Identification of key amino acid residues in the catalytic mechanism of diaminopropionate ammonialyase from Salmonella typhimurium

Diaminopropionate ammonialyase (DAPAL), a fold-typeII pyridoxal 5-phosphate-dependent enzyme, catalyzes the ,-elimination of diaminopropionate (DAP) to pyruvate and ammonia. DAPAL was able to utilize both d- and l-DAP as substrates with almost equal efficiency. Mutational analysis of functionally important residues such as Thr385, Asp125 and Asp194 was carried out to understand the mechanism by which the isomers are hydrolyzed. Further, the putative residues involved in the formation of disulfide bond Cys271 and Cys299 were also mutated. T385S, T385D sDAPAL were as active with dl-DAP as substrate as sDAPAL, whereas the later exhibited a threefold increase in catalytic efficiency with d-Ser as substrate. Further analysis of these mutants suggested that DAPAL might follow an anti-E-2 mechanism of catalysis that does not involve the formation of a quinonoid intermediate. Of the two mutants of Asp125, D125E showed complete loss of activity with d-DAP as substrate, whereas the reaction with l-DAP was not affected significantly, demonstrating that Asp125 was essential for abstraction of protons from the d-isomer. By contrast, mutational analysis of Asp194 showed that the residue may not be directly involved in proton abstraction from l-DAP. sDAPAL does not form a disulfide bond in solution, although the position of Cys299 and Cys271 in the modeled structure of sDAPAL favored the formation of a disulfide bond. Further, unlike eDAPAL, sDAPAL could be activated by monovalent cations. Mutation of the cysteine residues showed that Cys271 may be involved in coordinating the monovalent cation, as observed in the case of other fold-typeII enzymes.

A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-kappa B nexus

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n = 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kappa B pathway association with high-risk group. NF-kappa B inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kappa B targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kappa B activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kappa B-activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kappa B pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. (C) 2013 AACR.

A scaled unscented transformation based directed Gaussian sum filter for nonlinear dynamic system identification

Impoverishment of particles, i.e. the discretely simulated sample paths of the process dynamics, poses a major obstacle in employing the particle filters for large dimensional nonlinear system identification. A known route of alleviating this impoverishment, i.e. of using an exponentially increasing ensemble size vis-a-vis the system dimension, remains computationally infeasible in most cases of practical importance. In this work, we explore the possibility of unscented transformation on Gaussian random variables, as incorporated within a scaled Gaussian sum stochastic filter, as a means of applying the nonlinear stochastic filtering theory to higher dimensional structural system identification problems. As an additional strategy to reconcile the evolving process dynamics with the observation history, the proposed filtering scheme also modifies the process model via the incorporation of gain-weighted innovation terms. The reported numerical work on the identification of structural dynamic models of dimension up to 100 is indicative of the potential of the proposed filter in realizing the stated aim of successfully treating relatively larger dimensional filtering problems. (C) 2013 Elsevier Ltd. All rights reserved.

Iterated gain-based stochastic filters for dynamic system identification

We propose a novel form of nonlinear stochastic filtering based on an iterative evaluation of a Kalman-like gain matrix computed within a Monte Carlo scheme as suggested by the form of the parent equation of nonlinear filtering (Kushner-Stratonovich equation) and retains the simplicity of implementation of an ensemble Kalman filter (EnKF). The numerical results, presently obtained via EnKF-like simulations with or without a reduced-rank unscented transformation, clearly indicate remarkably superior filter convergence and accuracy vis-a-vis most available filtering schemes and eminent applicability of the methods to higher dimensional dynamic system identification problems of engineering interest. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Identification and characterization of novel indole based small molecules as anticancer agents through SIRT1 inhibition

In our pursuit to develop new potential anticancer leads, we designed a combination of structural units of indole and substituted triazole; and a library of 1-{1-methyl-2-4-phenyl-5-(propan-2-ylsulfanyl)-4H-1,2,4-triazol-3-yl ]-1H-indol-3-yl}methanamine derivatives was synthesized and characterized. Cytotoxic evaluations of these molecules over a panel of three human cancer cell lines were carried out. Few molecules exhibited potent growth inhibitory action against the treated cancer cell lines at lower micro molar concentration. An in vitro assay investigation of these active compounds using recombinant human SIRT1 enzyme showed that one of the compounds (IT-14) inhibited the deacetylation activity of the enzyme. The in vivo study of IT-14 exemplified its promising action by reducing the prostate weight to the body weight ratio in prostate hyperplasia animal models. A remarkable decrease in the disruption of histoarchitecture of the prostate tissues isolated from IT-14 treated animal compared to that of the positive control was observed. The molecular interactions with SIRT1 enzyme were also supported by molecular docking simulations. Hence this compound can act as a lead molecule to treat prostatic hyperplasia. (C) 2013 Elsevier Masson SAS. All rights reserved.

