Analysis of nucleosome positioning landscapes enables gene discovery in the human malaria parasite Plasmodium falciparum.

BackgroundPlasmodium falciparum, the deadliest malaria-causing parasite, has an extremely AT-rich (80.7 %) genome. Because of high AT-content, sequence-based annotation of genes and functional elements remains challenging. In order to better understand the regulatory network controlling gene expression in the parasite, a more complete genome annotation as well as analysis tools adapted for AT-rich genomes are needed. Recent studies on genome-wide nucleosome positioning in eukaryotes have shown that nucleosome landscapes exhibit regular characteristic patterns at the 5'- and 3'-end of protein and non-protein coding genes. In addition, nucleosome depleted regions can be found near transcription start sites. These unique nucleosome landscape patterns may be exploited for the identification of novel genes. In this paper, we propose a computational approach to discover novel putative genes based exclusively on nucleosome positioning data in the AT-rich genome of P. falciparum.ResultsUsing binary classifiers trained on nucleosome landscapes at the gene boundaries from two independent nucleosome positioning data sets, we were able to detect a total of 231 regions containing putative genes in the genome of Plasmodium falciparum, of which 67 highly confident genes were found in both data sets. Eighty-eight of these 231 newly predicted genes exhibited transcription signal in RNA-Seq data, indicative of active transcription. In addition, 20 out of 21 selected gene candidates were further validated by RT-PCR, and 28 out of the 231 genes showed significant matches using BLASTN against an expressed sequence tag (EST) database. Furthermore, 108 (47%) out of the 231 putative novel genes overlapped with previously identified but unannotated long non-coding RNAs. Collectively, these results provide experimental validation for 163 predicted genes (70.6%). Finally, 73 out of 231 genes were found to be potentially translated based on their signal in polysome-associated RNA-Seq representing transcripts that are actively being translated.ConclusionOur results clearly indicate that nucleosome positioning data contains sufficient information for novel gene discovery. As distinct nucleosome landscapes around genes are found in many other eukaryotic organisms, this methodology could be used to characterize the transcriptome of any organism, especially when coupled with other DNA-based gene finding and experimental methods (e.g., RNA-Seq)

Level of agreement between objectively determined body composition and perceived body image in 6- To 8-year-old South African children- To Body Composition-Isotope Technique study

To assess the level of agreement between body size self-perception and actual body size determined by body mass index (BMI) z-score and body fatness measured by the deuterium dilution method (DDM) in South African children aged 6-8 years. A cross-sectional sample of 202 children (83 boys and 119 girls) aged 6-8 years from the Body Composition-Isotope Technique study (BC-IT) was taken. Subjective measures of body image (silhouettes) were compared with the objective measures of BMI z-score and body fatness measured by the DDM. The World Health Organization BMI z-scores were used to classify the children as underweight, normal, overweight, or obese. DDM-measured fatness was classified based on the McCarthy centile curves set at 2nd, 85th and 95th in conjunction with fatness cut-off points of 25% in boys and 30% in girls. Data were analyzed using SPSS v26. Of 202 children, 32.2%, 55.1%, 8.8%, and 2.4% perceived their body size as underweight, normal, overweight, and obese, respectively. Based on BMI z-score, 18.8%, 72.8%, 6.9%, and 1.5% were classified as underweight, normal, overweight, and obese, respectively. Body fatness measurement showed that 2.5%, 48.0%, 21.8%, and 29.7% were underweight, normal weight, overweight, and obese, respectively

Homology modeling and docking studies between HIV-1 protease and carbamic acid

96-100HIV-I protease (HIV-I PR) is aspartic protease enzyme which is essential for the life-cycle of HIV retrovirus. Homology structural model and function relation of HIV-I PR have solved the structure of HIV-I proteases. We created a homology model of HIV-I PR and the 3-D structure as template using with ICMPro software. The ICMPro homology modeling algorithm has demonstrated excellent accuracy in blind predictions. Moreover, recent results show that ICMPro models built with as little as 35% identity can be accurate enough to be successfully used in receptor based rational drug design. The closest homologue with the highest sequence identity of 38.395% was selected as representative model using YASARA tools. The model was validated using protein structure checking tools such as PROCHEK for reliability. A total of two pockets were predicted by the software. Once the pockets were predicted, the ligand was subjected to docking reaction using the docking module of ICMPro software. Based on the RMSD and energy values, the best docking orientation was selected. The better RMSD value of docking is 0.0066288. This study will be used in broad screening of inhibitors of the protein and can be further implemented in future drug designing

Hybrid Materials : A Metareview

The field of hybrid materials has grown so wildly in the last 30 years that writing a comprehensive review has turned into an impossible mission. Yet, the need for a general view of the field remains, and it would be certainly useful to draw a scientific and technological map connecting the dots of the very different subfields of hybrid materials, a map which could relate the essential common characteristics of these fascinating materials while providing an overview of the very different combinations, synthetic approaches, and final applications formulated in this field, which has become a whole world. That is why we decided to write this metareview, that is, a review of reviews that could provide an eagle's eye view of a complex and varied landscape of materials which nevertheless share a common driving force: the power of hybridization

Modeling Competing Risks for Progressively Censored Data Using Inverse Lindley Distribution

In this article, we present the competing risk analysis of progressively type-II censored system lifetime data, where each system contains more than one component in series. We assume that the lifetime of every component follows inverse Lindley distribution. We obtain the maximum likelihood estimates, asymptotic and bootstrap confidence intervals of the parameters of the component’s lifetime distribution. For same parameters, we also evaluate Bayes estimates, Bayesian credible and highest posterior density intervals. For numerical illustrations, we present an extensive simulation study considering different censoring schemes

Nanostructured Thick Electrode Strategies toward Enhanced Electrode–Electrolyte Interfaces

This article addresses the issue of bulk electrode design and the factors limiting the performance of thick electrodes. Indeed, one of the challenges for achieving improved performance in electrochemical energy storage devices (batteries or supercapacitors) is the maximization of the ratio between active and non-active components while maintaining ionic and electronic conductivity of the assembly. In this study, we developed and compared supercapacitor thick electrodes using commercially available carbons and utilising conventional, easily scalable methods such as spray coating and freeze-casting. We also compared different binders and conductive carbons to develop thick electrodes and analysed factors that determine the performance of such thick electrodes, such as porosity and tortuosity. The spray-coated electrodes showed high areal capacitances of 1428 mF cm−2 at 0.3 mm thickness and 2459 F cm−2 at 0.6 mm thickness

2D-DIGE as a strategy to identify serum protein biomarkers to monitor pharmacological efficacy in dopamine-dictated states of Parkinson’s disease and schizophrenia

Ashish Kumar Gupta,1 Gaurav Khunger Kumar,1 Komal Rani,1 Ruchika Pokhriyal,1 Mohd Imran Khan,1 Domada Ratna Kumar,1 Vinay Goyal,2 Manjari Tripathi,2 Rishab Gupta,3 Rakesh Kumar Chadda,3 Perumal Vanamail,4 Ashok Kumar Mohanty,5 Gururao Hariprasad1 1Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India; 2Department of Neurology, All India Institute of Medical Sciences, New Delhi 110029, India; 3Department of Psychiatry, All India Institute of Medical Sciences, New Delhi 110029, India; 4Department of Biostatistics, All India Institute of Medical Sciences, New Delhi 110029, India; 5Proteomics Facility, National Diary Research Institute, Karnal, Haryana 132001, India Objectives: Parkinson’s disease and schizophrenia are clinical scenarios that occur due to dopaminergic deficit and hyperactivity in the midbrain, respectively. Current pharmacological interventions for these two diseases therefore aim to restore normal dopamine levels in the midbrain. But during therapy, there is a overshooting of dopamine concentrations that result in hallucinations in Parkinson’s disease patients and extra-pyramidal symptoms in schizophrenic patients. This causes a lot of inconvenience to the patents and the clinicians. There are no tests currently available to monitor drug efficacy in these two neuropsychiatric diseases. Materials and methods: Parkinson’s disease and schizophrenic naïve patients were recruited. Serum proteins isolated from these two clinical phenotypes were labeled with fluorescent cyanine dyes and analyzed by two-dimensional difference in gel electrophoresis proteomic experiment. Differentially expressed spots that had consistent expression pattern across five sets of biological replicate gels were trypsin digested and subjected to mass spectrometric analysis for protein identification. Validation experiments were done for the identified proteins using antibody-based assay on a patient cohort that included naïve, treated, and those who had side effects. Results: Serum α- and β-globin chains were identified as differentially expressed proteins having threefold higher expressions in Parkinson’s patients as compared to schizophrenia. Interestingly, concentrations of these two proteins had an inverse correlation across clinical phenotypes in the dopaminergic spectrum. RBC contamination as a source for these proteins was ruled out. Conclusion: There is a clear association of free serum globin with dopaminergic clinical states. This lays a platform for protein biomarker–based monitoring of pharmacological efficacy in Parkinson’s disease and schizophrenia. Keywords: Parkinson’s disease, schizophrenia, gel-based proteomics, biomarkers, dopamine, pharmacological efficacy, difference gel electrophoresi