Cytokine control of neuronal phenotype

Diffusible proteins regulate neural development at a variety of stages. Using a novel neuronal culture assay, I have identified several cytokines that regulate the expression of neurotransmitters and neuropeptides in sympathetic neurons. These cytokines fall into two families. The first group is termed the neuropoietic cytokines, while including CDF/LIF, CNTF, OSM and GPA, induces expression of the same set of neuropeptide mRNAs in cultured sympathetic neurons. These four factors not only exhibit similar biological activities; they also share a predicted secondary structure and bind to a signal-transducing receptor subunit in common with IL-6 and IL-11. The latter two cytokines display a weaker activity in this assay. In addition, I find that several members of the TGF-β superfamily, activin A, BMP-2, and BMP-6, have a selective overlap with the neuropoietic family in the spectrum of neuropeptides that these cytokines induce in sympathetic neurons. Different patterns of neuropeptides induced by the TGF-β family members, however, demonstrate that the activities of these cytokines are distinct from those of the neuropoietic family. Another 30 cytokines are without detectable effect in this neuronal assay.

Activin A induces a set of neurotransmitters and neuropeptides that is somewhat similar to the phenotype of sympathetic neurons innervating sweat glands in rat footpads. In situ hybridization and RNase protection were carried out to test whether activins were involved in the phenotypic transition when sympathetic neurons contact sweat glands. I find that activin mRNA is present in both cholinergic and noradrenergic targets. Moreover, homogenates of footpads do not contain activin-like activity in the neuronal assay in vitro. Taken together, these data do not support activins as the best candidates for the sweat gland factor.

Several novel factors that regulate neuropeptide expression exist in heart cell conditioned medium. I attempted to purify these factors in collaboration with Dr. Jane Talvenheimo. Our results suggest that these factors are sensitive to the storage conditions used. Several modifications of purification strategy are discussed.

Structural requirements for catalysis: studies on RTEM-1 β-lactamase

β-lactamases are a group of enzymes that confer resistance to penam and cephem antibiotics by hydrolysis of the β-lactam ring, thereby inactivating the antibiotic. Crystallographic and computer modeling studies of RTEM-1 β-lactamase have indicated that Asp 132, a strictly conserved residue among the class A β-lactamases, appears to be involved in substrate binding, catalysis, or both. To study the contribution of residue 132 to β-lactamase function, site saturation mutagenesis was used to generate mutants coding for all 20 amino acids at position 132. Phenotypic screening of all mutants indicated that position 132 is very sensitive to amino acid changes, with only N132C, N132D, N132E, and N132Q showing any appreciable activity. Kinetic analysis of three of these mutants showed increases in K_M, along with substantial decreases in k_(cat). Efforts to trap a stable acyl-enzyme intermediate were unsuccessfuL These results indicate that residue 132 is involved in substrate binding, as well as catalysis, and supports the involvement of this residue in acylation as suggested by Strynadka et al.

Crystallographic and computer modeling studies of RTEM-1 β-lactamase have indicated that Lys 73 and Glu 166, two strictly conserved residues among the class A β-lactamases, appear to be involved in substrate binding, catalysis, or both. To study the contribution of these residues to β-lactamase function, site saturation mutagenesis was used to generate mutants coding for all 20 amino acids at positions 73 and 166. Then all 400 possible combinations of mutants were created by combinatorial mutagenesis. The colonies harboring the mutants were screened for growth in the presence of ampicillin. The competent colonys' DNA were sequenced, and kinetic parameters investigated. It was found that lysine is essential at position 73, and that position 166 only tolerated fairly conservative changes (Aspartic acid, Histidine, and Tyrosine). These functional mutants exhibited decreased kcat's, but K_M was close to wild-type levels. The results of the combinatorial mutagenesis experiments indicate that Lysis absolutely required for activity at position 73; no mutation at residue 166 can compensate for loss of the long side chain amine. The active mutants found--K73K/E166D, K73KIE166H, and K73KIE166Y were studied by kinetic analysis. These results reaffirmed the function of residue 166 as important in catalysis, specifically deacylation.

