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.

Design and analysis of nucleic acid reaction pathways

Nucleic acids are a useful substrate for engineering at the molecular level. Designing the detailed energetics and kinetics of interactions between nucleic acid strands remains a challenge. Building on previous algorithms to characterize the ensemble of dilute solutions of nucleic acids, we present a design algorithm that allows optimization of structural features and binding energetics of a test tube of interacting nucleic acid strands. We extend this formulation to handle multiple thermodynamic states and combinatorial constraints to allow optimization of pathways of interacting nucleic acids. In both design strategies, low-cost estimates to thermodynamic properties are calculated using hierarchical ensemble decomposition and test tube ensemble focusing. These algorithms are tested on randomized test sets and on example pathways drawn from the molecular programming literature. To analyze the kinetic properties of designed sequences, we describe algorithms to identify dominant species and kinetic rates using coarse-graining at the scale of a small box containing several strands or a large box containing a dilute solution of strands.

The isolation and partial biochemical analysis of Sindbis virus proteins

Recently, the amino acid sequences have been reported for several proteins, including the envelope glycoproteins of Sindbis virus, which all probably span the plasma membrane with a common topology: a large N-terminal, extracellular portion, a short region buried in the bilayer, and a short C-terminal intracellular segment. The regions of these proteins buried in the bilayer correspond to portions of the protein sequences which contain a stretch of hydrophobic amino acids and which have other common characteristics, as discussed. Reasons are also described for uncertainty, in some proteins more than others, as to the precise location of some parts of the sequence relative to the membrane.

The signal hypothesis for the transmembrane translocation of proteins is briefly described and its general applicability is reviewed. There are many proteins whose translocation is accurately described by this hypothesis, but some proteins are translocated in a different manner.

The transmembraneous glycoproteins E1 and E2 of Sindbis virus, as well as the only other virion protein, the capsid protein, were purified in amounts sufficient for biochemical analysis using sensitive techniques. The amino acid composition of each protein was determined, and extensive N-terminal sequences were obtained for E1 and E2. By these techniques E1 and E2 are indistinguishable from most water soluble proteins, as they do not contain an obvious excess of hydrophobic amino acids in their N-terminal regions or in the intact molecule.

The capsid protein was found to be blocked, and so its N-terminus could not be sequenced by the usual methods. However, with the use of a special labeling technique, it was possible to incorporate tritiated acetate into the N-terminus of the protein with good specificity, which was useful in the purification of peptides from which the first amino acids in the N-terminal sequence could be identified.

Nanomole amounts of PE2, the intracellular precursor of E2, were purified by an immuno-affinity technique, and its N-terminus was analyzed. Together with other work, these results showed that PE2 is not synthesized with an N-terminal extension, and the signal sequence for translocation is probably the N-terminal amino acid sequence of the protein. This N-terminus was found to be 80-90% blocked, also by Nacetylation, and this acetylation did not affect its function as a signal sequence. The putative signal sequence was also found to contain a glycosylated asparagine residue, but the inhibition of this glycosylation did not lead to the cleavage of the sequence.

Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.