Biology subjects

SENGUPTA, K.

Publications and source records attributed to SENGUPTA, K..

3 recordsLinked to original sources

bioRxiv2022

Elevated levels of lamin A promote HR and NHEJ-mediated repair mechanisms in etoposide-treated ovarian cancer cells

Lamins are emerging as major regulators in the maintenance of nuclear architecture and genome organization. Extensive research for the last two decades has enormously contributed to understanding the roles of lamins in various signaling mechanisms which are drastically modified in neoplasia. It is interesting to record that alteration in lamin A/C expression and distribution drives tumorigenesis of almost all tissues of human bodies. One of the important signatures of a cancer cell is its inability to repair DNA damage which befalls several genomic events that transform the cells to be sensitive to chemotherapeutic agents. This genomic and chromosomal instability is the most common feature found in cases of high-grade ovarian serous carcinoma. Here, we report elevated levels of lamins in OVCAR3 cells (High grade ovarian serous carcinoma cell line) in comparison to IOSE (Immortalised ovarian surface epithelial cells) and consequently altered damage repair machinery in OVCAR3. We have analyzed the changes in global gene expression as a sequel to DNA damage induced by etoposide in ovarian carcinoma where lamin A is particularly elevated in expression and reported some differentially expressed genes associated with pathways conferring cellular proliferation and chemoresistance. We highlight new avenues unraveling the role of upregulated lamin A in confronting chemically induced genomic instability in the context of high grade ovarian serous cancer through a combination of HR and NHEJ mechanisms.

cancer biology↗

bioRxiv2022

Microdynamics of lamin A Ig-fold domain regulates higher order assembly of the protein

Lamins maintain the shape and rigidity of the nucleus in the form of a proteinaceous scaffold underneath the inner nuclear membrane (INM) and provide anchorage to chromatin and other nuclear proteins. Mutations in the human LMNA gene encoding lamin A/C cause about 16 different diseases with distinct phenotypes collectively termed as laminopathies which affect primarily the muscle tissues as well as adipose tissues, neuromuscular junctions and multiple other organs in progeroid syndromes. Lamins contain several domains of which Ig-fold is one of the well characterized and structured domains that harbours many mutations leading to deleterious interactions with other nuclear proteins. In this work, we have elucidated the effects of 3 such mutations namely R453W, W498C and W498R on the dynamics and flexibility of the Ig-fold domain and the consequent effect on the assembly into lamina by live cell imaging, fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulations. From our experimental observations, we concluded that R453W has somewhat increased flexibility compared to the other mutants but lower than that of the wild type. This might be attributed due to an alteration of the salt bridges in the Ig fold domain of the mutant. This is the first report where such an alteration in the full length has been documented by gross changes in diffusional properties as a sequel to a mutation in the Ig fold domain

biochemistry↗

bioRxiv2020

Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL): A novel architecture for accurate diagnosis/prognosis of Ovarian Cancer

Nuclear morphological features are potent determining factors for clinical diagnostic approaches adopted by pathologists to analyse the malignant potential of cancer cells. Considering the structural alteration of nucleus in cancer cells, various groups have developed machine learning techniques based on variation in nuclear morphometric information like nuclear shape, size, nucleus-cytoplasm ratio and various non-parametric methods like deep learning have also been tested for analysing immunohistochemistry images of tissue samples for diagnosing various cancers. Our aim is to study the morphometric distribution of nuclear lamin proteins as a specific parameter in ovarian cancer tissues. Besides being the principal mechanical component of the nucleus, lamins also present a platform for binding of proteins and chromatin thereby serving a wide range of nuclear functions like maintenance of genome stability, chromatin regulation. Altered expression of lamins in different subtypes of cancer is now evident from data across the world. It has already been elucidated that in ovarian cancer, extent of alteration in nuclear shape and morphology can determine degree of genetic changes and thus can be utilized to predict the outcome of low to high form of serous carcinoma. In this work, we have performed exhaustive imaging of ovarian cancer versus normal tissue and introduced a novel Deep Hybrid Learning approach on the basis of the distribution of lamin proteins. Although developed with ovarian cancer datasets in view, this architecture would be of immense importance in accurate and fast diagnosis and prognosis of all types of cancer associated with lamin induced morphological changes and would perform across small/medium to large datasets with equal efficiency. Significance StatementWe have developed a novel Deep Hybrid Learning approach based on nuclear morphology to classify normal and ovarian cancer tissues with highest possible accuracy and speed. Ovarian cancer cells can be easily distinguished from their enlarged nuclear morphology as is evident from lamin A & B distribution pattern. This is the first report to invoke specific nuclear markers like lamin A & B instead of classical haematoxylin-eosin staining in an effort to build parametric datasets. Our approach has been shown to outperform the existing deep learning techniques in training and validation of datasets over a wide range. Therefore this method could be used as a robust model to predict malignant transformations of benign nuclei and thus be implemented in the diagnosis and prognosis of ovarian cancer in future. Most importantly, this method can be perceived as a generalized approach in the diagnosis for all types of cancer.

cancer biology↗