Shreya Roy Chowdhury

University of Massachusetts Chan Medical School

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Pallob Kundu

Bose Institute

Shrabani Basak

Bose Institute

Sib Sankar Roy

Indian Institute of Chemical Biology

Sunil K. Malonia

University of Massachusetts Chan Medical School

Tessa M. Simone

University of Massachusetts Chan Medical School

Narendra M. Dixit

Indian Institute of Science Bangalore

Tianyi Ye

University of Massachusetts Chan Medical School

Alok Mishra

University of Massachusetts Chan Medical School

Upasana Ray

The University of Texas MD Anderson Cancer Center

Lihua Julie Zhu

University of Massachusetts Chan Medical School

Cooperative Institutions

University of Massachusetts Chan Medical School

Bose Institute

Howard Hughes Medical Institute

Lawrence Berkeley National Laboratory

Indian Institute of Chemical Biology

Council of Scientific and Industrial Research

Indian Institute of Science Bangalore

University of Calcutta

Techno India University

PES University

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Gene regulatory networking reveals the molecular cue to lysophosphatidic acid‐induced metabolic adaptations in ovarian cancer cells

Molecular Oncology (2017)

Upasana Ray Shreya Chowdhury Madavan Vasudevan Kiran Bankar Susanta Roychoudhury

Extravasation and metastatic progression are two main reasons for the high mortality rate associated with cancer. The metastatic potential of cancer cells depends on a plethora of metabolic challenges prevailing within the tumor microenvironment. To achieve higher rates of proliferation, cancer cells reprogram their metabolism, increasing glycolysis and biosynthetic activities. Just why this metabolic reprogramming predisposes cells towards increased oncogenesis remains elusive. The accumulation of myriad oncolipids in the tumor microenvironment has been shown to promote the invasiveness of cancer cells, with lysophosphatidic acid (LPA) being one such critical factor enriched in ovarian cancer patients. Cellular bioenergetic studies confirm that oxidative phosphorylation is suppressed and glycolysis is increased with long exposure to LPA in ovarian cancer cells compared with non-transformed epithelial cells. We sought to uncover the regulatory complexity underlying this oncolipid-induced metabolic perturbation. Gene regulatory networking using RNA-Seq analysis identified the oncogene ETS-1 as a critical mediator of LPA-induced metabolic alterations for the maintenance of invasive phenotype. Moreover, LPA receptor-2 specific PtdIns3K-AKT signaling induces ETS-1 and its target matrix metalloproteases. Abrogation of ETS-1 restores cellular bioenergetics towards increased oxidative phosphorylation and reduced glycolysis, and this effect was reversed by the presence of LPA. Furthermore, the bioenergetic status of LPA-treated ovarian cancer cells mimics hypoxia through induction of hypoxia-inducible factor-1α, which was found to transactivate ets-1. Studies in primary tumors generated in syngeneic mice corroborated the in vitro findings. Thus, our study highlights the phenotypic changes induced by the pro-metastatic factor ETS-1 in ovarian cancer cells. The relationship between enhanced invasiveness and metabolic plasticity further illustrates the critical role of metabolic adaptation of cancer cells as a driver of tumor progression. These findings reveal oncolipid-induced metabolic predisposition as a new mechanism of tumorigenesis and propose metabolic inhibitors as a potential approach for future management of aggressive ovarian cancer.

10.1002/1878-0261.12046

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Citations (31)

Tomato miR398 knockout disrupts ROS dynamics during stress conferring heat tolerance but hypersusceptibility to necrotroph infection

Plant Molecular Biology (2025)

Shreya Chowdhury Ananya Mukherjee Raghuvir Singh Sushmita Talukdar Shrabani Basak

10.1007/s11103-025-01563-z

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Role of Parents in the Gender Role Identity Formation of Offspring: An Exploratory Study

Journal of Pediatrics Perinatology and Child Health (2024)

Shreya Chowdhury Suvosree Bhattacharya Surajit Bhattacharya Anjan Bhattacharya

Gender role identity tends to shape an individual’s self-concept, playing a significant role in shaping their behaviour and interpersonal relationship patterns across different societies, races, and ethnicities. This study explores the relationship between perceived parenting styles (authoritarian, authoritative, and permissive for both parents) and gender role identity in young adults, and also the pattern of gender role identity of parents and their offspring. The sample consisted of 100 young adults (M=20.60, SD=1.98) and both of their parents (father and mother). Both the young adults and their parents responded to the Bem Sex-Role Inventory, whereas only young adults responded to the Parental Authority Questionnaire. Descriptive statistics showed that out of 100 young adults, 38% identified as androgynous, followed by feminine (29%), masculine (12%), and undifferentiated (21%). 54% of male young adults rated themselves as androgynous, whereas 46% of females identified as feminine. A considerable proportion of the parents reported themselves as androgynous (both 48%). A significant correlation was found between permissive parenting style in mothers and undifferentiated gender role identity in young adults (r= 0.75, p= 8.1e-05). A positive, moderately significant correlation was also found between fathers' permissive parenting style and masculinity in young adults (r= 0.83, p= 0.00093). This information will help us understand parents' role in gender identity formation and may also help us formulate management plans in terms of the parent-child relationship and their gender role identity.

