Recombinase-based genetic circuits in human T cells for cellular immunotherapy

Treatments using a patient’s own T cells to target cancer have applied advances in genetic engineering and cancer immunotherapy as the basis for a powerful, targeted cell-based therapy. Tumor-infiltrating lymphocytes and T cells that have been genetically modified to express cancer antigen-specific receptors—such as T cell receptors (TCRs) and chimeric antigen receptors (CARs)—leverage the innate targeted cytotoxicity of T cells against cancer. Despite promising results in clinical trials, these therapies have elicited serious and sometimes fatal responses. These toxicities include killing of healthy tissue expressing lower levels of antigen, as well as an overstimulation of the immune response called cytokine release syndrome. These outcomes reflect the double-edged nature of T cell-based therapies: the same powerful capability that makes them strong therapeutic candidates can become fatal if modified cells are left to their own devices. T cell therapies would greatly benefit from the development of tools that enable doctors to have bedside control over a cell’s behavior and truly respond to each patient’s needs. The work of this thesis aims to develop genetic circuits that control T cell activity. This platform has been adapted to control when CARs are expressed and at what level. In contrast to the current approach where patients are treated to one therapeutic “state,” these genetic circuits will allow doctors to decide between multiple states defined by CAR expression through the addition of a drug. These circuits contain memory such that long-term administration of the drug is not required to maintain a change. I have designed an ON switch and an OFF switch to control when a CAR is expressed, and an EXPRESSION switch to increase CAR expression. I characterized the performance of these circuits to demonstrate their dynamics over time, as well as their ability to control T cell behavior. I also demonstrate that these circuits contain memory, and that they are tunable.2020-07-02T00:00:00

Identifying Hubs in Protein Interaction Networks

In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs), it is common to define an ad hoc degree scale that defines "hub" proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust.In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction.We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks

Identifying Hubs in Protein Interaction Networks

In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs), it is common to define an ad hoc degree scale that defines “hub” proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust.In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction.We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks.</p

Bimodality of PCC distribution for the HC network.

<p>Inclusion of non-hub nodes into the list of HC hubs leads to reduction in bi-modality of the average PCC distribution. This can be seen as the number of hubs included increases from 40 to 419 in the HC dataset. The panel on the left displays smoothed probability density functions corresponding to the average PCC distribution while the panel on the right displays the cumulative distribution functions. Percentiles refer to the percentages of top high degree nodes included in the hub set, following <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005344#pone.0005344-Batada1" target="_blank">[19]</a>.</p

Statistical significance of relative subgraph connectivity.

<p>Empirical P-values (dashed lines) for significance of the relative connectivity measure (solid lines) for all the four networks were computed using 10,000 random networks corresponding to each real network. P-values that are less than 10⁻⁴ can be identified by the circles on the x-axis in each panel.</p

Robustness of relative subgraph connectivity.

<p>Relative subgraph connectivity profiles for unperturbed versions of all four networks are shown, along with the corresponding profiles upon random addition and removal of 10% and 15% of the edges in the unperturbed networks.</p

A cartoon illustrating relative connectivity of subgraphs.

<p>Successive subgraphs are generated from a ranked degree list, and the relative connectivity <i>f</i> is computed from them. Each node is represented by a black center with a gray ‘halo’ whose size is proportional to the degree of the node. Note that newer nodes have smaller halos (lower degrees). Interactions involving newly added nodes are shown as dotted edges, while previously established interactions are shown as dark edges. Note that all subgraphs upto <i>G₄</i> are completely disconnected in this example.</p

Essential gene enrichment.

<p>Enrichment for essential genes among hubs relative to non-hubs, as measured by the Jensen-Shannon divergence (upper panels) and the P-value for the Kolmogorov-Smirnov test (lower panels).</p

Comparison between degree cutoffs for defining hubs by our relative connectivity based method and definitions used in the literature.

<p>Comparison between degree cutoffs for defining hubs by our relative connectivity based method and definitions used in the literature.</p

Dip statistics as a function of number of included hubs.

<p>Values of the dip statistic for all four networks studied as a function of the number of top degree nodes included in the hub set. The straight line marks the boundary between statistically significant and insignificant dip values (at 95% confidence).</p