enrichment analysis, functional class scoring, and topology-based methods) are severely influenced by crosstalk phenomena. Using real pathways and data, we show that in some cases pathways with significant P-values are not biologically meaningful, and that some biologically meaningful pathways with nonsignificant P-values become statistically significant when the crosstalk effects of other pathways are removed. We describe a technique able to detect, quantify, and correct crosstalk effects, as well as identify independent functional modules. We assessed this novel approach on data from four experiments involving three phenotypes and two species. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.The correct identification of the signaling and metabolic pathways involved in a given phenotype is a crucial step in the interpretation of high-throughput genomic experiments. Most approaches currently available for this purpose treat the pathways as independent. In fact, pathways can affect each other''s P-values through a phenomenon we refer to as crosstalk. This crosstalk may be due to the regulatory interactions among different pathways or to the gene overlap among pathways. In this work, we will use the term crosstalk to refer to the effect that pathways exercise on each other due to the presence of overlapping genes. Although it is intuitive that various pathways could influence each other, especially when they share genes, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. There are three major categories of methods that aim to identify significant pathways: enrichment analysis (e.g., Fisher''s exact test–hypergeometric) (Tavazoie et al. 1999; Draghici et al. 2003); functional scoring (e.g., GSEA) (Mootha et al. 2003; Subramanian et al. 2005); and topology-based methods (e.g., impact analysis) (Draghici et al. 2007; Tarca et al. 2009). Another classification of gene set analysis methods is based on the definition of the null hypothesis and divides the methods into competitive and self-contained (Goeman and Bühlmann 2007; Nam and Kim 2008). In this work, we focus on competitive methods, and in particular on the Fisher''s exact test, although the problems identified likely apply also for self-contained methods.Here we show that the results of all these methods are affected by crosstalk effects and that this phenomenon is related to the structure of the pathways. We propose the first approach that can (1) detect crosstalk when it exists, (2) quantify its magnitude, (3) correct for it, resulting in a more meaningful ranking among pathways in a specific biological condition, and (4) identify novel functional modules that can play an independent role and have different functions than the pathway they are currently located on. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.     

State of the art and recommendationsKangaroo mother care: application in a high‐tech environment

Since Kangaroo Mother Care (KMC) was developed in Colombia in the 1970s, two trends in clinical application emerged. In low income settings, the original KMC model is implemented. This consists of continuous (24 h/day, 7 days/week) and prolonged mother/parent–infant skin‐to‐skin contact; early discharge with the infant in the kangaroo position; (ideally) exclusive breastfeeding; and, adequate follow‐up. In affluent settings, intermittent KMC with sessions of one or a few hours skin‐to‐skin contact for a limited period is common. As a result of the increasing evidence of the benefits of KMC for both infants and families in all intensive care settings, KMC in a high‐tech environment was chosen as the topic for the first European Conference on KMC, and the clinical implementation of the KMC model in all types of settings was discussed at the 7th International Workshop on KMC. Kangaroo Mother Care protocols in high‐tech Neonatal Intensive Care Units (NICU) should specify criteria for initiation, kangaroo position, transfer to/from KMC, transport in kangaroo position, kangaroo nutrition, parents’ role, modification of the NICU environment, performance of care in KMC, and KMC in case of infant instability. Conclusion: Implementation of the original KMC method, with continuous skin‐to‐skin contact whenever possible, is recommended for application in high‐tech environments, although scientific evaluation should continue.     

Aryloxypropanolamine and catecholamine ligand interactions with the beta(1)-adrenergic receptor: evidence for interaction with distinct conformations of beta(1)-adrenergic receptors

Pharmacological responses to aryloxypropanolamines were examined in cells expressing rat or human beta(1)-adrenergic receptors (ARs) using adenylyl cyclase assays. The aryloxypropanolamines CGP 12177 and LY 362884, originally developed as beta(3)-AR agonists, were found to stimulate the beta(1)-AR. Interestingly, both CGP 12177 and LY 362884 exhibited an anomalous biphasic effect on beta(1)-AR. Low concentrations of either CGP 12177 or LY 362884 potently blocked isoproterenol-induced stimulation of beta(1)-AR, whereas higher concentrations of these compounds stimulated the beta(1)-AR. The unusual interaction of these aryloxypropanolamine ligands with the beta(1)-AR was further characterized using beta-AR antagonists. Activation of beta(1)-AR by CGP 12177 or LY 362884 was observed to be significantly more resistant to blockade by beta-AR antagonists compared with activation by catecholamines. These results suggest that catecholamines and aryloxypropanolamines interact with distinct active conformations of the beta(1)-AR: a state that is responsive to catecholamines and is blocked with high affinity by CGP 12177 and LY 362884, and a novel state that is activated by aryloxypropanolamines but is resistant to blockade by standard beta-AR antagonists. Moreover, dependence of antagonist affinity on agonist structure is unprecedented, and its implications on the use of beta-AR agonists such as CGP 12177 in receptor classification are discussed.     

Effect of infusion duration on valproate pharmacokinetics

The pharmacokinetics of intravenously administered valproic acid (VPA) were investigated in 16 healthy male volunteers in a single-dose, fasting, four-period, randomized, double-blind, placebo-controlled, parallel design study. Subjects were randomly assigned to be infused a single dose of sodium valproate equivalent to 1000 mg VPA or placebo over each of four different time periods. Valproate concentrations in plasma were determined using gas chromatography with flame ionization detection. The pharmacokinetic parameters were determined by both non-compartmental and model-dependent techniques. Analyses of variance (ANOVAs) were performed to detect any statistical differences among the regimens. Overall, the pharmacokinetics of valproate were similar after infusions of 5, 10, 30, and 60 min, with an average terminal-phase half-life of 15.9 h. There were modest differences in overall clearances among the regimens, with the 5 min infusion producing a mean area under the plasma concentration-time curve (AUC; 1877 μg·h ml^?1) that was significantly (13 to 16 per cent) higher than the means for the longer infusions (1614–1656 μg·h ml^?1). Differences in distribution were also noted as a function of infusion duration. The shortest duration produced a significantly smaller terminal volume of distribution (12.8 vs 14.2–15.1 l) and more rapid tissue equilibration. The α-phase rate constant declined from a mean of 5.1 h^?1 after the 5 min infusion to a mean of 0.9 h^?1 after the 60 min infusion. The distributional differences are almost certainly related to the saturable protein binding of valproate. However, the lower clearance after the 5 min infusion indicates that there may have also been partial saturation of one of the metabolic pathways of valproate during the distributive phase, and that the increase in f_u was smaller than the decrease in CL′_int, such that the product of f_u·CL′_int showed a net decrease.     

Emphysema in the renal allograft

Two diabetic patients in whom emphysematous pyelonephritis developed after renal transplantation are described. Clinical recognition of this unusual and serious infection is masked by the effects of immunosuppression. Abdominal radiographic, ultrasound, and computed tomography findings are discussed. The clinical presentation includes urinary tract infection, sepsis, and acute tubular malfunction of the allograft in insulin-dependent diabetics.