phenoVein—A Tool for Leaf Vein Segmentation and Analysis

Precise measurements of leaf vein traits are an important aspect of plant phenotyping for ecological and genetic research. Here, we present a powerful and user-friendly image analysis tool named phenoVein. It is dedicated to automated segmenting and analyzing of leaf veins in images acquired with different imaging modalities (microscope, macrophotography, etc.), including options for comfortable manual correction. Advanced image filtering emphasizes veins from the background and compensates for local brightness inhomogeneities. The most important traits being calculated are total vein length, vein density, piecewise vein lengths and widths, areole area, and skeleton graph statistics, like the number of branching or ending points. For the determination of vein widths, a model-based vein edge estimation approach has been implemented. Validation was performed for the measurement of vein length, vein width, and vein density of Arabidopsis (Arabidopsis thaliana), proving the reliability of phenoVein. We demonstrate the power of phenoVein on a set of previously described vein structure mutants of Arabidopsis (hemivenata, ondulata3, and asymmetric leaves2-101) compared with wild-type accessions Columbia-0 and Landsberg erecta-0. phenoVein is freely available as open-source software.Leaf veins are an important aspect of leaf structure and responsible for both the mechanical support of leaves and the long-distance transport of water, nutrients, and photoassimilates (Onoda et al., 2011; Malinowski, 2013). The molecular mechanisms by which vascular tissues acquire their identities are yet largely unknown (Roschzttardtz et al., 2014), and there is high interest in analyzing and evaluating traits of veins or leaf venation networks and their genetic regulation. The impact of vein density on photosynthesis is a major investigated topic (Sack and Scoffoni, 2013). During the last decade, a positive correlation between leaf venation and photosynthesis has been observed (Sack and Holbrook, 2006; Brodribb et al., 2007). An optimization of photosynthetic rates was shown to occur by spatial coordination between leaf vein and stomatal densities (Zhang et al., 2012; Carins Murphy et al., 2014; Fiorin et al., 2015). Additionally, there is interest in the impact of vein density on interveinal distances (Dengler et al., 1994; McKown and Dengler, 2009) and the effect of climate, habitat, or growth form on vein density (Sack and Scoffoni, 2013; Scoffoni et al., 2015) or vein width with respect to leaf hydraulic conductance (Feild and Brodribb, 2013; Xiong et al., 2015). Other researchers are particularly interested in the evolution from C3 to C4 plants, which requires higher vein density (Gowik and Westhoff, 2011) and led to selecting for variation of vein density within species (e.g. in a mutant collection by Feldman et al., 2014).Leaf venation studies analyzing traits of veins and venation networks are generally performed on microscopic images of leaves that are properly cleared after harvest. For very small leaves, e.g. the cotyledons or the first leaves (leaves 2–5) of Arabidopsis (Arabidopsis thaliana), basic traits, such as total vein length or vein density (vein length per leaf area), can be achieved manually. However, for larger leaves, manual vein segmentation may become tedious, and at least partially automated analysis is needed for studies on large series of leaf collections. Furthermore, the quantification of vein widths and in particular mean values of vein width of certain vein pieces of interest can hardly be achieved manually. Dedicated image processing tools are, therefore, needed to support researchers for fast and reliable data analysis.A number of software tools have been published that are either specifically made or adapted to analyze leaf veins. These programs have some common properties, like image processing functionalities for vein/areole segmentation and trait extraction. However, they differ in handling strategies or vein parameter analysis methods. A general overview on plant image analysis tools is collected in an online database at http://www.plant-image-analysis.org (Lobet et al., 2013). Programs allowing automated or semiautomated analysis of leaf venation parameters are, for example, a method to extract leaf venation patterns (Rolland-Lagan et al., 2009), the leaf extraction and analysis framework graphical user interface LeafGUI (Price et al., 2011), the leaf image analysis interface LIMANI (Dhondt et al., 2012), the user-interactive vessel generation analysis tool VESGEN (Vickerman et al., 2009; Parsons-Wingerter et al., 2014), and the software network extraction from images NEFI (Dirnberger et al., 2015). Nevertheless, for the analysis of large-scale leaf vein phenotyping experiments, there are certain needs that are only partly covered by each of the approaches and programs mentioned above. Specifically, the following properties are needed: (1) automated vein segmentation with optional manual correction; (2) invariance of the segmentation procedure to inhomogeneous illumination or brightness variations in the leaf image; (3) automated determination of total vein length and projected leaf area; (4) a well-defined and automated determination of vein widths, which is, as far as possible, independent of user chosen thresholds; (5) ability to process large high-resolution images of whole leafs; and (6) full transparency of the source code as well as offline availability of the tool. To provide these functionalities, we developed the user-friendly analysis tool phenoVein. It features automated leaf vein segmentation based on advanced image filtering techniques and includes determination of various vein traits, particularly a model-based vein width estimation. phenoVein allows easy and fast visual control and manual correction on the automatically achieved skeleton of the veins enabled by a real-time overlay of the segmented leaf vein structures on the original image. The length measurement algorithm of phenoVein was validated against complete manual segmentation. We evaluated the impact of image resolution on the results, which has recently been discussed (Price et al., 2014; Sack et al., 2014), and tested whether the orientation (angle) of a leaf on an image may affect the results as suspected from image analysis theory on binary skeleton length measurements (Russ, 2011). To show the powerful phenotyping capabilities of phenoVein, we analyzed the venation traits of leaves of Arabidopsis at different developmental stages (cotyledons, pooled leaves 1 + 2, and leaf 6) harvested from previously described venation mutants and corresponding wild-type lines: asymmetric leaves2-101 (as2-101), ondulata3 (ond3), and hemivenata2 (hve-2) versus Columbia-0 (Col-0) and Landsberg erecta-0 (Ler-0; Semiarti et al., 2001; Alonso-Peral et al., 2006; Robles et al., 2010; Pérez-Pérez et al., 2011). We offer the source code of phenoVein to the public as open-source software that can be further adapted or improved (for details, see “Materials and Methods”).     

