Selection analyses of insertional mutants using subgenic-resolution arrays

Drugs against superbugs: private lessons from bacteriophages

Bacterial genomics has provided a plethora of potential targets for antibacterial drug discovery, however, success in the hunt for new antibiotics will hinge on selecting targets with the highest potential. A recent paper by Liu and coworkers describes a new approach to target selection that uncovers strategies used by bacteriophage to disable bacteria. The method uses key phage proteins to identify and validate vulnerable targets and exploits them further in the identification of new antibacterial leads.

Mathematical models of diffusion-constrained polymerase chain reactions: basis of high-throughput nucleic acid assays and simple self-organizing systems

DNA templates amplified by polymerase chain reaction in thin polyacrylamide gels form diffusion-constrained amplicons called "polonies" (polymerase colonies) that have been used to phase DNA haplotypes over long distances, to analyse RNA splice variants, and to assay other phenomena of biological interest. We present two sets of mathematical models, one for single polony growth (SPGM) and one for two polony interaction (TPIM), that will be used to optimize polony technology. The models provide detailed predictions of polony yield, concentration profiles, growth of isolated polonies, and the interaction of neighboring polonies. The TPIM explains an experimental observation that nearby polonies deform against each other rather than interpenetrate, an effect important for optimizing polony protocols. However, the TPIM also predicts that polonies may invade each other with a complex geometry when sufficiently close. Polonies are also of interest as simple abiotic systems that exhibit lifelike properties of self-organization, growth, and development, and the models may also apply to biological phenomena involving propagation through tethering and diffusion. Our polony modeling software is available at our web site.

Enabling inverse metabolic engineering through genomics

Inverse metabolic engineering (IME) is a powerful framework for engineering cellular phenotypes. Progress in this field has been limited by a lack of comprehensive methods for efficiently identifying the genetic basis of relevant phenotypes. Advances in genomics technologies, including DNA microarrays and gene sequencing, have dramatically improved our ability to relate changes in phenotype with associated changes in genotype. When applied in the context of IME, these tools should enable the integration of "evolutionary" and "direct" approaches to engineering cell physiology, which should improve our understanding of the complex interactions affecting the expression, evolution and engineering of traits in natural and industrial hosts.

Assaying gene function by growth competition experiment

High-throughput screening and analysis is one of the emerging paradigms in biotechnology. In particular, high-throughput methods are essential in the field of functional genomics because of the vast amount of data generated in recent and ongoing genome sequencing efforts. In this report we discuss integrated functional analysis methodologies which incorporate both a growth competition component and a highly parallel assay used to quantify results of the growth competition. Several applications of the two most widely used technologies in the field, i.e., transposon mutagenesis and deletion strain library growth competition, and individual applications of several developing or less widely reported technologies are presented.

Transposon-based strategies for microbial functional genomics and proteomics

Transposons are mobile genetic elements that can relocate from one genomic location to another. As well as modulating gene expression and contributing to genome plasticity and evolution, transposons are remarkably diverse molecular tools for both whole-genome and single-gene studies in bacteria, yeast, and other microorganisms. Efficient but simple in vitro transposition reactions now allow the mutational analysis of previously recalcitrant microorganisms. Transposon-based signature-tagged mutagenesis and genetic footprinting strategies have pinpointed essential genes and genes that are crucial for the infectivity of a variety of human and other pathogens. Individual proteins and protein complexes can be dissected by transposon-mediated scanning linker mutagenesis. These and other transposon-based approaches have reaffirmed the usefulness of these elements as simple yet highly effective mutagens for both functional genomic and proteomic studies of microorganisms.

Flux coupling analysis of genome-scale metabolic network reconstructions

In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v(1) and v(2), are (1) directionally coupled, if a non-zero flux for v(1) implies a non-zero flux for v(2) but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v(1) implies a non-zero, though variable, flux for v(2) and vice versa; or (3) fully coupled, if a non-zero flux for v(1) implies not only a non-zero but also a fixed flux for v(2) and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations.

Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization

The advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. In this work, the computational OptKnock framework is introduced for suggesting gene deletion strategies leading to the overproduction of chemicals or biochemicals in E. coli. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) must be accompanied, due to stoichiometry, by the production of a desired product. Computational results for gene deletions for succinate, lactate, and 1,3-propanediol (PDO) production are in good agreement with mutant strains published in the literature. While some of the suggested deletion strategies are straightforward and involve eliminating competing reaction pathways, many others suggest complex and nonintuitive mechanisms of compensating for the removed functionalities. Finally, the OptKnock procedure, by coupling biomass formation with chemical production, hints at a growth selection/adaptation system for indirectly evolving overproducing mutants.

Experimental determination and system level analysis of essential genes in Escherichia coli MG1655

Defining the gene products that play an essential role in an organism's functional repertoire is vital to understanding the system level organization of living cells. We used a genetic footprinting technique for a genome-wide assessment of genes required for robust aerobic growth of Escherichia coli in rich media. We identified 620 genes as essential and 3,126 genes as dispensable for growth under these conditions. Functional context analysis of these data allows individual functional assignments to be refined. Evolutionary context analysis demonstrates a significant tendency of essential E. coli genes to be preserved throughout the bacterial kingdom. Projection of these data over metabolic subsystems reveals topologic modules with essential and evolutionarily preserved enzymes with reduced capacity for error tolerance.

Using DNA microarray data to understand the ionizing radiation resistance of Deinococcus radiodurans

In a recent paper, Liu et al. documented the changes in gene expression as stationary phase Deinococcus radiodurans cultures recover from acute exposure to gamma radiation. Given that the biochemical details of the response of D. radiodurans to ionizing radiation are poorly understood, this work represents an important first step towards achieving an understanding of the ionizing radiation resistance in this species.

Parallel competition analysis of Saccharomyces cerevisiae strains differing by a single base using polymerase colonies

We describe a strategy to analyze the impact of single nucleotide mutations on protein function. Our method utilizes a combination of yeast functional complementation, growth competition of mutant pools and polyacrylamide gel immobilized PCR. A system was constructed in which the yeast PGK1 gene was expressed from a plasmid-borne copy of the gene in a PGK1 deletion strain of Saccharomyces cerevisiae. Using this system, we demonstrated that the enrichment or depletion of PGK1 point mutants from a mixed culture was consistent with the expected results based on the isolated growth rates of the mutants. Enrichment or depletion of individual point mutants was shown to result from increases or decreases, respectively, in the specific activities of the encoded proteins. Further, we demonstrate the ability to analyze the functional effect of many individual point mutations in parallel. By functional complementation of yeast deletions with human homologs, our technique could be readily applied to the functional analysis of single nucleotide polymorphisms in human genes of medical interest.

Genetic strategies for antibacterial drug discovery

The availability of genome sequences is revolutionizing the field of microbiology. Genetic methods are being modified to facilitate rapid analysis at a genome-wide level and are blossoming for human pathogens that were previously considered intractable. This revolution coincided with a growing concern about the emergence of microbial drug resistance, compelling the pharmaceutical industry to search for new antimicrobial agents. The availability of the new technologies, combined with many genetic strategies, has changed the way that researchers approach antibacterial drug discovery.

Definition of the Escherichia coli MC4100 genome by use of a DNA array

We have used an Escherichia coli K-12 whole-genome array based on the DNA sequence of strain MG1655 as a tool to identify deletions in another E. coli K-12 strain, MC4100, by probing the array with labeled chromosomal DNA. Despite the continued widespread use of MC4100 as an experimental system, the specific genetic relationship of this strain to the sequenced K-12 derivative MG1655 has not been resolved. MC4100 was found to contain four deletions, ranging from 1 to 97 kb in size. The exact nature of three of the deletions was previously unresolved, and the fourth deletion was altogether unknown.

Information overload: assigning genetic functionality in the age of genomics and large-scale screening

As more and more genome sequences are completed, it is becoming increasingly evident that our understanding of the function of most bacterial gene products is lacking. This is frustrating, particularly in the study of pathogens, where an understanding of the role of individual gene products would probably facilitate the development of novel antimicrobials and vaccines. Recently, we devised a technique known as virulence-attenuated pool (VAP) screening to help assign genetic functionality to gene products that the pathogen Vibrio cholerae requires for colonization. This screen and potential new applications of the VAP technique are discussed here.

Analysis of optimality in natural and perturbed metabolic networks

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.

From genetic footprinting to antimicrobial drug targets: examples in cofactor biosynthetic pathways

Novel drug targets are required in order to design new defenses against antibiotic-resistant pathogens. Comparative genomics provides new opportunities for finding optimal targets among previously unexplored cellular functions, based on an understanding of related biological processes in bacterial pathogens and their hosts. We describe an integrated approach to identification and prioritization of broad-spectrum drug targets. Our strategy is based on genetic footprinting in Escherichia coli followed by metabolic context analysis of essential gene orthologs in various species. Genes required for viability of E. coli in rich medium were identified on a whole-genome scale using the genetic footprinting technique. Potential target pathways were deduced from these data and compared with a panel of representative bacterial pathogens by using metabolic reconstructions from genomic data. Conserved and indispensable functions revealed by this analysis potentially represent broad-spectrum antibacterial targets. Further target prioritization involves comparison of the corresponding pathways and individual functions between pathogens and the human host. The most promising targets are validated by direct knockouts in model pathogens. The efficacy of this approach is illustrated using examples from metabolism of adenylate cofactors NAD(P), coenzyme A, and flavin adenine dinucleotide. Several drug targets within these pathways, including three distantly related adenylyltransferases (orthologs of the E. coli genes nadD, coaD, and ribF), are discussed in detail.

Genome-wide screening for trait conferring genes using DNA microarrays

We report a DNA microarray-based method for genome-wide monitoring of competitively grown transformants to identify genes whose overexpression confers a specific cellular phenotype. Whereas transcriptional profiling identifies differentially expressed genes that are correlated with particular aspects of the cellular phenotype, this functional genomics approach determines genes that result in a specific physiology. This parallel gene-trait mapping method consists of transforming a strain with a genomic library, enriching the cell population in transformants containing the trait conferring gene(s), and finally using DNA microarrays to simultaneously isolate and identify the enriched gene inserts. Various methods of enrichment can be used; here, genes conferring low-level antibiotic resistance were identified by growth in selective media. We demonstrated the method by transforming Escherichia coli cells with a genomic E. coli library and selecting for transformants exhibiting a growth advantage in the presence of the anti-microbial agent Pine-Sol. Genes conferring Pine-Sol tolerance (19 genes) or sensitivity (27 genes) were identified by hybridizing, on DNA microarrays containing 1,160 E. coli gene probes, extra-chromosomal DNA isolated from transformed cells grown in the presence of various levels of Pine-Sol. Results were further validated by plating and sequencing of individual colonies, and also by assessing the Pine-Sol resistance of cells transformed with enriched plasmid library or individual resistance genes identified by the microarrays. Applications of this method beyond antibiotic resistance include identification of genes resulting in resistance to chemotherapeutic agents, genes yielding resistance to toxic products (recombinant proteins, chemical feedstocks) in industrial fermentations, genes providing enhanced growth in cell culture or high cell density fermentations, genes facilitating growth on unconventional substrates, and others.