The Nymphaeales (waterlilly and relatives) lineage has diverged as the second branch of basal angiosperms and comprises of two families: Cabombaceae and Nymphaceae. The classification of Nymphaeales and phylogeny within the flowering plants are quite intriguing as several systems (Thorne system, Dahlgren system, Cronquist system, Takhtajan system and APG III system (Angiosperm Phylogeny Group III system) have attempted to redefine the Nymphaeales taxonomy. There have been also fossil records consisting especially of seeds, pollen, stems, leaves and flowers as early as the lower Cretaceous. Here we present an in silico study of the order Nymphaeales taking maturaseK (matK) and internal transcribed spacer (ITS2) as biomarkers for phylogeny reconstruction (using character-based methods and Bayesian approach) and identification of motifs for DNA barcoding.

Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools.

Since the initial annotation of microRNAs (miRNAs) in 2001, many studies have sought to identify additional miRNAs experimentally or computationally in various species. MiRNAs act with the Argonaut family of proteins to regulate target messenger RNAs (mRNAs) post-transcriptionally. Currently, researches mainly focus on single miRNA function study. Considering that members in the same miRNA family might participate in the same pathway or regulate the same target(s) and thus share similar biological functions, people can explore useful knowledge from high quality miRNA family architecture.

Multiple transcription factors (TFs) are involved in the generation of gene expression patterns, such as tissue-specific gene expression and pleiotropic immune responses. However, how combinations of TFs orchestrate diverse gene expression patterns is poorly understood. Here we propose a new measure for regulatory motif co-occurrence and a new methodology to systematically identify TF pairs significantly co-occurring in a set of promoter sequences.

Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses.

Prediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored.

Gene expression data are noisy due to technical and biological variability. Consequently, analysis of gene expression data is complex. Different statistical methods produce distinct sets of genes. In addition, selection of expression p-value (EPv) threshold is somewhat arbitrary. In this study, we aimed to develop novel literature based approaches to integrate functional information in analysis of gene expression data.

Voltage-gated sodium channels (Na(V)s) underlie the upstroke of the action potential in the excitable tissues of nerve and muscle. After opening, Na(V)s rapidly undergo inactivation, a crucial process through which sodium conductance is negatively regulated. Disruption of inactivation by inherited mutations is an established cause of lethal cardiac arrhythmia, epilepsy, or painful syndromes. Intracellular calcium ions (Ca(2+)) modulate sodium channel inactivation, and multiple players have been suggested in this process, including the cytoplasmic Na(V) C-terminal region including two EF-hands and an IQ motif, the Na(V) domain III-IV linker, and calmodulin. Calmodulin can bind to the IQ domain in both Ca(2+)-bound and Ca(2+)-free conditions, but only to the DIII-IV linker in a Ca(2+)-loaded state. The mechanism of Ca(2+) regulation, and its composite effect(s) on channel gating, has been shrouded in much controversy owing to numerous apparent experimental inconsistencies. Herein, we attempt to summarize these disparate data and propose a novel, to our knowledge, physiological mechanism whereby calcium ions promote sodium current facilitation due to Ca(2+) memory at high-action-potential frequencies where Ca(2+) levels may accumulate. The available data suggest that this phenomenon may be disrupted in diseases where cytoplasmic calcium ion levels are chronically high and where targeted phosphorylation may decouple the Ca(2+) regulatory machinery. Many Na(V) disease mutations associated with electrical dysfunction are located in the Ca(2+)-sensing machinery and misregulation of Ca(2+)-dependent channel modulation is likely to contribute to disease phenotypes.

Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum. We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers, and use these patterns to predict localization for new genes. We analyze images of in-situ hybridization (ISH) experiments, which we represent using histograms of local binary patterns (LBP) and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje, granular, molecular and white matter layer. On held-out data, the layer classifiers achieve accuracy above 94% (AUC) by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision. When applied to the full mouse genome, the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers. Many genes localized to the Purkinje layer are likely to be expressed in astrocytes, and many others are involved in lipid metabolism, possibly due to the unusual size of Purkinje cells.

The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFNγ) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFNγ inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFNγ induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types.

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

Tri-methylated H3 lysine 4 (H3K4me3) is associated with transcriptionally active genes, but its function in the transcription process is still unclear. Point mutations in the catalytic domain of ATX1 (ARABIDOPSIS TRITHORAX1), a H3K4 methyltransferase, and RNAi knockdowns of subunits of the AtCOMPASS-like (Arabidopsis Complex Proteins Associated with Set) were used to address this question. We demonstrate that both ATX1 and AtCOMPASS-like are required for high level accumulation of TBP (TATA-binding protein) and Pol II at promoters and that this requirement is independent of the catalytic histone modifying activity. However, the catalytic function is critically required for transcription as H3K4me3 levels determine the efficiency of transcription elongation. The roles of H3K4me3, ATX1, and AtCOMPASS-like may be of a general relevance for transcription of Trithorax-activated eukaryotic genes.

