During the last two decades several nanoscale materials were engineered for industrial and medical applications. Among them carbon nanotubes (CNTs) are the most exploited nanomaterials with global production of around 1000 tons/year. Besides several commercial benefits of CNTs, the fiber-like structures and their bio-persistency in lung tissues raise serious concerns about the possible adverse human health effects resembling those of asbestos fibers. In this review, we present a comparative analysis between CNTs and asbestos fibers using the following four parameters: (1) fibrous needle-like shape, (2) bio-persistent nature, (3) high surface to volume ratio and (4) capacity to adsorb toxicants/pollutants on the surface. We also compare mechanisms underlying the toxicity caused by certain diameters and lengths of CNTs and asbestos fibers using downstream pathways associated with altered gene expression data from both asbestos and CNT exposure. Our results suggest that indeed certain types of CNTs are emulating asbestos fiber as far as associated toxicity is concerned.

Medicinal plants are the main natural pools for the primary health care system, ethno-medicine, as well as traditional Indian system of several medicines. Uttarakhand also known as ‘Herbal State’, is a rich source of medicinal plants and traditional medicinal knowledge. A great deal of information about medicinal plants of Uttarakhand is scattered in different forms. Although many medicinal plant databases are available, currently there is no cohesive manually curated database of medicinal plants widely distributed in Uttarakhand state. A comprehensive database has been developed, known as the Uttarakhand Medicinal Plants Database (UMPDB). UMPDB provides extensive information on botanical name, common name, taxonomy, genomic taxonomy id, habit, habitat, location in Uttarakhand, part use, medicinal use, genomic information (including number of nucleotides, proteins, ESTs), chemical information, and scientific literature. Annotated medicinal plants integrated in the current version of the database were collected from the existing books, databases, and available literature. The current version of UMPDB contains the 1127 records of medicinal plants which belong to 153 plant families distributed across 13 districts of Uttarakhand. The primary goal of developing this database is to provide traditional, genomic, and chemical descriptions of the medicinal plants exclusively found in various regions of Uttarakhand. We anticipate that embedded information in the database would help users to readily obtain desired information.

CCCTC-binding factor (CTCF) plays fundamental roles in transcriptional regulation and chromatin architecture maintenance. CTCF is also a tumour suppressor frequently mutated in cancer, however, the structural and functional impact of mutations have not been examined. We performed molecular and structural characterisation of five cancer-specific CTCF missense zinc finger (ZF) mutations occurring within key intra- and inter-ZF residues. Functional characterisation of CTCF ZF mutations revealed a complete (L309P, R339W, R377H) or intermediate (R339Q) abrogation as well as an enhancement (G420D) of the anti-proliferative effects of CTCF. DNA binding at select sites was disrupted and transcriptional regulatory activities abrogated. Molecular docking and molecular dynamics confirmed that mutations in residues specifically contacting DNA bases or backbone exhibited loss of DNA binding. However, R339Q and G420D were stabilised by the formation of new primary DNA bonds, contributing to gain-of-function. Our data confirm that a spectrum of loss-, change- and gain-of-function impacts on CTCF zinc fingers are observed in cell growth regulation and gene regulatory activities. Hence, diverse cellular phenotypes of mutant CTCF are clearly explained by examining structure–function relationships.

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.