Identification of LncRNAs as Therapeutic Targets in Chronic Lymphocytic Leukemia

Chronic Lymphocytic Leukemia (CLL) is one kind of blood cancer that has a very heterogeneous biological background, which results in diverse stages of the CLL and complex treatment strategies. However, a small part of the tumor may disappear without receiving any treatment. This condition is known as “spontaneous regression” and occurs as a result of a poorly investigated mechanism. Exposing the underlying causes of this condition can lead to a novel treatment approach for CLL and other types of cancer. While most such mechanisms have been assumed to be directly linked to protein coding genes, a recent approach was aimed to carry out more comprehensive studies by focusing on non-protein coding genes as well as protein-coding genes at the RNA level. In this article, we applied in-silico analysis of total RNA expression data from 24 CLL samples to determine possible regulatory mechanisms of spontaneous regression in CLL. These were selected by comparing spontaneous regression with progressive samples of CLL at the transcriptional level. As a result, 33 lncRNAs were found to be significantly differentially expressed among these conditions based on differential gene expression analysis. Current study suggested lncRNAs, PTPN22-AS1, PCF11-AS1, SYNGAP1-AS1, PRRT3-AS1 and H1FX-AS1 as potential therapeutic targets to trigger spontaneous regression. Eventually, the results presented here reveal new insights into the spontaneous regression and the relation with the non-coding RNAs, particularly lncRNAs

Investigations on Binding Pattern of Kinase Inhibitors with PPAR γ

Peroxisome proliferator-activated receptor gamma (PPARγ) is a potential target for the treatment of several disorders. In view of several FDA approved kinase inhibitors, in the current study, we have investigated the interaction of selected kinase inhibitors with PPARγ using computational modeling, docking, and molecular dynamics simulations (MDS). The docked conformations and MDS studies suggest that the selected KIs interact with PPARγ in the ligand binding domain (LBD) with high positive predictive values. Hence, we have for the first time shown the plausible binding of KIs in the PPARγ ligand binding site. The results obtained from these in silico investigations warrant further evaluation of kinase inhibitors as PPARγ ligands in vitro and in vivo

The GPI Anchor Signal Sequence Dictates the Folding and Functionality of the Als5 Adhesin from Candida albicans

Background: Proteins destined to be Glycosylphosphatidylinositol (GPI) anchored are translocated into the ER lumen completely before the C-terminal GPI anchor attachment signal sequence (SS) is removed by the GPI-transamidase and replaced by a pre-formed GPI anchor precursor. Does the SS have a role in dictating the conformation and function of the protein as well? Methodology/Principal Findings: We generated two variants of the Als5 protein without and with the SS in order to address the above question. Using a combination of biochemical and biophysical techniques, we show that in the case of Als5, an adhesin of C. albicans, the C-terminal deletion of 20 amino acids (SS) results in a significant alteration in conformation and function of the mature protein. Conclusions/Significance: We propose that the locking of the conformation of the precursor protein in an alternate conformation from that of the mature protein is one probable strategy employed by the cell to control the behaviour an

Single Residue Mutation in Active Site of Serine Acetyltransferase Isoform 3 from <em>Entamoeba histolytica</em> Assists in Partial Regaining of Feedback Inhibition by Cysteine

<div><p>The cysteine biosynthetic pathway is essential for survival of the protist pathogen <i>Entamoeba histolytica,</i> and functions by producing cysteine for countering oxidative attack during infection in human hosts. Serine acetyltransferase (SAT) and O-acetylserine sulfhydrylase (OASS) are involved in cysteine biosynthesis and are present in three isoforms each. While EhSAT1 and EhSAT2 are feedback inhibited by end product cysteine, EhSAT3 is nearly insensitive to such inhibition. The active site residues of EhSAT1 and of EhSAT3 are identical except for position 208, which is a histidine residue in EhSAT1 and a serine residue in EhSAT3. A combination of comparative modeling, multiple molecular dynamics simulations and free energy calculation studies showed a difference in binding energies of native EhSAT3 and of a S208H-EhSAT3 mutant for cysteine. Mutants have also been generated <i>in vitro</i>, replacing serine with histidine at position 208 in EhSAT3 and replacing histidine 208 with serine in EhSAT1. These mutants showed decreased affinity for substrate serine, as indicated by K_m, compared to the native enzymes. Inhibition kinetics in the presence of physiological concentrations of serine show that IC50 of EhSAT1 increases by about 18 folds from 9.59 µM for native to 169.88 µM for H208S-EhSAT1 mutant. Similar measurements with EhSAT3 confirm it to be insensitive to cysteine inhibition while its mutant (S208H-EhSAT3) shows a gain of cysteine inhibition by 36% and the IC50 of 3.5 mM. Histidine 208 appears to be one of the important residues that distinguish the serine substrate from the cysteine inhibitor.</p> </div