Exploratory research

10.26502/jppch.74050178

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Adjusting for specificity of symptoms reveals higher prevalence of asymptomatic SARS-CoV-2 infections than previously estimated

medRxiv (Cold Spring Harbor Laboratory) (2024)

Akshay Tiwari Shreya Chowdhury Ananthu James Budhaditya Chatterjee Narendra M. Dixit

Accurate estimates of the prevalence of asymptomatic SARS-CoV-2 infections, ψ, have been important for understanding and forecasting the trajectory of the COVID-19 pandemic. Two-part population-based surveys, which test the infection status and also assess symptoms, have been used to estimate ψ. Here, we identified a widely prevalent confounding effect that compromises these estimates and devised a formalism to adjust for it. The symptoms associated with SARS-CoV-2 infection are not all specific to SARS-CoV-2. They can be triggered by a host of other conditions, such as influenza virus infection. By not accounting for the source of the symptoms, the surveys may misclassify individuals experiencing symptoms from other conditions as symptomatic for SARS-CoV-2, thus underestimating ψ. We developed a rigorous formalism to adjust for this confounding effect and derived a facile formula for the adjusted prevalence, ψ adj . We applied it to data from 50 published serosurveys, conducted on the general populations from 28 nations. We found that ψ adj was significantly higher than the reported prevalence, ψ c (P=3×10 -8 ). The median ψ adj was ~60%, whereas the median ψ c was ~40%. In several instances, ψ adj exceeded ψ c by >100%. These findings suggest that asymptomatic infections have been far more prevalent than previously estimated. Our formalism can be readily deployed to obtain more accurate estimates of ψ from standard population-based surveys, without additional data collection. The findings have implications for understanding COVID-19 epidemiology and devising more effective interventions.

2019-20 coronavirus outbreak

10.1101/2024.09.11.24313462

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Twitter Bot Detection and Ranking using Supervised Machine Learning Models

K Sujith Shreya Chowdhury Arsh Goyal A Hegde Ramamoorthy Srinath

The rise in use of social media has led to a sharp increase in the number of social bot accounts on platforms like Twitter. A majority of these accounts are used for malicious activities such as opinion mangling, hashtag spamming and directing users to spam URLs. These bots can cause societalunrest and manipulation if they are left unchecked and are not removed. This paper proposes a supervised machine learning approach with multiple models to not only detect these bots but also analyse the impact of these accounts to the community and rank them. Separate machine learning models are created to classify accounts based on user metadata, tweet content and posting history. The models are trained on the MIB dataset. A custom rule based algorithm is used to combine the predictions of these models to achieve an accuracy of 95.7%. This algorithm gives a better accuracy than any model gives in isolation.

Learning to Rank

Rank (graph theory)

Supervised Learning

Isolation

10.1109/icdsaai55433.2022.10028860

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Heightened miR6024-NLR interactions facilitate necrotrophic pathogenesis in tomato

Plant Molecular Biology (2022)

Sayani Dey Arijita Sarkar Shreya Chowdhury Raghuvir Singh Ananya Mukherjee

10.1007/s11103-022-01270-z

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Citations (19)

Comparative study of neural networks and k-means classification in web usage mining

International Conference for Internet Technology and Secured Transactions (2010)

Prakash Raghavendra Shreya Chowdhury Srilekha Vedula Kameswari

There are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user's interests. In this paper, we model user behaviour as a vector of the time he spends at each URL, and further classify a new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, artificial neural networks, and artificial neural networks with standardised inputs were implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour.

