Golnaz Vahedi

Golnaz Vahedi 

Assistant Professor of Genetics
Institute for Immunology
Core member of the Epigenetics Institute
Perelman School of Medicine
University of Pennsylvania
CV
Room 310 BRB II/III, 421 Curie Boulevard
Philadelphia, PA 19104-6160
Email: vahedi at pennmedicine.upenn.edu

The Vahedi Laboratory

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 determined during development and after immune activation. 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 signficant 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 the single-cell level. 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, epigenomics 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 molecular or cell biology 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 Projects 2018-2019 (Rotation, Thesis, Postdoc)

This article very nicely summarizes our lab's philosophy and the kind of projects available for trainees.

1) Prediction of CAR T cell integration sites.

2) How is the 3D genome reorganized in T cells after activation?

3) Which transcription factors play key roles organizing the 3D chromatin architecture of T cells?

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

5) Novel tools to decipher transcription factor grammar from sequence and epigenomics data using machine learning.

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

7) Deciphering the contributions of genetics and epigenetics in type 1 diabetes development.

8) Can viruses change the 3D genome organization of infected host cells?

Trainees are welcome to join our weekly computational journal club on Fridays at 4pm (301 BRB).

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