Molecular mechanisms of interleukin-2 gene inducibility: developmental control and combinatorial action of transcription factors

Interleukin-2 is one of the lymphokines secreted by T helper type 1 cells upon activation mediated by T-cell receptor (TCR) and accessory molecules. The ability to express IL-2 is correlated with T-lineage commitment and is regulated during T cell development and differentiation. Understanding the molecular mechanism of how IL-2 gene inducibility is controlled at each transition and each differentiation process of T-cell development is to understand one aspect of T-cell development. In the present study, we first attempted to elucidate the molecular basis for the developmental changes of IL-2 gene inducibility. We showed that IL-2 gene inducibility is acquired early in immature CD4- CD8-TCR- thymocytes prior to TCR gene rearrangement. Similar to mature T cells, a complete set of transcription factors can be induced at this early stage to activate IL-2 gene expression. The progression of these cells to cortical CD4^+CD8^+TCR^(1o) cells is accompanied by the loss of IL-2 gene inducibility. We demonstrated that DNA binding activities of two transcription factors AP-1 and NF-AT are reduced in cells at this stage. Further, the loss of factor binding, especially AP-1, is attributable to the reduced ability to activate expression of three potential components of AP-1 and NF-AT, including c-Fos, FosB, and Fra-2. We next examined the interaction of transcription factors and the IL-2 promoter in vivo by using the EL4 T cell line and two non-T cell lines. We showed an all-or-none phenomenon regarding the factor-DNA interaction, i.e., in activated T cells, the IL-2 promoter is occupied by sequence-specific transcription factors when all the transcription factors are available; in resting T cells or non-T cells, no specific protein-DNA interaction is observed when only a subset of factors are present in the nuclei. Purposefully reducing a particular set of factor binding activities in stimulated T cells using pharmacological agents cyclosporin A or forskolin also abolished all interactions. The results suggest that a combinatorial and coordinated protein-DNA interaction is required for IL-2 gene activation. The thymocyte experiments clearly illustrated that multiple transcription factors are regulated during intrathymic T-cell development, and this regulation in tum controls the inducibility of the lineage-specific IL-2 gene. The in vivo study of protein-DNA interaction stressed the combinatorial action of transcription factors to stably occupy the IL-2 promoter and to initiate its transcription, and provided a molecular mechanism for changes in IL-2 gene inducibility in T cells undergoing integration of multiple environmental signals.

Vibrational predissociation spectroscopy of mass-selected ionic clusters

This thesis presents structural investigations of molecular ions and ionic clusters using vibrational predissociation spectroscopy. Experimentally, a pulsed beam of the mass-selected ion is crossed by a tunable infrared laser beam generated by a Nd:YAG pumped LiNbO_3 optical parametric oscillator. The resulting fragment ion is mass-analyzed and detected, with its intensity as a function of the laser wavelength being the "action" spectrum of the parent ion. In the case of SiH_7^+, we observed a vibrational band centered at 3866 cm^(-1) with clear P, Q, R branches, which is assigned as a perturbed H_2 stretch. The absence of a second H_2 band suggests that the ion forms a symmetric complex with a structure H_2•SiH_3^+•H_2 , in contrast to the species CH_7^+, which has the structure CH_5^+•H_2. The infrared spectra of NO_2^+(H_2O)_n clusters exhibit a marked change with cluster size, indicating that an intracluster reaction occurs with sufficient solvation. Specifically, in NO_2^+(H_2O)_n clusters where n≤3, H_2O binds to a nitronium ion core; but at n=4 the NO_2^+ reacts, transforming the cluster to a new structure of H_3O^+•(H_2O)_(n_2)•HNO_3. For protonated chlorine nitrate, we have observed two distinct isomers previously predicted by ab initio calculations: NO_2^+•(HOC1), the lowest energy isomer, and (ClO)(HO)NO^+, a covalently bonded isomer about 20 kcal/mol higher in energy. Both isomers decompose to NO_2^+ and HOCl upon photo-excitation. These results for HClONO_2^+ lend strong support to the involvement of an ionic mechanism in the reaction of ClONO_2 on polar stratospheric cloud surfaces, a critical step in the dramatic springtime depletion of ozone over Antarctica. Current research activities on halide-solvent clusters and metal-ligand complexes as well as technological improvements of the apparatus are also discussed.

