Showing papers by "James J. Collins published in 2007"

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Abstract: Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Dispersing biofilms with engineered enzymatic bacteriophage

Abstract: Synthetic biology involves the engineering of biological organisms by using modular and generalizable designs with the ultimate goal of developing useful solutions to real-world problems. One such problem involves bacterial biofilms, which are crucial in the pathogenesis of many clinically important infections and are difficult to eradicate because they exhibit resistance to antimicrobial treatments and removal by host immune systems. To address this issue, we engineered bacteriophage to express a biofilm-degrading enzyme during infection to simultaneously attack the bacterial cells in the biofilm and the biofilm matrix, which is composed of extracellular polymeric substances. We show that the efficacy of biofilm removal by this two-pronged enzymatic bacteriophage strategy is significantly greater than that of nonenzymatic bacteriophage treatment. Our engineered enzymatic phage substantially reduced bacterial biofilm cell counts by ≈4.5 orders of magnitude (≈99.997% removal), which was about two orders of magnitude better than that of nonenzymatic phage. This work demonstrates the feasibility and benefits of using engineered enzymatic bacteriophage to reduce bacterial biofilms and the applicability of synthetic biology to an important medical and industrial problem.

Gyrase Inhibitors Induce an Oxidative Damage Cellular Death Pathway in Escherichia Coli

Abstract: Modulation of bacterial chromosomal supercoiling is a function of DNA gyrase-catalyzed strand breakage and rejoining. This reaction is exploited by both antibiotic and proteic gyrase inhibitors, which trap the gyrase molecule at the DNA cleavage stage. Owing to this interaction, doublestranded DNA breaks are introduced and replication machinery is arrested at blocked replication forks. This immediately results in bacteriostasis and ultimately induces cell death. Here we demonstrate, through a series of phenotypic and gene expression analyses, that superoxide and hydroxyl radical oxidative species are generated following gyrase poisoning and play an important role in cell killing by gyrase inhibitors. We show that superoxide-mediated oxidation of iron–sulfur clusters promotes a breakdown of iron regulatory dynamics; in turn, iron misregulation drives the generation of highly destructive hydroxyl radicals via the Fenton reaction. Importantly, our data reveal that blockage of hydroxyl radical formation increases the survival of gyrase-poisoned cells. Together, this series of biochemical reactions appears to compose a maladaptive response, that serves to amplify the primary effect of gyrase inhibition by oxidatively damaging DNA, proteins and lipids. Molecular Systems Biology 13 March 2007; doi:10.1038/msb4100135 Subject Categories: cellular metabolism; microbiology & pathogenesis

Combinatorial promoter design for engineering noisy gene expression.

Abstract: Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO2 operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose–response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental–computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.

A network biology approach to prostate cancer

Abstract: Received 1.8.06; accepted 22.12.06 There is a need to identify genetic mediators of solid-tumor cancers, such as prostate cancer, where invasion and distant metastases determine the clinical outcome of the disease. Whole-genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by a condition from the hundreds to thousands of genes that exhibit changes in expression. Here, we show that reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease. We apply our method to non-recurrent primary and metastatic prostate cancer data, and identify the androgen receptor gene (AR) among the top genetic mediators and the AR pathway as a highly enriched pathway for metastatic prostate cancer. These results were not obtained on the basis of expression change alone. We further demonstrate that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers. This work shows that a network biology approach can be used advantageously to identify the genetic mediators and mediating pathways associated with a disease. Molecular Systems Biology 13 February 2007; doi:10.1038/msb4100125 Subject Categories: metabolic and regulatory networks; molecular biology of disease

Reading Shop Windows in Globalized Neighborhoods: Multilingual Literacy Practices and Indexicality.

Abstract: Shop and cafe signs in multiple languages are familiar features of polyglot immigrant neighborhoods. This paper examines such signs, presenting photographic, observational, and interview data from ...

