Biomedical subjects

Ryan T Gill

Publications and source records attributed to Ryan T Gill.

11 recordsLinked to original sources

PubMed2006

SCALEs: multiscale analysis of library enrichment.

We report a genome-wide, multiscale approach to simultaneously measure the effect that the increased copy of each gene and/or operon has on a desired trait or phenotype. The method involves (i) growth selections on a mixture of several different plasmid-based genomic libraries of defined insert sizes or SCALEs, (ii) microarray studies of enriched plasmid DNA, and a (iii) mathematical multiscale analysis that precisely identifies the relevant genetic elements. This approach allows for identification of all single open reading frames and larger multigene fragments within a genomic library that alter the expression of a given phenotype. We have demonstrated this method in Escherichia coli by monitoring, in parallel, a population of >10(6) genomic library clones of different insert sizes, throughout continuous selections over a period of 100 generations.

DNA Fragmentation↗

PubMed2006

Broad host range vectors for stable genomic library construction.

We describe the construction of 36 stable vectors for genomic library construction in gram-negative species. These vectors contain the pBBR1 replicon that has been shown to stably replicate in every gram-negative species tested. The plasmids also contain bidirectional, rho-independent transcriptional terminators flanking the multiple cloning site, which allows for greater insert stability, and thus, greater genomic representation. Each vector varies in its antibiotic resistance cassette, mobilization function, and promoter used to express insert sequences. These vectors should prove useful in the screening of highly representational genomic libraries in a broad variety of gram-negative species.

Cloning, Molecular↗

PubMed2006

A comparison of alternative 60-mer probe designs in an in-situ synthesized oligonucleotide microarray.

BACKGROUND: DNA microarrays have proven powerful for functional genomics studies. Several technologies exist for the generation of whole-genome arrays. It is well documented that 25mer probes directed against different regions of the same gene produce variable signal intensity values. However, the extent to which this is true for probes of greater length (60mers) is not well characterized. Moreover, this information has not previously been reported for whole-genome arrays designed against bacteria, whose genomes may differ substantially in characteristics directly affecting microarray performance. RESULTS: We report here an analysis of alternative 60mer probe designs for an in-situ synthesized oligonucleotide array for the GC rich, beta-proteobacterium Burkholderia cenocepacia. Probes were designed using the ArrayOligoSel3.5 software package and whole-genome microarrays synthesized by Agilent, Inc. using their in-situ, ink-jet technology platform. We first validated the quality of the microarrays as demonstrated by an average signal to noise ratio of >1000. Next, we determined that the variance of replicate probes (1178 total probes examined) of identical sequence was 3.8% whereas the variance of alternative probes (558 total alternative probes examined) designs was 9.5%. We determined that depending upon the definition, about 2.4% of replicate and 7.8% of alternative probes produced outlier conclusions. Finally, we determined none of the probe design subscores (GC content, internal repeat, binding energy and self annealment) produced by ArrayOligoSel3.5 were predictive or probes that produced outlier signals. CONCLUSION: Our analysis demonstrated that the use of multiple probes per target sequence is not essential for in-situ synthesized 60mer oligonucleotide arrays designed against bacteria. Although probes producing outlier signals were identified, the use of ratios results in less than 10% of such outlier conclusions. We also determined that several different measures commonly utilized in probe design were not predictive of outlier probes.

DNA Probes↗

PubMed2006

A comparative study of metabolic engineering anti-metabolite tolerance in Escherichia coli.

A problem in strain engineering is that mutations that benefit the expression of a phenotype in one environment may impose a cost to biological fitness in a new environment. The overall objective of this study was to improve understanding of this phenomenon within the context of a classic anti-metabolite selection strategy. We have engineered Escherichia coli using three mutagenesis techniques (chemical mutagenesis, insertional mutagenesis, and plasmid-based overexpression) and assessed the relative costs and benefits to biological fitness of mutants selected for tolerance to five amino acid analogs whose target amino acids (glutamatic acid, aspartic acid, tryptophan, glycine, and serine) differ in metabolic connectivity and biosynthetic energy requirements. Our major findings include (i) the fold increase in anti-metabolite tolerance, independent of mutagenesis strategy, was much greater for aspartic acid beta-hydroxamate (AAH) compared to all other tested hydroxamates, (ii) increased tolerance to glutamic acid gamma-hydroxamate (GAH) was not achieved using any of the mutagenesis strategies, and (iii) characteristics of the anti-metabolite, rather than those of the corresponding metabolite, were more important in determining the ability to increase tolerance.

Drug Tolerance↗

PubMed2006

Reverse engineering antibiotic sensitivity in a multidrug-resistant Pseudomonas aeruginosa isolate.

Antibiotic resistance is a pervasive and growing clinical problem. We describe an evaluation of a reverse engineering approach for identifying cellular mechanisms and genes that could be manipulated to increase antibiotic sensitivity in a resistant Pseudomonas aeruginosa isolate. We began by chemically mutating a broadly resistant isolate of P. aeruginosa and screening for mutants with increased sensitivity to the aminoglycoside amikacin, followed by performing whole-genome transcriptional profiling of the mutant and wild-type strains to characterize the global changes occurring as a result of the mutations. We then performed a series of assays to characterize the mechanisms involved in the increased sensitivity of the mutant strains. We report four primary results: (i) mutations that increase sensitivity occur at a high frequency (10(-2)) relative to the frequency of those that increase resistance (10(-5) to 10(-10)) and occur at a frequency 10(4) higher than the frequency of a single point mutation; (ii) transcriptional profiles were altered in sensitive mutants, resulting in overall expression patterns more similar to those of the sensitive laboratory strain PAO1 than those of the parental resistant strain; (iii) genes found from transcriptional profiling had the more dramatic changes in expression-encoded functions related to cellular membrane permeability and aminoglycoside modification, both of which are known aminoglycoside resistance mechanisms; and finally, (iv) even though we did not identify the specific sites of mutation, several different follow-up MIC assays suggested that the mutations responsible for increased sensitivity differed between sensitive mutants.

Amikacin↗

PubMed2005

Organic acid toxicity, tolerance, and production in Escherichia coli biorefining applications.

Organic acids are valuable platform chemicals for future biorefining applications. Such applications involve the conversion of low-cost renewable resources to platform sugars, which are then converted to platform chemicals by fermentation and further derivatized to large-volume chemicals through conventional catalytic routes. Organic acids are toxic to many of the microorganisms, such as Escherichia coli, proposed to serve as biorefining platform hosts at concentrations well below what is required for economical production. The toxicity is two-fold including not only pH based growth inhibition but also anion-specific effects on metabolism that also affect growth. E. coli maintain viability at very low pH through several different tolerance mechanisms including but not limited to the use of decarboxylation reactions that consume protons, ion transporters that remove protons, increased expression of known stress genes, and changing membrane composition. The focus of this mini-review is on organic acid toxicity and associated tolerance mechanisms as well as several examples of successful organic acid production processes for E. coli.

Journal Article↗

PubMed2005

Amino acid content of recombinant proteins influences the metabolic burden response.

Recombinant protein production in Escherichia coli often results in a dramatic cellular stress response best characterized by a decrease in overall cell fitness. We determined that the primary sequence (the amino acid sequence) of the recombinant protein alone plays an important role in mitigating this response. To do so, we created two polypeptides, modeled after the 39-40 amino acid Defensin class of proteins, which contained exclusively the five least (PepAA; His, Trp, Tyr, Phe, Met), or most (PepCO: Ala, Glu, Gln, Asp, Asn) abundant amino acids in E. coli. We determined that overexpression of PepAA resulted in a drastic decrease in growth rate compared to overexpression of PepCO, our model Defensin protein MGD-1, or the 26 amino acid polypeptide contained within the pET-3d vector backbone. We further determined, using Affymetrix E. coli gene chips, that differences among the whole-genome transcriptional responses of these model systems were best characterized by altered expression of genes whose products are involved in translation, transport, or metabolic functions as opposed to stress response genes. Based on these results, we confirmed that translation efficiency was significantly reduced in cells overexpressing PepAA compared with the other model polypeptides evaluated.

Amino Acid Sequence↗

PubMed2005

Visualization of protoplast fusion and quantitation of recombination in fused protoplasts of auxotrophic strains of Escherichia coli.

Protoplast fusion has been used to combine genes from different organisms to create strains with desired properties. A recently developed variant on this approach, genome shuffling, involves generation of a genetically heterogeneous population of a single organism, followed by recursive protoplast fusion to allow recombination of mutations within the fused protoplasts. These are powerful techniques for engineering of microbial strains for desirable industrial properties. However, there is a prevailing opinion that it will be difficult to use these methods for engineering of Gram-negative bacteria because the outer membrane makes protoplast fusion more difficult. Here we describe the successful use of protoplast fusion in Escherichia coli. Using two auxotrophic strains of E. coli, we obtained prototrophic strains by recombination in fused protoplasts at frequencies of 0.05-0.7% based on the number of protoplasts subjected to fusion. This frequency is three-four orders of magnitude better than those previously reported for recombination in fused protoplasts of Gram-negative bacteria such as E. coli and Providencia alcalifaciens.

Artificial Gene Fusion↗

PubMed2004

Mapping phenotypic landscapes using DNA micro-arrays.

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.

Animals↗

PubMed2003

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.

Bacteria↗

PubMed2002

Genome-wide dynamic transcriptional profiling of the light-to-dark transition in Synechocystis sp. strain PCC 6803.

We report the results of whole-genome transcriptional profiling of the light-to-dark transition with the model photosynthetic prokaryote Synechocystis sp. strain PCC 6803 (Synechocystis). Experiments were conducted by growing Synechocystis cultures to mid-exponential phase and then exposing them to two cycles of light/dark conditions, during which RNA samples were obtained. These samples were probed with a full-genome DNA microarray (3,169 genes, 20 samples) as well as a partial-genome microarray (88 genes, 29 samples). We concluded that (i) 30-min sampling intervals accurately captured transcriptional dynamics throughout the light/dark transition, (ii) 25% of the Synechocystis genes (783 genes) responded positively to the presence of light, and (iii) the response dynamics varied greatly for individual genes, with a delay of up to 120 to 150 min for some genes. Four classes of genes were identified on the basis of their dynamic gene expression profiles: class I (108 genes, 30-min response time), class II (279 genes, 60 to 90 min), class III (258 genes, 120 to 150 min), and class IV (138 genes, 180 min). The dynamics of several transcripts from genes involved in photosynthesis and primary energy generation are discussed. Finally, we applied Fisher discriminant analysis to better visualize the progression of the overall transcriptional program throughout the light/dark transition and to determine those genes most indicative of the lighting conditions during growth.

Cyanobacteria↗