Hypocholesterolemic Effect of Potent Peptide and Bioactive Fraction from Pigeon Pea By-Products in Wistar Rats

International Journal of Peptide Research and Therapeutics - Elevation high plasma cholesterol plays a significant role in promoting the incidence of atherosclerosis and coronary heart disease. In...     

Black Spot: a platform for automated and rapid estimation of leaf area from scanned images

Leaf area and its derivatives (e.g. specific leaf area) are widely used in ecological assessments, especially in the fields of plant–animal interactions, plant community assembly, ecosystem functioning and global change. Estimating leaf area is highly time-consuming, even when using specialized software to process scanned leaf images, because manual inputs are invariably required for scale detection and leaf surface digitisation. We introduce Black Spot Leaf Area Calculator (hereafter, Black Spot), a technique and stand-alone software package for rapid and automated leaf area assessment from images of leaves taken with standard flatbed scanners. Black Spot operates on comprehensive rule-sets for colour band ratios to carry out pixel-based classification which isolates leaf surfaces from the image background. Importantly, the software extracts information from associated image meta-data to detect image scale, thereby eliminating the need for time-consuming manual scale calibration. Black Spot’s output provides the user with estimates of leaf area as well as classified images for error checking. We tested this method and software combination on a set of 100 leaves of 51 different plant species collected from the field. Leaf area estimates generated using Black Spot and by manual processing of the images using an image editing software generated statistically identical results. Mean error rate in leaf area estimates from Black Spot relative to manual processing was ?0.4 % (SD = 0.76). The key advantage of Black Spot is the ability to rapidly batch process multi-species datasets with minimal user effort and at low cost, thus making it a valuable tool for field ecologists.     

Deep sequencing of the oral microbiome reveals signatures of periodontal disease

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ~90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.     

Concentration Sensing by the Moving Nucleus in Cell Fate Determination: A Computational Analysis

During development of the vertebrate neuroepithelium, the nucleus in neural progenitor cells (NPCs) moves from the apex toward the base and returns to the apex (called interkinetic nuclear migration) at which point the cell divides. The fate of the resulting daughter cells is thought to depend on the sampling by the moving nucleus of a spatial concentration profile of the cytoplasmic Notch intracellular domain (NICD). However, the nucleus executes complex stochastic motions including random waiting and back and forth motions, which can expose the nucleus to randomly varying levels of cytoplasmic NICD. How nuclear position can determine daughter cell fate despite the stochastic nature of nuclear migration is not clear. Here we derived a mathematical model for reaction, diffusion, and nuclear accumulation of NICD in NPCs during interkinetic nuclear migration (INM). Using experimentally measured trajectory-dependent probabilities of nuclear turning, nuclear waiting times and average nuclear speeds in NPCs in the developing zebrafish retina, we performed stochastic simulations to compute the nuclear trajectory-dependent probabilities of NPC differentiation. Comparison with experimentally measured nuclear NICD concentrations and trajectory-dependent probabilities of differentiation allowed estimation of the NICD cytoplasmic gradient. Spatially polarized production of NICD, rapid NICD cytoplasmic consumption and the time-averaging effect of nuclear import/export kinetics are sufficient to explain the experimentally observed differentiation probabilities. Our computational studies lend quantitative support to the feasibility of the nuclear concentration-sensing mechanism for NPC fate determination in zebrafish retina.     

Nitrogen fixation ability explains leaf chemistry and arbuscular mycorrhizal responses to fertilization

Atmospheric nitrogen (N) and phosphorus (P) deposition rates are predicted to drastically increase in the coming decades. The ecosystem level consequences of these increases will depend on how plant tissue nutrient concentrations, stoichiometry and investment in nutrient uptake mechanisms such as arbuscular mycorrhizal fungi (AMF) change in response to increased nutrient availability, and how responses differ between plant functional types. Using a factorial nutrient addition experiment with seedlings of multiple N-fixing and non-N-fixing tree species, we examined whether leaf chemistry and AMF responses differ between these dominant woody plant functional groups of tropical savanna and dry forest ecosystems. We found that N-fixers have remarkably stable foliar chemistry that stays constant with external input of nutrients. Non-N-fixers responded to N and N + P addition by increasing both concentrations and total amounts of foliar N, but showed a corresponding decrease in P concentrations while total amounts of foliar P stayed constant, suggesting a ‘dilution’ of tissue P with increased N availability. Non-N-fixers also showed an increase in N:P ratios with N and N + P addition, probably driven by both an increase in N and a decrease in P concentrations. AMF colonization decreased with N + P addition in non-N-fixers and increased with N and N + P addition in N-fixers, suggesting differences in their nutrient acquisition roles in the two plant functional groups. Our results suggest that N-fixers and non-N-fixers can differ significantly in their responses to N and P deposition, with potential consequences for future nutrient and carbon cycling in savanna and dry forest ecosystems.     

A novel role of BELL1-like homeobox genes, PENNYWISE and POUND-FOOLISH, in floral patterning

Flowers are determinate shoots comprised of perianth and reproductive organs displayed in a whorled phyllotactic pattern. Floral organ identity genes display region-specific expression patterns in the developing flower. In Arabidopsis, floral organ identity genes are activated by LEAFY (LFY), which functions with region-specific co-regulators, UNUSUAL FLORAL ORGANS (UFO) and WUSCHEL (WUS), to up-regulate homeotic genes in specific whorls of the flower. PENNYWISE (PNY) and POUND-FOOLISH (PNF) are redundant functioning BELL1-like homeodomain proteins that are expressed in shoot and floral meristems. During flower development, PNY functions with a co-repressor complex to down-regulate the homeotic gene, AGAMOUS (AG), in the outer whorls of the flower. However, the function of PNY as well as PNF in regulating floral organ identity in the central whorls of the flower is not known. In this report, we show that combining mutations in PNY and PNF enhance the floral patterning phenotypes of weak and strong alleles of lfy, indicating that these BELL1-like homeodomain proteins play a role in the specification of petals, stamens and carpels during flower development. Expression studies show that PNY and PNF positively regulate the homeotic genes, APETALA3 and AG, in the inner whorls of the flower. Moreover, PNY and PNF function in parallel with LFY, UFO and WUS to regulate homeotic gene expression. Since PNY and PNF interact with the KNOTTED1-like homeodomain proteins, SHOOTMERISTEMLESS (STM) and KNOTTED-LIKE from ARABIDOPSIS THALIANA2 (KNAT2) that regulate floral development, we propose that PNY/PNF-STM and PNY/PNF-KNAT2 complexes function in the inner whorls to regulate flower patterning events.