EhCoactosin Stabilizes Actin Filaments in the Protist Parasite Entamoeba histolytica

Entamoeba histolytica is a protist parasite that is the causative agent of amoebiasis, and is a highly motile organism. The motility is essential for its survival and pathogenesis, and a dynamic actin cytoskeleton is required for this process. EhCoactosin, an actin-binding protein of the ADF/cofilin family, participates in actin dynamics, and here we report our studies of this protein using both structural and functional approaches. The X-ray crystal structure of EhCoactosin resembles that of human coactosin-like protein, with major differences in the distribution of surface charges and the orientation of terminal regions. According to in vitro binding assays, full-length EhCoactosin binds both F- and G-actin. Instead of acting to depolymerize or severe F-actin, EhCoactosin directly stabilizes the polymer. When EhCoactosin was visualized in E. histolytica cells using either confocal imaging or total internal reflectance microscopy, it was found to colocalize with F-actin at phagocytic cups. Over-expression of this protein stabilized F-actin and inhibited the phagocytic process. EhCoactosin appears to be an unusual type of coactosin involved in E. histolytica actin dynamics.     

Microbial biofilm ecology,in silico study of quorum sensing receptor-ligand interactions and biofilm mediated bioremediation

Archives of Microbiology - Biofilms are structured microbial communities of single or multiple populations in which microbial cells adhere to a surface and get embedded in extracellular polymeric...     

Microbiota Dynamics in Patients Treated with Fecal Microbiota Transplantation for Recurrent Clostridium difficile Infection

Clostridium difficile causes antibiotic-associated diarrhea and pseudomembraneous colitis and is responsible for a large and increasing fraction of hospital-acquired infections. Fecal microbiota transplantation (FMT) is an alternate treatment option for recurrent C. difficile infection (RCDI) refractory to antibiotic therapy. It has recently been discussed favorably in the clinical and scientific communities and is receiving increasing public attention. However, short- and long-term health consequences of FMT remain a concern, as the effects of the transplanted microbiota on the patient remain unknown. To shed light on microbial events associated with RCDI and treatment by FMT, we performed fecal microbiota analysis by 16S rRNA gene amplicon pyrosequencing of 14 pairs of healthy donors and RCDI patients treated successfully by FMT. Post-FMT patient and healthy donor samples collected up to one year after FMT were studied longitudinally, including one post-FMT patient with antibiotic-associated relapse three months after FMT. This analysis allowed us not only to confirm prior reports that RCDI is associated with reduced diversity and compositional changes in the fecal microbiota, but also to characterize previously undocumented post-FMT microbiota dynamics. Members of the Streptococcaceae, Enterococcaceae, or Enterobacteriaceae were significantly increased and putative butyrate producers, such as Lachnospiraceae and Ruminococcaceae were significantly reduced in samples from RCDI patients before FMT as compared to post-FMT patient and healthy donor samples. RCDI patient samples showed more case-specific variations than post-FMT patient and healthy donor samples. However, none of the bacterial groups were invariably associated with RCDI or successful treatment by FMT. Overall microbiota compositions in post-FMT patients, specifically abundances of the above-mentioned Firmicutes, continued to change for at least 16 weeks after FMT, suggesting that full microbiota recovery from RCDI may take much longer than expected based on the disappearance of diarrheal symptoms immediately after FMT.     

Combination of sulfamethoxazole and selenium in anticancer therapy: a novel approach

Sulfonamides have been reported to possess substantial antitumor activity as they act as carbonic anhydrase inhibitors. In addition, selenium appears to have a protective effect at various stages of cancer due to its antioxidant property, enhanced carcinogen detoxification, inhibition of cell invasion, and by inhibiting angiogenesis. Here, in the present study we aimed to evaluate and synergize the cytotoxic activity of sulfonamide and selenium (SM+SE) as effective therapy in the treatment of DENA-induced HCC. Hepatocarcinogeneis was induced by a single intraperitoneal injection of diethylnitrosamine (DENA) (200 mg/kg) in phosphate buffer. 30 Male Wistar rats used in this study were divided randomly into five equal groups (n = 6). DENA-administered animals showed significant alteration (p < 0.001) in liver-specific enzymes—glutamate oxaloacetate transaminase (SGOT), serum glutamate pyruvate transaminase (SGPT), alkaline phosphatase (ALP), and Alpha fetoproteins (AFP), and also induced severe histopathological changes in the hepatic tissues. Interestingly, treatment with (SE+SE) (SM 30 mg/kg + SE 3 mg/kg) significantly reduced (P < 0.001, P < 0.001, P < 0.001, P < 0.001) the elevated AFP, SGOT, SGPT, and ALP levels, respectively, suggesting that combination therapy of SM+SE has a potential to treat DENA-induced liver damage.     

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.     

A protocol for culturing Drosophila melanogaster stage 9 egg chambers for live imaging

This protocol describes a method for the dissection of egg chambers from intact Drosophila females and culture conditions that permit live imaging of them, with a particular emphasis on stage 9. This stage of development is characterized by oocyte growth and patterning, outer follicle cell rearrangement and migration of border cells. Although in vitro culture of egg chambers of later developmental stages has long been possible, until recently stage 9 egg chambers could only be kept alive for short periods, did not develop normally, and border cell migration failed entirely. We have established culture conditions that support overall egg chamber development including border cell migration in vitro. This protocol makes possible direct observation of molecular and cellular dynamics in both wild-type and mutant egg chambers, and opens the door to testing of pharmacological inhibitors and the use of biosensors. The entire protocol takes approximately 24 h while the preparation of egg chambers for live imaging requires only 15-20 min.     

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.     

Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum

Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.