The Beta Cell in Its Cluster: Stochastic Graphs of Beta Cell Connectivity in the Islets of Langerhans

Pancreatic islets of Langerhans consist of endocrine cells, primarily α, β and δ cells, which secrete glucagon, insulin, and somatostatin, respectively, to regulate plasma glucose. β cells form irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Due to the central functional significance of this local connectivity in the placement of β cells in an islet, it is important to characterize it quantitatively. However, quantification of the seemingly stochastic cytoarchitecture of β cells in an islet requires mathematical methods that can capture topological connectivity in the entire β-cell population in an islet. Graph theory provides such a framework. Using large-scale imaging data for thousands of islets containing hundreds of thousands of cells in human organ donor pancreata, we show that quantitative graph characteristics differ between control and type 2 diabetic islets. Further insight into the processes that shape and maintain this architecture is obtained by formulating a stochastic theory of β-cell rearrangement in whole islets, just as the normal equilibrium distribution of the Ornstein-Uhlenbeck process can be viewed as the result of the interplay between a random walk and a linear restoring force. Requiring that rearrangements maintain the observed quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that β-cell rearrangement is dependent on its connectivity in order to maintain an optimal cluster size in both normal and T2D islets.     

Effect of hormones and antihormones on phospholipase A2 activity in human endometrial stromal cells

Phospholipase A₂ activity was studied in isolated human endometrial predecidual cells, and in human endometrium collected from day 19–23 of the menstrual cycle, by performing a radiochemical assay. Phospholipase A₂ activity on day 20 was significantly higher than other days (P < 0.001), and the activity was found to gradually decrease after day 20 of the menstrual cycle. The effects of the hormones estradiol and progesterone, and antihormones tamoxifen and RU 486, were studied on the phospholipase A₂ activity in isolated predecidual stromal cells. Estradiol produced a significant stimulatory effect (P < 0.001) on phospholipase A₂ activity in predecidual cells, and this effect was antagonized by tamoxifen. The combination of estradiol and tamoxifen was significantly different from estradiol alone (P < 0.001), but not from tamoxifen alone. RU 486 alone significantly increased (P < 0.001) phospholipase A₂ activity in predecidual stromal cells. However, progesterone had no effect on phospholipase A₂ activity in predecidual stromal cells.     

Nutrients determine the spatial architecture of Paracoccus sp. biofilm

Bacterial biofilms adapt and shape their structure in response to varied environmental conditions. A statistical methodology was adopted in this study to empirically investigate the influence of nutrients on biofilm structural parameters deduced from confocal scanning laser microscope images of Paracoccus sp.W1b, a denitrifying bacterium. High concentrations of succinate, Mg⁺⁺, Ca⁺⁺, and Mn⁺⁺ were shown to enhance biofilm formation whereas higher concentration of iron decreased biofilm formation. Biofilm formed at high succinate was uneven with high surface to biovolume ratio. Higher Mg⁺⁺ or Ca⁺⁺ concentrations induced cohesion of biofilm cells, but contrasting biofilm architectures were detected. Biofilm with subpopulation of pillar-like protruding cells was distributed on a mosaic form of monolayer cells in medium with 10 mM Mg⁺⁺. 10 mM Ca⁺⁺ induced a dense confluent biofilm. Denitrification activity was significantly increased in the Mg⁺⁺- and Ca⁺⁺-induced biofilms. Chelator treatment of various biofilm ages indicated that divalent cations are important in the initial stages of biofilm formation.     

