Stress Conditions Modulate the Chromatin Interactions Network in Arabidopsis.

Stresses have been known to cause various responses like cellular physiology, gene regulation, and genome remodeling in the organism to cope and survive. Here, we assessed the impact of stress conditions on the chromatin-interactome network of Arabidopsis thaliana. We identified thousands of chromatin interactions in native as well as in salicylic acid treatment and high temperature conditions in a genome-wide fashion. Our analysis revealed the definite pattern of chromatin interactions and stress conditions could modulate the dynamics of chromatin interactions. We found the heterochromatic region of the genome actively involved in the chromatin interactions. We further observed that the establishment or loss of interactions in response to stress does not result in the global change in the expression profile of interacting genes; however, interacting regions (genes) containing motifs for known TFs showed either lower expression or no difference than non-interacting genes. The present study also revealed that interactions preferred among the same epigenetic state (ES) suggest interactions clustered the same ES together in the 3D space of the nucleus. Our analysis showed that stress conditions affect the dynamics of chromatin interactions among the chromatin loci and these interaction networks govern the folding principle of chromatin by bringing together similar epigenetic marks.

Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

In-silico analysis of Sirt2 from Schistosoma mansoni: structures, conformations, and interactions with inhibitors.

Sirtuins are NAD+-dependent lysine deacetylases member of the class III HDAC family. These are demonstrated to be therapeutic targets in parasitic diseases like schistosomiasis. Observations suggested that sirtuin enzyme is necessary for the functionality of fe/male reproductive system, due to which SmSirt2 is treated as a potential therapeutic target. There are no structural and molecular features of SmSirt2 have been reported yet. In this study, homology modeling has been used to determine the three-dimensional features of the SmSITRT2. Further, structure validation has been performed by energy minimization and Ramachandran plot. Validated structures are further subjected to molecular docking and virtual screening to find the best lead molecules for downstream analysis. Ten lead molecules were selected while comparing virtual screening of hSirt2 and SmSirt2 both. These leads are further compared with AKG2 which is known inhibitor of hSirt2 (-8.8 kcal/mol). Out of selected 10 leads, four of them (ZINC23995485 (-9.5 kcal/mol), ZINC53298162 (-9.4 kcal/mol), ZINC70927268 (-10.0 kcal/mol), ZINC89878705 (-11.2 kcal/mol)) have shown better interaction with SmSirt2, in which ZINC89878705 (-11.2 kcal/mol) shows a more compact packing as compared to AKG2 and rest of ligands. These molecules could be further subject to in vitro study and model of SmSirt2 has been proposed for further structure-based drug design projects concerning sirtuins from Schistosoma mansoni.