pcaMethods - a bioconductor package providing PCA methods for incomplete data

pcaMethods is a Bioconductor compliant library for computing principal component analysis (PCA) on incomplete data sets. The results can be analyzed directly or used to estimate missing values to enable the use of missing value sensitive statistical methods. The package was mainly developed with microarray and metabolite data sets in mind, but can be applied to any other incomplete data set as well

Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al

Dynamic Allostery in the Methionine Repressor Revealed by Force Distribution Analysis

Many fundamental cellular processes such as gene expression are tightly regulated by protein allostery. Allosteric signal propagation from the regulatory to the active site requires long-range communication, the molecular mechanism of which remains a matter of debate. A classical example for long-range allostery is the activation of the methionine repressor MetJ, a transcription factor. Binding of its co-repressor SAM increases its affinity for DNA several-fold, but has no visible conformational effect on its DNA binding interface. Our molecular dynamics simulations indicate correlated domain motions within MetJ, and quenching of these dynamics upon SAM binding entropically favors DNA binding. From monitoring conformational fluctuations alone, it is not obvious how the presence of SAM is communicated through the largely rigid core of MetJ and how SAM thereby is able to regulate MetJ dynamics. We here directly monitored the propagation of internal forces through the MetJ structure, instead of relying on conformational changes as conventionally done. Our force distribution analysis successfully revealed the molecular network for strain propagation, which connects collective domain motions through the protein core. Parts of the network are directly affected by SAM binding, giving rise to the observed quenching of fluctuations. Our results are in good agreement with experimental data. The force distribution analysis suggests itself as a valuable tool to gain insight into the molecular function of a whole class of allosteric proteins

Metabolomic Response of Calotropis procera Growing in the Desert to Changes in Water Availability

Water availability is a major limitation for agricultural productivity. Plants growing in severe arid climates such as deserts provide tools for studying plant growth and performance under extreme drought conditions. The perennial species Calotropis procera used in this study is a shrub growing in many arid areas which has an exceptional ability to adapt and be productive in severe arid conditions. We describe the results of studying the metabolomic response of wild C procera plants growing in the desert to a one time water supply. Leaves of C. procera plants were taken at three time points before and 1 hour, 6 hours and 12 hours after watering and subjected to a metabolomics and lipidomics analysis. Analysis of the data reveals that within one hour after watering C. procera has already responded on the metabolic level to the sudden water availability as evidenced by major changes such as increased levels of most amino acids, a decrease in sucrose, raffinose and maltitol, a decrease in storage lipids (triacylglycerols) and an increase in membrane lipids including photosynthetic membranes. These changes still prevail at the 6 hour time point after watering however 12 hours after watering the metabolomics data are essentially indistinguishable from the prewatering state thus demonstrating not only a rapid response to water availability but also a rapid response to loss of water. Taken together these data suggest that the ability of C. procera to survive under the very harsh drought conditions prevailing in the desert might be associated with its rapid adjustments to water availability and losses

Merging transcriptomics and metabolomics - advances in breast cancer profiling

Background Combining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information. Methods Breast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS. Results In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses. Conclusions Combining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels. See Commentary: http://www.biomedcentral.com/1741-7015/8/7

Transcriptome analysis of extended-spectrum ß-lactamase-producing Escherichia coli and methicillin-resistant Staphylococcus aureus exposed to cefotaxime

