Unravelling metal speciation in the microenvironment surrounding phytoplankton cells to improve predictions of metal bioavailability.

A lack of knowledge on metal speciation in the microenvironment surrounding phytoplankton cells (i.e., the phycosphere) represents an impediment to accurately predicting metal bioavailability. Phycosphere pH and O2 concentrations from a diversity of algae species were compiled. For marine algae in the light, the average increases were 0.32 pH units and 0.17 mM O2 in the phycosphere, whereas in the dark the average decreases were 0.10 pH units and 0.03 mM O2, in comparison to bulk seawater. In freshwater algae, the phycosphere pH increased by 1.28 units, whereas O2 increased by 0.38 mM in the light. Equilibrium modeling showed that the pH alteration influenced the chemical species distribution (i.e., free ion, inorganic complexes, and organic complexes) of Al, Cd, Co, Cu, Fe, Hg, Mn, Ni, Pb, Sc, Sm, and Zn in the phycosphere, and the O2 fluctuation increased oxidation rates of Cu(I), Fe(II) and Mn(II) from 2 to 938-fold. The pH/O2-induced changes in phycosphere metal chemistry were larger for freshwater algae than for marine species. Reanalyses of algal metal uptake data in the literature showed that uptake of the trivalent metals (Sc, Sm and Fe), in addition to divalent metals, can be better predicted after considering the phycosphere chemistry

Consideration of the bioavailability of metal/metalloid species in freshwaters: experiences regarding the implementation of biotic ligand model-based approaches in risk assessment frameworks

After the scientific development of Biotic Ligand Models (BLMs) in recent decades these models are now considered suitable for implementation in regulatory risk assessment of metals in freshwater bodies. The approach has been developed over several years and has been described in many peer-reviewed publications. The original complex BLMs have been applied in prospective risk assessment reports for metals and metal compounds and are also recommended as suitable concepts for the evaluation of monitoring data in the context of the European Water Framework Directive. Currently, several user-friendly BLM-based bioavailability software tools are available for assessing the aquatic toxicity of a limited number of metals (mainly copper, nickel, and zinc). These tools need only a basic set of water parameters as input (e.g., pH, hardness, dissolved organic matter and dissolved metal concentration). Such tools seem appropriate to foster the implementation in routine water quality assessments. This work aims to review the existing bioavailability-based regulatory approaches and the application of available BLM-based bioavailability tools for this purpose. Advantages and possible drawbacks of these tools (e.g., feasibility, boundaries of validity) are discussed, and recommendations for further implementation are given

Unique establishment of procephalic head segments is supported by the identification of cis-regulatory elements driving segment-specific segment polarity gene expression in Drosophila

Anterior head segmentation is governed by different regulatory mechanisms than those that control trunk segmentation in Drosophila. For segment polarity genes, both initial mode of activation as well as cross-regulatory interactions among them differ from the typical genetic circuitry in the trunk and are unique for each of the procephalic segments. In order to better understand the segment-specific gene network responsible for the procephalic expression of the earliest active segment polarity genes wingless and hedgehog, we started to identify and analyze cis-regulatory DNA elements of these genes. For hedgehog, we could identify a cis-regulatory element, ic-CRE, that mediates expression specifically in the posterior part of the intercalary segment and requires promoter-specific interaction for its function. The intercalary stripe is the last part of the metameric hedgehog expression pattern that appears during embryonic development, which probably reflects the late and distinct establishment of this segment. The identification of a cis-regulatory element that is specific for one head segment supports the mutant-based observation that the expression of segment polarity genes is governed by a unique gene network in each of the procephalic segments. This provides further indication that the anterior-most head segments represent primary segments, which are set up independently, in contrast to the secondary segments of the trunk, which resemble true repetitive units

GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae

Motivation Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data. Availability and implementation GEMMER is freely available at http://gemmer.barberislab.com. Source code, written in Python, JavaScript library D3js, PHP and JSON, is freely available at https://github.com/barberislab/GEMMER.</p

The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise.

Transcriptional stochasticity can be measured by counting the number of mRNA molecules per cell. Cell-to-cell variability is best captured in terms of concentration rather than molecule counts, because reaction rates depend on concentrations. We combined single-molecule mRNA counting with single-cell volume measurements to quantify the statistics of both transcript numbers and concentrations in human cells. We compared three cell clones that differ only in the genomic integration site of an identical constitutively expressed reporter gene. The transcript number per cell varied proportionally with cell volume in all three clones, indicating concentration homeostasis. We found that the cell-to-cell variability in the mRNA concentration is almost exclusively due to cell-to-cell variation in gene expression activity, whereas the cell-to-cell variation in mRNA number is larger, due to a significant contribution of cell volume variability. We concluded that the precise relationship between transcript number and cell volume sets the biological stochasticity of living cells. This study highlights the importance of the quantitative measurement of transcript concentrations in studies of cell-to-cell variability in biology