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.

Use of RT-PCR and DNA microarrays to characterize RNA recovered by non-invasive tape harvesting of normal and inflamed skin.

We describe a non-invasive approach for recovering RNA from the surface of skin via a simple tape stripping procedure that permits a direct quantitative and qualitative assessment of pathologic and physiologic biomarkers. Using semi-quantitative RT-PCR we show that tape-harvested RNA is comparable in quality and utility to RNA recovered by biopsy. It is likely that tape-harvested RNA is derived from epidermal cells residing close to the surface and includes adnexal structures and present data showing that tape and biopsy likely recover different cell populations. We report the successful amplification of tape-harvested RNA for hybridization to DNA microarrays. These experiments showed no significant gene expression level differences between replicate sites on a subject and minimal differences between a male and female subject. We also compared the array generated RNA profiles between normal and 24 h 1% SLS-occluded skin and observed that SLS treatment resulted in statistically significant changes in the expression levels of more than 1,700 genes. These data establish the utility of tape harvesting as a non-invasive method for capturing RNA from human skin and support the hypothesis that tape harvesting is an efficient method for sampling the epidermis and identifying select differentially regulated epidermal biomarkers.

Effects of exercise on gene expression in human peripheral blood mononuclear cells.

Exercise leads to increases in circulating levels of peripheral blood mononuclear cells (PBMCs) and to a simultaneous, seemingly paradoxical increase in both pro- and anti-inflammatory mediators. Whether this is paralleled by changes in gene expression within the circulating population of PBMCs is not fully understood. Fifteen healthy men (18-30 yr old) performed 30 min of constant work rate cycle ergometry (approximately 80% peak O2 uptake). Blood samples were obtained preexercise (Pre), end-exercise (End-Ex), and 60 min into recovery (Recovery), and gene expression was measured using microarray analysis (Affymetrix GeneChips). Significant differential gene expression was defined with a posterior probability of differential expression of 0.99 and a Bayesian P value of 0.005. Significant changes were observed from Pre to End-Ex in 311 genes, from End-Ex to Recovery in 552 genes, and from Pre to Recovery in 293 genes. Pre to End-Ex upregulation of PBMC genes related to stress and inflammation [e.g., heat shock protein 70 (3.70-fold) and dual-specificity phosphatase-1 (4.45-fold)] was followed by a return of these genes to baseline by Recovery. The gene for interleukin-1 receptor antagonist (an anti-inflammatory mediator) increased between End-Ex and Recovery (1.52-fold). Chemokine genes associated with inflammatory diseases [macrophage inflammatory protein-1alpha (1.84-fold) and -1beta (2.88-fold), and regulation-on-activation, normal T cell expressed and secreted (1.34-fold)] were upregulated but returned to baseline by Recovery. Exercise also upregulated growth and repair genes such as epiregulin (3.50-fold), platelet-derived growth factor (1.55-fold), and hypoxia-inducible factor-I (2.40-fold). A single bout of heavy exercise substantially alters PBMC gene expression characterized in many cases by a brisk activation and deactivation of genes associated with stress, inflammation, and tissue repair.

How noisy and replicable are DNA microarry data?

This paper analyses variability in highly replicated measurements of DNA microarray data conducted on nylon filters and Affymetrix GeneChips with different cDNA targets, filters, and imaging technology. Replicability is assessed quantitatively using correlation analysis as a global measure and differential expression analysis and ANOVA at the level of individual genes.

A mathematical model for the branched chain amino acid biosynthetic pathways of Escherichia coli K12.

As a first step toward the elucidation of the systems biology of the model organism Escherichia coli, it was our goal to mathematically model a metabolic system of intermediate complexity, namely the well studied end product-regulated pathways for the biosynthesis of the branched chain amino acids L-isoleucine, L-valine, and L-leucine. This has been accomplished with the use of kMech (Yang, C.-R., Shapiro, B. E., Mjolsness, E. D., and Hatfield, G. W. (2005) Bioinformatics 21, in press), a Cellerator (Shapiro, B. E., Levchenko, A., Meyerowitz, E. M., Wold, B. J., and Mjolsness, E. D. (2003) Bioinformatics 19, 677-678) language extension that describes a suite of enzyme reaction mechanisms. Each enzyme mechanism is parsed by kMech into a set of fundamental association-dissociation reactions that are translated by Cellerator into ordinary differential equations. These ordinary differential equations are numerically solved by Mathematica. Any metabolic pathway can be simulated by stringing together appropriate kMech models and providing the physical and kinetic parameters for each enzyme in the pathway. Writing differential equations is not required. The mathematical model of branched chain amino acid biosynthesis in E. coli K12 presented here incorporates all of the forward and reverse enzyme reactions and regulatory circuits of the branched chain amino acid biosynthetic pathways, including single and multiple substrate (Ping Pong and Bi Bi) enzyme kinetic reactions, feedback inhibition (allosteric, competitive, and non-competitive) mechanisms, the channeling of metabolic flow through isozymes, the channeling of metabolic flow via transamination reactions, and active transport mechanisms. This model simulates the results of experimental measurements.

