Santosh B. Noronha

Distinctive microRNA signature of medulloblastomas associated with the WNT signaling pathway

AIM Medulloblastoma is a malignant brain tumor that occurs predominantly in children. Current risk stratification based on clinical parameters is inadequate for accurate prognostication. MicroRNA expression is known to be deregulated in various cancers and has been found to be useful in predicting tumor behavior. In order to get a better understanding of medulloblastoma biology, miRNA profiling of medulloblastomas was carried out in parallel with expression profiling of protein-coding genes. MATERIALS AND METHODS miRNA profiling of medulloblastomas was carried out using Taqman Low Density Array v 1.0 having 365 human microRNAs. In parallel, genome-wide expression profiling of protein-coding genes was carried out using Affymetrix gene 1.0 ST arrays. RESULTS Both the profiling studies identified four molecular subtypes of medulloblastomas. Expression levels of select protein-coding genes and miRNAs could classify an independent set of medulloblastomas. Twelve of 31 medulloblastomas were found to overexpress genes belonging to the canonical WNT signaling pathway and carry a mutation in CTNNB1 gene. A number of miRNAs like miR-193a, miR-224/miR-452 cluster, miR-182/miR-183/miR-96 cluster, and miR-148a having potential tumor/metastasis suppressive activity were found to be overexpressed in the WNT signaling associated medulloblastomas. Exogenous expression of miR-193a and miR-224, two miRNAs that have the highest WNT pathway specific upregulation, was found to inhibit proliferation, increase radiation sensitivity and reduce anchorage-independent growth of medulloblastoma cells. CONCLUSION Expression level of tumor/metastasis suppressive miRNAs in the WNT signaling associated medulloblastomas is likely to determine their response to treatment, and thus, these miRNAs would be important biomarkers for risk stratification within the WNT signaling associated medulloblastomas.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Biofilm formation in Escherichia coli cra mutants is impaired due to down-regulation of curli biosynthesis

Cra is a pleiotropic regulatory protein that controls carbon and energy flux in enteric bacteria. Recent studies have shown that Cra also regulates other cell processes and influences biofilm formation. The purpose of the present study was to investigate the role of Cra in biofilm formation in Escherichia coli. Congo red-binding studies suggested that curli biosynthesis is impaired in cra mutants. Microarray analysis of wild-type and mutant E. coli cultivated in conditions promoting biofilm formation revealed that the curli biosynthesis genes, csgBAC and csgDEFG, are poorly expressed in the mutant, suggesting that transcription of genes required for curli production is regulated by Cra. Four putative Cra-binding sites were identified in the curli intergenic region, which were experimentally validated by performing electromobility shift assays. Site-directed mutagenesis of three Cra-binding sites in the promoter region of the csgDEFG operon suggests that Cra activates transcription of this operon upon binding to operator regions both downstream and upstream of the transcription start site. Based on the Cra-binding sites identified in this and other studies, the Cra consensus sequence is refined.

Analyzing metabolic variations in different bacterial strains, historical perspectives and current trends - example E. coli

The analysis of metabolic differences in bacterial strains is a useful tool for the development of strains with desired growth and production properties. Several methods are available for the evaluation and understanding of the differences: Biochemical methods to measure metabolites concentration and enzyme activity, mathematical methods to analyze metabolic fluxes through the various pathways, proteomic methods to identify expressed proteins, and genomic methods to detect and measure gene expression. A combination of the various methods is required to obtain a comprehensive understanding of metabolic activities. The genomic methods provide substantial amount information on global gene expression but do not always reflect the actual activity of the individual components. The review focuses on the different methodologies and their use, as well as historical overview of the evaluation of the differences between Escherichia coli K and E. coli B.

Comparison of pyruvate decarboxylases from Saccharomyces cerevisiae and Komagataella pastoris (Pichia pastoris)

Pyruvate decarboxylases (PDCs) are a class of enzymes which carry out the non-oxidative decarboxylation of pyruvate to acetaldehyde. These enzymes are also capable of carboligation reactions and can generate chiral intermediates of substantial pharmaceutical interest. Typically, the decarboxylation and carboligation processes are carried out using whole cell systems. However, fermentative organisms such as Saccharomyces cerevisiae are known to contain several PDC isozymes; the precise suitability and role of each of these isozymes in these processes is not well understood. S. cerevisiae has three catalytic isozymes of pyruvate decarboxylase (ScPDCs). Of these, ScPDC1 has been investigated in detail by various groups with the other two catalytic isozymes, ScPDC5 and ScPDC6 being less well characterized. Pyruvate decarboxylase activity can also be detected in the cell lysates of Komagataella pastoris, a Crabtree-negative yeast, and consequently it is of interest to investigate whether this enzyme has different kinetic properties. This is also the first report of the expression and functional characterization of pyruvate decarboxylase from K. pastoris (PpPDC). This investigation helps in understanding the roles of the three isozymes at different phases of S. cerevisiae fermentation as well as their relevance for ethanol and carboligation reactions. The kinetic and physical properties of the four isozymes were determined using similar conditions of expression and characterization. ScPDC5 has comparable decarboxylation efficiency to that of ScPDC1; however, the former has the highest rate of reaction, and thus can be used for industrial production of ethanol. ScPDC6 has the least decarboxylation efficiency of all three isozymes of S. cerevisiae. PpPDC in comparison to all isozymes of S. cerevisiae is less efficient at decarboxylation. All the enzymes exhibit allostery, indicating that they are substrate activated.