Dirac spectra, summation formulae, and the spectral action

Noncommutative geometry is a source of particle physics models with matter Lagrangians coupled to gravity. One may associate to any noncommutative space (A, H, D) its spectral action, which is defined in terms of the Dirac spectrum of its Dirac operator D. When viewing a spin manifold as a noncommutative space, D is the usual Dirac operator. In this paper, we give nonperturbative computations of the spectral action for quotients of SU(2), Bieberbach manifolds, and SU(3) equipped with a variety of geometries. Along the way we will compute several Dirac spectra and refer to applications of this computation.

Synthetic molecular machines for active self-assembly : prototype algorithms, designs, and experimental study

Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast–all while remaining functional.

This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of “active self-assembly” of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology’s numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules.

One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved.

One might think that because a system is Turing-complete, capable of computing “anything,” that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not “computations” in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface.

Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors “energetically incomplete” programmable behaviors. This class of behaviors includes any behavior where a passive physical system simply does not have enough physical energy to perform the specified tasks in the requisite amount of time.

As we will demonstrate and prove, a sufficiently expressive implementation of an “active” molecular self-assembly approach can achieve these behaviors. Using an external source of fuel solves part of the the problem, so the system is not “energetically incomplete.” But the programmable system also needs to have sufficient expressive power to achieve the specified behaviors. Perhaps surprisingly, some of these systems do not even require Turing completeness to be sufficiently expressive.

Building on a large variety of work by other scientists in the fields of DNA nanotechnology, chemistry and reconfigurable robotics, this thesis introduces several research contributions in the context of active self-assembly.

We show that simple primitives such as insertion and deletion are able to generate complex and interesting results such as the growth of a linear polymer in logarithmic time and the ability of a linear polymer to treadmill. To this end we developed a formal model for active-self assembly that is directly implementable with DNA molecules. We show that this model is computationally equivalent to a machine capable of producing strings that are stronger than regular languages and, at most, as strong as context-free grammars. This is a great advance in the theory of active self- assembly as prior models were either entirely theoretical or only implementable in the context of macro-scale robotics.

We developed a chain reaction method for the autonomous exponential growth of a linear DNA polymer. Our method is based on the insertion of molecules into the assembly, which generates two new insertion sites for every initial one employed. The building of a line in logarithmic time is a first step toward building a shape in logarithmic time. We demonstrate the first construction of a synthetic linear polymer that grows exponentially fast via insertion. We show that monomer molecules are converted into the polymer in logarithmic time via spectrofluorimetry and gel electrophoresis experiments. We also demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism. This shows the growth of a population of polymers in logarithmic time. We characterize the DNA insertion mechanism that we utilize in Chapter 4. We experimentally demonstrate that we can control the kinetics of this re- action over at least seven orders of magnitude, by programming the sequences of DNA that initiate the reaction.

In addition, we review co-authored work on programming molecular robots using prescriptive landscapes of DNA origami; this was the first microscopic demonstration of programming a molec- ular robot to walk on a 2-dimensional surface. We developed a snapshot method for imaging these random walking molecular robots and a CAPTCHA-like analysis method for difficult-to-interpret imaging data.

A study of protein targeting reveals insights into mitigating protein aggregation