Extracellular Vesicles: A Prevalent Tool for Microbial Gene Delivery?

Genetic plasticity of prokaryotic microbial communities is largely dependent on the ongoing exchange of genetic determinants by Horizontal Gene Transfer (HGT). HGT events allow beneficial genetic transitions to occur throughout microbial life, thus promoting adaptation to changing environmental conditions. Here, the significance of secreted vesicles in mediating HGT between microorganisms is discussed, while focusing on the benefits gained by vesicle‐mediated gene delivery and its occurrence under different environmental cues. The potential use of secreted DNA‐harboring vesicles as a mechanism of currently unresolved HGT events in eukaryotic microbes is further discussed.     

The Caenorhabditis elegans A��1�C42 Model of Alzheimer Disease Predominantly Expresses A��3�C42

Transgenic expression of human amyloid β (Aβ) peptide in body wall muscle cells of Caenorhabditis elegans has been used to better understand aspects of Alzheimer disease (AD). In human aging and AD, Aβ undergoes post-translational changes including covalent modifications, truncations, and oligomerization. Amino truncated Aβ is increasingly recognized as potentially contributing to AD pathogenesis. Here we describe surface-enhanced laser desorption ionization-time of flight mass spectrometry mass spectrometry of Aβ peptide in established transgenic C. elegans lines. Surprisingly, the Aβ being expressed is not full-length 1–42 (amino acids) as expected but rather a 3–42 truncation product. In vitro analysis demonstrates that Aβ_3–42 self-aggregates like Aβ_1–42, but more rapidly, and forms fibrillar structures. Similarly, Aβ_3–42 is also the more potent initiator of Aβ_1–40 aggregation. Seeded aggregation via Aβ_3–42 is further enhanced via co-incubation with the transition metal Cu(II). Although unexpected, the C. elegans model of Aβ expression can now be co-opted to study the proteotoxic effects and processing of Aβ_3–42.Numerous studies support a role for aggregating Aβ³ in mediating the toxicity that underlies AD (1, 2). However, several key questions remain central to understanding how AD and Aβ pathology are related. What is the connection between Aβ aggregation and toxicity? Is there a specific toxic Aβ conformation or species? How and why does aging impact on Aβ precipitation? Significant effort to address these questions has been invested in the use of vertebrate and simple invertebrate model organisms to simulate neurodegenerative diseases through transgenic expression of human Aβ (3). From these models, several novel insights into the proteotoxicity of Aβ have been gained (4–7).Human Aβ (e.g. in brain, cerebrospinal fluid, or plasma) is not found as a single species but rather as diverse mixtures of various modified, truncated, and cross-linked forms (8–10). Specific truncations, covalent modifications, and cross-linked oligomers of Aβ have potentially important roles in determining Aβ-associated neurotoxicity. For example, N-terminal truncations of Aβ have increased abundance in AD, rapidly aggregate, and are neurotoxic (9, 11). Furthermore, the N-terminal glutamic acid residue of Aβ_3–42 can be cyclized to pyroglutamate (Aβ_3(pE)-42) (12), which may be particularly important in AD pathogenesis (13, 14). Aβ_3(pE)-42 is a significant fraction of total Aβ in AD brain (15), accounting for more than 50% of Aβ accumulated in plaques (16). Aβ_3(pE)-42 seeds Aβ aggregation (17), confers proteolytic resistance, and is neurotoxic (13). Recently, glutaminyl cyclase (QC) has been proposed to catalyze, in vivo, pyroglutamate formation of Aβ_3(pE)-40/42 (14, 18). Aβ_1–42 itself cannot be cyclized by QC to Aβ_3(pE)-42 (19), unlike Aβ that commences with an N-terminal glutamic acid-residue (e.g. Aβ_3–42 and Aβ_11–42) (20). QC has broad expression in mammalian brain (21, 22), and its inhibition attenuates accumulation of Aβ_3(pE)-42 into plaques and improves cognition in a transgenic mouse model of AD that overexpresses human amyloid precursor protein (14). N-terminal truncations at position 3 have been reported in senile plaques (23, 24); however, the process that generates Aβ_3–42 is unknown. Currently there are no reported animal models of Aβ_3–42 expression.Advances in surface-enhanced laser desorption ionization-time of flight mass spectrometry (SELDI-TOF MS) analysis now facilitate accurate identification of particular Aβ species. Using this technology, we examined well characterized C. elegans transgenic models of AD that develop amyloid aggregates (25, 26) to see whether the human Aβ they express is post-translationally modified.     