A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification

A Monte Carlo filter, based on the idea of averaging over characteristics and fashioned after a particle-based time-discretized approximation to the Kushner-Stratonovich (KS) nonlinear filtering equation, is proposed. A key aspect of the new filter is the gain-like additive update, designed to approximate the innovation integral in the KS equation and implemented through an annealing-type iterative procedure, which is aimed at rendering the innovation (observation prediction mismatch) for a given time-step to a zero-mean Brownian increment corresponding to the measurement noise. This may be contrasted with the weight-based multiplicative updates in most particle filters that are known to precipitate the numerical problem of weight collapse within a finite-ensemble setting. A study to estimate the a-priori error bounds in the proposed scheme is undertaken. The numerical evidence, presently gathered from the assessed performance of the proposed and a few other competing filters on a class of nonlinear dynamic system identification and target tracking problems, is suggestive of the remarkably improved convergence and accuracy of the new filter. (C) 2013 Elsevier B.V. All rights reserved.

Identification of heat shock factor binding protein in Plasmodium falciparum

Background: Heat shock factor binding protein (HSBP) was originally discovered in a yeast two-hybrid screen as an interacting partner of heat shock factor (HSF). It appears to be conserved in all eukaryotes studied so far, with yeast being the only exception. Cell biological analysis of HSBP in mammals suggests its role as a negative regulator of heat shock response as it appears to interact with HSF only during the recovery phase following exposure to heat stress. While the identification of HSF in the malaria parasite is still eluding biologists, this study for the first time, reports the presence of a homologue of HSBP in Plasmodium falciparum. Methods: PfHSBP was cloned and purified as his-tag fusion protein. CD (Circular dichroism) spectroscopy was performed to predict the secondary structure. Immunoblots and immunofluorescence approaches were used to study expression and localization of HSBP in P. falciparum. Cellular fractionation was performed to examine subcellular distribution of PfHSBP. Immunoprecipitation was carried out to identify HSBP interacting partner in P. falciparum. Results: PfHSBP is a conserved protein with a high helical content and has a propensity to form homo-oligomers. PfHSBP was cloned, expressed and purified. The in vivo protein expression profile shows maximal expression in trophozoites. The protein was found to exist in oligomeric form as trimer and hexamer. PfHSBP is predominantly localized in the parasite cytosol, however, upon heat shock, it translocates to the nucleus. This study also reports the interaction of PfHSBP with PfHSP70-1 in the cytoplasm of the parasite. Conclusions: This study emphasizes the structural and biochemical conservation of PfHSBP with its mammalian counterpart and highlights its potential role in regulation of heat shock response in the malaria parasite. Analysis of HSBP may be an important step towards identification of the transcription factor regulating the heat shock response in P. falciparum.

Titanium promoted reduction of imines with Grignards, silanes, and zinc: identification of a new mechanism with silanes

Aldimines react with reducing agents, such as Grignards, phenylsilane or zinc in the presence of titanium(IV) isopropoxide to form amines and reductively coupled imines (diamines). Using deuterium labeled reagents, the mechanism of reduction to form amines is described. Reducing agents, such as the Grignard and zinc result in the formation of low valent titanium (LVT), which in turn reduces the imine. On the other hand, phenylsilane reacts by a distinctly different mechanism and where a hydrogen atom from silicon is directly transferred to the titanium coordinated imine. (c) 2014 Elsevier Ltd. All rights reserved.

Identification of miRNAs from French bean (Phaseolus vulgaris) under low nitrate stress

Objective: In this study, we report the role of miRNAs involved under nitrogen starvation from widely grown vegetable crop, French bean. In recent years, a great deal of attention has been paid to the elucidation of miRNAs involved in low nitrate stress. Methods: To identify miRNAs expressed under stress, cDNA libraries were analyzed. Results: We reported the nine potential miRNAs with 67 targets involved in nutrient transporters and other stress specific genes. Among the miRNA sequences obtained 6 sequences belong to miR172 family, one with miR169. RT-PCR analysis of expression of miR172 family was induced upon low nitrate stress while miR169 family was repressed. In addition, Pvu-SN7b and Pvu-miR16 may be new members of miRNA172 and miR169 families, respectively. Conclusion: The targets of Pvu-SN7b were major protein kinases, one among which is the Protein Kinase CK2. CK2 Kinase is found to involve in transcription-directed signaling, gene control and cell-cycle regulation. Other targets of Pvu-SN7b were involved in DNA-dependent transcription regulation, photo-periodism, calcium-mediated signaling. Pvu-miR16 targets Thymidine kinase, the key enzyme of deoxy-nucleotide synthesis. The cleavage of these targets affects cell proliferation there by affecting nodule formation. Pvu-miR8 inhibits translation of its target protein Pre-protein translocase, a membrane-bound protein transporter involved in trans-membrane protein transportation. Together these results denote the response and role of miRNAs to nitrate-limiting conditions in French bean.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Role of Vertex Index in Substructure Identification and Activity Prediction: A Study on Antitubercular Activity of a Series of Acid Alkyl Ester Derivatives

