Reproducibility of gene expression across generations of Affymetrix microarrays

BACKGROUND: The development of large-scale gene expression profiling technologies is rapidly changing the norms of biological investigation. But the rapid pace of change itself presents challenges. Commercial microarrays are regularly modified to incorporate new genes and improved target sequences. Although the ability to compare datasets across generations is crucial for any long-term research project, to date no means to allow such comparisons have been developed. In this study the reproducibility of gene expression levels across two generations of Affymetrix GeneChips(® )(HuGeneFL and HG-U95A) was measured. RESULTS: Correlation coefficients were computed for gene expression values across chip generations based on different measures of similarity. Comparing the absolute calls assigned to the individual probe sets across the generations found them to be largely unchanged. CONCLUSION: We show that experimental replicates are highly reproducible, but that reproducibility across generations depends on the degree of similarity of the probe sets and the expression level of the corresponding transcript

Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

Blue lasing at room temperature in high quality factor GaN/AlInN microdisks with InGaN quantum wells

The authors report on the achievement of optically pumped III-V nitride blue microdisk lasers operating at room temperature. Controlled wet chemical etching of an AlInN interlayer lattice matched to GaN allows forming inverted cone pedestals. Whispering gallery modes are observed in the photoluminescence spectra of InGaN∕GaN quantum wells embedded in the GaN microdisks. Typical quality factors of several thousands are found (Q>4000). Laser action at ∼420nm is achieved under pulsed excitation at room temperature for a peak power density of 400kW/cm2. The lasing emission linewidth is down to 0.033nm

Carrier-density-dependent recombination dynamics of excitons and electron-hole plasma in m-plane InGaN/GaN quantum wells

We study the carrier-density-dependent recombination dynamics in m-plane InGaN/GaN multiple quantum wells in the presence of n-type background doping by time-resolved photoluminescence. Based on Fermi's golden rule and Saha's equation, we decompose the radiative recombination channel into an excitonic and an electron-hole pair contribution, and extract the injected carrier-density-dependent bimolecular recombination coefficients. Contrary to the standard electron-hole picture, our results confirm the strong influence of excitons even at room temperature. Indeed, at 300 K, excitons represent up to 63 +/- 6% of the photoexcited carriers. In addition, following the Shockley-Read-Hall model, we extract the electron and hole capture rates by deep levels and demonstrate that the increase in the effective lifetime with injected carrier density is due to asymmetric capture rates in presence of an n-type background doping. Thanks to the proper determination of the density-dependent recombination coefficients up to high injection densities, our method provides a way to evaluate the importance of Auger recombination

Successful network inference from time-series data using Mutual Information Rate

This work uses an information-based methodology to infer the connectivity of complex systems from observed time-series data. We first derive analytically an expression for the Mutual Information Rate (MIR), namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between two finite-length low-resolution noisy time-series, and then apply it after a proper normalization for the identification of the connectivity structure of small networks of interacting dynamical systems. In particular, we show that our methodology successfully infers the connectivity for heterogeneous networks, different time-series lengths or coupling strengths, and even in the presence of additive noise. Finally, we show that our methodology based on MIR successfully infers the connectivity of networks composed of nodes with different time-scale dynamics, where inference based on Mutual Information fails

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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From Violation to Reconstruction: The Process of Self-Renewal Associated with Chronic Fatigue Syndrome

Chronic Fatigue Syndrome (CFS) is a contested condition that generates scepticism and occupies a marginalised position within medical and social contexts. The thesis examines the illness experiences, and specifically the experiences of self, for people affected with CFS. Using qualitative inquiry, a substantive theory related to the process of self-renewal and adaptation associated with CFS is explicated. The theory encompasses the trajectory of CFS from onset to chronicity, and in exceptional instances, recovery. Illness narratives were derived from in-depth, semi-structured interviews of 19 adults, including 16 people affected with, and 3 people recovered from, CFS. Data was coded and analysed using a grounded theory approach. Analysis generated two parallel narratives that defined the illness experience of CFS: the narrative of the illness biographies and the narrative of self, specifically the struggling and diminished self seeking renewal. The illness biographies encompassed the stories of symptoms and their explanations, the encounters that ensued and their contentious milieu. The narrative of self was the primary narrative. It articulated the negative consequences to self and personhood associated with CFS, named the Violation of Self, and the consequent efforts of participants to decrease the struggle and violation by use of the Guardian Response and the Reconstructing Response. The Guardian Response provided protection and self-reclamation. The Reconstructing Response fostered self-renewal and meaning. The two narratives were bridged by the threats of CFS. That is, the illness biographies were accompanied by threats of disruption related to chronic illness, and by threats of invalidation that arose from CFS as a contested condition. In turn, these threats provided the catalyst to the violation and responses as described in the narrative of self. Under different conditions the relative strengths of violation, guardianship or reconstruction fluctuated, and it was these fluctuations that presented the participants with the ongoing struggle of CFS