A unique chloroplast Signal Recognition Particle (SRP) in green plants is primarily dedicated to the post-translational targeting of light harvesting chlorophyll-a/b binding (LHC) proteins. Our study of the thermodynamics and kinetics of the GTPases of the system demonstrates that GTPase complex assembly and activation are highly coupled in the chloroplast GTPases, suggesting they may forego the GTPase activation step as a key regulatory point. This reflects adaptations of the chloroplast SRP to the delivery of their unique substrate protein. Devotion to one highly hydrophobic family of proteins also may have allowed the chloroplast SRP system to evolve an efficient chaperone in the cpSRP43 subunit. To understand the mechanism of disaggregation, we showed that LHC proteins form micellar, disc-shaped aggregates that present a recognition motif (L18) on the aggregate surface. Further molecular genetic and structure-activity analyses reveal that the action of cpSRP43 can be dissected into two steps: (i) initial recognition of L18 on the aggregate surface; and (ii) aggregate remodeling, during which highly adaptable binding interactions of cpSRP43 with hydrophobic transmembrane domains of the substrate protein compete with the packing interactions within the aggregate. We also tested the adaptability of cpSRP43 for alternative substrates, specifically in attempts to improve membrane protein expression and inhibition of amyloid beta fibrillization. These preliminary results attest to cpSRP43’s potential as a molecular chaperone and provides the impetus for further engineering endeavors to address problems that stem from protein aggregation.

Engineering multi-step electron tunneling systems in proteins

Multi-step electron tunneling, or “hopping,” has become a fast-developing research field with studies ranging from theoretical modeling systems, inorganic complexes, to biological systems. In particular, the field is exploring hopping mechanisms in new proteins and protein complexes, as well as further understanding the classical biological hopping systems such as ribonuclease reductase, DNA photolyases, and photosystem II. Despite the plethora of natural systems, only a few biologically engineered systems exist. Engineered hopping systems can provide valuable information on key structural and electronic features, just like other kinds of biological model systems. Also, engineered systems can harness common biologic processes and utilize them for alternative reactions. In this thesis, two new hopping systems are engineered and characterized.

The protein Pseudomonas aeruginosa azurin is used as a building block to create the two new hopping systems. Besides being well studied and amenable to mutation, azurin already has been used to successfully engineer a hopping system. The two hopping systems presented in this thesis have a histidine-attached high potential rhenium 4,7-dimethyl-1,10-phenanthroline tricarbonyl [Re(dmp)(CO)3] + label which, when excited, acts as the initial electron acceptor. The metal donor is the type I copper of the azurin protein. The hopping intermediates are all tryptophan, an amino acid mutated into the azurin at select sites between the photoactive metal label and the protein metal site. One system exhibits an inter-molecular hopping through a protein dimer interface; the other system undergoes intra-molecular multi-hopping utilizing a tryptophan “wire.” The electron transfer reactions are triggered by excitation of the rhenium label and monitored by UV-Visible transient absorption, luminescence decays measurements, and time-resolved Infrared spectroscopy (TRIR). Both systems were structurally characterized by protein X-ray crystallography.

The Voting Rights Act, Shelby County, and Redistricting: Improving Estimates of Racially Polarized Voting in a Multiple-Election Context

The Supreme Court’s decision in Shelby County has severely limited the power of the Voting Rights Act. I argue that Congressional attempts to pass a new coverage formula are unlikely to gain the necessary Republican support. Instead, I propose a new strategy that takes a “carrot and stick” approach. As the stick, I suggest amending Section 3 to eliminate the need to prove that discrimination was intentional. For the carrot, I envision a competitive grant program similar to the highly successful Race to the Top education grants. I argue that this plan could pass the currently divided Congress.

Without Congressional action, Section 2 is more important than ever before. A successful Section 2 suit requires evidence that voting in the jurisdiction is racially polarized. Accurately and objectively assessing the level of polarization has been and continues to be a challenge for experts. Existing ecological inference methods require estimating polarization levels in individual elections. This is a problem because the Courts want to see a history of polarization across elections.

I propose a new 2-step method to estimate racially polarized voting in a multi-election context. The procedure builds upon the Rosen, Jiang, King, and Tanner (2001) multinomial-Dirichlet model. After obtaining election-specific estimates, I suggest regressing those results on election-specific variables, namely candidate quality, incumbency, and ethnicity of the minority candidate of choice. This allows researchers to estimate the baseline level of support for candidates of choice and test whether the ethnicity of the candidates affected how voters cast their ballots.

Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement

Over the last century, the silicon revolution has enabled us to build faster, smaller and more sophisticated computers. Today, these computers control phones, cars, satellites, assembly lines, and other electromechanical devices. Just as electrical wiring controls electromechanical devices, living organisms employ "chemical wiring" to make decisions about their environment and control physical processes. Currently, the big difference between these two substrates is that while we have the abstractions, design principles, verification and fabrication techniques in place for programming with silicon, we have no comparable understanding or expertise for programming chemistry.

In this thesis we take a small step towards the goal of learning how to systematically engineer prescribed non-equilibrium dynamical behaviors in chemical systems. We use the formalism of chemical reaction networks (CRNs), combined with mass-action kinetics, as our programming language for specifying dynamical behaviors. Leveraging the tools of nucleic acid nanotechnology (introduced in Chapter 1), we employ synthetic DNA molecules as our molecular architecture and toehold-mediated DNA strand displacement as our reaction primitive.

Abstraction, modular design and systematic fabrication can work only with well-understood and quantitatively characterized tools. Therefore, we embark on a detailed study of the "device physics" of DNA strand displacement (Chapter 2). We present a unified view of strand displacement biophysics and kinetics by studying the process at multiple levels of detail, using an intuitive model of a random walk on a 1-dimensional energy landscape, a secondary structure kinetics model with single base-pair steps, and a coarse-grained molecular model that incorporates three-dimensional geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Our findings are consistent with previously measured or inferred rates for hybridization, fraying, and branch migration, and provide a biophysical explanation of strand displacement kinetics. Our work paves the way for accurate modeling of strand displacement cascades, which would facilitate the simulation and construction of more complex molecular systems.

In Chapters 3 and 4, we identify and overcome the crucial experimental challenges involved in using our general DNA-based technology for engineering dynamical behaviors in the test tube. In this process, we identify important design rules that inform our choice of molecular motifs and our algorithms for designing and verifying DNA sequences for our molecular implementation. We also develop flexible molecular strategies for "tuning" our reaction rates and stoichiometries in order to compensate for unavoidable non-idealities in the molecular implementation, such as imperfectly synthesized molecules and spurious "leak" pathways that compete with desired pathways.

We successfully implement three distinct autocatalytic reactions, which we then combine into a de novo chemical oscillator. Unlike biological networks, which use sophisticated evolved molecules (like proteins) to realize such behavior, our test tube realization is the first to demonstrate that Watson-Crick base pairing interactions alone suffice for oscillatory dynamics. Since our design pipeline is general and applicable to any CRN, our experimental demonstration of a de novo chemical oscillator could enable the systematic construction of CRNs with other dynamic behaviors.

Understanding Micro- and Macro-Mechanics of Soil Liquefaction: A Necessary Step for Field-Scale Assessment

Liquefaction is a devastating instability associated with saturated, loose, and cohesionless soils. It poses a significant risk to distributed infrastructure systems that are vital for the security, economy, safety, health, and welfare of societies. In order to make our cities resilient to the effects of liquefaction, it is important to be able to identify areas that are most susceptible. Some of the prevalent methodologies employed to identify susceptible areas include conventional slope stability analysis and the use of so-called liquefaction charts. However, these methodologies have some limitations, which motivate our research objectives. In this dissertation, we investigate the mechanics of origin of liquefaction in a laboratory test using grain-scale simulations, which helps (i) understand why certain soils liquefy under certain conditions, and (ii) identify a necessary precursor for onset of flow liquefaction. Furthermore, we investigate the mechanics of liquefaction charts using a continuum plasticity model; this can help in modeling the surface hazards of liquefaction following an earthquake. Finally, we also investigate the microscopic definition of soil shear wave velocity, a soil property that is used as an index to quantify liquefaction resistance of soil. We show that anisotropy in fabric, or grain arrangement can be correlated with anisotropy in shear wave velocity. This has the potential to quantify the effects of sample disturbance when a soil specimen is extracted from the field. In conclusion, by developing a more fundamental understanding of soil liquefaction, this dissertation takes necessary steps for a more physical assessment of liquefaction susceptibility at the field-scale.