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RESEARCH & FUNDING

Research Areas

Why Do We Study Female Reproductive Tissues?

Data from the Institute of Health Metrics and Evaluation (IHME) and Global Burden of Disease (GBD) in 2019 show that India leads the world in gynecological disease-related deaths per 100,000 population. This emphasizes the critical need for improved healthcare strategies to address these conditions. Moreover, gynecological cancers pose a major health challenge for women worldwide, including in India. According to the National Cancer Registry Programme (NCRP), cancers of the female reproductive system, such as those affecting the uterine cervix, uterine corpus, ovaries, and vulva, rank second in incidence among gynecological cancers in India, with only breast cancer having a higher rate. Various diseases and malignancies are linked to gynecological tissues, and hysterectomy remains one of the most common major surgeries performed globally and in India.

Understanding the normal and pathological physiology of female reproductive organs is crucial due to their inherent complexity. These tissues and organs exhibit a rich diversity in cellular structures and functions, which play vital roles in reproduction and overall health. Our research aims to uncover tissue-specific cellular and molecular alterations that occur during development and in various disease states. By investigating these changes, we can gain insights into the multifaceted nature of gynecological conditions. Furthermore, we will address the challenges posed by treatment resistance, which often arises from the heterogeneity and plasticity of cells within these tissues. This complexity makes it essential to explore the unique characteristics of female reproductive systems to develop new therapies. Ultimately, our findings may enhance our understanding of how to better treat gynecological diseases and improve reproductive health of women.

Regulatory Elements of the Genome

Both coding and noncoding regulatory elements in the genome play critical roles in driving cellular, molecular, and tissue heterogeneity and plasticity. Coding sequences produce proteins that direct key cellular functions, while noncoding regulatory elements—such as promoters, enhancers, silencers, and long noncoding RNAs—control when, where, and how these genes are expressed. This regulation contributes to cell-type specificity, allowing different tissues to develop distinct functional identities despite sharing the same genomic blueprint. Noncoding regions, particularly enhancers, also play a key role in cellular plasticity, the ability of cells to adapt or reprogram their function, which is critical during tissue development and regeneration. By studying these regulatory elements, we gain insights into how gene expression patterns are fine-tuned during development, helping us understand normal tissue formation. In disease and malignancies, dysregulation of these elements can lead to altered cellular states, driving tumor heterogeneity and promoting drug resistance. Mapping these elements in cancer can reveal mechanisms of resistance, offering potential targets for therapeutic intervention, and guiding the development of treatments that address tumor plasticity and heterogeneity to combat relapse and resistance.

NextGen Sequencing Approaches

Genome, epigenome, and transcriptome-based Next-Generation Sequencing (NGS) approaches have revolutionized the study of gene regulation by providing comprehensive insights into regulatory elements that control gene expression. Genomic sequencing identifies the complete DNA sequence, enabling the discovery of regulatory regions such as promoters, enhancers, and silencers. Epigenomic sequencing, including techniques like ChIP-seq, ATAC-seq, and bisulfite sequencing, maps DNA modifications (e.g., methylation) and histone modifications, offering insights into chromatin accessibility and how these modifications regulate gene activity. Transcriptome sequencing (RNA-seq) captures the complete set of RNA transcripts, revealing how regulatory elements control gene expression patterns across different cell types, conditions, or disease states. Together, these NGS approaches allow for the precise mapping of regulatory elements and their functional impact. For instance, integrating epigenomic and transcriptomic data can help identify enhancer elements that drive tissue-specific expression or pinpoint dysregulated gene networks in cancer. Advances in multiomic approaches now enable the simultaneous profiling of the genome, epigenome, and transcriptome, enhancing our ability to link specific regulatory elements with functional outcomes, paving the way for understanding complex gene regulation mechanisms in health and disease.

Regulome In Female Reproductive Tissues

Coding and noncoding regulatory elements in the genome, along with epigenomic modifications, are crucial for understanding the tissue heterogeneity and physiological changes in female reproductive tissues across different life stages. Coding regions of the genome provide the instructions for proteins that mediate key processes like cell division, hormone signaling, and tissue remodeling. Noncoding regulatory elements, such as enhancers, promoters, and long noncoding RNAs, modulate when and where these genes are expressed, contributing to the diversity of cellular functions in tissues like the endometrium, ovaries, and placenta. Epigenetic marks, including DNA methylation and histone modifications, further regulate gene expression by making chromatin more or less accessible, allowing dynamic responses to hormonal and environmental signals. These regulatory mechanisms govern tissue development, cyclic processes like menstruation, and the complex adaptations required during pregnancy, both prenatal and postnatal. During menopause, shifts in epigenomic regulation impact hormone levels and tissue homeostasis, contributing to aging-related changes. Dysregulation of these elements can lead to diseases such as endometriosis, polycystic ovary syndrome (PCOS), and reproductive cancers. By integrating genomic and epigenomic data, we can better understand how these regulatory networks drive tissue heterogeneity, and their role in gynecological diseases and malignancies, enabling targeted therapies and improved reproductive health outcomes.

Computational Modeling of Regulome

Computational predictive models and statistical approaches are invaluable for modeling the coding and noncoding regulatory elements in the genome and epigenome, offering deep insights into the complex regulation of female reproductive tissues. These models integrate high-dimensional data from genomics, transcriptomics, and epigenomics to predict how various regulatory elements, such as enhancers, promoters, and long noncoding RNAs, control gene expression in specific tissue contexts. By applying statistical techniques like machine learning, these models can identify patterns and interactions within large datasets that are difficult to discern manually, allowing researchers to uncover the regulatory networks driving key processes like menstruation, pregnancy, and menopause. In addition, epigenomic modeling helps track how chromatin modifications influence tissue-specific gene expression, shedding light on the dynamic nature of tissue heterogeneity. Predictive models also aid in identifying the genetic and epigenetic drivers of diseases such as endometriosis, PCOS, and reproductive cancers, highlighting potential biomarkers and therapeutic targets. By improving our understanding of these regulatory mechanisms, computational approaches can optimize treatment strategies, develop personalized therapies, and improve reproductive health outcomes for women, paving the way for more effective prevention and management of gynecological disorders and malignancies.