Root Canal Morphology of Mandibular Second Pre-Molar and Mandibular Second Molar in Dravidian Population: An Ex Vivo study

Mandibular second pre-molar and mandibular second molar have gained a reputation for having aberrant anatomy. Different studies have looked at the root canal morphology of mandibular second pre-molar and mandibular second molar over the years and reported a fairly high percentage of these teeth to have more than one canal.26 There seems to be a racial predisposition for the presence of two or more canals in mandibular second premolar and mandibular second molar.29 The aim of the present study was to investigate the root canal morphology of mandibular second pre-molar and mandibular second molar in South Indian population using Spiral CT and clearing technique. 400 mandibular second pre-molars and 400 mandibular second molars were collected, cleaned and air dried. Teeth were arranged in wax sheet for Spiral CT imaging. Images were reconstructed and stored in computer. Teeth were labeled and access cavity was prepared. Each teeth were decalcified and cleared. The comparative evaluation of the specimens were performed. The following parameters were investigated, I. Number of roots II. Shape of the roots III. Shape of the root canals IV. Number of canals and canal configuration (According to Vertucci’s & Gulabivala’s classification). From this root canal morphology study of mandibular second pre-molar and mandibular second molar in South Indian population, it was concluded that, 1. Single rooted mandibular second pre-molar with single canal is the most common canal configuration in South Indian population. 2. Two rooted mandibular second pre-molar in this study was in higher prevalence next to Kuwait population. 3. Two rooted mandibular second molar with two mesial canals and one distal canal is the most common canal configuration in South Indian population. 4. Two rooted mandibular second molar with two mesial canals and one distal canal were found to have higher prevalence than all other populations. 5. This was the only study which reports four rooted mandibular second molar. 6. C-shaped canals were found in mandibular second molar in South Indian population and it was approximately similar to Caucasian population. 7. There was no C-shaped canal found in mandibular second pre-molar. 8. There was no middle mesial canal found in mandibular second molar

Microwave assisted reaction, photophysical studies and antibacterial activities of simple sugar chalcone derivatives

Aldol condensation is adopted for the synthesis of sugar chalcone derivatives from b-C-glycosidic ketones with various aromatic aldehydes under basic condition with both conventional as well as microwave condition. Microwave assisted reaction gave an excellent yield. Products obtained were characterized using (1H, 13C) and elemental analysis. Sugar chalcone derivatives exhibited an excellent antibacterial activity.

From Cancer Sequencing Data to Neoantigen Prediction: A Reusable Pipeline using Snakemake

Neoantigens are newly formed peptides formed by somatic mutations that are capable of inducing tumor-specific T-cell recognition. Because neoantigens are expressed specifically in tumor cells, prediction of these neoantigens can lead to personalized immunotherapies for the treatment of cancers. This process involves many steps, the most crucial of which is identification of expressed somatic mutations (or variants) using next generation sequencing data. After evaluating multiple bioinformatics tools for somatic mutation calling, we selected GATK (Genome Analysis ToolKit) for its ability to accurately call expected mutations. There are other steps that need to be performed before and after identification of somatic mutations as well and these include mapping, duplicate marking, annotation of mutation calls, and filtering of mutation calls. We developed a pipeline using the workflow management system Snakemake to perform these steps in order to identify somatic mutations from whole exome and RNA-Seq data. By making this into a snakemake workflow, we are able to easily extend upon it and add more steps as was done for neoantigen prediction. Furthermore, Snakemake submits slurm jobs for each individual step and can intelligently adjust the runtime and processing load for those jobs. This makes it simple to run even very large samples through the pipeline. We have evaluated this pipeline using RNA sequencing and whole exome sequencing data from 46 Multiple Myeloma cell lines and have identified hundreds of expressed mutations per cell line. This reusable and expandable pipeline can serve as a useful resource for other researchers looking to identify expressed mutations and make neoantigen predictions from cancer sequencing data

Structural Variant Detection Tools Struggle with Whole Exome Sequencing (WES) Data

Whole exome sequencing (WES) is a targeted sequencing technique that sequences only the protein-coding regions of the genome. As WES is significantly cheaper than whole genome sequencing (WGS) while still providing meaningful information, WES has become a respected tool in identifying small genetic variants underlying diseases. It is also used, but less commonly, to identify large-scale structural variants (SVs) which because of their size and complexity, are more difficult to detect using short-read sequencing data. SVs are genome alterations spanning fifty or more base pairs and have been linked to the onset or progression of certain diseases, such as Multiple Myeloma (MM). Multiple bioinformatics tools are available for the identification of structural variants from genomic data; however, it is important to benchmark their accuracies and efficiencies, particularly when dealing with exome data. Using exome sequencing data from 71 Multiple Myeloma cell lines, we benchmarked six established SV identification tools by comparing their results to each cell-line’s known SVs. We utilized the Texas Advanced Computing Center (TACC) to parallelly run our workflows on these samples. When comparing the SVs detected by each tool to the SVs expected in these cell lines, the results brought to light the challenges of detecting SVs using short read WES data. At the chromosomal level of these known SVs, only two of six tools had a recall greater than 25%. At the coordinate level, no tool had a recall greater than 20%. These tools have been used in published studies to identify SVs from WES data; their poor recall in these MM cell-lines may indicate the need for WES-specific SV detection tools in the future

Meta-Analysis of Microarray Data Using a Pathway-Based Approach Identifies a 37-Gene Expression Signature for Systemic Lupus Erythematosus in Human Peripheral Blood Mononuclear Cells

A number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE. Methods: In this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set. Results: Differentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set. Conclusions: The novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature. Please see Research Highlight: http://genomemedicine.com/content/3/5/30Office of Women's Health, US Food and Drug Administration (OWH) 07-09-0001-SATexas Institute for Drug and Diagnostic Development (TI-3D

Structural characterization of the Hepatitis C Virus NS3 protease from genotype 3a: The basis of the genotype 1b vs. 3a inhibitor potency shift

AbstractThe first structural characterization of the genotype 3a Hepatitis C Virus NS3 protease is reported, providing insight into the differential susceptibility of 1b and 3a proteases to certain inhibitors. Interaction of the 3a NS3 protease with a P2–P4 macrocyclic and a linear phenethylamide inhibitor was investigated. In addition, the effect of the NS4A cofactor binding on the conformation of the protease was analyzed. Complexation of NS3 with the phenethylamide inhibitor significantly stabilizes the protease but binding does not involve residues 168 and 123, two key amino acids underlying the different inhibition of genotype 1b vs. 3a proteases by P2–P4 macrocycles. Therefore, we studied the dynamic behavior of these two residues in the phenethylamide complex, serving as a model of the situation in the apo 3a protein, in order to explore the structural basis of the inhibition potency shift between the proteases of the genotypes 1b and 3a

Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern. © 2014 Chikina, Sealfon

Key steps in the structure-based optimization of the hepatitis C virus NS3/4A protease inhibitor SCH503034

Crystal structures of protease/inhibitor complexes guided optimization of the buried nonpolar surface area thereby maximizing hydrophobic binding. The resulting potent tripeptide inhibitor is in clinical trials