Biomedical subjects

Eric M Knight

Publications and source records attributed to Eric M Knight.

5 recordsLinked to original sources

PubMed2006

Systems approach to refining genome annotation.

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.

Biological Transport↗

PubMed2006

Transcriptional regulation of the fad regulon genes of Escherichia coli by ArcA.

ArcA is a global transcription factor required for optimal growth of Escherichia coli during anaerobic growth. In this study, the role of ArcA on the transcriptional regulatory subnetwork of the fad regulon was investigated. Gene expression profiles of deletion mutants (Delta arcA, Delta fadR and Delta arcA/Delta fadR) indicated that (i) ArcA is a major transcription factor for the transcriptional regulation of fatty acid metabolism in the absence of oxygen, and (ii) ArcA and FadR cooperatively regulate the fad regulon under anaerobic conditions. To determine the direct interaction between ArcA and the promoters of the fad regulon genes, chromatin immunoprecipitation (ChIP) analysis was performed. ChIP analysis suggested that ArcA directly binds to the promoter regions of the fad regulon genes in vivo. An ArcA-binding motif was identified from known binding sequences and predicted putative binding sites in the promoter regions of the fad regulon genes. These results indicate that ArcA directly represses the expression of fad regulon genes during anaerobic growth.

Anaerobiosis↗

PubMed2006

PCR-based tandem epitope tagging system for Escherichia coli genome engineering.

Biological discovery in the postgenomic era requires a systematic and high-throughput experimental approach. To this end, a versatile PCR-based tandem epitope tagging system is described, which inserts a tandem epitope coding sequence into any desired position of the Escherichia coli chromosome. Template plasmids were constructed that carry tandem copies of the epitope encoding sequence, Flp recombinase target (FRT) sites, and antibiotic resistance genes. The linear DNA fragment, amplified from the template plasmid with extensions homologous to the end of the target gene and part of its downstream region, was transformed into E. coli K-12 MG1655 harboring the bacteriophage gamma Red recombination system. The antibiotic resistance gene was then removed from the inserted heterologous PCR fragment using Flp recombinase. This epitope tagging system was applied to global transcription factors of E. coli to obtain proteins fused with tandem c-myc epitope tags. The tandem myc epitope-fused transcription factors were successfully detected by Western blot analysis and chromatin immunoprecipitation with increased detection sensitivity and higher yield. Higher copy numbers of the epitope molecule allowed the use of more stringent experimental conditions to increase the signal-to-noise ratio in subsequent experimental applications. Furthermore, judging from the measurement of gene expression using reverse transcription PCR (RT-PCR), the epitope-fused transcription factors retained their normal function for gene regulation in vivo.

Blotting, Western↗

PubMed2005

In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

The development and validation of new methods to help direct rational strain design for metabolite overproduction remains an important problem in metabolic engineering. Here we show that computationally predicted E. coli strain designs, calculated from a genome-scale metabolic model, can lead to successful production strains and that adaptive evolution of the engineered strains can lead to improved production capabilities. Three strain designs for lactate production were implemented yielding a total of 11 evolved production strains that were used to demonstrate the utility of this integrated approach. Strains grown on 2 g/L glucose at 37 degrees C showed lactate titers ranging from 0.87 to 1.75 g/L and secretion rates that were directly coupled to growth rates.

Adaptation, Physiological↗

PubMed2004

Integrating high-throughput and computational data elucidates bacterial networks.

The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.

Aerobiosis↗