Analysis of a transcriptional network involving PU.1, Notch, and Gata3 in the lymphomyeloid lineage decision during early T-cell development

Hematopoiesis is a well-established system used to study developmental choices amongst cells with multiple lineage potentials, as well as the transcription factor network interactions that drive these developmental paths. Multipotent progenitors travel from the bone marrow to the thymus where T-cell development is initiated and these early T-cell precursors retain lineage plasticity even after initiating a T-cell program. The development of these early cells is driven by Notch signaling and the combinatorial expression of many transcription factors, several of which are also involved in the development of other cell lineages. The ETS family transcription factor PU.1 is involved in the development of progenitor, myeloid, and lymphoid cells, and can divert progenitor T-cells from the T-lineage to a myeloid lineage. This diversion of early T-cells by PU.1 can be blocked by Notch signaling. The PU.1 and Notch interaction creates a switch wherein PU.1 in the presence of Notch promotes T-cell identity and PU.1 in the absence of Notch signaling promotes a myeloid identity. Here we characterized an early T-cell cell line, Scid.adh.2c2, as a good model system for studying the myeloid vs. lymphoid developmental choice dependent on PU.1 and Notch signaling. We then used the Scid.adh.2c2 system to identify mechanisms mediating PU.1 and Notch signaling interactions during early T-cell development. We show that the mechanism by which Notch signaling is protecting pro-T cells is neither degradation nor modification of the PU.1 protein. Instead we give evidence that Notch signaling is blocking the PU.1-driven inhibition of a key set of T-regulatory genes including Myb, Tcf7, and Gata3. We show that the protection of Gata3 from PU.1-mediated inhibition, by Notch signaling and Myb, is important for retaining a T-lineage identity. We also discuss a PU.1-driven mechanism involving E-protein inhibition that leads to the inhibition of Notch target genes. This is mechanism may be used as a lockdown mechanism in pro-T-cells that have made the decision to divert to the myeloid pathway.

Neural network design for switching network control

A neural network is a highly interconnected set of simple processors. The many connections allow information to travel rapidly through the network, and due to their simplicity, many processors in one network are feasible. Together these properties imply that we can build efficient massively parallel machines using neural networks. The primary problem is how do we specify the interconnections in a neural network. The various approaches developed so far such as outer product, learning algorithm, or energy function suffer from the following deficiencies: long training/ specification times; not guaranteed to work on all inputs; requires full connectivity.

Alternatively we discuss methods of using the topology and constraints of the problems themselves to design the topology and connections of the neural solution. We define several useful circuits-generalizations of the Winner-Take-All circuitthat allows us to incorporate constraints using feedback in a controlled manner. These circuits are proven to be stable, and to only converge on valid states. We use the Hopfield electronic model since this is close to an actual implementation. We also discuss methods for incorporating these circuits into larger systems, neural and nonneural. By exploiting regularities in our definition, we can construct efficient networks. To demonstrate the methods, we look to three problems from communications. We first discuss two applications to problems from circuit switching; finding routes in large multistage switches, and the call rearrangement problem. These show both, how we can use many neurons to build massively parallel machines, and how the Winner-Take-All circuits can simplify our designs.

Next we develop a solution to the contention arbitration problem of high-speed packet switches. We define a useful class of switching networks and then design a neural network to solve the contention arbitration problem for this class. Various aspects of the neural network/switch system are analyzed to measure the queueing performance of this method. Using the basic design, a feasible architecture for a large (1024-input) ATM packet switch is presented. Using the massive parallelism of neural networks, we can consider algorithms that were previously computationally unattainable. These now viable algorithms lead us to new perspectives on switch design.

Structural Characterization of Pro-inflammatory and Anti-inflammatory Immunoglobulin G Fc Proteins

Immunoglobulin G (IgG) is central in mediating host defense due to its ability to target and eliminate invading pathogens. The fragment antigen binding (Fab) regions are responsible for antigen recognition; however the effector responses are encoded on the Fc region of IgG. IgG Fc displays considerable glycan heterogeneity, accounting for its complex effector functions of inflammation, modulation and immune suppression. Intravenous immunoglobulin G (IVIG) is pooled serum IgG from multiple donors and is used to treat individuals with autoimmune and inflammatory disorders such as rheumatoid arthritis and Kawasaki’s disease, respectively. It contains all the subtypes of IgG (IgG1-4) and over 120 glycovariants due to variation of an Asparagine 297-linked glycan on the Fc. The species identified as the activating component of IVIG is sialylated IgG Fc. Comparisons of wild type Fc and sialylated Fc X-ray crystal structures suggests that sialylation causes an increase in conformational flexibility, which may be important for its anti-inflammatory properties.

Although glycan modifications can promote the anti-inflammatory properties of the Fc, there are amino acid substitutions that cause Fcs to initiate an enhanced immune response. Mutations in the Fc can cause up to a 100-fold increase in binding affinity to activating Fc gamma receptors located on immune cells, and have been shown to enhance antibody dependent cell-mediated cytotoxicity. This is important in developing therapeutic antibodies against cancer and infectious diseases. Structural studies of mutant Fcs in complex with activating receptors gave insight into new protein-protein interactions that lead to an enhanced binding affinity.

Together these studies show how dynamic and diverse the Fc region is and how both protein and carbohydrate modifications can alter structure, leading to IgG Fc’s switch from a pro-inflammatory to an anti-inflammatory protein.