A High-throughput Genomic Tool： Diversity Array Technology Complementary for Rice Genotyping

Diversity array technology （DART^TM） was a genotyping tool characterized gel-independent and high throughput. The main purpose of present study is to validate DArT for rice （Oryza sativa L.）genotyping in a high throughput manner. Technically, the main objective was to generate a rice general purpose gene pool, and optimize this genomic tool in order to evaluate rice germplasm genetic diversity. To achieve this, firstly, a generalpurpose DArT array was developed. Ten representatives from 24 varieties were hybridized with the general-purpose array to determine the informativeness of the clones printed on the array. The informative 1 152 clones were re-arrayed on a slide and used to fingerprint 17 of 24 germplasms. Hybridizing targets prepared from the germplasm to be assayed to the DNA array gave DNA fingerprints of germplasms. Raw data were normalized and transformed into binary data, which were then analyzed by using NTSYSpc （Numerical taxonomy system for cluster and ordination analysis, v. 2.02j） software package. The graphically displayed dendrogram derived from the array experimental data was matched with simple sequence repeats genotyping outline and varieties＇ pedigree deviation of the different varieties. Considering DArT is a sequence-independent genotyping approach, it will be applied in studies of the genetic diversity and the gene mapping of diverse of organisms, especially for those crops with less-developed molecular markers.     

PredSL： A Tool for the N-terminal Sequence-based Prediction of Protein Subcellular Localization

The ability to predict the subcellular localization of a protein from its sequence is of great importance, as it provides information about the protein＇s function. We present a computational tool, PredSL, which utilizes neural networks, Markov chains, profile hidden Markov models, and scoring matrices for the prediction of the subcellular localization of proteins in eukaryotic cells from the N-terminal amino acid sequence. It aims to classify proteins into five groups： chloroplast, thylakoid, mitochondrion, secretory pathway, and ＂other＂. When tested in a fivefold cross-validation procedure, PredSL demonstrates 86.7% and 87.1% overall accuracy for the plant and non-plant datasets, respectively. Compared with TargetP, which is the most widely used method to date, and LumenP, the results of PredSL are comparable in most cases. When tested on the experimentally verified proteins of the Saccharomyces cerevisiae genome, PredSL performs comparably if not better than any available algorithm for the same task. Furthermore, PredSL is the only method capable for the prediction of these subcellular localizations that is available as a stand-alone application through the URL： http：//bioinformatics.biol.uoa.gr/PredSL/.     

Peptidomimetics: A Synthetic Tool for Inhibiting Protein–Protein Interactions in Cancer

International Journal of Peptide Research and Therapeutics - Protein–protein interactions (PPI) are vital in modulating biochemical pathways in many biological processes. Inhibiting PPI is a...     

Dimethyl Sulfoxide Is Feasible for Plant Tubulin Assembly In vitro： A Comprehensive Analysis

It is much more difficult for tubulin from plant sources to polymerize in vitro than tubulin from animal sources. Taxol, a most widely used reagent in microtubule studies, enhances plant microtubule assembly, but hinders microtubule dynamics. Dimethyl sulfoxide (DMSO), a widely used reagent in animal microtubule studies, is a good candidate for the investigation of plant microtubule assembly in vitro. However, proper investigation is lacking about the effects of DMSO on plant microtubule assembly in vitro. In the present study, DMSO was used to establish optimal conditions for the polymerization of plant tubulin. Tubulin, purified from lily pollen, polymerizes into microtubules at a critical concentration of 1.2 mg/mL in the presence of 10% DMSO. The polymers appear to have a normal microtubule structure, as revealed by electron microscopy. In the presence of 10% DMSO, microtubule polymerization decreases when the pH of the medium is increased from 6.5 to 7.4. Both the polymerization rate and the mass of the polymers increase as temperature increases from 25 to 40 ℃. Tubulin polymerizes and depolymerizes along with cycling of temperature, from 37 to 4 ℃, or following the addition to or the removal of Ca^2 from the medium. When incubated with nuclei isolated from tobacco BY-2 suspension cells, tubulin assembles onto the nuclear surface in the presence of 10% DMSO. Labeling lily pollen tubulin with 5- (and 6-) carboxytetramethyl-rhodamine succinimidyl ester (NHS-rhodamine) was performed successfully in the presence of 10% DMSO. Labeled tubulin assembles into a radial structure on the surface of BY-2 nuclei. The polymerization of lily pollen tubulin is also enhanced by microtubule-associated proteins from animal sources in the presence of 10% DMSO. All the experimental results indicate that plant tubulin functions normally in the presence of DMSO. Therefore, DMSO is an appropriate reagent for plant tubulin polymerization and investigation of plant microtubules in vitro.