Background: Plants have developed various sophisticated mechanisms to cope up with climate extremes and different stress conditions, especially by involving specific transcription factors (TFs). The members of the WRKY TF family are well known for their role in plant development, phytohormone signaling and developing resistance against biotic or abiotic stresses. In this study, we performed a genome-wide screening to identify and analyze the WRKY TFs in pearl millet (Pennisetum glaucum; PgWRKY), which is one of the most widely grown cereal crops in the semi-arid regions. Results: A total number of 97 putative PgWRKY proteins were identified and classified into three major Groups (I-III) based on the presence of WRKY DNA binding domain and zinc-finger motif structures. Members of Group II have been further subdivided into five subgroups (IIa-IIe) based on the phylogenetic analysis. In-silico analysis of PgWRKYs revealed the presence of various cis-regulatory elements in their promoter region like ABRE, DRE, ERE, EIRE, Dof, AUXRR, G-box, etc., suggesting their probable involvement in growth, development and stress responses of pearl millet. Chromosomal mapping evidenced uneven distribution of identified 97 PgWRKY genes across all the seven chromosomes of pearl millet. Synteny analysis of PgWRKYs established their orthologous and paralogous relationship among the WRKY gene family of Arabidopsis thaliana, Oryza sativa and Setaria italica. Gene ontology (GO) annotation functionally categorized these PgWRKYs under cellular components, molecular functions and biological processes. Further, the differential expression pattern of PgWRKYs was noticed in different tissues (leaf, stem, root) and under both drought and salt stress conditions. The expression pattern of PgWRKY33, PgWRKY62 and PgWRKY65 indicates their probable involvement in both dehydration and salinity stress responses in pearl millet. Conclusion: Functional characterization of identified PgWRKYs can be useful in delineating their role behind the natural stress tolerance of pearl millet against harsh environmental conditions. Further, these PgWRKYs can be employed in genome editing for millet crop improvement.

The increase in antibiotic resistance has become a major health concern in recent times. It is therefore essential to identify novel antibacterial targets as well as discover and develop new antibacterial agents. FtsZ, a highly conserved bacterial protein, is responsible for initiation of cell division in bacteria. FtsZ functions inside the cells are tightly regulated and any perturbation in its functions leads to inhibition of bacterial division. Recent reports indicate that small molecules targeting FtsZ functions may be used as a lead to develop new antibacterial agents. To identify small molecules targeting FtsZ and inhibiting bacterial division, we screened an FDA approved drug library of 800 molecules using independent computational, biochemical and microbial approach. From this screen, we identified doxorubicin; an anthracycline molecule that inhibits E. coli division and forms filamentous cell. Fluorescence binding assay shows that doxorubicin interacts strongly with FtsZ. A detail biochemical analysis demonstrated that doxorubicin inhibits FtsZ assembly and its GTPase activity through binding to a site different than the GTP binding site. Further, using molecular docking, we identified a probable doxorubicin binding site in FtsZ. A number of single amino acid mutations at the identified binding site in FtsZ resulted in several-fold decrease in the FtsZ affinity for doxorubicin indicating the importance of this site for doxorubicin interaction. Our study suggests the presence of a novel binding site in FtsZ that interacts with the small molecules and can be targeted for the screening and development of new antibacterial agents.

In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein–protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.

Circular RNAs (circRNAs) are a large family of noncoding RNAs that have emerged as novel regulators of gene expression. However, little is known about the function of circRNAs in pancreatic β-cells. Here, transcriptomic analysis of mice pancreatic islet RNA-sequencing data identified 77 differentially expressed circRNAs between mice fed with a normal diet and a high-fat diet. Surprisingly, multiple circRNAs were derived from the intron 2 of the preproinsulin 2 (Ins2) gene and are termed as circular intronic (ci)-Ins2. The expression of ci-Ins2 transcripts in mouse pancreatic islets, and βTC6 cells were confirmed by reverse transcription PCR, DNA sequencing, and RNase R treatment experiments. The level of ci-Ins2 was altered in βTC6 cells upon exposure to elevated levels of palmitate and glucose. Computational analysis predicted the interaction of several RNA-binding proteins with ci-Ins2 and their flanking region, suggesting their role in the ci-Ins2 function or biogenesis. Additionally, bioinformatics analysis predicted the association of several microRNAs with ci-Ins2. Gene ontology and pathway analysis of genes targeted by miRNAs associated with ci-Ins2 suggested the regulation of several key biological processes. Together, our findings indicate that differential expression of circRNAs, especially ci-Ins2 transcripts, may regulate β-cell function and may play a critical role in the development of diabetes.

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.