Delineating the Mechanism of Fragility at BCL6 Breakpoint Region Associated With Translocations in Diffuse Large B Cell Lymphoma

BCL6 translocation is one of the most common chromosomal translocations in cancer and results in its enhanced expression in germinal center B cells. It involves the fusion of BCL6 with any of its twenty-six Ig and non-Ig translocation partners associated with diffuse large B cell lymphoma (DLBCL). Despite being discovered long back, the mechanism of BCL6 fragility is largely unknown. Analysis of the translocation breakpoints in 5\u27 UTR of BCL6 reveals the clustering of most of the breakpoints around a region termed Cluster II. In silico analysis of the breakpoint cluster sequence identified sequence motifs that could potentially fold into non-B DNA. Results revealed that the Cluster II sequence folded into overlapping hairpin structures and identified sequences that undergo base pairing at the stem region. Further, the formation of cruciform DNA blocked DNA replication. The sodium bisulfite modification assay revealed the single-strandedness of the region corresponding to hairpin DNA in both strands of the genome. Further, we report the formation of intramolecular parallel G4 and triplex DNA, at Cluster II. Taken together, our studies reveal that multiple non-canonical DNA structures exist at the BCL6 cluster II breakpoint region and contribute to the fragility leading to BCL6 translocation in DLBCL patients

Rational Mutational Analysis of a Multidrug MFS Transporter CaMdr1p of Candida albicans by Employing a Membrane Environment Based Computational Approach

CaMdr1p is a multidrug MFS transporter of pathogenic Candida albicans. An over-expression of the gene encoding this protein is linked to clinically encountered azole resistance. In-depth knowledge of the structure and function of CaMdr1p is necessary for an effective design of modulators or inhibitors of this efflux transporter. Towards this goal, in this study, we have employed a membrane environment based computational approach to predict the functionally critical residues of CaMdr1p. For this, information theoretic scores which are variants of Relative Entropy (Modified Relative Entropy REM) were calculated from Multiple Sequence Alignment (MSA) by separately considering distinct physico-chemical properties of transmembrane (TM) and inter-TM regions. The residues of CaMdr1p with high REM which were predicted to be significantly important were subjected to site-directed mutational analysis. Interestingly, heterologous host Saccharomyces cerevisiae, over-expressing these mutant variants of CaMdr1p wherein these high REM residues were replaced by either alanine or leucine, demonstrated increased susceptibility to tested drugs. The hypersensitivity to drugs was supported by abrogated substrate efflux mediated by mutant variant proteins and was not attributed to their poor expression or surface localization. Additionally, by employing a distance plot from a 3D deduced model of CaMdr1p, we could also predict the role of these functionally critical residues in maintaining apparent inter-helical interactions to provide the desired fold for the proper functioning of CaMdr1p. Residues predicted to be critical for function across the family were also found to be vital from other previously published studies, implying its wider application to other membrane protein families

A fact sheet on communal riots in India

“This occasional paper of Public Policy Research Centre has tried to see hard facts related to major riots in India with an objective to have dispassionate and objective analysis of the communal riots in India. The objective behind this monograph is to help objective researchers and analysts.

Machine-learning-based integrative –‘omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction

Summary: Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED