Prediction of photoperiodic regulators from quantitative gene circuit models

Photoperiod sensors allow physiological adaptation to the changing seasons. The external coincidence hypothesis postulates that a light-responsive regulator is modulated by a circadian rhythm. Sufficient data are available to test this quantitatively in plants, though not yet in animals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their lightsensitive proteins are thought to form an external coincidence sensor. We use 40 timeseries of molecular data to model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, the models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modelling makes this complexity explicit and may thus contribute to crop improvement

Adipocyte-specific protein tyrosine phosphatase 1B deletion increases lipogenesis, adipocyte cell size and is a minor regulator of glucose homeostasis

Peer reviewedPublisher PD

Randomization in Laboratory Procedure Is Key to Obtaining Reproducible Microarray Results

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects

Randomization in Laboratory Procedure Is Key to Obtaining Reproducible Microarray Results

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects

Aplastic anemia associated with interferon alpha 2a in a patient with chronic hepatitis C virus infection: a case report

<p>Abstract</p> <p>Introduction</p> <p>Hepatitis-associated aplastic anemia is a common syndrome in patients with bone marrow failure. However, hepatitis-associated aplastic anemia is an immune-mediated disease that does not appear to be caused by any of the known hepatitis viruses including hepatitis C virus. In addition, to the best of our knowledge there are no reported cases of patients with chronic hepatitis C virus infection developing aplastic anemia associated with pegylated interferon alpha 2a treatment.</p> <p>Case presentation</p> <p>We report the case of a 46-year-old Greek man who developed severe aplastic anemia during treatment with pegylated interferon alpha 2a for chronic hepatitis C virus infection. He presented with generalized purpura and bruising, as well as pallor of the skin and mucous membranes. His blood tests showed pancytopenia. He underwent allogeneic bone marrow transplantation after completing two courses of immunosuppressive therapy with antithymocyte globulin and cyclosporin A.</p> <p>Conclusions</p> <p>The combination of a specific environmental precipitant represented by the hepatitis C virus infection, an altered metabolic detoxification pathway due to treatment with pegylated interferon alpha 2a and a facilitating genetic background such as polymorphism in metabolic detoxification pathways and specific human leukocyte antigen genes possibly conspired synergistically in the development of aplastic anemia in this patient. Our case clearly shows that the causative role of pegylated interferon alpha 2a in the development of aplastic anemia must not be ignored.</p

Does the diurnal cycle of cortisol explain the relationship between physical performance and cognitive function in older adults?

Background Regular physical activity is a promising strategy to treat and prevent cognitive decline. The mechanisms that mediate these benefits are not fully clear but physical activity is thought to attenuate the harmful effects of chronic psychological stress and hypercortisolism on cognition. However, the circadian pattern of cortisol secretion is complex and it is not known which aspects are most closely associated with increased cognitive function and better physical performance. This is the first study to simultaneously measure cognitive function, the diurnal cycle of salivary cortisol and physical performance in older adults, without cognitive impairment (n = 30) and with amnestic Mild Cognitive Impairment (aMCI) (n = 30). Results Regression analysis showed that better cognitive function was associated with better physical performance. A greater variance in cortisol levels across the day from morning to evening was associated with better cognitive function and physical performance. Conclusions The results support the idea that a more dynamic cortisol secretion pattern is associated with better cognitive function and physical performance even in the presence of cognitive impairment, but our results could not confirm a mediating role in this relationship

Bovine liver slices combined with an androgen transcriptional activation assay: an in-vitro model to study the metabolism and bioactivity of steroids

Previously we described the properties of a rapid and robust yeast androgen bioassay for detection of androgenic anabolic compounds, validated it, and showed its added value for several practical applications. However, biotransformation of potent steroids into inactive metabolites, or vice versa, is not included in this screening assay. Within this context, animal-friendly in-vitro cellular systems resembling species-specific metabolism can be of value. We therefore investigated the metabolic capacity of precision-cut slices of bovine liver using 17β-testosterone (T) as a model compound, because this is an established standard compound for assessing the metabolic capacity of such cellular systems. However, this is the first time that slice metabolism has been combined with bioactivity measurements. Moreover, this study also involves bioactivation of inactive prohormones, for example dehydroepiandrosterone (DHEA) and esters of T, and although medium extracts are normally analyzed by HPLC, here the metabolites formed were identified with more certainty by ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC–TOFMS) with accurate mass measurement. Metabolism of T resulted mainly in the formation of the less potent phase I metabolites 4-androstene-3,17-dione (4-AD), the hydroxy-T metabolites 6α, 6β, 15β, and 16α-OH-T, and the phase II metabolite T-glucuronide. As a consequence the overall androgenic activity, as determined by the yeast androgen bioassay, decreased. In order to address the usefulness of bovine liver slices for activation of inactive steroids, liver slices were exposed to DHEA and two esters of T. This resulted in an increase of androgenic activity, because of the formation of 4-AD and T

Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor α agonists

BACKGROUND: Fibrates are a unique hypolipidemic drugs that lower plasma triglyceride and cholesterol levels through their action as peroxisome proliferator-activated receptor alpha (PPARα) agonists. The activation of PPARα leads to a cascade of events that result in the pharmacological (hypolipidemic) and adverse (carcinogenic) effects in rodent liver. RESULTS: To understand the molecular mechanisms responsible for the pleiotropic effects of PPARα agonists, we treated mouse primary hepatocytes with three PPARα agonists (bezafibrate, fenofibrate, and WY-14,643) at multiple concentrations (0, 10, 30, and 100 μM) for 24 hours. When primary hepatocytes were exposed to these agents, transactivation of PPARα was elevated as measured by luciferase assay. Global gene expression profiles in response to PPARα agonists were obtained by microarray analysis. Among differentially expressed genes (DEGs), there were 4, 8, and 21 genes commonly regulated by bezafibrate, fenofibrate, and WY-14,643 treatments across 3 doses, respectively, in a dose-dependent manner. Treatments with 100 μM of bezafibrate, fenofibrate, and WY-14,643 resulted in 151, 149, and 145 genes altered, respectively. Among them, 121 genes were commonly regulated by at least two drugs. Many genes are involved in fatty acid metabolism including oxidative reaction. Some of the gene changes were associated with production of reactive oxygen species, cell proliferation of peroxisomes, and hepatic disorders. In addition, 11 genes related to the development of liver cancer were observed. CONCLUSION: Our results suggest that treatment of PPARα agonists results in the production of oxidative stress and increased peroxisome proliferation, thus providing a better understanding of mechanisms underlying PPARα agonist-induced hepatic disorders and hepatocarcinomas

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone