Selva Rupa C. immanuel

selva rupa C. immanuel

Rupa's Doctoral Thesis work is entitled "Metabolic Reprogramming of the Cell: Investigating Systemic Signatures in Cancer"

She defended her thesis on September 21st 2018.

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Rupa is a DST-INSPIRE fellow, working with Dr. Anu Raghunathan in the MICE lab at CSIR-NCL. She has a Bachelors degree in Biotechnology and Masters in Nanoscience and Nanotechnology from University of Madras, Chennai. Her doctoral research is focused on differential analysis of drug susceptible and resistant brain cancer cells using systems biology approaches, with special emphasis on metabolic reprogramming in the model glioblastom cell line "U87MG". She is also an awardee of the Newton Bhabha doctoral fellowship and worked in the labs of Dr. Philip Day and Prof. Douglas Kell at University of Manchester, UK during her PhD tenure.

Skills and Expertise:

Animal cell culture techniques, Fluorescence microscopy, Flow cytometry (FACS), qPCR, Molecular Biology, High-throughput analysis of NGS exome data and Flux balance analysis (FBA) of Metabolic models in MATLAB platform, Gene knockouts in E. coli.

Doctoral Research Summary:

Glioblastoma, the most severe form of brain cancer is even more complex due to its inherent heterogeneity, as the only drug temozolomide (TMZ) used to treat it is being rendered less useful due to chemoresistance. To understand the difference between cells of glioblastoma that are resistant or susceptible to TMZ, a resistant population of neurospheroidal cells (NSP) from an authenticated model cell line U87MG have been isolated. These were further characterized extensively using whole exome sequencing, limited gene expression profiling, metabolite profiling, growth-resistance profiling and respiration phenotyping to understand the genotype and the differential phenotypes of its molecular components. To complement data-driven analysis, a predictive constraints-based flux balance model of human metabolism was also used to develop context-specific models for U87MG and NSP. These models computed accurately the growth phenotypes of each cell and also predicted the metabolic reprogramming through varied flux distribution profiles. Alternate pathways were thus identified that can be targeted to kill such resistant cancer cells. Such pipelines are scalable to other cancer types and can be translated to clinical studies.

Projects involved:

Glioblastoma - Metabolic Reprogramming in Temozolomide Resistance Cells.
Systems Metabolic Engineering for increased Tryptophan biosynthesis in E. coli.

Publications:

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