She-pin Hung

Global gene expression profiling in Escherichia coli K12. The effects of leucine-responsive regulatory protein.

Leucine-responsive regulatory protein (Lrp) is a global regulatory protein that affects the expression of multiple genes and operons in bacteria. Although the physiological purpose of Lrp-mediated gene regulation remains unclear, it has been suggested that it functions to coordinate cellular metabolism with the nutritional state of the environment. The results of gene expression profiles between otherwise isogenic lrp + andlrp − strains of Escherichia colisupport this suggestion. The newly discovered Lrp-regulated genes reported here are involved either in small molecule or macromolecule synthesis or degradation, or in small molecule transport and environmental stress responses. Although many of these regulatory effects are direct, others are indirect consequences of Lrp-mediated changes in the expression levels of other global regulatory proteins. Because computational methods to analyze and interpret high dimensional DNA microarray data are still an early stage, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe a Bayesian statistical framework for a posterior estimate of the standard deviation of gene measurements based on a limited number of replications. We also describe an algorithm to compute a posterior estimate of differential expression for each gene based on the experiment-wide global false positive and false negative level for a DNA microarray data set. This allows the experimenter to compute posterior probabilities of differential expression for each individual differential gene expression measurement.

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high‐dimensional DNA microarray experiments. We discuss methods using a regularized t‐test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t‐test when only a few experimental replicates are available. We also describe a computational method for calculating the global false‐positive and false‐negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment‐wide false‐positive and ‐negative levels driven by experimental error and biological variance.

Recombinant Human Collagen and Biomimetic Variants Using a De Novo Gene Optimized for Modular Assembly

A collagen-mimetic polymer that can be easily engineered with specific cell-responsive and mechanical properties would be of significant interest for fundamental cell-matrix studies and applications in regenerative medicine. However, oligonucleotide-based synthesis of full-length collagen has been encumbered by the characteristic glycine-X-Y sequence repetition, which promotes mismatched oligonucleotide hybridizations during de novo gene assembly. In this work, we report a novel, modular synthesis strategy that yields full-length human collagen III and specifically defined variants. We used a computational algorithm that applies codon degeneracy to design oligonucleotides that favor correct hybridizations while disrupting incorrect ones for gene synthesis. The resulting recombinant polymers were expressed in Saccharomyces cerevisiae engineered with prolyl-4-hydroxylase. Our modular approach enabled mixing-and-matching domains to fabricate different combinations of collagen variants that contained different secretion signals at the N-terminus and cysteine residues imbedded within the triple-helical domain at precisely defined locations. This work shows the flexibility of our strategy for designing and assembling specifically tailored biomimetic collagen polymers with re-engineered properties.