Size matters: network inference tackles the genome scale

Abstract: The growing importance of microarray data challenges biologists, and especially the systems biology community, to come up with genome-scale analysis methods that can convert the large quantity of available high-throughput data into high-quality systems-level insights. One area of systems-level analysis that has received considerable attention in recent years is that of inferring molecular-level regulation, with frequent focus on transcriptional regulatory networks (Kholodenko et al, 1997; Tavazoie et al, 1999; Gardner et al, 2003; Segal et al, 2003; Beer and Tavazoie, 2004; Yu et al, 2004; di Bernardo et al, 2005; Gardner and Faith, 2005; Woolf et al, 2005; Margolin et al, 2006; Faith et al, 2007). As microarrays provide a tool for measuring transcript levels of the whole genome, recent interest has shifted to inferring networks on a genome scale. The less-studied organisms are a natural starting point for such mapping, as it is for these organisms that the rapid, genome-scale identification of regulatory structure is most needed. In a recent study, Bonneau et al (2006) apply the Inferelator, their elegant new algorithm, for inferring gene networks, to precisely such a little-studied but important organism. Specifically, the authors focus on Halobacterium NRC-I, a model archaeon (DasSarma et al, 2006), to show that, at least for a small genome, it is possible to determine a sizeable portion of the transcriptional regulatory network from microarrays without much prior knowledge. This choice of an organism has two practical advantages. First, the salt-loving NRC-I is one of a handful of Halobacteria for which transformation techniques have been well studied, allowing in vivo validation of network predictions. Second, NRC-I's genome is relatively small and thus, its regulation ought to be comparatively easy to reconstruct. Small genome or not, putting high-throughput profiling technologies to work on the genome scale requires a confluence of robust algorithms, biologically plausible simplifying assumptions, and a robust verification strategy. The work of Bonneau et al (2006) is a good example, using multiple tools in the bioinformatics toolbox to build a credible blueprint of a transcriptional-regulatory network involving thousands of genes and more than 100 transcription factors. In order to appreciate the need for a well-structured approach to regulatory mapping, consider the mathematical and biological scope of this cross-disciplinary problem. The tiny archaeon Halobacterium NRC-I contains about 2400 genes. For each one of these, the goal is to understand the transcriptional regulatory apparatus—that is about 2400 question marks, each with thousands of possible answers in the form of a set of transcriptional regulators. Put that against a typical compendium size of several hundred chips for a given organism, and you get what is known as a ‘small n, large p' problem, where the number of possible parameters (regulators), p, dwarfs the number of data points (microarrays), n, available to define them. This problem gets considerably worse for complex organisms, where a larger number of available microarrays are more than offset by the vast complexity of large genomes, alternate splice variants, and multiple layers of regulation. For network inference algorithms, ‘small n, large p' means dearth of data and very high computational demands. As if this computational complexity were not bad enough, there is the inherent high dimensionality in the biological realm. Regulation happens in the domains of mRNA, proteins, metabolites, kinases, acetylases, and so on, and through a variety of pleiotropic perturbations and influences, such as salinity, temperature, and cell-wall permeability. As the best high-throughput data capture only mRNA, one must make simplifying assumptions and skip many important parameters. Bonneau and colleagues' best simplifying assumption is to focus on predicting the targets of transcription factors in the network, along with some key environmental influences. When only transcription factors are allowed to regulate other genes, the ‘p' in the ‘small n, large p' problem is no longer so big. In fact, at 120, it is smaller than the number of chips (268) used in this study. To further constrain the network learning problem, the Inferelator performs a pre-processing step of bi-clustering—organizing experimental data by both genes and conditions. This algorithm, the cMonkey (Reiss et al, 2006), allows further reduction of dimensionality by collapsing genes into conditionally coexpressed modules. cMonkey identified 300 such bi-clusters, and 159 individual genes that could not be grouped, a nearly six-fold reduction in dimensionality. Crucially, as the composition of the culture medium used for the microarray-profiled experiments is known, each bi-cluster's grouping of genes by experimental condition suggests plausible metabolic or environmental effectors of regulation. The authors exploit this benefit of their approach in one of their verifying experiments. Bi-clustering, therefore, serves two ends: it limits the number of genes, and thus variables to reconstruct, to fewer than 500 (including only 80 TFs and metabolites), and places each predicted regulatory interaction into an experiment-specific context. The problem now becomes mathematically well-posed, and the authors solve it using LASSO regression, a sparse regression method designed just for such computationally difficult problems (Tibshirani, 1996). LASSO works by selecting a small set of the most likely regulators of a given gene, and simultaneously determines a quantitative influence function relating regulator expression to target expression (Figure 1). In addition, the authors extend the LASSO algorithm beyond its typical linear domain by including piecewise and nonlinear terms in the regression to model saturation effects and pairwise combinatorial regulation. With this approach, the authors construct a model of transcription regulation in Halobacterium that matches 80 transcription factors to 500 predicted gene targets and captures the putative metabolic controllers of these pathways. This is an impressive result, both in size and regulatory complexity, particularly in light of the relatively modest size of the experimental data set (i.e., 268 microarrays). Moreover, this represents a dramatic leap in our understanding of this little-studied organism. Figure 1 (A) Schematic diagram of a hypothetical bacterial operon, represented by a single gene Y, which is regulated by a protein X1 and a protein complex X2X3. (B) Within its dynamic range, the level of the transcript y may be modeled as a function of transcripts ... Having obtained the first-pass transcriptional blueprint, Bonneau and colleagues ask the obligatory next question: how much do we trust this network? In network inference, three broad types of verification are possible: computational verification through cross-validation, in vivo verification, and literature-driven curation. To be effective, the last approach should leverage a large data set documenting connectivity known in the literature, such as TransFac (Matys et al, 2003) or RegulonDB (Salgado et al, 2006). This type of verification not being available for Halobacterium, the authors vigorously pursue the former two, including knockout experimentation and ChIP-chip analysis, demonstrating that their network can serve as a reliable and useful blueprint of Halobacterium NRC-I's transcriptional regulation. Bonneau et al (2006) show the feasibility of mapping a genome-scale regulatory network from a modestly sized compendium of microarrays, an important success for the systems biology community. As microarray technology continues to improve and costs drop, growing databases of microarrays present an opportunity to infer ever more complex regulatory networks in both microbes and higher organisms. Abundance of data fuels the need for a network inference case study that would clearly map the boundaries of what is possible with today's network mapping algorithms. To this end, we believe that the once and future model organisms like Escherichia coli and Saccharomyces cerevisiae, buoyed by extensive bodies of literature and large databases such as RegulonDB, SGD (Christie et al, 2004), and TransFac, may represent attractive short-term targets for network inference studies. In addition to the use of curated data sets, it may be possible to seed organisms with small synthetic in vivo networks, the connectivity of which is known by design, and to measure the success of network reconstruction on the whole by success or failure to reconstruct the seed. We are aware of at least one lab doing such work (Cantone et al, 2006). Biological yardsticks in general will gain in importance, as they supplement in silico testing and usher in algorithms' transition from design to practical use, and from simple organisms to higher eukaryotes. Challenges remain, but we see the immediate future of network inference as promising and bright. Molecular biologists have long been looking for ways to generate more oomph from their microarrays. Systems biology may have some answers, and we laud Bonneau and colleagues for providing an illuminating step in that direction.