SD-8, a novel therapeutic agent active against multidrug-resistant Gram positive cocci

Anti-bacterial drug resistance is one of the most critical concerns among the scientist worldwide. The novel antimicrobial decapeptide SD-8 is designed and its minimal inhibitory concentration and therapeutic index (TI) was found in the range of 1–8 μg/ml and 45–360, respectively, against major group of Gram positive pathogens (GPP). The peptide was also found to be least hemolytic at a concentration of 180 μg/ml, i.e., nearly 77 times higher than its average effective concentration. The kinetics assay showed that the killing time is 120 min for methicillin-sensitive Staphylococcus aureus (MSSA) and 90 min for methicillin-resistant S. aureus (MRSA). Membrane permeabilization is the cause of peptide antimicrobial activity as shown by the transmission electron microscopy studies. The peptide showed the anti-inflammatory property by inhibiting COX-2 with a K _D and K _i values of 2.36 × 10⁻⁹ and 4.8 × 10⁻⁸ M, respectively. The peptide was also found to be effective in vivo as derived from histopathological observations in a Staphylococcal skin infection rat model with MRSA as causative organism.     

Paxillin family members function as Csk-binding proteins that regulate Lyn activity in human and murine platelets

SFKs (Src family kinases) contribute importantly to platelet function in haemostasis. SFK activity is controlled by Csk (C-terminal Src kinase), which phosphorylates a C-terminal tyrosine residue on SFKs, resulting in inhibition of SFK activity. Csk is recruited to sites of SFK activity by tyrosine-phosphorylated Csk-binding proteins. Paxillin, a multidomain adaptor protein, has been shown to act as a Csk-binding protein and to inhibit Src activity during growth factor signalling. Human platelets express Hic-5, a member of the paxillin family; however, its ability to act as a Csk-binding protein has not been characterized. We sought to identify and characterize the ability of paxillin family members to act as Csk-binding proteins during platelet activation. We found that murine and human platelets differ in the complement of paxillin family members expressed. Human platelets express Hic-5, whereas murine platelets express paxillin and leupaxin in addition to Hic-5. In aggregating human platelets, Hic-5 was tyrosine phosphorylated and recruited Csk via its SH2 domains. In aggregating murine platelets, however, Csk bound preferentially to paxillin, even though both paxillin and Hic-5 were abundantly present and became tyrosine phosphorylated. The SFK Lyn, but not Src or Fyn, was associated with paxillin family members in resting and aggregated human and murine platelets. Lyn, however, was phosphorylated on its C-terminal inhibitory tyrosine residue only following platelet aggregation, which was coincident with recruitment of Csk to paxillin and/or Hic-5 in a manner dependent on prior alpha(IIb)beta3 engagement. These observations support the notion that Hic-5 and paxillin function as negative feedback regulators of SFKs in aggregated platelets and that, when both are present, paxillin is preferentially used.     

HPCA1 and HSL3: two plasma membrane proteins that probably cooperate to modulate H2O2 signalling under drought conditions

Plant Growth Regulation - Ion transporters are essential for plant growth and development, and play key roles not only in acquisition/ transportation of essential ions from the surrounding and...     

Structure–sequence features based prediction of phosphosites of serine/threonine protein kinases of Mycobacterium tuberculosis

Elucidation of signaling events in a pathogen is potentially important to tackle the infection caused by it. Such events mediated by protein phosphorylation play important roles in infection, and therefore, to predict the phosphosites and substrates of the serine/threonine protein kinases, we have developed a Machine learning-based approach for Mycobacterium tuberculosis serine/threonine protein kinases using kinase-peptide structure–sequence data. This approach utilizes features derived from kinase three-dimensional-structure environment and known phosphosite sequences to generate support vector machine (SVM)-based kinase-specific predictions of phosphosites of serine/threonine protein kinases (STPKs) with no or scarce data of their substrates. SVM outperformed the four machine learning algorithms we tried (random forest, logistic regression, SVM, and k-nearest neighbors) with an area under the curve receiver-operating characteristic value of 0.88 on the independent testing dataset and a 10-fold cross-validation accuracy of ~81.6% for the final model. Our predicted phosphosites of M. tuberculosis STPKs form a useful resource for experimental biologists enabling elucidation of STPK mediated posttranslational regulation of important cellular processes.