Intercalation vs Adsorption Strategies of Myo-Inositol Hexakisphosphate into Zn-Fe Layered Double Hydroxide: A Tiff between Anion Exchange and Coprecipitation.

Myo-inositol hexakisphosphates (IHPs) or phytates are the most abundant organic phosphates having the potential to serve as a phosphorus reserve in soil. Understanding the fate of IHP interaction with soil minerals tends to be crucial for its efficient storage and utilization as a slow-release organic phosphate fertilizer. We have systematically compared the effective intercalation strategy of a phytate onto Zn-Fe layered double hydroxide (LDH) acting as storage/carrier material through coprecipitation and anion exchange. Powder X-ray diffraction, X-ray photoelectron spectroscopy, elemental analysis, thermogravimetric analysis, FTIR spectra, and molecular modeling demonstrated the formation of phytate-intercalated Zn-Fe LDH through coprecipitation with a maximum loading of 41.34% (w/w) in the pH range of ∼9-10 in a vertical alignment through monolayer formation. No intercalation product was obtained from the anion exchange method, which was concluded based on the absence of shifting in the XRD (003) peak. A change in the zeta potential values from positive to negative and subsequent increase in solution pH, with decreasing phytate concentration, are suggestive of adsorption of IHP onto the LDH surface. The batch adsorption data were best fitted with Langmuir isotherm equation and followed the pseudo-second-order kinetic model. The maximum adsorption capacity was found to be 45.87 mg g-1 at a temperature of 25 ± 0.5 °C and pH 5.63.

Revealing the Structural Organization of Gamma-irradiated Starch Granules Using Polarization-resolved Second Harmonic Generation Microscopy.

Starch is a semi-crystalline macromolecule with the presence of amorphous and crystalline components. The amorphous amylose and crystalline amylopectin regions in starch granules are susceptible to certain physical modifications, such as gamma irradiation. Polarization-resolved second harmonic generation (P-SHG) microscopy in conjunction with SHG-circular dichroism (CD) was used to assess the three-dimensional molecular order and inherent chirality of starch granules and their reaction to different dosages of gamma irradiation. For the first time, the relationship between starch achirality (χ21/χ16 and χ22/χ16) and chirality (χ14/χ16) determining susceptibility tensor ratios has been elucidated. The results showed that changes in the structure and orientation of long-chain amylopectin were supported by the decrease in the SHG anisotropy factor and the χ22/χ16 ratio. Furthermore, SHG-CD illustrated the molecular tilt angle by revealing the arrangement of amylopectin molecules pointing either upward or downward owing to molecular polarity.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer.

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.

Estimation of the proportion of true null hypotheses under sparse dependence: Adaptive FDR controlling in microarray data.

The proportion of non-differentially expressed genes is an important quantity in microarray data analysis and an appropriate estimate of the same is used to construct adaptive multiple testing procedures. Most of the estimators for the proportion of true null hypotheses based on the thresholding, maximum likelihood and density estimation approaches assume independence among the gene expressions. Usually, sparse dependence structure is natural in modelling associations in microarray gene expression data and hence it is necessary to develop methods for accommodating the sparse dependence well within the framework of existing estimators. We propose a clustering based method to put genes in the same group that are not coexpressed using the estimated high dimensional correlation structure under sparse assumption as dissimilarity matrix. This novel method is applied to three existing estimators for the proportion of true null hypotheses. Extensive simulation study shows that the proposed method improves an existing estimator by making it less conservative and the corresponding adaptive Benjamini-Hochberg algorithm more powerful. The proposed method is applied to a microarray gene expression dataset of colorectal cancer patients and the results show gain in terms of number of differentially expressed genes. The R code is available at https://github.com/aniketstat/Proportiontion-of-true-null-under-sparse-dependence-2021.

Integrated computational approach toward discovery of multi-targeted natural products from Thumbai (Leucas aspera) for attuning NKT cells.

