Ocean net heat flux influences seasonal to interannual patterns of plankton abundance

This is the final version. Available from the publisher via the DOI in this record.Changes in the net heat flux (NHF) into the ocean have profound impacts on global climate. We analyse a long-term plankton time-series and show that the NHF is a critical indicator of ecosystem dynamics. We show that phytoplankton abundance and diversity patterns are tightly bounded by the switches between negative and positive NHF over an annual cycle. Zooplankton increase before the transition to positive NHF in the spring but are constrained by the negative NHF switch in autumn. By contrast bacterial diversity is decoupled from either NHF switch, but is inversely correlated (r = 20.920) with the magnitude of the NHF. We show that the NHF is a robust mechanistic tool for predicting climate change indicators such as spring phytoplankton bloom timing and length of the growing season.Natural Environment Research Council (NERC)European Union: 7th Framework ProgrammeINTERREG IV

Ocean Net Heat Flux Influences Seasonal to Interannual Patterns of Plankton Abundance

Changes in the net heat flux (NHF) into the ocean have profound impacts on global climate. We analyse a long-term plankton time-series and show that the NHF is a critical indicator of ecosystem dynamics. We show that phytoplankton abundance and diversity patterns are tightly bounded by the switches between negative and positive NHF over an annual cycle. Zooplankton increase before the transition to positive NHF in the spring but are constrained by the negative NHF switch in autumn. By contrast bacterial diversity is decoupled from either NHF switch, but is inversely correlated (r=-0.920) with the magnitude of the NHF. We show that the NHF is a robust mechanistic tool for predicting climate change indicators such as spring phytoplankton bloom timing and length of the growing season

Transcriptome analyses of mouse and human mammary cell subpopulations reveal multiple conserved genes and pathways

INTRODUCTION: Molecular characterization of the normal epithelial cell types that reside in the mammary gland is an important step toward understanding pathways that regulate self-renewal, lineage commitment, and differentiation along the hierarchy. Here we determined the gene expression signatures of four distinct subpopulations isolated from the mouse mammary gland. The epithelial cell signatures were used to interrogate mouse models of mammary tumorigenesis and to compare with their normal human counterpart subsets to identify conserved genes and networks. METHODS: RNA was prepared from freshly sorted mouse mammary cell subpopulations (mammary stem cell (MaSC)-enriched, committed luminal progenitor, mature luminal and stromal cell) and used for gene expression profiling analysis on the Illumina platform. Gene signatures were derived and compared with those previously reported for the analogous normal human mammary cell subpopulations. The mouse and human epithelial subset signatures were then subjected to Ingenuity Pathway Analysis (IPA) to identify conserved pathways. RESULTS: The four mouse mammary cell subpopulations exhibited distinct gene signatures. Comparison of these signatures with the molecular profiles of different mouse models of mammary tumorigenesis revealed that tumors arising in MMTV-Wnt-1 and p53-/- mice were enriched for MaSC-subset genes, whereas the gene profiles of MMTV-Neu and MMTV-PyMT tumors were most concordant with the luminal progenitor cell signature. Comparison of the mouse mammary epithelial cell signatures with their human counterparts revealed substantial conservation of genes, whereas IPA highlighted a number of conserved pathways in the three epithelial subsets. CONCLUSIONS: The conservation of genes and pathways across species further validates the use of the mouse as a model to study mammary gland development and highlights pathways that are likely to govern cell-fate decisions and differentiation. It is noteworthy that many of the conserved genes in the MaSC population have been considered as epithelial-mesenchymal transition (EMT) signature genes. Therefore, the expression of these genes in tumor cells may reflect basal epithelial cell characteristics and not necessarily cells that have undergone an EMT. Comparative analyses of normal mouse epithelial subsets with murine tumor models have implicated distinct cell types in contributing to tumorigenesis in the different models