Wolbachia are maternally inherited symbiotic bacteria, commonly found in arthropods, which are able to manipulate the reproduction of their host in order to maximise their transmission. The evolutionary history of endosymbionts like Wolbachia can be revealed by integrating information on infection status in natural populations with patterns of sequence variation in Wolbachia and host mitochondrial genomes. Here we use whole-genome resequencing data from 290 lines of Drosophila melanogaster from North America, Europe, and Africa to predict Wolbachia infection status, estimate relative cytoplasmic genome copy number, and reconstruct Wolbachia and mitochondrial genome sequences. Overall, 63% of Drosophila strains were predicted to be infected with Wolbachia by our in silico analysis pipeline, which shows 99% concordance with infection status determined by diagnostic PCR. Complete Wolbachia and mitochondrial genomes show congruent phylogenies, consistent with strict vertical transmission through the maternal cytoplasm and imperfect transmission of Wolbachia. Bayesian phylogenetic analysis reveals that the most recent common ancestor of all Wolbachia and mitochondrial genomes in D. melanogaster dates to around 8,000 years ago. We find evidence for a recent global replacement of ancestral Wolbachia and mtDNA lineages, but our data suggest that the derived wMel lineage arose several thousand years ago, not in the 20th century as previously proposed. Our data also provide evidence that this global replacement event is incomplete and is likely to be one of several similar incomplete replacement events that have occurred since the out-of-Africa migration that allowed D. melanogaster to colonize worldwide habitats. This study provides a complete genomic analysis of the evolutionary mode and temporal dynamics of the D. melanogaster-Wolbachia symbiosis, as well as important resources for further analyses of the impact of Wolbachia on host biology.

Epigenetic regulation of gene expression, including by Polycomb Group (PcG) proteins, may depend on heritable chromatin states, but how these states can be propagated through mitosis is unclear. Using immunofluorescence and biochemical fractionation, we find PcG proteins associated with mitotic chromosomes in Drosophila S2 cells. Genome-wide sequencing of chromatin immunoprecipitations (ChIP-SEQ) from mitotic cells indicates that Posterior Sex Combs (PSC) is not present at well-characterized PcG targets including Hox genes in mitosis, but does remain at a subset of interphase sites. Many of these persistent sites overlap with chromatin domain borders described by Sexton et al. (2012), which are genomic regions characterized by low levels of long range contacts. Persistent PSC binding sites flank both Hox gene clusters. We hypothesize that disruption of long-range chromatin contacts in mitosis contributes to PcG protein release from most sites, while persistent binding at sites with minimal long-range contacts may nucleate re-establishment of PcG binding and chromosome organization after mitosis.

Aggregation is a social behavior that varies between and within species, providing a model to study the genetic basis of behavioral diversity. In the nematode Caenorhabditis elegans, aggregation is regulated by environmental context and by two neuromodulatory pathways, one dependent on the neuropeptide receptor NPR-1 and one dependent on the TGF-β family protein DAF-7. To gain further insight into the genetic regulation of aggregation, we characterize natural variation underlying behavioral differences between two wild-type C. elegans strains, N2 and CB4856. Using quantitative genetic techniques, including a survey of chromosome substitution strains and QTL analysis of recombinant inbred lines, we identify three new QTLs affecting aggregation in addition to the two known N2 mutations in npr-1 and glb-5. Fine-mapping with near-isogenic lines localized one QTL, accounting for 5%-8% of the behavioral variance between N2 and CB4856, 3' to the transcript of the GABA neurotransmitter receptor gene exp-1. Quantitative complementation tests demonstrated that this QTL affects exp-1, identifying exp-1 and GABA signaling as new regulators of aggregation. exp-1 interacts genetically with the daf-7 TGF-β pathway, which integrates food availability and population density, and exp-1 mutations affect the level of daf-7 expression. Our results add to growing evidence that genetic variation affecting neurotransmitter receptor genes is a source of natural behavioral variation.

In the title compound, C(15)H(12)ClNO, the isoxazoline ring adopts an envelope conformation with the C atom bearing an unsubstituted phenyl ring as the flap atom. The chlorinated phenyl group is nearly in-plane with the four coplanar atoms of the heterocycle and the corresponding mean planes enclosing an angle of 1.16 (7)°. The unsubstituted phenyl group attached to the envelope flap atom approaches a nearly perpendicular orientation relative to the isoxazoline ring with a dihedral angle of 74.93 (7)°. In the crystal, weak C-H⋯O, C-H⋯N and C-H⋯π inter-actions connect the mol-ecules into layers perpendicular to the a axis.

Hepatocellular carcinoma (HCC) is a common cancer associated with a poor prognosis. Bevacizumab is a monoclonal antibody that binds vascular endothelial growth factor, a mediator of tumor angiogenesis. Bevacizumab is currently under investigation as treatment for HCC. We performed a systematic review of the efficacy and safety of bevacizumab for the treatment of advanced HCC.

We present a fast computational method to efficiently screen enzyme activity. In the presented method, the effect of mutations on the barrier height of an enzyme-catalysed reaction can be computed within 24 hours on roughly 10 processors. The methodology is based on the PM6 and MOZYME methods as implemented in MOPAC2009, and is tested on the first step of the amide hydrolysis reaction catalyzed by the Candida Antarctica lipase B (CalB) enzyme. The barrier heights are estimated using adiabatic mapping and shown to give barrier heights to within 3 kcal/mol of B3LYP/6-31G(d)//RHF/3-21G results for a small model system. Relatively strict convergence criteria (0.5 kcal/(molÅ)), long NDDO cutoff distances within the MOZYME method (15 Å) and single point evaluations using conventional PM6 are needed for reliable results. The generation of mutant structures and subsequent setup of the semiempirical calculations are automated so that the effect on barrier heights can be estimated for hundreds of mutants in a matter of weeks using high performance computing.

Topoisomerase I (Top1) is a proven target for cancer therapeutics. Recent data from the Fluorouracil, Oxaliplatin, CPT-11: Use and Sequencing (FOCUS) trial demonstrated that nuclear staining of Top1 correlates with chemotherapeutic efficacy. Such a correlation may help identify patients likely to respond to Top1 inhibitors and illuminate their mechanism of action. Cellular response to Top1 inhibitors is complex, but Top1 target engagement is a necessary first step in this process. This paper reports the development and validation of a quantitative immunoassay for Top1 in tumors.