Prediction and Analysis of Canonical EF Hand Loop and Qualitative Estimation of Ca²⁺ Binding Affinity

<div><p>The diversity of functions carried out by EF hand-containing calcium-binding proteins is due to various interactions made by these proteins as well as the range of affinity levels for Ca²⁺ displayed by them. However, accurate methods are not available for prediction of binding affinities. Here, amino acid patterns of canonical EF hand sequences obtained from available crystal structures were used to develop a classifier that distinguishes Ca²⁺-binding loops and non Ca²⁺-binding regions with 100% accuracy. To investigate further, we performed a proteome-wide prediction for <i>E. histolytica</i>, and classified known EF-hand proteins. We compared our results with published methods on the E. <i>histolytica</i> proteome scan, and demonstrated our method to be more specific and accurate for predicting potential canonical Ca²⁺-binding loops. Furthermore, we annotated canonical EF-hand motifs and classified them based on their Ca²⁺-binding affinities using support vector machines. Using a novel method generated from position-specific scoring metrics and then tested against three different experimentally derived EF-hand-motif datasets, predictions of Ca²⁺-binding affinities were between 87 and 90% accurate. Our results show that the tool described here is capable of predicting Ca²⁺-binding affinity constants of EF-hand proteins. The web server is freely available at <a href="http://202.41.10.46/calb/index.html" target="_blank">http://202.41.10.46/calb/index.html</a>.</p></div

The Performance of SVM Models with different learning parameters on D1 and D2 dataset.

<p>Using binary patterns and AA (amino acid) composition [γ <b>(g)</b> (in RBF kernel), c: parameter for trade-off between training error & margin] where SN–sensitivity, SP–specificity, ACC-accuracy, MCC–Matthews Correlation Coefficient.</p

The binding free energy of EhSAT1 and EhSAT3 complexes.

<p>The table shows the detailed contribution of energy components calculated using Poisson Boltzmann Surface Area (MM-PBSA) method for EhSAT1, EhSAT3 and their mutants to evaluate their binding activity. Here ΔE_Ele, electrostatic interactions; ΔE_Vdw, van der Waals interactions, ΔE_MM = ΔE _Ele+ΔE_Vdw, ΔG_sol-ele: polar solvation free energy are calculated by solving the Poisson-Boltzmann equation PB; ΔG_sol-np, non-polar solvation free energy, ΔG_polar = ΔE _Ele+ΔG_sol-ele; ΔG_nonpolar = ΔE_Vdw+ΔG_sol-np, ΔG_Bind = estimated total binding free energy.</p

Kinetic study of EhSAT3.

<p>Michaelis Menten representation of velocity values plotted against the serine concentrations for EhSAT3 native and mutant (S208H). Kinetic studies were done in 50 mM Tris buffer pH 8.0 keeping acetyl CoA concentration constant at 0.1 mM and varying concentration of serine from 10 µM to 500 µM. The inhibition studies were done in presence of 5 µM and 10 µM of cysteine. K_m was calculated by the Michaelis Menten equation using Sigma Plot software. Standard deviations are calculated from the three independent experiments for each substrate concentration values. EhSAT3 activity was not affected by the mutation but the mutant EhSAT3 show increased sensitiveness to cysteine inhibition.</p