What is marine biodiversity? Towards common concepts and their implications for assessing biodiversity status

Biodiversity' is one of the most common keywords used in environmental sciences, spanning from research to management, nature conservation, and consultancy. Despite this, our understanding of the underlying concepts varies greatly, between and within disciplines as well as among the scientists themselves. Biodiversity can refer to descriptions or assessments of the status and condition of all or selected groups of organisms, from the genetic variability, to the species, populations, communities, and ecosystems. However, a concept of biodiversity also must encompass understanding the interactions and functions on all levels from individuals up to the whole ecosystem, including changes related to natural and anthropogenic environmental pressures. While biodiversity as such is an abstract and relative concept rooted in the spatial domain, it is central to most international, European, and national governance initiatives aimed at protecting the marine environment. These rely on status assessments of biodiversity which typically require numerical targets and specific reference values, to allow comparison in space and/or time, often in association with some external structuring factors such as physical and biogeochemical conditions. Given that our ability to apply and interpret such assessments requires a solid conceptual understanding of marine biodiversity, here we define this and show how the abstract concept can and needs to be interpreted and subsequently applied in biodiversity assessments

DPI-ELISA: a fast and versatile method to specify the binding of plant transcription factors to DNA in vitro

<p>Abstract</p> <p>Background</p> <p>About 10% of all genes in eukaryote genomes are predicted to encode transcription factors. The specific binding of transcription factors to short DNA-motifs influences the expression of neighbouring genes. However, little is known about the DNA-protein interaction itself. To date there are only a few suitable methods to characterise DNA-protein-interactions, among which the EMSA is the method most frequently used in laboratories. Besides EMSA, several protocols describe the effective use of an ELISA-based transcription factor binding assay e.g. for the analysis of human NFκB binding to specific DNA sequences.</p> <p>Results</p> <p>We provide a unified protocol for this type of ELISA analysis, termed DNA-Protein-Interaction (DPI)-ELISA. Qualitative analyses with His-epitope tagged plant transcription factors expressed in <it>E. coli </it>revealed that EMSA and DPI-ELISA result in comparable and reproducible data. The binding of <it>At</it>bZIP63 to the C-box and <it>At</it>WRKY11 to the W2-box could be reproduced and validated by both methods. We next examined the physical binding of the C-terminal DNA-binding domains of <it>At</it>WRKY33, <it>At</it>WRKY50 and <it>At</it>WRKY75 to the W2-box. Although the DNA-binding domain is highly conserved among the WRKY proteins tested, the use of the DPI-ELISA discloses differences in W2-box binding properties between these proteins. In addition to these well-studied transcription factor families, we applied our protocol to <it>At</it>BPC2, a member of the so far uncharacterised plant specific Basic Pentacysteine transcription factor family. We could demonstrate binding to GA/TC-dinucleotide repeat motifs by our DPI-ELISA protocol. Different buffers and reaction conditions were examined.</p> <p>Conclusions</p> <p>We successfully applied our DPI-ELISA protocol to investigate the DNA-binding specificities of three different classes of transcription factors from <it>Arabidopsis thaliana</it>. However, the analysis of the binding affinity of any DNA-binding protein to any given DNA sequence can be performed <it>via </it>this method. The DPI-ELISA is cost efficient, less time-consuming than other methods and provides a qualitative and quantitative readout. The presented DPI-ELISA protocol is accompanied by advice on trouble-shooting, which will enable scientists to rapidly establish this versatile and easy to use method in their laboratories.</p

The Minus-End-Directed Kinesin OsDLK Shuttles to the Nucleus and Modulates the Expression of Cold-Box Factor 4

The transition to terrestrial plants was accompanied by a progressive loss of microtubule minus-end-directed dynein motors. Instead, the minus-end-directed class-XIV kinesins expanded considerably, likely related to novel functions. One of these motors, OsDLK (Dual Localisation Kinesin from rice), decorates cortical microtubules but moves into the nucleus in response to cold stress. This analysis of loss-of-function mutants in rice indicates that OsDLK participates in cell elongation during development. Since OsDLK harbours both a nuclear localisation signal and a putative leucin zipper, we asked whether the cold-induced import of OsDLK into the nucleus might correlate with specific DNA binding. Conducting a DPI-ELISA screen with recombinant OsDLKT (lacking the motor domain), we identified the Opaque2 motif as the most promising candidate. This motif is present in the promoter of NtAvr9/Cf9, the tobacco homologue of Cold-Box Factor 4, a transcription factor involved in cold adaptation. A comparative study revealed that the cold-induced accumulation of NtAvr9/Cfp9 was specifically quelled in transgenic BY−2 cells overexpressing OsDLK-GFP. These findings are discussed as a working model, where, in response to cold stress, OsDLK partitions from cortical microtubules at the plasma membrane into the nucleus and specifically modulates the expression of genes involved in cold adaptation

PCN94 - Cost-effectiveness and preference for follow-up scenarios following breast cancer