Tuberculosis (TB) is a life threatening disease caused due to infection from Mycobacterium tuberculosis (Mtb). That most of the TB strains have become resistant to various existing drugs, development of effective novel drug candidates to combat this disease is a need of the day. In spite of intensive research world-wide, the success rate of discovering a new anti-TB drug is very poor. Therefore, novel drug discovery methods have to be tried. We have used a rule based computational method that utilizes a vertex index, named `distance exponent index (D-x)' (taken x = -4 here) for predicting anti-TB activity of a series of acid alkyl ester derivatives. The method is meant to identify activity related substructures from a series a compounds and predict activity of a compound on that basis. The high degree of successful prediction in the present study suggests that the said method may be useful in discovering effective anti-TB compound. It is also apparent that substructural approaches may be leveraged for wide purposes in computer-aided drug design.

Identification of a novel AMPK-PEA15 axis in the anoikis-resistant growth of mammary cells

Introduction: Matrix detachment triggers anoikis, a form of apoptosis, in most normal epithelial cells, while acquisition of anoikis resistance is a prime requisite for solid tumor growth. Of note, recent studies have revealed that a small population of normal human mammary epithelial cells (HMECs) survive in suspension and generate multicellular spheroids termed `mammospheres'. Therefore, understanding how normal HMECs overcome anoikis may provide insights into breast cancer initiation and progression. Methods: Primary breast tissue-derived normal HMECs were grown as adherent monolayers or mammospheres. The status of AMP-activated protein kinase (AMPK) and PEA15 signaling was investigated by immunoblotting. Pharmacological agents and an RNA interference (RNAi) approach were employed to gauge their roles in mammosphere formation. Immunoprecipitation and in vitro kinase assays were undertaken to evaluate interactions between AMPK and PEA15. In vitro sphere formation and tumor xenograft assays were performed to understand their roles in tumorigenicity. Results: In this study, we show that mammosphere formation by normal HMECs is accompanied with an increase in AMPK activity. Inhibition or knockdown of AMPK impaired mammosphere formation. Concomitant with AMPK activation, we detected increased Ser(116) phosphorylation of PEA15, which promotes its anti-apoptotic functions. Inhibition or knockdown of AMPK impaired PEA15 Ser(116) phosphorylation and increased apoptosis. Knockdown of PEA15, or overexpression of the nonphosphorylatable S116A mutant of PEA15, also abrogated mammosphere formation. We further demonstrate that AMPK directly interacts with and phosphorylates PEA15 at Ser(116) residue, thus identifying PEA15 as a novel AMPK substrate. Together, these data revealed that AMPK activation facilitates mammosphere formation by inhibition of apoptosis, at least in part, through Ser(116) phosphorylation of PEA15. Since anoikis resistance plays a critical role in solid tumor growth, we investigated the relevance of these findings in the context of breast cancer. Significantly, we show that the AMPK-PEA15 axis plays an important role in the anchorage-independent growth of breast cancer cells both in vitro and in vivo. Conclusions: Our study identifies a novel AMPK-PEA15 signaling axis in the anchorage-independent growth of both normal and cancerous mammary epithelial cells, suggesting that breast cancer cells may employ mechanisms of anoikis resistance already inherent within a subset of normal HMECs. Thus, targeting the AMPK-PEA15 axis might prevent breast cancer dissemination and metastasis.

Iterated stochastic filters with additive updates for dynamic system identification: Annealing-type iterations and the filter bank

A nonlinear stochastic filtering scheme based on a Gaussian sum representation of the filtering density and an annealing-type iterative update, which is additive and uses an artificial diffusion parameter, is proposed. The additive nature of the update relieves the problem of weight collapse often encountered with filters employing weighted particle based empirical approximation to the filtering density. The proposed Monte Carlo filter bank conforms in structure to the parent nonlinear filtering (Kushner-Stratonovich) equation and possesses excellent mixing properties enabling adequate exploration of the phase space of the state vector. The performance of the filter bank, presently assessed against a few carefully chosen numerical examples, provide ample evidence of its remarkable performance in terms of filter convergence and estimation accuracy vis-a-vis most other competing filters especially in higher dimensional dynamic system identification problems including cases that may demand estimating relatively minor variations in the parameter values from their reference states. (C) 2014 Elsevier Ltd. All rights reserved.