Autoantibody Profiling of Glioma Serum Samples to Identify Biomarkers Using Human Proteome Arrays

The heterogeneity and poor prognosis associated with gliomas, makes biomarker identification imperative. Here, we report autoantibody signatures across various grades of glioma serum samples and sub-categories of glioblastoma multiforme using Human Proteome chips containing ~17000 full-length human proteins. The deduced sets of classifier proteins helped to distinguish Grade II, III and IV samples from the healthy subjects with 88, 89 and 94% sensitivity and 87, 100 and 73% specificity, respectively. Proteins namely, SNX1, EYA1, PQBP1 and IGHG1 showed dysregulation across various grades. Sub-classes of GBM, based on its proximity to the sub-ventricular zone, have been reported to have different prognostic outcomes. To this end, we identified dysregulation of NEDD9, a protein involved in cell migration, with probable prognostic potential. Another subcategory of patients where the IDH1 gene is mutated, are known to have better prognosis as compared to patients carrying the wild type gene. On a comparison of these two cohorts, we found STUB1 and YWHAH proteins dysregulated in Grade II glioma patients. In addition to common pathways associated with tumourigenesis, we found enrichment of immunoregulatory and cytoskeletal remodelling pathways, emphasizing the need to explore biochemical alterations arising due to autoimmune responses in glioma.

Evaluation of risedronate as an antibiofilm agent

Escherichia coli cra null mutants have been reported in the literature to be impaired in biofilm formation. To develop E. coli biofilm-inhibiting agents for prevention and control of adherent behaviour, analogues of a natural Cra ligand, fructose-1,6-bisphosphate, were identified based on two-dimensional similarity to the natural ligand. Of the analogues identified, those belonging to the bisphosphonate class of drug molecules were selected for study, as these are approved for clinical use in humans and their safety has been established. Computational and in vitro studies with purified Cra protein showed that risedronate sodium interacted with residues in the fructose-1,6-bisphosphate-binding site. Using a quantitative biofilm assay, risedronate sodium, at a concentration of 300-400 μM, was found to decrease E. coli and Salmonella pullorum biofilm formation by >60 %. Risedronate drastically reduced the adherence of E. coli cells to a rubber Foley urinary catheter, demonstrating its utility in preventing the formation of biofilm communities on medical implant surfaces. The use of risedronate, either alone or in combination with other agents, to prevent the formation of biofilms on surfaces is a novel finding that can easily be translated into practical applications.

Protein structure prediction aided by geometrical and probabilistic constraints.

Database‐assisted ab initio protein structure prediction methods have exhibited considerable promise in the recent past, with several implementations being successful in community‐wide experiments (CASP). We have employed combinatorial optimization techniques toward solving the protein structure prediction problem. A Monte Carlo minimization algorithm has been employed on a constrained search space to identify minimum energy configurations. The search space is constrained by using radius of gyration cutoffs, the loop backbone dihedral probability distributions, and various secondary structure packing conformations. Simulations have been carried out on several sequences and 1000 conformations have been initially generated. Of these, 50 best candidates have then been selected as probable conformations. The search for the optimum has been simplified by incorporating various geometrical constraints on secondary structural elements using distance restraint potential functions. The advantages of the reported methodology are its simplicity, and modifiability to include other geometric and probabilistic restraints.

Engineering of yeast pyruvate decarboxylase for enhanced selectivity towards carboligation

The aim of the study was to increase production of (R)-PAC by altering carboligation activity of Pdc in Saccharomyces cerevisiae. Pdc1 activity was modified by over-expression as well as changing the rate of decarboxylation and carboligation by site specific mutation in Pdc1. Over-expression of mutant Pdc1 resulted in 50 ± 2.5% increase in levels of (R)-PAC in wild-type and further 30-40% in pdc null background. The combination of mutant Pdc1 in pdc null background was successfully evaluated for production of (R)-PAC at industrial scale. This is the first report of enhancing (R)-PAC product in yeast by recombinant technology with capability of commercial production.

Prediction of Loops in G Protein-Coupled Receptor Homology Models: Effect of Imprecise Surroundings and Constraints

In the present study, we explored the extent to which inaccuracies inherent in homology models of the transmembrane helical cores of G protein-coupled receptors (GPCRs) can impact loop prediction. We demonstrate that loop prediction in homology models is much more difficult than loop reconstruction in crystal structures because of the imprecise positioning of loop anchors. Deriving information from 17 recently available GPCR crystal structures, we estimated all of the possible errors that could occur in loop anchors as the result of comparative modeling. Subsequently, we performed an exhaustive analysis to decipher the effect of these errors on loop modeling using ICM High Precision Sampling. The influence of the presence of other extracellular loops was also explored. Our results reveal that the error space of modeled loop residues is much larger than that of the anchor residues, although modeling a particular extracellular loop in the presence of other extracellular loops provides constraints that help in predicting near-native loop conformations observed in crystal structures. This implies that errors in loop anchor positions introduce increased uncertainty in the modeled loop coordinates. Therefore, for the success of any GPCR structure prediction algorithm, minimizing errors in the helical end points is likely to be critical for successful loop modeling.