A multi-omics-based approach targeting the plant-based natural products from Thumbai (Leucas aspera), an important yet untapped potential source of many therapeutic agents for myriads of immunological conditions and genetic disorders, was conceptualized to reconnoiter its potential biomedical application. A library of 79 compounds from this plant was created, out of which 9 compounds qualified the pharmacokinetics parameters. Reverse pharmacophore technique for target fishing of the screened compounds was executed through which renin receptor (ATP6AP2) and thymidylate kinase (DTYMK) were identified as potential targets. Network biology approaches were used to comprehend and validate the functional, biochemical and clinical relevance of the targets. The target-ligand interaction and subsequent stability parameters at molecular scale were investigated using multiple strategies including molecular modeling, pharmacophore approaches and molecular dynamics simulation. Herein, isololiolide and 4-hydroxy-2-methoxycinnamaldehyde were substantiated as the lead molecules exhibiting comparatively the best binding affinity against the two putative protein targets. These natural lead products from L. aspera and the combinatorial effects may have plausible medical applications in a wide variety of neurodegenerative, genetic and developmental disorders. The lead molecules also exhibit promising alternative in diagnostics and therapeutics through immuno-modulation targeting natural killer T-cell function in transplantation-related pathogenesis, autoimmune and other immunological disorders.Communicated by Ramaswamy H. Sarma.

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network.

Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

Identification of novel human renin inhibitors through a combined approach of pharmacophore modelling, molecular DFT analysis and in silico screening.

Renin is an aspartyl protease of the renin-angiotensin system (RAS) and the first enzyme of the biochemical pathway for the generation of angiotensin II - a potent vasoconstrictor involved in the maintenance of cardiovascular homeostasis and the regulation of blood pressure. High enzymatic specificity of renin and its involvement in the catalysis of the rate-limiting step of the RAS hormone system qualify it as a good target for inhibition of hypertension and other associated diseases. Ligand-based pharmacophore model (Hypo1) was generated from a training set of 24 compounds with renin inhibitory activity. The best hypothesis consisted of one Hydrogen Bond Acceptor (HBA), three Hydrophobic Aliphatic (HY-Al) and one Ring Aromatic (AR) features. This well-validated pharmacophore hypothesis (correlation coefficient 0.95) was further utilized as a 3D query to screen database compounds, which included structures from two natural product repositories. These screened compounds were further analyzed for drug-likeness and ADMET studies. The compounds which satisfied the qualifying criteria were then subjected to molecular docking and Density Functional Theory (DFT) analysis in order to discern their atomic level interactions at the active site of the 3D structure of rennin. The pharmacophore-based modelling that has been used to generate the novel findings of the present study would be an avant-garde approach towards the development of potent inhibitors of renin.

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors.

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Veber's drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.

Comparative analysis of codon usage bias in Crenarchaea and Euryarchaea genome reveals differential preference of synonymous codons to encode highly expressed ribosomal and RNA polymerase proteins.

The present study was undertaken to investigate the pattern of optimal codon usage in Archaea. Comparative analysis was executed to understand the pattern of codon usage bias between the high expression genes (HEG) and the whole genomes in two Archaeal phyla, Crenarchaea and Euryarchaea. The G+C% of the HEG was found to be less in comparison to the genome G+C% in Crenarchaea, whereas reverse was the case in Euryarchaea. The preponderance of U/A ending codons that code for HEG in Crenarchaea was in sharp contrast to the C/G ended ones in Euryarchaea. The analysis revealed prevalence of Uending codons even within theWWY(nucleotide ambiguity code) families in Crenarchaea vis-à-vis Euryarchaea, bacteria and Eukarya. No plausible interpretation of the observed disparity could be made either in the context of tRNA gene composition or genome G+C%. The results in this study attested that the preferential biasness for codons in HEG of Crenarchaea might be different from Euryarchaea. The main highlights are (i) varied CUB in the HEG and in the whole genomes in Euryarchaea and Crenarchaea. (ii) Crenarchaea was found to have some unusual optimal codons (OCs) compared to other organisms. (iii) G+C% (and GC3) of the HEG were different from the genome G+C% in the two phyla. (iv) Genome G+C% and tRNA gene number failed to explain CUB in Crenarchaea. (v) Translational selection is possibly responsible for A+T rich OCs in Crenarchaea.