Item Details

title

USING UNSUPERVISED LEARNING TECHNIQUES TO UNDERSTAND CD8+ T CELL DIFFERENTIATION DURING VIRAL INFECTION AND TUMOR DEVELOPMENT

author

Liu, Mingyong

abstract

The goal of this work was to use unsupervised learning techniques to understand the effects of gammaherpesvirus latency and autoimmune disease on CD8+ T cell differentiation. To examine how memory CD8+ T cells generated during latency differ from those primed in acute or chronic viral infection, we adoptively transferred naive P14 CD8+ T cells into uninfected recipients, and compared their phenotype and function following infection with the Armstrong (acute) or Clone 13 (chronic) strains of LCMV, or MHV68 expressing the LCMV epitope DbGP33-41. By performing k-means clustering and generating self organizing maps (SOM), we observed increased short-lived effector-like and terminally-differentiated CD8+ T cells following latent infection. Memory CD8+ T cells generated during latency had intermediate function and expansion capabilities compared to those primed in acute or chronic infection. To understand how autoimmunity affects the immune response against primary tumors, we transplanted H31m1 sarcoma cells into wildtype and TREX1D18N/D18N mice, and compared CD8+ T cell differentiation and tumor growth. We found D18N mice had greater tumor rejection and dendritic cell maturation, and their CD8+ tumor infiltrating lymphocytes had reduced inhibitory receptors expression but lower levels of cytokine production. Therefore, these results demonstrate memory CD8+ T cells from latent infection occupy an intermediate differentiation space, and autoimmunity increases antitumor response.

subject

Acute viral infection

Autoimmune diease

CD8+ T cell

Chronic viral infection

Latent viral infection

Unsupervised machine learning

contributor

Grayson, Jason M (committee chair)

Perrino, Fred W (committee member)

Zhou, Xiaobo (committee member)

date

2017-06-15T08:36:03Z (accessioned)

2018-06-14T08:30:10Z (available)

2017 (issued)

degree

Biomedical Science – MS (discipline)

embargo

2018-06-14 (terms)

identifier

http://hdl.handle.net/10339/82214 (uri)

language

en (iso)

publisher

Wake Forest University

type

Thesis