The identity of the residue responsible for enhancing the active site serine (Ser 70) in RTEM-1 β-lactamase has been disputed for some time. Recently, analysis of a crystal structure of RTEM-1 β-lactamase with covalently bound intermediate was published, and it was suggested that Lys 73, a strictly conserved residue among the class A β-lactamases, was acting as a general base, activating Ser 70. For this to be possible, the pK_a of Lys 73 would have to be depressed significantly. In an attempt to assay the pK_a of Lys 73, the mutation K73C was made. This mutant protein can be reacted with 2-bromoethylamine, and activity is restored to near wild type levels. ^(15)N-2-bromoethylamine hydrobromide and ^(13)C-2-bromoethylamine hydrobromide were synthesized. Reacting these compounds with the K73C mutant gives stable isotopic enrichment at residue 73 in the form of aminoethylcysteine, a lysine homologue. The pK_a of an amine can be determined by NMR titration, following the change in chemical shift of either the ^(15)N-amine nuclei or adjacent Be nuclei as pH is changed. Unfortunately, low protein solubility, along with probable label scrambling in the Be experiment, did not permit direct observation of either the ^(15)N or ^(13)C signals. Indirect detection experiments were used to observe the protons bonded directly to the ^(13)C atoms. Two NMR signals were seen, and their chemical shift change with pH variation was noted. The peak which was determined to correspond to the aminoethylcysteine residue shifted from 3.2 ppm down to 2.8 ppm over a pH range of 6.6 to 12.5. The pK_a of the amine at position 73 was determined to be ~10. This indicates that residue 73 does not function as a general base in the acylation step of the reaction. However the experimental measurement takes place in the absence of substrate. Since the enzyme undergoes conformational changes upon substrate binding, the measured pK_a of the free enzyme may not correspond to the pK_a of the enzyme substrate complex.

Role of feedback and dynamics in a gene regulatory network

Cells exhibit a diverse repertoire of dynamic behaviors. These dynamic functions are implemented by circuits of interacting biomolecules. Although these regulatory networks function deterministically by executing specific programs in response to extracellular signals, molecular interactions are inherently governed by stochastic fluctuations. This molecular noise can manifest as cell-to-cell phenotypic heterogeneity in a well-mixed environment. Single-cell variability may seem like a design flaw but the coexistence of diverse phenotypes in an isogenic population of cells can also serve a biological function by increasing the probability of survival of individual cells upon an abrupt change in environmental conditions. Decades of extensive molecular and biochemical characterization have revealed the connectivity and mechanisms that constitute regulatory networks. We are now confronted with the challenge of integrating this information to link the structure of these circuits to systems-level properties such as cellular decision making. To investigate cellular decision-making, we used the well studied galactose gene-regulatory network in \textit{Saccharomyces cerevisiae}. We analyzed the mechanism and dynamics of the coexistence of two stable on and off states for pathway activity. We demonstrate that this bimodality in the pathway activity originates from two positive feedback loops that trigger bistability in the network. By measuring the dynamics of single-cells in a mixed sugar environment, we observe that the bimodality in gene expression is a transient phenomenon. Our experiments indicate that early pathway activation in a cohort of cells prior to galactose metabolism can accelerate galactose consumption and provide a transient increase in growth rate. Together these results provide important insights into strategies implemented by cells that may have been evolutionary advantageous in competitive environments.

A genetic and biochemical analysis of pre-mRNA splicing in Saccharomyces cerevisiae

Pre-mRNA splicing requires interaction of cis- acting intron sequences with trans -acting factors: proteins and small nuclear ribonucleoproteins (snRNPs). The assembly of these factors into a large complex, the spliceosome, is essential for the subsequent two step splicing reaction. First, the 5' splice site is cleaved and free exon 1 and a lariat intermediate (intron- exon2) form. In the second reaction the 3' splice site is cleaved the exons ligated and lariat intron released. A combination of genetic and biochemical techniques have been used here to study pre-mRNA splicing in yeast.

Yeast introns have three highly conserved elements. We made point mutations within these elements and found that most of them affect splicing efficiency in vivo and in vitro, usually by inhibiting spliceosome assembly.

To study trans -acting splicing factors we generated and screened a bank of temperature- sensitive (ts) mutants. Eleven new complementation groups (prp17 to prp27) were isolated. The four phenotypic classes obtained affect different steps in splicing and accumulate either: 1) pre-mRNA, 2) lariat intermediate, 3) excised intron or 4) both pre-mRNA and intron. The latter three classes represent novel phenotypes. The excised intron observed in one mutant: prp26 is stabilized due to protection in a snRNP containing particle. Extracts from another mutant: prpl8 are heat labile and accumulate lariat intermediate and exon 1. This is especially interesting as it allows analysis of the second splicing reaction. In vitro complementation of inactivated prp18 extracts does not require intact snRNPs. These studies have also shown the mutation to be in a previously unknown splicing protein. A specific requirement for A TP is also observed for the second step of splicing. The PRP 18 gene has been cloned and its polyadenylated transcript identified.

Robust Control of Evolutionary Dynamics

The application of principles from evolutionary biology has long been used to gain new insights into the progression and clinical control of both infectious diseases and neoplasms. This iterative evolutionary process consists of expansion, diversification and selection within an adaptive landscape - species are subject to random genetic or epigenetic alterations that result in variations; genetic information is inherited through asexual reproduction and strong selective pressures such as therapeutic intervention can lead to the adaptation and expansion of resistant variants. These principles lie at the center of modern evolutionary synthesis and constitute the primary reasons for the development of resistance and therapeutic failure, but also provide a framework that allows for more effective control.

A model system for studying the evolution of resistance and control of therapeutic failure is the treatment of chronic HIV-1 infection by broadly neutralizing antibody (bNAb) therapy. A relatively recent discovery is that a minority of HIV-infected individuals can produce broadly neutralizing antibodies, that is, antibodies that inhibit infection by many strains of HIV. Passive transfer of human antibodies for the prevention and treatment of HIV-1 infection is increasingly being considered as an alternative to a conventional vaccine. However, recent evolution studies have uncovered that antibody treatment can exert selective pressure on virus that results in the rapid evolution of resistance. In certain cases, complete resistance to an antibody is conferred with a single amino acid substitution on the viral envelope of HIV.

The challenges in uncovering resistance mechanisms and designing effective combination strategies to control evolutionary processes and prevent therapeutic failure apply more broadly. We are motivated by two questions: Can we predict the evolution to resistance by characterizing genetic alterations that contribute to modified phenotypic fitness? Given an evolutionary landscape and a set of candidate therapies, can we computationally synthesize treatment strategies that control evolution to resistance?

To address the first question, we propose a mathematical framework to reason about evolutionary dynamics of HIV from computationally derived Gibbs energy fitness landscapes -- expanding the theoretical concept of an evolutionary landscape originally conceived by Sewall Wright to a computable, quantifiable, multidimensional, structurally defined fitness surface upon which to study complex HIV evolutionary outcomes.

To design combination treatment strategies that control evolution to resistance, we propose a methodology that solves for optimal combinations and concentrations of candidate therapies, and allows for the ability to quantifiably explore tradeoffs in treatment design, such as limiting the number of candidate therapies in the combination, dosage constraints and robustness to error. Our algorithm is based on the application of recent results in optimal control to an HIV evolutionary dynamics model and is constructed from experimentally derived antibody resistant phenotypes and their single antibody pharmacodynamics. This method represents a first step towards integrating principled engineering techniques with an experimentally based mathematical model in the rational design of combination treatment strategies and offers predictive understanding of the effects of combination therapies of evolutionary dynamics and resistance of HIV. Preliminary in vitro studies suggest that the combination antibody therapies predicted by our algorithm can neutralize heterogeneous viral populations despite containing resistant mutations.