The architecture of pre-mRNAs affects mechanisms of splice-site pairing.

The exon/intron architecture of genes determines whether components of the spliceosome recognize splice sites across the intron or across the exon. Using in vitro splicing assays, we demonstrate that splice-site recognition across introns ceases when intron size is between 200 and 250 nucleotides. Beyond this threshold, splice sites are recognized across the exon. Splice-site recognition across the intron is significantly more efficient than splice-site recognition across the exon, resulting in enhanced inclusion of exons with weak splice sites. Thus, intron size can profoundly influence the likelihood that an exon is constitutively or alternatively spliced. An EST-based alternative-splicing database was used to determine whether the exon/intron architecture influences the probability of alternative splicing in the Drosophila and human genomes. Drosophila exons flanked by long introns display an up to 90-fold-higher probability of being alternatively spliced compared with exons flanked by two short introns, demonstrating that the exon/intron architecture in Drosophila is a major determinant in governing the frequency of alternative splicing. Exon skipping is also more likely to occur when exons are flanked by long introns in the human genome. Interestingly, experimental and computational analyses show that the length of the upstream intron is more influential in inducing alternative splicing than is the length of the downstream intron. We conclude that the size and location of the flanking introns control the mechanism of splice-site recognition and influence the frequency and the type of alternative splicing that a pre-mRNA transcript undergoes.

Global gene expression profiling in Escherichia coli K12: effects of oxygen availability and ArcA.

The ArcAB two-component system of Escherichia coli regulates the aerobic/anaerobic expression of genes that encode respiratory proteins whose synthesis is coordinated during aerobic/anaerobic cell growth. A genomic study of E. coli was undertaken to identify other potential targets of oxygen and ArcA regulation. A group of 175 genes generated from this study and our previous study on oxygen regulation (Salmon, K., Hung, S. P., Mekjian, K., Baldi, P., Hatfield, G. W., and Gunsalus, R. P. (2003) J. Biol. Chem. 278, 29837-29855), called our gold standard gene set, have p values <0.00013 and a posterior probability of differential expression value of 0.99. These 175 genes clustered into eight expression patterns and represent genes involved in a large number of cell processes, including small molecule biosynthesis, macromolecular synthesis, and aerobic/anaerobic respiration and fermentation. In addition, 119 of these 175 genes were also identified in our previous study of the fnr allele. A MEME/weight matrix method was used to identify a new putative ArcA-binding site for all genes of the E. coli genome. 16 new sites were identified upstream of genes in our gold standard set. The strict statistical analyses that we have performed on our data allow us to predict that 1139 genes in the E. coli genome are regulated either directly or indirectly by the ArcA protein with a 99% confidence level.

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(+) and lrp(-) strains of Escherichia coli support 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.

Global gene expression profiling in Escherichia coli K12. The effects of oxygen availability and FNR.

The work presented here is a first step toward a long term goal of systems biology, the complete elucidation of the gene regulatory networks of a living organism. To this end, we have employed DNA microarray technology to identify genes involved in the regulatory networks that facilitate the transition of Escherichia coli cells from an aerobic to an anaerobic growth state. We also report the identification of a subset of these genes that are regulated by a global regulatory protein for anaerobic metabolism, FNR. Analysis of these data demonstrated that the expression of over one-third of the genes expressed during growth under aerobic conditions are altered when E. coli cells transition to an anaerobic growth state, and that the expression of 712 (49%) of these genes are either directly or indirectly modulated by FNR. The results presented here also suggest interactions between the FNR and the leucine-responsive regulatory protein (Lrp) regulatory networks. Because computational methods to analyze and interpret high dimensional DNA microarray data are still at an early stage, and because basic issues of data analysis are still being sorted out, 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 an approach for identifying gene expression patterns (clusters) obtained from multiple perturbation experiments based on a subset of genes that exhibit high probability for differential expression values.