Towards an efficient computational mining approach to identify EST-SSR markers

Microsatellites are the markers of choice due to their high abundance reproducibility, degree of polymorphism and co-dominant nature. These are mainly used for studying the genetic variability in different species and Marker assisted selection. Expressed Sequence Tags (ESTs) serve as the main resource for Simple Sequence Repeats (SSRs). The computational approach for detecting SSRs and developing SSR markers from EST-SSRs is preferred over the conventional methods as it reduces time and cost to a great extent. The available EST sequence databases, various web interfaces and standalone tools provide the platform for an easy analysis of the EST sequences leading to the development of potential EST-SSR Markers. This paper is an overview of in silico approach to develop SSR Markers from the EST sequence using some of the most efficient tools that are available freely for academic purpose.     

Therapeutic siRNA silencing in inflammatory monocytes in mice

Excessive and prolonged activity of inflammatory monocytes is a hallmark of many diseases with an inflammatory component. In such conditions, precise targeting of these cells could be therapeutically beneficial while sparing many essential functions of the innate immune system, thus limiting unwanted effects. Inflammatory monocytes-but not the noninflammatory subset-depend on the chemokine receptor CCR2 for localization to injured tissue. Here we present an optimized lipid nanoparticle and a CCR2-silencing short interfering RNA that, when administered systemically in mice, show rapid blood clearance, accumulate in spleen and bone marrow, and localize to monocytes. Efficient degradation of CCR2 mRNA in monocytes prevents their accumulation in sites of inflammation. Specifically, the treatment attenuates their number in atherosclerotic plaques, reduces infarct size after coronary artery occlusion, prolongs normoglycemia in diabetic mice after pancreatic islet transplantation, and results in reduced tumor volumes and lower numbers of tumor-associated macrophages.     

Soy‐Derived Phytochemical Genistein Modifies Chromatome Topology to Restrict Cancer Cell Proliferation

Epidemiological data indicate that human cancer risk is significantly reduced by the consumption of soy‐based foods containing the “phytoestrogen” genistein, which can signal via host cell estrogen receptors. While additional chemoprotective effects of genistein induced by epigenetic factors have also been reported, the key molecules and mechanisms involved are poorly defined. We therefore investigated genistein effects on chromatin‐bound proteins in the estrogen receptor‐deficient cell line MDA‐MB‐231 which is insensitive to phytoestrogen signaling. After exposure to low‐dose genistein for >1 month, MDA‐MB‐231 cells exhibited stable epigenetic alterations that are analyzed via partial MNase digestion and TMT‐based quantitative proteomics. 3177 chromatin‐bound proteins are identified with high confidence, including 882 molecules that displayed altered binding topology after cell conditioning with genistein. Prolonged phytochemical exposure conferred heritable changes in the binding topology of key epigenetic regulators including ATRX, SUV39H1/H2, and HP1BP3 that are preserved in untreated progeny, resulting in sustained downregulation of proliferation genes and reduced cell growth. These data indicate that soy derivative genistein exerts complex estrogen receptor‐independent effects on the epigenome likely to influence tumorigenesis by restricting cell growth.     

Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato

An artificial neural network (ANN) was used to analyze photometric features extracted from the digitized images of leaves from in vitro-regenerated potato plants for non-invasive estimation of chlorophyll content. A MATLAB®-based, feed-forward, backpropagation-type network was developed for an input layer (three input elements), with one hidden layer (one node) and one output layer representing the predicted chlorophyll content. A significant influence of training function during optimization of ANN modeling was observed. Among the 11 training functions tested, “trainlm” was found to be the best on the basis of comparative analysis of root-mean-square error (RMSE) at zero epoch. A significant correlation between the model-predicted and Soil-Plant Analysis Development (SPAD) meter-measured relative chlorophyll contents was obtained when the mean brightness ratio (rgb) parameters were used. Compared to a red (R), green (G), and blue (B) color space model, the rgb model exhibited better performance with a significant correlation (R ² = 0.85). Incorporation of photometric features, such as luminosity (L), blue (B)/L, and green (G)/L, with rgb failed to improve the performance of the network. The developed Intelligent image analysis (IIA) system was able to estimate in real time the chlorophyll content of in vitro-regenerated leaves for assessment of plant nutrient status during micropropagation.     

Kinetic asymmetry as a key source of functional diversity in biochemical networks

From the analysis of the dynamic properties of various symmetric and asymmetric kinetic schemes, the present report demonstrates that all kinetic schemes, which can be hypothetically divided into two equal halves about an axis of mirror symmetry, are endowed with structural metastability under mass-closed conditions. In mass-closed symmetric schemes, absolute symmetry in reaction conditions in two halves is essential for the occurrence of ordered dynamic behaviour. Even an infinitesimal deviation from the symmetry relations instantaneously drives such systems from limit-cycles to turbulence. Reaction schemes with no axes of symmetry may exhibit a large variety of complex, structurally stable temporal order for wide ranges of values of system parameters and variables. Kinetic asymmetry, therefore, may confer to biochemical networks the functional diversity as well as stability against environmental perturbations.     

The regions of securin and cyclin B proteins recognized by the ubiquitination machinery are natively unfolded

The proteins securin and cyclin B are destroyed in mitosis by the ubiquitin/proteasome system. This destruction is important to mitotic progression. The N-terminal regions of these proteins contain the sequence features recognized by the ubiquitination system. We have demonstrated using circular dichroism and 1-D and 2-D nuclear magnetic resonance that these rather substantial regions are natively unfolded. Based on these findings, we propose a model that helps to explain previously enigmatic observations.