Candidate After the completion of my Ph.D. in Bioinformatics, I joined the Center for Statistical Genetics at the University of Michigan (U-M) as research fellow and acquired extensive training in analysis on high- dimensional biological data. I uncovered a strong interest in studying the genetics and genomics of psoriasis when working with Dr. James Elder at the U-M, and I developed a fascination in understanding the functional roles of long non-coding RNAs (lncRNAs) in cutaneous diseases. I joined the Department of Dermatology at the U-M as a faculty in summer of 2015, with secondary appointments in the Department of Computational Medicine & Bioinformatics and the Department of Biostatistics. In addition, I direct the new Center for Cutaneous Bioinformatics within the Department of Dermatology, and serve to supervise and implement an analysis pipeline for studies investigating the immunological mechanisms for different skin diseases. Career Development Plan I aim to become a future leader in combining in silico discovery and bench experiments to advance biomedical research in autoimmune skin disorders. My objective in seeking a Mentored Research Scientist Development Award is to acquire the additional knowledge, training, and experience necessary for me to become an independent scholar in developing novel systems biology approaches to decipher the pathology and mechanisms of cutaneous diseases. The five year training proposed will provide knowledge and experience in aspects that are critical to my success, and they are: i) To develop knowledge and experimental skills in cutaneous biology --- achieved by guidance from Dr. Elder (investigative dermatology), intense research meetings/conferences, and practical laboratory experience in cutaneous research; ii) To develop knowledge and skills to study immunological systems of autoimmune skin diseases --- achieved by supervision from Dr. Johann Gudjonsson (skin immunology), attending formal Immunology courses and seminars, and earning laboratory experience from immunology experiments; iii) To advance skills in developing statistical and computational approaches --- accomplished by mentoring from Dr. Goncalo Abecasis (computational biologist), research meetings, and conducting research projects requiring advanced skills and knowledge in quantitative science; iv) To cultivate my professional development through enhancing scientific connections, grantsmanship skills, and educator portfolio --- achieved by establishing connections with colleagues during meetings, visiting King?s College London as scholar, attending a grantsmanship workshop and bootcamp, and learning mentoring skills through teaching formal classes and mentoring research students. Through the intensive and comprehensive training, I will be well grounded in conducting basic science experiments and also be able to capitalize my advanced knowledge in quantitative science to model mechanisms in cutaneous diseases. Research Project The research project will use psoriasis as a disease model to study the roles of lncRNAs in complex cutaneous disorders. I will test the hypotheses that (i): some lncRNAs are key causal elements and potentiate pathogenic inflammatory reactions in psoriasis development and (ii) by combining in silico predictions and in vitro validations we are able to provide comprehensive characterization of skin-expressing lncRNAs in keratinocytes and lymphocytes to infer their pathological implications for psoriasis. This work will demonstrate how we can take advantages of the genomic data to develop an integrative biology framework to provide novel biological insights and understand pathological roles of lncRNAs. Significance Psoriasis is a chronic immune-mediated skin disease with complex genetic architecture. It is estimated that over 4 million Americans and 100 million people worldwide suffer from this disease. While genetic association studies have revealed the disease loci are highly enriched in non-coding regions, it is very challenging to translate genetic signals to biologic effects. In fact, most of the causal genes have not yet been identified. Our preliminary results showed that lncRNA is a class of gene that has largely been understudied for their roles in psoriasis, and both genetic and transcriptomic data suggested they can play important functions in psoriasis pathogenesis. By combining in silico analysis and in vitro validation we can expand our knowledge of lncRNAs in skin biology, and generate important hypotheses for future experiments. The results of this project can also identify novel biomarkers, and ultimately assist in the therapeutic drug discovery.
Psoriasis is a chronic immune-mediated skin disease with complex genetic architecture, and affects over 4 million Americans and 100 million people worldwide. Long non-coding RNAs (lncRNAs) is a class of gene that has largely been understudied, and recent studies suggested their potential roles in autoimmune diseases. This project aims to expand our knowledge of lncRNAs in skin biology, and advance identification of lncRNAs that play functional roles in psoriasis pathogenesis.
|Patrick, Matthew T; Stuart, Philip E; Raja, Kalpana et al. (2018) Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nat Commun 9:4178|
|Patrick, Matthew T; Raja, Kalpana; Miller, Keylonnie et al. (2018) Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach. J Invest Dermatol :|
|Tsoi, Lam C; Patrick, Matthew T; Elder, James T (2018) Research Techniques Made Simple: Using Genome-Wide Association Studies to Understand Complex Cutaneous Disorders. J Invest Dermatol 138:e23-e29|
|Enerbäck, C; Sandin, C; Lambert, S et al. (2018) The psoriasis-protective TYK2 I684S variant impairs IL-12 stimulated pSTAT4 response in skin-homing CD4+ and CD8+ memory T-cells. Sci Rep 8:7043|