Stimulation of Macrophage Oxygen Free Radical Production and Lymphocyte Blastogenic Response by Immunization with Porins

Porins were prepared from smooth strain of Salmonella typhi 0–901 and chemotype rough mutant of S. typhimurium Ra-30. Porins could significantly stimulate the immune systems of mice. Immunization of mice with the porins provoked synthesis of anti-porin antibodies. Macrophages from the immunized mice showed increased capacity to generate oxygen free radicals, and lymphocytes from these mice showed proliferative response to the porins. Thus porins may play a role in providing protection from salmonellosis by stimulating the antibody production and increasing the capacity of macrophages to generate oxygen free radicals along with stimulation of lymphocytes.     

Murine model of hepatic breast cancer

Background and aimsBreast cancer is the most common cancer in women and the second leading cause of cancer-related deaths in this population. Breast cancer related deaths have declined due to screening and adjuvant therapies, yet a driving clinical need exists to better understand the cause of the deadliest aspect of breast cancer, metastatic disease. Breast cancer metastasizes to several distant organs, the liver being the third most common site. To date, very few murine models of hepatic breast cancer exist.MethodsIn this study, a novel murine model of liver breast cancer using the MDA-MB-231 cell line is introduced as an experimental (preclinical) model.ResultsHistological typing revealed consistent hepatic breast cancer tumor foci. Common features of the murine model were vascular invasion, lung metastasis and peritoneal seeding.ConclusionsThe novel murine model of hepatic breast cancer established in this study provides a tool to be used to investigate mechanisms of hepatic metastasis and to test potential therapeutic interventions.     

Characterization of Staphylococcus epidermidis Polynucleotide phosphorylase and its interactions with ribonucleases RNase J1 and RNase J2

Polynucleotide phosphorylase catalyzes both 3′-5′ exoribonuclease and polyadenylation reactions. The crystal structure of Staphylococcus epidermidis PNPase revealed a bound phosphate in the PH2 domain of each protomer coordinated by three adjacent serine residues. Mutational analysis suggests that phosphate coordination by these serine residues is essential to maintain the catalytic center in an active conformation. We note that PNPase forms a complex with RNase J1 and RNase J2 without substantially altering either exo-ribonuclease or polyadenylation activity of this enzyme. This decoupling of catalytic activity from protein-protein interactions suggests that association of these endo- or exo-ribonucleases with PNPase could be more relevant for cellular localization or concerted targeting of structured RNA for recycling.     

Characterization of somatostatin receptors and associated signaling pathways in pancreas of R6/2 transgenic mice

The present study describes the status of somatostatin receptors (SSTRs) and their colocalization with insulin (β), glucagon (α) and somatostatin (δ) producing cells in the pancreatic islets of 11 weeks old R6/2 Huntington's Disease transgenic (HD tg) and age-matched wild type (wt) mice. We also determined expression of tyrosine hydroxylase (TH), glutamic acid decarboxylase (GAD) and presynaptic marker synaptophysin (SYP) in addition to signal transduction pathways associated with diabetes. In R6/2 mice, islets are relatively smaller in size, exhibit enhanced expression and nuclear inclusion of mHtt along with the loss of insulin, glucagon and somatostatin expression. In comparison to wt, R6/2 mice display enhanced mRNA for all SSTRs except SSTR2. In the pancreatic lysate, SSTR1, 4 and 5 immunoreactivity decreases whereas SSTR3 immunoreactivity increases with no discernible changes in SSTR2 immunoreactivity. Furthermore, at the cellular level, R6/2 mice exhibit a receptor specific distributional pattern of SSTRs like immunoreactivity and colocalization with β, α and δ cells. While GAD expression is increased, TH and SYP immunoreactivity was decreased in R6/2 mice, anticipating a cross-talk between the CNS and pancreas in diabetes pathophysiology. We also dissected out the changes in signaling pathway and found decreased activation and expression of PKA, AKT, ERK1/2 and STAT3 in R6/2 mice pancreas. These findings suggest that the impaired organization of SSTRs within islets may lead to perturbed hormonal regulation and signaling. These interconnected complex events might shed new light on the pathogenesis of diabetes in neurodegenerative diseases and the role of SSTRs in potential therapeutic intervention.     

Selection of relevant features from amino acids enables development of robust classifiers

Machine learning (ML) has been extensively applied to develop models and to understand high-throughput data of biological processes. However, new ML models, trained with novel experimental results, are required to build regularly for more precise predictions. ML methods can build models from numeric data, whereas biological data are generally textual (DNA, protein sequences) or images and needs feature calculation algorithms to generate quantitative features. Programming skills along with domain knowledge are required to develop these algorithms. Therefore, the process of knowledge discovery through ML is decelerated due to lack of generic tools to construct features and to build models directly from the data. Hence, we developed a schema that calculates about 5,000 features, selects relevant features and develops protein classifiers from the training data. To demonstrate the general applicability and robustness of our method, fungal adhesins and nuclear receptor proteins were used for building classifiers which outperformed existing classifiers when tested on independent data. Next, we built a classifier for mitochondrial proteins of Plasmodium falciparum which causes human malaria because the latest corresponding classifiers are not publically accessible. Our classifier attained 98.18 % accuracy and 0.95 Matthews correlation coefficient by fivefold cross-validation and outperformed existing classifiers on independent test set. We implemented this schema as user-friendly and open source application Pro-Gyan (http://code.google.com/p/pro-gyan/), to build and share executable classifiers without programming knowledge.