OBJECTIVES: About one in every eight women in The Netherlands develops breast cancer. Every year, 11,000 new cases are registered and about 3500 women die of breast cancer. Prognosis after primary treatment for patients with breast cancer is improving. This leads to an increased number of patients in follow-up, which leads to increased workload. One of the main goals of follow-up is to improve the survival of patients. This study aims to determine a more individualized follow-up by modelling costeffectiveness of various follow-up scenarios and by determining individual preferences by using a discrete choice experiment (DCE). METHODS: A discrete-event state-transition model was developed to estimate the cost-effectiveness of all scenarios for all patient groups. In addition, a discrete choice experiment (DCE) was designed to establish patient preferences. The DCE incorporated three process attributes (duration of follow-up, frequency and type of consult) and data were collected in a sample of 125 breast cancer patients. Patients had to complete all 18 choice sets that were generated from the three attributes. RESULTS: The modelling study revealed recommendations for follow-up in different age categories. Patients younger than 40 and patients with unfavorable tumor characteristics (>3 lymph nodes, tumor size >2 cm) can benefit from a more intensive follow-up of five or possibly ten years. Patients older than 40 but younger than 70 years old sometimes benefit from a more intensive follow-up; e.g. when younger than 50 and tumor size >2 cm. The DCE, however, showed that patients chose maximum levels of follow-up independent from age and their individual clinical risk profile. Duration of follow-up and type of consult (either hospital visit or telephone) weighted approximately 0.43 and 0.50 respectively. The frequency of follow-up (either once or twice a year) was least important (0.07). CONCLUSIONS: The model showed that follow-up may be individualized according to risk profile and age. However, patients preferred long and intensive follow-up strategies after breast cancer treatment. Taking into account individual patient preferences it may be recommended to reduce the frequency of follow-up to once a year. The service delivery by nurse practioners is well appreciated and another means for improving cost-effective follow-up

K-band Spectroscopy of Clusters in NGC 4038/4039

Integral field spectroscopy in the K-band (1.9-2.4um) was performed on four IR-bright star clusters and the two nuclei in NGC 4038/4039 (``The Antennae''). Two of the clusters are located in the overlap region of the two galaxies, and together comprise ~25% of the total 15um and ~10% of the total 4.8 GHz emission from this pair of merging galaxies. The other two clusters, each of them spatially resolved into two components, are located in the northern galaxy, one in the western and one in the eastern loop of blue clusters. Comparing our analysis of Brgamma, CO band-heads, He I (2.058um), Halpha (from archival HST data), and V-K colors with stellar population synthesis models indicates that the clusters are extincted (A_V ~ 0.7 - 4.3 mags) and young, displaying a significant age spread (4-13 Myrs). The starbursts in the nuclei are much older (65 Myrs), with the nucleus of NGC 4038 displaying a region of recent star formation northward of its K-band peak. Using our derived age estimates and assuming the parameters of the IMF (Salpeter slope, upper mass cut-off of 100 M_sun, Miller-Scalo between 1 M_sun and 0.1 M_sun), we find that the clusters have masses between 0.5 and 5 * 10^6M_sun.Comment: 10 pages, 3 figures, ApJ accepte

Age-related Changes of Plasma Bile Acid Concentrations in Healthy Adults : Results from the Cross-Sectional KarMeN Study

Bile acids (BA) play an important role in lipid metabolism. They facilitate intestinal lipid absorption, and BA synthesis is the main catabolic pathway for cholesterol. The objective of this study was to investigate associations of age, sex, diet (fat intake) and parameters of lipid metabolism (triglycerides, LDL, HDL, body fat content) with fasting plasma BA concentration of healthy individuals. Fasting plasma samples from a cross-sectional study were used to determine the concentrations of 14 BA using an LC-MS stable isotope dilution assay. Triglycerides, LDL and HDL were analyzed by standard clinical chemistry methods and body fat content was measured with a DXA instrument. The dietary fat intake of the 24 h period prior to the sampling was assessed on the basis of a 24 h recall. Subsequent statistical data processing was done by means of a median regression model. Results revealed large inter-individual variations. Overall, higher median plasma concentrations of BA were observed in men than in women. Quantile regression showed significant interactions of selected BA with age and sex, affecting primarily chenodeoxycholic acid and its conjugates. No associations were found for LDL and the amount of fat intake (based on the percentage of energy intake from dietary fat as well as total fat intake). Additional associations regarding body fat content, HDL and triglycerides were found for some secondary BA plasma concentrations. We conclude that age and sex are associated with the fasting plasma concentrations. Those associations are significant and need to be considered in studies investigating the role of BA in the human metabolism

Genes and gene clusters related to genotype and drought induced variation in saccharification potential, lignin content, and wood anatomical traits in Populus nigra:Saccharification, Wood Anatomy and Gene Clusters

Wood is a renewable resource that can be employed for the production of second generation biofuels by enzymatic saccharification and subsequent fermentation. Knowledge on how the saccharification potential is affected by genotype-related variation of wood traits and drought is scarce. Here, we used three Populus nigra genotypes from habitats differing in water availability to (i) investigate the relationships between wood anatomy, lignin content and saccharification and (ii) identify genes and co-expressed gene clusters related to genotype and drought-induced variation in wood traits and saccharification potential. The three poplar genotypes differed in wood anatomy, lignin content and saccharification potential. Drought resulted in reduced cambial activity, decreased vessel and fibre lumina, and increased the saccharification potential. The saccharification potential was unrelated to lignin content as well as to most wood anatomical traits. RNA sequencing of the developing xylem revealed that 1.5% of the analysed genes were differentially expressed in response to drought, while 67% differed among the genotypes. Weighted gene correlation network analysis identified modules of co-expressed genes correlated with saccharification potential. These modules were enriched in gene ontology terms related to cell wall polysaccharide biosynthesis and modification and vesicle transport, but not to lignin biosynthesis. Among the most strongly saccharification-correlated genes, those with regulatory functions, especially kinases were prominent. We further identified transcription factors whose transcript abundances differed among genotypes, and which were co45 regulated with genes for biosynthesis and modifications of hemicelluloses and pectin. Overall, our study suggests that the regulation of pectin and hemicellulose metabolism is a promising target for improving wood quality of second generation bioenergy crops. The causal relationship of the identified genes and pathways with saccharification potential needs to be validated in further experiments.publishersversionPeer reviewe

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

<p>Abstract</p> <p>Background</p> <p>For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or <it>in-silico </it>deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.</p> <p>Results</p> <p>Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.</p> <p>Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.</p> <p>Conclusions</p> <p>The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.</p

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p