Previous studies on bacterial response to antibiotics mainly focused on susceptible strains. Here we characterized the transcriptional responses of distinct cephalosporin-resistant bacteria of public health relevance to cefotaxime (CTX), a cephalosporin widely used in clinical practice. Adaptation to therapeutic concentrations of CTX (30 µg/ml) was investigated by RNA sequencing in mid-exponential phase cultures of a methicillin-resistant Staphylococcus aureus (MRSA) and two genetically diverse E. coli producing CTX-M-15 or CMY-2 β-lactamase following genome sequencing and annotation for each strain. MRSA showed the most notable adaptive changes in the transcriptome after exposure to CTX, mainly associated with cell envelope functions. This reprogramming coincided with a transient reduction in cell growth, which also occurred in the CMY-2-producing E. coli but not in the CTX-M-15-producing strain. Re-establishment of growth in the CMY-2 producer proceeded without any notable adaptive transcriptional response, while limited reprogramming of gene transcription was observed in the CTX-M-15 producer. Our data show that the transcriptional response of CTX-resistant bacteria to CTX depends on the bacterial species, level of resistance and resistance determinant involved. Gene products induced in the presence of CTX may play an essential role for bacterial survival during therapy and merit further investigation as possible targets for potentiating CTX

Conformational Control of the Binding of the Transactivation Domain of the MLL Protein and c-Myb to the KIX Domain of CREB

The KIX domain of CBP is a transcriptional coactivator. Concomitant binding to the activation domain of proto-oncogene protein c-Myb and the transactivation domain of the trithorax group protein mixed lineage leukemia (MLL) transcription factor lead to the biologically active ternary MLL∶KIX∶c-Myb complex which plays a role in Pol II-mediated transcription. The binding of the activation domain of MLL to KIX enhances c-Myb binding. Here we carried out molecular dynamics (MD) simulations for the MLL∶KIX∶c-Myb ternary complex, its binary components and KIX with the goal of providing a mechanistic explanation for the experimental observations. The dynamic behavior revealed that the MLL binding site is allosterically coupled to the c-Myb binding site. MLL binding redistributes the conformational ensemble of KIX, leading to higher populations of states which favor c-Myb binding. The key element in the allosteric communication pathways is the KIX loop, which acts as a control mechanism to enhance subsequent binding events. We tested this conclusion by in silico mutations of loop residues in the KIX∶MLL complex and by comparing wild type and mutant dynamics through MD simulations. The loop assumed MLL binding conformation similar to that observed in the KIX∶c-Myb state which disfavors the allosteric network. The coupling with c-Myb binding site faded, abolishing the positive cooperativity observed in the presence of MLL. Our major conclusion is that by eliciting a loop-mediated allosteric switch between the different states following the binding events, transcriptional activation can be regulated. The KIX system presents an example how nature makes use of conformational control in higher level regulation of transcriptional activity and thus cellular events

pcaGoPromoter - An R Package for Biological and Regulatory Interpretation of Principal Components in Genome-Wide Gene Expression Data

Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms (biological processes), and an indication of the involvement of possible transcription factors

Cranial Growth and Variation in Edmontosaurs (Dinosauria: Hadrosauridae): Implications for Latest Cretaceous Megaherbivore Diversity in North America

The well-sampled Late Cretaceous fossil record of North America remains the only high-resolution dataset for evaluating patterns of dinosaur diversity leading up to the terminal Cretaceous extinction event. Hadrosaurine hadrosaurids (Dinosauria: Ornithopoda) closely related to Edmontosaurus are among the most common megaherbivores in latest Campanian and Maastrichtian deposits of western North America. However, interpretations of edmontosaur species richness and biostratigraphy have been in constant flux for almost three decades, although the clade is generally thought to have undergone a radiation in the late Maastrichtian. We address the issue of edmontosaur diversity for the first time using rigorous morphometric analyses of virtually all known complete edmontosaur skulls. Results suggest only two valid species, Edmontosaurus regalis from the late Campanian, and E. annectens from the late Maastrichtian, with previously named taxa, including the controversial Anatotitan copei, erected on hypothesized transitional morphologies associated with ontogenetic size increase and allometric growth. A revision of North American hadrosaurid taxa suggests a decrease in both hadrosaurid diversity and disparity from the early to late Maastrichtian, a pattern likely also present in ceratopsid dinosaurs. A decline in the disparity of dominant megaherbivores in the latest Maastrichtian interval supports the hypothesis that dinosaur diversity decreased immediately preceding the end Cretaceous extinction event

Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment

As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms