Garcinol loaded vitamin E TPGS emulsified PLGA nanoparticles: preparation, physicochemical characterization, in vitro and in vivo studies

Garcinol (GAR) is a naturally occurring polyisoprenylated phenolic compound. It has been recently investigated for its biological activities such as antioxidant, anti-inflammatory, anti ulcer, and antiproliferative effect on a wide range of human cancer cell lines. Though the outcomes are very promising, its extreme insolubility in water remains the main obstacle for its clinical application. Herein we report the formulation of GAR entrapped PLGA nanoparticles by nanoprecipitation method using vitamin E TPGS as an emulsifier. The nanoparticles were characterized for size, surface morphology, surface charge, encapsulation efficiency and in vitro drug release kinetics. The MTT assay depicted a high amount of cytotoxicity of GAR-NPs in B16F10, HepG2 and KB cells. A considerable amount of cell apoptosis was observed in B16f10 and KB cell lines. In vivo cellular uptake of fluorescent NPs on B16F10 cells was also investigated. Finally the GAR loaded NPs were radiolabeled with technetium-99m with >95% labeling efficiency and administered to B16F10 melanoma tumor bearing mice to investigate the in vivo deposition at the tumor site by biodistribution and scintigraphic imaging study. In vitro cellular uptake studies and biological evaluation confirm the efficacy of the formulation for cancer treatmen

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: http://sysbio.icm.edu.pl/secstruct and http://code.google.com/p/cmater-bioinfo

Genetic diversity and differentiation among populations of the Indian eri silkworm, Samia cynthia ricini, revealed by ISSR markers

Samia cynthia ricini (Lepidoptera:Saturniidae), the Indian eri silkworm, contributes significantly to the production of commercial silk and is widely distributed in the Brahmaputra river valley in North-Eastern India. Due to over exploitation coupled with rapid deforestation, most of the natural populations of S. cynthia ricini are dwindling rapidly and its preservation has become an important goal. Assessment of the genetic structure of each population is a prerequisite for a sustainable conservation program. DNA fingerprinting to detect genetic variation has been used in different insect species not only between populations, but also between individuals within a population. Since, information on the genetic basis of phenotypic variability and genetic diversity within the S. cynthia ricini populations is scanty, inter simple sequence repeat (ISSR) system was used to assess genetic diversity and differentiation among six commercially exploited S. cynthia ricini populations. Twenty ISSR primers produced 87% of inter population variability among the six populations. Genetic distance was lowest between the populations Khanapara (E5) and Mendipathar (E6) (0.0654) and highest between Dhanubhanga (E4) and Titabar (E3) (0.3811). Within population, heterozygosity was higher in Borduar (E2) (0.1093) and lowest in Titabar (E3) (0.0510). Highest gene flow (0.9035) was between E5 and E6 and the lowest (0.2172) was between E3 and E5. Regression analysis showed positive correlation between genetic distance and geographic distance among the populations. The high GST value (0.657) among the populations combined with low gene flow contributes significantly to the genetic differentiation among the S. cynthia ricini populations. Based on genetic diversity, these populations can be considered as different ecotypes and in situ conservation of them is recommended

The Arabidopsis plastid-signalling mutant gun1 (genomes uncoupled1) shows altered sensitivity to sucrose and abscisic acid and alterations in early seedling development

Developing seedlings of the Arabidopsis gun1 (genomes uncoupled1) mutant, which is defective in retrograde plastid-to-nucleus signalling, show several previously unrecognized mutant phenotypes. gun1 seedlings accumulated less anthocyanin than wild-type seedlings when grown in the presence of 2% (w/v) sucrose, due to lower amounts of transcripts of early anthocyanin biosynthesis genes in gun1. Norflurazon and lincomycin, which induce retrograde signalling, further decreased the anthocyanin content of sucrose-treated seedlings, and altered the temporal pattern of anthocyanin accumulation. Lincomycin treatment altered the spatial pattern of sucrose-induced anthocyanin accumulation, suggesting that plastids provide information for the regulation of anthocyanin biosynthesis in Arabidopsis seedlings. The temporal pattern of accumulation of LHCB1 transcripts differed between wild-type and gun1 seedlings, and gun1 seedlings were more sensitive to sucrose suppression of LHCB1 transcript accumulation than wild-type seedlings. Growth and development of gun1 seedlings was more sensitive to exogenous 2% sucrose than wild-type seedlings and, in the presence of lincomycin, cotyledon expansion was enhanced in gun1 seedlings compared to the wild type. gun1 seedlings were more sensitive than wild-type seedlings to the inhibition of seedling growth and development by abscisic acid. These observations clearly implicate GUN1 and plastid signalling in the regulation of seedling development and anthocyanin biosynthesis, and indicate a complex interplay between sucrose and plastid signalling pathways

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art

FunPred 3.0: improved protein function prediction using protein interaction network

Proteins are the most versatile macromolecules in living systems and perform crucial biological functions. In the advent of the post-genomic era, the next generation sequencing is done routinely at the population scale for a variety of species. The challenging problem is to massively determine the functions of proteins that are yet not characterized by detailed experimental studies. Identification of protein functions experimentally is a laborious and time-consuming task involving many resources. We therefore propose the automated protein function prediction methodology using in silico algorithms trained on carefully curated experimental datasets. We present the improved protein function prediction tool FunPred 3.0, an extended version of our previous methodology FunPred 2, which exploits neighborhood properties in protein–protein interaction network (PPIN) and physicochemical properties of amino acids. Our method is validated using the available functional annotations in the PPIN network of Saccharomyces cerevisiae in the latest Munich information center for protein (MIPS) dataset. The PPIN data of S. cerevisiae in MIPS dataset includes 4,554 unique proteins in 13,528 protein–protein interactions after the elimination of the self-replicating and the self-interacting protein pairs. Using the developed FunPred 3.0 tool, we are able to achieve the mean precision, the recall and the F-score values of 0.55, 0.82 and 0.66, respectively. FunPred 3.0 is then used to predict the functions of unpredicted protein pairs (incomplete and missing functional annotations) in MIPS dataset of S. cerevisiae. The method is also capable of predicting the subcellular localization of proteins along with its corresponding functions. The code and the complete prediction results are available freely at: https://github.com/SovanSaha/FunPred-3.0.git

Ophthalmoplegia associated with head and neck malignancies: a case series

Ophthalmoplegia is the paralysis or weakness of the eye muscles. It can affect one or more of the six muscles that hold the eye in place and control its movement. Total ophthalmoplegia refers to the paralysis of all the muscles in the eye. People affected by ophthalmoplegia with orbital apex involvement may have double or blurred vision. They may also experience an inability to position the eyes in sync. Some may also have a hard time moving both eyes in every direction, and many will have drooping of their eyelids. Causes of ophthalmoplegia along with orbital apex involvement can be multiple sclerosis, trauma infarction, brain injury, stroke, thyroid disease, infection, associated with sinonasal malignancy, nasopharyngeal carcinoma, invasive fungal infections needing surgical removal and brain tumours