Perelman School of Medicine at the University of Pennsylvania

Penn Institute for Immunology

Golnaz Vahedi

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Assistant Professor of Genetics
Department: Genetics

Contact information
421 Curie Boulevard
Philadelphia, PA 19104
B.Sc. (Electrical Engineering)
Sharif University of Technology, Iran, 2001.
M.Sc. (Electrical Engineering)
University of Alberta, Canada, 2005.
Ph.D. (Electrical Engineering)
Texas A&M University, Texas, 2009.
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Description of Research Expertise

Our laboratory is multidisciplinary, integrating computational and cutting-edge experimental approaches to develop a single to collective cell understanding of gene regulation in T cells.

What is the goal of our research? The overarching goal of our hybrid wet and dry laboratory is to exploit the epigenome in addition to mouse and human genetics to understand how T cell identity is established. Why the epigenome? Information encoded in DNA is interpreted, modified, and propagated as chromatin. The diversity of inputs encountered by immune cells demands a matching capacity for transcriptional outcomes provided by the combinatorial and dynamic nature of epigenetic processes. Advances in genome editing and genome-wide analyses have revealed unprecedented complexity of chromatin pathways involved in the immune response, offering explanations to long-standing questions and presenting new challenges.

Why is this an important goal? Mechanistic and comprehensive studying of factors controlling T cell's fate through the epigenome can be used to reprogram T cells to enhance or suppress their function. Reprogramming T cells at will using genetic and epigenetic engineering can have significant implications in cancer or autoimmune diseases such as psoriasis and type 1 diabetes.

How do we do research? We measure epigenomic modifications of the linear genome using bulk assays such as ChIP-seq and ATAC-seq. The three-dimensional (3D) organization of the genome also plays a crucial role in carrying out the instructions encoded in its linear sequence. We create high-resolution maps of 3D genome interactions in primary T cells using HiChiP which only requires hundred thousand cells. Our lab invested in single-cell technology and we were the first to publish maps of chromatin accessibility at individual T cells. We take advantage of natural genetic variation as an in vivo mutagenesis screen to assess the genome-wide effects of sequence variation on transcription factor binding, 3D genome organization, and transcriptional outcomes in primary T cells. As a result of our computational expertise, we also harvest the vast troves of big data that immunologists and other researchers are pouring into public repositories. Our data integrations rely on available computational pipelines. Furthermore, we develop novel computational techniques to fully understand the complexity of multidimensional epigenomics datasets in T cells.

What is our training goal? Biology in the 21st century is arguably the most data-rich science of the most intricately regulated dynamical systems that any discipline has to offer. We view quantitative and computational biology as intrinsic parts of the biological discipline. Our lab has an efficient and cohesive environment for trainees with no computational backgrounds to get familiar with programming and standard genomics pipelines. Trainees with previous computational expertise will be immersed in biological problems with significant implications in human health and disease. They are able to devise novel methods generating new hypotheses which can be further tested in the wet lab using genetic approaches.

List of Rotation Projects 2019-2020.

1) What is the underlying mechanism through which the lineage-restricted 3D genome organization is established in T cells?

2) How do endogenous retroelements control gene regulation in T cells?

3) How can common genetic variations associated with type 1 diabetes change the 3D genome organization of T cells?

4) How does the chimeric antigen receptor (CAR) integration change the linear and 3D genome organization of T cells?

5) What are the epigenetic mechanisms through which the transcription factor TCF-1 creates accessible chromatin in T cells? (PMID: 29466756)

7) Exploiting natural genetic variations in multiple mouse strains to decipher transcription factor grammar in T cell development.

Selected Publications

Choobdar, S., Ahsen, M.E., Crawford, J., Tomasoni, J., Fang, T., Lamparter, D., Lin, J., Hescott, B., Hu, X., Mercer, J., Natoli, T., Narayan, R., The DREAM Module Identification Challenge Consortium (Cai, S. and Vahedi, G.), Subramanian, A., Zhang, J.D., Stolovitzky, G., Kutalik, Z., Lage, K., Slonim D.K., Saez-Rodriguez, J., Cowen L.J., Bergmann, S., and Marbach, D.: Assessment of network module identification across complex diseases. Nature Methods In press, September 2019.

Vacchio M.S., Ciucci T., Gao Y., Watanabe, M., Balmaceno-Criss, M., McGinty, MT, Xiao HQ, McConkey C., Zhao, Y., Shetty, J., Tran B., Pepper, M., Vahedi, G., Jenkins, MK, McGavern, DB, Bosselut, R.: A Thpok-directed transcriptional circuitry promotes Bcl6 and Maf to orchestrate T follicular helper differentiation. Immunity 51: 1-14, September 2019.

Petrovic J., Zhou Y, Fasolino M, Goldman N, Schwartz G, Mumbach M, Nguyen S, Rome K, Sela, Y, Zapataro Z, Blacklow S, Kruhlak M, Aster J, Shi J, Joyce E, Little S, Vahedi G, Pear, WS, Faryabi RB: Oncogenic Notch promotes long-range regulatory interactions within hyperconnected 3D cliques. Molecular Cell 73(6): 1174-1190. March 2019.

Cai S, Georgakilas G, Johnson JL, Vahedi G: A cosine similarity-based method to infer variability of chromatin accessibility at the single-cell level. Frontiers in Genetics Page: 9:319, August 2018.

Johnson, J.L., Georgakilas, G., Petrovic, J., Kurachi, M., Cai, S., Harly, C., Pear, W.S., Bhandoola, A., Wherry, E., Vahedi G.: Lineage-determining transcription factor TCF-1 initiates the epigenetic identity of T cells. Immunity. Cell Press, 48(2): 243-257, February 2018 Notes: Featured on the cover of the journal and received a News and Views.

Johnson J.L. and Vahedi G.: Epigenome: a dynamic vehicle for transmitting and recording cytokine signalling. CSHL Perspectives, Cold Spring Harbor Laboratory Press Page: a028779, March 2018.

Johnson, B.S., Zhao, Y., Fasolino, M.,Lamonica, J.M, Kim, Y.J., Georgakilas, G., Wood, K.H., Bu, D., Cui, Y., Goffin, D., Vahedi, G., Kim, T.H., Zhou, Z: Biotin tagging of MeCP2 reveals contextual insights into the Rett syndrome transcriptome. Nature Medicine 10(23): 1203-1214, October 2017.

Pauken, K. E., Sammons, M. A., Odorizzi, P. M., Manne, S., Godec, J., Khan, O., Drake, A. M., Chen, Z., Sen, D., Kurachi, M., Barnitz, R. A., Bartman, C., Bengsch, B., Huang, A. C., Schenkel, J. M., Vahedi, G., Haining, W. N., Berger, S. L., Wherry, E. J.: Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354(6316), December 2016.

Vahedi, G., Kanno, Y., Furumoto, Y., Jiang, K., Parker, S. C., Erdos, M. R., Davis, S. R., Roychoudhuri, R., Restifo, N. P., Gadina, M., Tang, Z., Ruan, Y., Collins, F. S., Sartorelli, V., O'Shea, J. J.: Super-enhancers delineate disease-associated regulatory nodes in T cells. Nature 520(7548): 558-62, April 2015.

Vahedi, G., Takahashi, H., Nakayamada, S., Sun, H. W., Sartorelli, V., Kanno, Y., O'Shea, J. J.: STATs shape the active enhancer landscape of T cell populations. Cell 151(5): 981-93, November 2012.

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Last updated: 08/28/2019
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