Occurrence and stability of anion–π interactions between phosphate and nucleobases in functional RNA molecules

We present a systematic structural and energetic characterization of phosphate(OP)–nucleobase anion…π stacking interactions in RNAs. We observed OP–nucleobase stacking contacts in a variety of structural motifs other than regular helices and spanning broadly diverse sequence distances. Apart from the stacking between a phosphate and a guanine or a uracil two-residue upstream in specific U-turns, such interactions in RNA have been scarcely characterized to date. Our QM calculations showed an energy minimum at a distance between the OP atom and the nucleobase plane centroid slightly below 3 Å for all the nucleobases. By sliding the OP atom over the nucleobase plane we localized the optimal mutual positioning of the stacked moieties, corresponding to an energy minimum below -6 kcal•mol⁻¹, for all the nucleobases, consistently with the projections of the OP atoms over the different π-rings we observed in experimental occurrences. We also found that the strength of the interaction clearly correlates with its electrostatic component, pointing to it as the most relevant contribution. Finally, as OP–uracil and OP–guanine interactions represent together 86% of the instances we detected, we also proved their stability under dynamic conditions in model systems simulated by state-of-the art DFT-MD calculations.     

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.     

Immune Response to Dengue Virus Infection in Pediatric Patients in New Delhi,India—Association of Viremia,Inflammatory Mediators and Monocytes with Disease Severity

Dengue virus, a mosquito-borne flavivirus, is a causative agent for dengue infection, which manifests with symptoms ranging from mild fever to fatal dengue shock syndrome. The presence of four serotypes, against which immune cross-protection is short-lived and serotype cross-reactive antibodies that might enhance infection, pose a challenge to further investigate the role of virus and immune response in pathogenesis. We evaluated the viral and immunological factors that correlate with severe dengue disease in a cohort of pediatric dengue patients in New Delhi. Severe dengue disease was observed in both primary and secondary infections. Viral load had no association with disease severity but high viral load correlated with prolonged thrombocytopenia and delayed recovery. Severe dengue cases had low Th1 cytokines and a concurrent increase in the inflammatory mediators such as IL-6, IL-8 and IL-10. A transient increase in CD14⁺CD16⁺ intermediate monocytes was observed early in infection. Sorting of monocytes from dengue patient peripheral blood mononuclear cells revealed that it is the CD14+ cells, but not the CD16+ or the T or B cells, that were infected with dengue virus and were major producers of IL-10. Using the Boruta algorithm, reduced interferon-α levels and enhanced aforementioned pro-inflammatory cytokines were identified as some of the distinctive markers of severe dengue. Furthermore, the reduction in the levels of IL-8 and IL-10 were identified as the most significant markers of recovery from severe disease. Our results provide further insights into the immune response of children to primary and secondary dengue infection and help us to understand the complex interplay between the intrinsic factors in dengue pathogenesis.     

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.