puma: a Bioconductor package for propagating uncertainty in microarray analysis

BACKGROUND: Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied. RESULTS: puma is a Bioconductor package incorporating a suite of analysis methods for use on Affymetrix GeneChip data. puma extends the differential expression detection methods of previous work from the 2-class case to the multi-factorial case. puma can be used to automatically create design and contrast matrices for typical experimental designs, which can be used both within the package itself but also in other Bioconductor packages. The implementation of differential expression detection methods has been parallelised leading to significant decreases in processing time on a range of computer architectures. puma incorporates the first R implementation of an uncertainty propagation version of principal component analysis, and an implementation of a clustering method based on uncertainty propagation. All of these techniques are brought together in a single, easy-to-use package with clear, task-based documentation. CONCLUSION: For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. These methods can be used to improve results from more traditional analyses of microarray data. puma also offers improvements in terms of scope and speed of execution over previously available methods. puma is recommended for anyone working with the Affymetrix GeneChip platform for gene expression analysis and can also be applied more generally

Sensory Symptom Profiles and Co-Morbidities in Painful Radiculopathy

Painful radiculopathies (RAD) and classical neuropathic pain syndromes (painful diabetic polyneuropathy, postherpetic neuralgia) show differences how the patients express their sensory perceptions. Furthermore, several clinical trials with neuropathic pain medications failed in painful radiculopathy. Epidemiological and clinical data of 2094 patients with painful radiculopathy were collected within a cross sectional survey (painDETECT) to describe demographic data and co-morbidities and to detect characteristic sensory abnormalities in patients with RAD and compare them with other neuropathic pain syndromes. Common co-morbidities in neuropathic pain (depression, sleep disturbance, anxiety) do not differ considerably between the three conditions. Compared to other neuropathic pain syndromes touch-evoked allodynia and thermal hyperalgesia are relatively uncommon in RAD. One distinct sensory symptom pattern (sensory profile), i.e., severe painful attacks and pressure induced pain in combination with mild spontaneous pain, mild mechanical allodynia and thermal hyperalgesia, was found to be characteristic for RAD. Despite similarities in sensory symptoms there are two important differences between RAD and other neuropathic pain disorders: (1) The paucity of mechanical allodynia and thermal hyperalgesia might be explained by the fact that the site of the nerve lesion in RAD is often located proximal to the dorsal root ganglion. (2) The distinct sensory profile found in RAD might be explained by compression-induced ectopic discharges from a dorsal root and not necessarily by nerve damage. These differences in pathogenesis might explain why medications effective in DPN and PHN failed to demonstrate efficacy in RAD

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N \u3e 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies

Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA – a Bayesian approach

<p>Abstract</p> <p>Background</p> <p>Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge.</p> <p>Results</p> <p>A computing tool (ArrayLeaRNA) has been developed for gene expression analysis. It implements a Bayesian approach which is based on the Gumbel distribution and uses printed genomic DNA control features for normalization and for estimation of the parameters of the Bayesian model and prior knowledge from predicted operon structure. The method is compared with two other approaches: the classical LOWESS normalization followed by a two fold cut-off criterion and the OpWise method (Price, et al. 2006. BMC Bioinformatics. 7, 19), a published Bayesian approach also using predicted operon structure. The three methods were compared on experimental datasets with prior knowledge of gene expression. With ArrayLeaRNA, data normalization is carried out according to the genomic features which reflect the results of equally transcribed genes; also the statistical significance of the difference in expression is based on the variability of the equally transcribed genes. The operon information helps the classification of genes with low confidence measurements.</p> <p>ArrayLeaRNA is implemented in Visual Basic and freely available as an Excel add-in at <url>http://www.ifr.ac.uk/safety/ArrayLeaRNA/</url></p> <p>Conclusion</p> <p>We have introduced a novel Bayesian model and demonstrated that it is a robust method for analysing microarray expression profiles. ArrayLeaRNA showed a considerable improvement in data normalization, in the estimation of the experimental variability intrinsic to each hybridization and in the establishment of a clear boundary between non-changing and differentially expressed genes. The method is applicable to data derived from hybridizations of labelled cDNA samples as well as from hybridizations of labelled cDNA with genomic DNA and can be used for the analysis of datasets where differentially regulated genes predominate.</p