The extraction by soil and absorption by plants of applied zinc and cadmium

In five consecutive years lettuce, spinach, spring wheat, endive and maize were grown in pots and the effects of native and soil-applied Zn and Cd on plant Zn and Cd concentrations were studied. The normal interactive pattern was antagonistic, Zn reducing plant Cd uptake, and conversely, but less so. Only in loam soil Zn and Cd were synergistic to some extent, plant Zn uptake increasing with applied Cd.When relating total soil Cd/Zn to plant Cd/Zn separate sets of data could be distinguished for loam and sandy soil, each fitting a straight line. The use of 0.1 M CaCl₂ instead of total extractable soil Cd/Zn makes the two sets of data to coalesce around a single straight line. All crops were found to show a positive linear relationship between 0.1 M CaCl₂-extractable soil Cd/Zn and plant Cd/Zn.     

Versatile Trans-Replication Systems for Chikungunya Virus Allow Functional Analysis and Tagging of Every Replicase Protein

Chikungunya virus (CHIKV; genus Alphavirus, family Togaviridae) has recently caused several major outbreaks affecting millions of people. There are no licensed vaccines or antivirals, and the knowledge of the molecular biology of CHIKV, crucial for development of efficient antiviral strategies, remains fragmentary. CHIKV has a 12 kb positive-strand RNA genome, which is translated to yield a nonstructural (ns) or replicase polyprotein. CHIKV structural proteins are expressed from a subgenomic RNA synthesized in infected cells. Here we have developed CHIKV trans-replication systems, where replicase expression and RNA replication are uncoupled. Bacteriophage T7 RNA polymerase or cellular RNA polymerase II were used for production of mRNAs for CHIKV ns polyprotein and template RNAs, which are recognized by CHIKV replicase and encode for reporter proteins. CHIKV replicase efficiently amplified such RNA templates and synthesized large amounts of subgenomic RNA in several cell lines. This system was used to create tagged versions of ns proteins including nsP1 fused with enhanced green fluorescent protein and nsP4 with an immunological tag. Analysis of these constructs and a matching set of replicon vectors revealed that the replicases containing tagged ns proteins were functional and maintained their subcellular localizations. When cells were co-transfected with constructs expressing template RNA and wild type or tagged versions of CHIKV replicases, formation of characteristic replicase complexes (spherules) was observed. Analysis of mutations associated with noncytotoxic phenotype in CHIKV replicons showed that a low level of RNA replication is not a pre-requisite for reduced cytotoxicity. The CHIKV trans-replicase does not suffer from genetic instability and represents an efficient, sensitive and reliable tool for studies of different aspects of CHIKV RNA replication process.     

Applications of a new subspace clustering algorithm (COSA) in medical systems biology

A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies. Doris Damian, Matej Orešič, and Elwin Verheij contributed equally to this work.     

Inhibitors of alphavirus entry and replication identified with a stable Chikungunya replicon cell line and virus-based assays

Chikungunya virus (CHIKV), an alphavirus, has recently caused epidemic outbreaks and is therefore considered a re-emerging pathogen for which no effective treatment is available. In this study, a CHIKV replicon containing the virus replicase proteins together with puromycin acetyltransferase, EGFP and Renilla luciferase marker genes was constructed. The replicon was transfected into BHK cells to yield a stable cell line. A non-cytopathic phenotype was achieved by a Pro718 to Gly substitution and a five amino acid insertion within non-structural protein 2 (nsP2), obtained through selection for stable growth. Characterization of the replicon cell line by Northern blotting analysis revealed reduced levels of viral RNA synthesis. The CHIKV replicon cell line was validated for antiviral screening in 96-well format and used for a focused screen of 356 compounds (natural compounds and clinically approved drugs). The 5,7-dihydroxyflavones apigenin, chrysin, naringenin and silybin were found to suppress activities of EGFP and Rluc marker genes expressed by the CHIKV replicon. In a concomitant screen against Semliki Forest virus (SFV), their anti-alphaviral activity was confirmed and several additional inhibitors of SFV with IC₅₀ values between 0.4 and 24 µM were identified. Chlorpromazine and five other compounds with a 10H-phenothiazinyl structure were shown to inhibit SFV entry using a novel entry assay based on a temperature-sensitive SFV mutant. These compounds also reduced SFV and Sindbis virus-induced cytopathic effect and inhibited SFV virion production in virus yield experiments. Finally, antiviral effects of selected compounds were confirmed using infectious CHIKV. In summary, the presented approach for discovering alphaviral inhibitors enabled us to identify potential lead structures for the development of alphavirus entry and replication phase inhibitors as well as demonstrated the usefulness of CHIKV replicon and SFV as biosafe surrogate models for anti-CHIKV screening.     

Inferring differences in the distribution of reaction rates across conditions

Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms. With the use of a small in silico hypothetical network, based on only the topology and directionality of a known pathway, several regulation scenarios can be formulated. Confronting these scenarios with experimental data results in a short list of possible pathway regulation mechanisms associated with the reversal of correlations between conditions. This procedure allows for the formulation of regulation scenarios without detailed prior knowledge of kinetics and for the inference of reaction rate changes without rate information. The method was applied to experimental time-resolved metabolomics data from multiple short-term perturbation-response experiments in S. cerevisiae across aerobic and anaerobic conditions. The method's output was validated against a detailed kinetic model of glycolysis in S. cerevisiae, which showed that the method can indeed infer the correct regulation scenario.     

Statistical data processing in clinical proteomics

This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature.     

Group-wise ANOVA simultaneous component analysis for designed <Emphasis Type="Italic">omics</Emphasis> experiments

Introduction

Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation.

Objectives

We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret.

Methods

GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices.

Results

The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes.

Conclusions

GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.