Serum concentrations of chlorinated dibenzo-p-dioxins and dibenzofurans among former Michigan trichlorophenol and pentachlorophenol workers.

Abstract: This study examines serum levels of 2,3,7,8-substituted chlorinated dioxins and furans, and PCBs for 375 Michigan workers with potential chlorophenol exposure, 37 Worker Referents, and 71 Community Referents. The chlorophenol workers were last exposed to trichlorophenol and/or pentachlorophenol 26-62 years ago. Employees working only in the trichlorophenol units had mean lipid-adjusted 2378-tetrachlorodibenzo-p-dioxin (TCDD) levels of 15.9 ppt compared with 6.5 ppt in the Worker Referents. Employees working only in the pentachlorophenol units had mean lipid-adjusted levels for 123478-H6CDD of 16.1 ppt, 123678-H6CDD of 150.6 ppt, 123789-H6CDD of 20.2 ppt, 1234678-H7CDD of 192.6 ppt, and OCDD of 2,594.0 ppt compared with the Worker Referent levels for the same congeners of 7.5, 74.7, 8.6, 68.7, and 509.1 ppt, respectively. All furan and PCB levels among workers in the trichlorophenol and/or pentachlorophenol departments were similar to the Worker Referents. The Tradesmen who worked throughout the plant had dioxin congener profiles consistent with both trichlorophenol and pentachlorophenol exposures. PCB levels and levels of 23478-P5CDF, 123478-H6CDF, and 123678-H6CDF were also greater in these Tradesmen than in the Worker Referents. The Worker Referent group had higher levels of dioxins and furans than the Community Referents indicating the potential for exposure outside the chlorophenol departments at the site. Distinct patterns of dioxin congeners were found many years after exposure among workers with different chlorophenol exposures. Furthermore, past trichlorophenol exposures were readily distinguishable from past pentachlorophenol exposures based on serum dioxin evaluations among workers. These data can be used to better assess dioxin exposures in future health studies.