Similarity (geometry)

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Targeted apoptosis in ovarian cancer cells through mitochondrial dysfunction in response to Sambucus nigra agglutinin

Cell Death and Disease (2017)

Shreya Chowdhury Upasana Ray Bishnu P. Chatterjee Sib Sankar Roy

Abstract Ovarian carcinoma (OC) patients encounter the severe challenge of clinical management owing to lack of screening measures, chemoresistance and finally dearth of non-toxic therapeutics. Cancer cells deploy various defense strategies to sustain the tumor microenvironment, among which deregulated apoptosis remains a versatile promoter of cancer progression. Although recent research has focused on identifying agents capable of inducing apoptosis in cancer cells, yet molecules efficiently breaching their survival advantage are yet to be classified. Here we identify lectin, Sambucus nigra agglutinin (SNA) to exhibit selectivity towards identifying OC by virtue of its specific recognition of α -2, 6-linked sialic acids. Superficial binding of SNA to the OC cells confirm the hyper-sialylated status of the disease. Further, SNA activates the signaling pathways of AKT and ERK1/2, which eventually promotes de-phosphorylation of dynamin-related protein-1 (Drp-1). Upon its translocation to the mitochondrial fission loci Drp-1 mediates the central role of switch in the mitochondrial phenotype to attain fragmented morphology. We confirmed mitochondrial outer membrane permeabilization resulting in ROS generation and cytochrome-c release into the cytosol. SNA response resulted in an allied shift of the bioenergetics profile from Warburg phenotype to elevated mitochondrial oxidative phosphorylation, altogether highlighting the involvement of mitochondrial dysfunction in restraining cancer progression. Inability to replenish the SNA-induced energy crunch of the proliferating cancer cells on the event of perturbed respiratory outcome resulted in cell cycle arrest before G2/M phase. Our findings position SNA at a crucial juncture where it proves to be a promising candidate for impeding progression of OC. Altogether we unveil the novel aspect of identifying natural molecules harboring the inherent capability of targeting mitochondrial structural dynamics, to hold the future for developing non-toxic therapeutics for treating OC.

Warburg Effect

Sambucus nigra

10.1038/cddis.2017.77

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Targeting the GPI transamidase subunit GPAA1 abrogates the CD24 immune checkpoint in ovarian cancer

Cell Reports (2024)

Alok Mishra Tianyi Ye Shahid Banday Ritesh P. Thakare Chinh Tran-To Su

CD24 is frequently overexpressed in ovarian cancer and promotes immune evasion by interacting with its receptor Siglec10, present on tumor-associated macrophages, providing a "don't eat me" signal that prevents targeting and phagocytosis by macrophages. Factors promoting CD24 expression could represent novel immunotherapeutic targets for ovarian cancer. Here, using a genome-wide CRISPR knockout screen, we identify GPAA1 (glycosylphosphatidylinositol anchor attachment 1), a factor that catalyzes the attachment of a glycosylphosphatidylinositol (GPI) lipid anchor to substrate proteins, as a positive regulator of CD24 cell surface expression. Genetic ablation of GPAA1 abolishes CD24 cell surface expression, enhances macrophage-mediated phagocytosis, and inhibits ovarian tumor growth in mice. GPAA1 shares structural similarities with aminopeptidases. Consequently, we show that bestatin, a clinically advanced aminopeptidase inhibitor, binds to GPAA1 and blocks GPI attachment, resulting in reduced CD24 cell surface expression, increased macrophage-mediated phagocytosis, and suppressed growth of ovarian tumors. Our study highlights the potential of targeting GPAA1 as an immunotherapeutic approach for CD24

CD24

10.1016/j.celrep.2024.114041

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Asymptomatic SARS-CoV-2 infections tend to occur less frequently in developed nations

medRxiv (Cold Spring Harbor Laboratory) (2023)

Shreya Chowdhury Akshay Tiwari Ananthu James Budhaditya Chatterjee Narendra M. Dixit

ABSTRACT Unlike severe infections, asymptomatic infections occur independently of healthcare access and reflect the natural immunity to SARS-CoV-2. What determines their prevalence, ψ , and its variation across nations is unknown. We conducted a systematic review of serosurveys performed on samples representative of national populations before vaccination and the emergence of variants. The studies that met our selection criteria together sampled 4,58,489 individuals and yielded estimates of ψ in 33 nations. Using random-effects modeling, we found the pooled global ψ to be 45.3% (95% CI: 33.6%-57.5%). ψ varied widely across nations (range: 6%-96%; I 2 =99.7%), highlighting the enormous underlying variation in the natural immunity to SARS-CoV-2. Performing meta-regression with national-level metrics, we found that the human development index (HDI) was negatively correlated with ψ (p=10 −13 ; R 2 =65.5%). More developed nations thus experienced less frequent asymptomatic infections on average. These findings have implications for unraveling the origins of asymptomatic infections and for future pandemic preparedness.

Pandemic

Preparedness

10.1101/2023.12.14.23299954

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