Working on the cutting edge of a field is a unique opportunity to advance the limits of human knowledge. Currently, I spend my time working with two groups to discover new insights into the nature of life, and ways we can make life better.
I specialize in the application of biological and computational techniques to problem solving in the life sciences.
Macroautophagy is a process responsible for degrading and recycling cellular material, including damaged organelles and toxic aggregations of protein. Upregulation of autophagy is strongly linked with increased lifespan, and dysfunction in autophagy pathways is implicated in a tremendous variety of diseases including cancer, Alzheimer's disease, and Huntington's disease.
I am investigating the structural and biochemical bases of mammalian autophagy, in particular the mechanisms underlying autophagosome-lysosome fusion. Our group uses techniques ranging from x-ray crystallography and cryo-electron microscopy to mass spectrometry and live cell imaging to find and place new pieces of the autophagy puzzle.
Ordinarily, when a tissue biopsy or sample is excised from a patient, a pathologist examines it under a microscope and makes semi-quantitative assessments based on a set of standards. This approach was the best option for a long time (and still is in many cases), but technological advances are allowing quantitative morphometric analysis of tissue samples using computer vision-based algorithms.
Currently, I'm investigating the potential of data not traditionally considered by clinicians to improve cancer diagnostic and prognostic modelling. These data sources include computer vision analysis of the cancer stroma and information about the history and origin of patients. for the diagnosis and prognosis of prostate cancer patients. By integrating data from a variety of non-traditional sources, we hope to create more powerful models that can improve patient care and advance our understanding of the disease.
Bhargava, H.K., Leo, P., Elliott, R., Janowcyzk, A., Whitney, J., Gupta, S., Fu, P., Yamoah, K., Rebbeck, T., Feldman, D., Lal, P., Madabhushi, A., (2018), Digital features of stromal morphology in prostate cancer differ between African-Americans and Caucasians and are prognostic of recurrence following prostatectomy. Under Review.
Bhargava, H.K., Leo, P., Elliott, R., Janowcyzk, A., Whitney, J., Gupta, S., Yamoah, K., Rebbeck, T., Feldman, D., Lal, P., Madabhushi, A., (2018), Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence. Poster presented at the American Society of Clinical Oncology (ASCO) Annual Meeting. J Clin Oncol 36, 2018 (suppl; abstr 12075).
My specialty is extracting massive datasets from a variety of sources, finding a useful signal, and making the solution easy to use.
Frameworks: NumPy/SciPy, Pandas, TensorFlow, node.js, Jupyter
Techniques: Classifier development, convolutional and recurrent neural networks, computer vision, process automation, full stack software development, image processing
Structural Biology: X-Ray Crystallography, Small Angle X-Ray Scattering (SAXS)
Protein Mass Spectrometry: Proteomics (IP-MS), Hydrogen-Deuterium Exchange (HDX-MS)
Protein Engineering: High throughput library design and synthesis, directed evolution, robotic automation.
Biochemistry: Protein purification, Isothermal Titration Calorimetry (ITC), Multi-Angle Light Scattering (MALS), Western blotting, Fluorescence Anisotropy
Cell Culture: E. Coli, insect cells, mammalian cells (HeLa, HEK293), primary neuronal culture
Miscellaneous: Complex cloning, fluorescence microscopy, virus production, small-molecule NMR spectroscopy.
Fluorescent calcium indicators are widely used in neuroscience research to image neuronal activity in vivo. I worked to engineer a novel class of calcium indicator that is composed of a calcium sensing domain combined with a covalent capture domain that binds a small- molecule fluorophore. Such an indicator presents advantages over both protein-only and small-molecule- only indicators, namely targetability, modularity, and photostability. This project involved high-throughput techniques for protein engineering, chemical engineering, and collaboration with neuroscientists to create the best tools possible.
Bhargava, H.K., Deo, C., Lavis, L.D., Schreiter, E.R., (2017), A Small Molecule + Protein Hybrid Calcium Indicator. Poster presented at HHMI Janelia Research Campus Undergraduate Scholars Poster Session.
My first independent research project was the structural and biochemical characterization of a protein complex that regulates gene transcription by releasing RNA polymerase II from a promoter-proximally stalled state in ~50% of metazoan genes. This complex is hijacked by HIV-1 during viral infection, and its dysfunction is linked with the pathogenesis of a variety of cancers.
In the course of the project, I conducted Hydrogen-Deuterium exchange experiments to determine the binding site of the endogenous competitor to the HIV Tat protein, Brd4. In addition, I attempted to solve the crystal structure of Brd4 in complex with P-TEFb, but succeeded only in solving the structure of free P-TEFb. The project came to an end when the Hurley Lab elected to move focus away from transcriptional regulation in 2017.
Bhargava, H.K., Schulze-Gahmen, U., Stjepanovic, G., Hurley, J.H. (2017), Structural and Biochemical Analysis of the Brd4:P-TEFb Complex. Poster presented at the NIH Structural Biology Related to HIV/AIDS Conference.