A tunable genetic switch for regulating gene expression

Abstract: The present invention relates generally the field of genetics, in particular methods, compositions and systems for controlling the inducible expression of transgenes, while eliminating background expression of transgene expression. The present invention relates to methods of use of the compositions and systems as disclosed herein for controlling the inducible expression of transgenes while eliminating background expression of transgene expression, such as use in, for example, the in generation of transgenic animals, use in therapeutic application and use in assays. In some embodiments, the present invention relates to a system of controlled expression of RNAi molecules which target binding sites in the untranslated regions of transgene, thereby the expression of the transgene is modulated and leakiness is reduced. The compositions and methods of the present invention can be used to for therapy, prophylaxis, research and diagnostics in diseases and disorders which afflict mammalian species, generation of transgenic animals, in the study of biological processes as well as for enhance performance of agricultural crops.

Compositions and methods for antibiotic potentiation and drug discovery

Abstract: The present invention provides methods for identifying target genes whose partial or complete functional inactivation potentiates the activity of an antibiotic agent, e.g., a quinolone antibiotic. The invention further provides methods for identifying agents that modulate expression of target genes or that modulate activity of expression products of target genes. Agents identified according to various methods of the invention potentiate the activity of antibiotics such as quinolones, aminoglycosides, peptide antibiotics and β-lactams. Also provided are agents that suppress and/or retard resistance to antibiotics. The inventive methods provide potentiating agents and compositions comprising potentiating agents and antibiotics. Such agents and compositions can be used for inhibiting growth or survival of a microbial cell or of treating a subject suffering from or susceptible to a microbial infection.

Method to determine transcriptional regulation pathways in organisms

Abstract: The invention relates to computer-implemented methods and systems for identifying regulatory relationships between expressed regulating polypeptides and targets of the regulatory activities of such regulating polypeptides. More specifically, the invention provides a new method for identifying regulatory dependencies between biochemical species in a cell. In particular embodiments, provided are computer-implemented methods for identifying a regulatory interaction between a transcription factor and a gene target of the transcription factor, or between a transcription factor and a set of gene targets of the transcription factor. Further provided are genome-scale methods for predicting regulatory interactions between a set of transcription factors and a corresponding set of transcriptional target substrates thereof.

Reca inhibitors with antibiotic activity, compositions and methods of use

Abstract: The present invention is directed to the use of RecA inhibitors as antibiotic agents, and provides RecA inhibitors useful in treating infections. Also provided are various compositions and methods associated with RecA inhibition.

Taking the inventory inside single cells

Abstract: Despite having an increasingly accurate parts list for biological cells, much is left to discover about how these parts act together to create functional cells, and how distinct individual cells interact to create functional tissues and organs. Biologists are increasingly aware of the cell-to-cell variability in molecule copy numbers—a trend that is revealed by several new techniques, including one that permits counting molecules in single cells.

Commutateur génétique ajustable pour réguler une expression génétique

Abstract: La presente invention concerne de maniere generale le domaine de la genetique, en particulier des procedes, des compositions et des systemes pour reguler l'expression inductible de transgenes, tout en eliminant une expression de fond d'une expression transgenique. La presente invention concerne des procedes d'utilisation des compositions et des systemes decrits par les presentes pour reguler l'expression inductible de transgenes tout en eliminant une expression de fond d'une expression transgenique, telle qu'une utilisation par exemple dans la generation d'animaux transgeniques, une utilisation dans une application therapeutique et une utilisation dans des analyses. Dans certains modes de realisation, la presente invention concerne un systeme d'expression regulee de molecules d'ARNi avec des sites de liaison cibles dans les zones n'ayant pas subi une translation de transgene, de sorte que l'expression du transgene est modulee, et la possibilite de fuite est reduite. Les compositions et procedes de la presente invention peuvent etre utilises pour la therapie, la prophylaxie, la recherche et le diagnostic de maladies et de troubles qui affectent les mammiferes, la generation d'animaux transgeniques, dans l'etude de processus biologiques de meme que pour ameliorer la performance de recoltes agricoles.

Inhibiteurs reca à activité antibiotique, compositions et méthodes d'utilisation

Abstract: La presente invention concerne l'utilisation d'inhibiteurs RecA comme agents antibiotiques, et permet d'obtenir des inhibiteurs RecA utiles pour traiter des infections. Elle concerne aussi plusieurs compositions et methodes associees avec une inhibition RecA.