Chennubhotla receives NSF grant

Chennubhotla_croppedDr. Chakra Chennubhotla received an NSF grant for $768K. He will be working with Dr. Frederick Quinn and his former graduate student, Dr. Shannon Quinn.

Title: OrNet: Large-scale analysis of organellar network evolution

They will be working to characterize spatio-temporal evolution of organellar social networks in host cells infected by bacterial pathogens from a large collection of time-lapse microscopy videos.

Chakra Chennubhotla, Ph.D., Assistant Professor, Computational & Systems Biology, University of Pittsburgh

Frederick Quinn, Ph.D., Department Head Athletic Association Professor of Infectious Diseases, University of Georgia

Shannon Quinn, Ph.D., Assistant Professor of Computer Science, University of Georgia

Benos and Kaminski awarded NIH R01 grant

IMG_2581_sm_detailDrs. Takis Benos (Pitt) and Naftali Kaminski (Yale) were awarded by NIH 2.9M over four years to perform genomic analysis to study the tissue and cellular heterogeneity in IPF.


Abstract:

“Genomic Analysis of Tissue and Cellular Heterogeneity in IPF”
Understanding the molecular networks that underlie disease tissue characteristics will lead to better understanding of the disease, its mechanism and will eventually help design more rational, mechanism-based therapeutic interventions.  The purpose of this grant is to study tissue characteristics in Idiopathic Pulmonary Fibrosis (IPF), a chronic and progressive lung disease with significant morbidity and mortality, for which at present there is no effective treatment other than lung transplantation. The study will undertake the following specific aims: (1) Identification of the unique genomic and transcriptomics characteristics of histologically defined lung microenvironments. (2) Determination of the cellular contribution to the genomic and epigenomic changes in the IPF lung. (3) Generate a dynamic regulatory model of IPF based on genomic data and perform preliminary experimental validation of model predictions. The data and analyses will be incorporated into a simple, intuitive, web-based interface, IPFmap, that will allow investigators to interactively mine the data, use analytical tools, integrate their own data into these analyses, and provide seamless access to complementary databases enabling development of therapies.

Quinn and Chennubhotla published in Science Translational Medicine

Science Translational Medicine, has published research from one of our Pitt faculty, Dr. Chakra Chennubhotla, and his former graduate student, Dr. Shannon Quinn.

shannonchakra

Shannon P. Quinn, Maliha J. Zahid, John R. Durkin, Richard J. Francis, Cecilia W. Lo, and S. Chakra Chennubhotla (2015) “Automated identification of abnormal respiratory ciliary motion in nasal biopsies” Science Translational Medicine Vol. 7, Issue 299, pp. 299ra124.

 

Editor’s summary:

The movement of tiny cilia can be used to detect various lung and heart diseases. Normally, these cilia beat in unison to move foreign particles and mucus out of the body. When diseased, the cilia adopt asynchronous motions, which can be observed in nasal or bronchial biopsies under a microscope and in turn be used for diagnosis. To reduce the subjective nature of diagnostics involving manual evaluation of ciliary motion, Quinn et al. devised an computational framework that objectively quantifies ciliary motion in digital biopsy videos. In their approach, ciliary motion is characterized as a “dynamic texture,” much like a flickering flame or billowing smoke. The ciliary motion was broken down into elemental components, which were then pieced together to create a digital “signature” capturing cilia rotation and deformation as functions of time and magnitude. Using these digital signatures, the authors were able to formulate ciliary motion predictions for two independent cohorts from different institutions that included patients with primary ciliary dyskinesia, congenital heart disease, and heterotaxy. Their computational framework was able to correctly identify ciliary motion defects in over 90% of patients. Such a “black box” method will allow untrained medical professionals to sensitively diagnose challenging ciliopathies.