Showing papers by "Sushmita Paul published in 2020"
TL;DR: This study demonstrates a novel example of an activator role of ZEB1 for the epigenetic landscape in colorectal tumor cells and identifies a self-reinforcing loop for Z EB1 expression and found that the SETD1B associated active chromatin mark H3K4me3 was enriched at the ZEB 1 promoter in EMT cells.
Abstract: Epigenetic deregulation remarkably triggers mechanisms associated with tumor aggressiveness like epithelial-mesenchymal transition (EMT). Since EMT is a highly complex, but also reversible event, epigenetic processes such as DNA methylation or chromatin alterations must be involved in its regulation. It was recently described that loss of the cell cycle regulator p21 was associated with a gain in EMT characteristics and an upregulation of the master EMT transcription factor ZEB1. In this study, in silico analysis was performed in combination with different in vitro and in vivo techniques to identify and verify novel epigenetic targets of ZEB1, and to proof the direct transcriptional regulation of SETD1B by ZEB1. The chorioallantoic-membrane assay served as an in vivo model to analyze the ZEB1/SETD1B interaction. Bioinformatical analysis of CRC patient data was used to examine the ZEB1/SETD1B network under clinical conditions and the ZEB1/SETD1B network was modeled under physiological and pathological conditions. Thus, we identified a self-reinforcing loop for ZEB1 expression and found that the SETD1B associated active chromatin mark H3K4me3 was enriched at the ZEB1 promoter in EMT cells. Moreover, clinical evaluation of CRC patient data showed that the simultaneous high expression of ZEB1 and SETD1B was correlated with the worst prognosis. Here we report that the expression of chromatin modifiers is remarkably dysregulated in EMT cells. SETD1B was identified as a new ZEB1 target in vitro and in vivo. Our study demonstrates a novel example of an activator role of ZEB1 for the epigenetic landscape in colorectal tumor cells.
47 citations
TL;DR: This paper has a special focus on tracking of Indian viral sequences submitted in public domain, as this gene has been reported with extensive positive selection as well as potential drug target and is important to observe the changes in NSP3 gene.
Abstract: As recently classified as a pandemic by WHO, novel Corononavirus 2019 has affected almost every corner of the globe causing human deaths in a range of hundred thousands. The virus having its roots in Wuhan (China) has been spread over the world by its own property to change itself accordingly. These changes correspond to its transmission and pathogenicity due to which the concept of social distancing appeared into the picture. In this paper, a few findings from the whole genome sequence analysis of viral genome sequences submitted from India are presented. The data used for analysis comprises 440 collective genome sequences of virus submitted in GenBank, GISAID, and SRA projects, from around the world as well as 28 viral sequences from India. Multiple sequence alignment of all genome sequences was performed and analysed. A novel non-synonymous mutation 4809C>T (S1515F) in NSP3 gene of SARS-CoV2 Indian strains is reported along with other frequent and important changes from around the world: 3037C>T, 14408C>T, and 23403A>G. The novel change was observed in samples collected in the month of March, whereas was found to be absent in samples collected in January with the respective persons travel history to China. Phylogenetic analysis clustered the sequences with this change as one separate clade. Mutation was predicted as stabilising change by insilco tool DynaMut. A second patient in the world to our knowledge with multiple (Wuhan and USA) strain contraction was observed in this study. The infected person is among the two early infected patients with travel history to China. Strains sequenced in Iran stood out to have different variants, as most of the reported frequent variants were not observed. The objective of this paper is to highlight the similarities and changes observed in the submitted Indian viral strains. This helps to keep track on the activity, that how virus is changing into a new subtype. Major strains observed were European with the novel change in India and other being emergent clade of Iran. Its important to observe the changes in NSP3 gene, as this gene has been reported with extensive positive selection as well as potential drug target. Extensive Positive Selection Drives the Evolution of Nonstructural Proteins. With the limited number of sequences this was the only frequent novel non-synonymous change observed from Indian strains, thereby making this change vulnerable for investigation in future. This paper has a special focus on tracking of Indian viral sequences submitted in public domain.
13 citations
TL;DR: Investigation of the cross-regulation of the mouse macrophage transcriptome by IFN-γ and by TDM or its synthetic analogue trehalose-6,6-dibehenate revealed a significant degree of negative regulation of IFN–γ–induced Ag presentation and antimicrobial gene expression by the mycobacterial cord factor that may contribute to myc Cobacterial persistence.
Abstract: Mycobacteria survive in macrophages despite triggering pattern recognition receptors and T cell-derived IFN-γ production. Mycobacterial cord factor trehalose-6,6-dimycolate (TDM) binds the C-type lectin receptor MINCLE and induces inflammatory gene expression. However, the impact of TDM on IFN-γ-induced macrophage activation is not known. In this study, we have investigated the cross-regulation of the mouse macrophage transcriptome by IFN-γ and by TDM or its synthetic analogue trehalose-6,6-dibehenate (TDB). As expected, IFN-γ induced genes involved in Ag presentation and antimicrobial defense. Transcriptional programs induced by TDM and TDB were highly similar but clearly distinct from the response to IFN-γ. The glycolipids enhanced expression of a subset of IFN-γ-induced genes associated with inflammation. In contrast, TDM/TDB exerted delayed inhibition of IFN-γ-induced genes, including pattern recognition receptors, MHC class II genes, and IFN-γ-induced GTPases, with antimicrobial function. TDM downregulated MHC class II cell surface expression and impaired T cell activation by peptide-pulsed macrophages. Inhibition of the IFN-γ-induced GTPase GBP1 occurred at the level of transcription by a partially MINCLE-dependent mechanism that may target IRF1 activity. Although activation of STAT1 was unaltered, deletion of Socs1 relieved inhibition of GBP1 expression by TDM. Nonnuclear Socs1 was sufficient for inhibition, suggesting a noncanonical, cytoplasmic mechanism. Taken together, unbiased analysis of transcriptional reprogramming revealed a significant degree of negative regulation of IFN-γ-induced Ag presentation and antimicrobial gene expression by the mycobacterial cord factor that may contribute to mycobacterial persistence.
10 citations
TL;DR: The proposed algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is found to generate more robust, integrated, and functionally enriched mi RNA-m RNA modules in cervical cancer.
Abstract: Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.
9 citations
TL;DR: A novel method for feature weighting based on robust regression fit is developed and has been demonstrated on different data sets to identify similar groups of patients that represent a cancer subtype.
Abstract: Identification of cancer subtypes is critically important for understanding the heterogeneity present in tumors. Integrating information from multiple sources, homogeneous groups for cancer can be identified. However, there is a lack of computational approaches to identify histological subtypes among the patients suffering from different types of cancers. Assigning weight to the biomarkers prior to the integration of multiple information sources for the same set of samples can play an important role in cancer subtypes identification, which has not been explored previously. Sub-typing of cancers can help in analyzing shared molecular profiles between different histological subtypes of solid tumors. A novel method for feature weighting based on robust regression fit is developed in this study. The weight is utilized to find similarity between patients individually from each of the information sources. Here, miRNA and mRNA expression profiles across the same set of samples have been used. Patient-similarity networks, that are generated from each of the expression profiles are then integrated using the approach of Similarity Network Fusion. Finally, Spectral clustering is applied on the fused network to identify similar groups of patients that represent a cancer subtype. The effectiveness of the proposed method has been demonstrated on different data sets.
1 citations