An integrated biotechnology platform for developing sustainable chemical processes.

Genomatica has established an integrated computational/experimental metabolic engineering platform to design, create, and optimize novel high performance organisms and bioprocesses. Here we present our platform and its use to develop E. coli strains for production of the industrial chemical 1,4-butanediol (BDO) from sugars. A series of examples are given to demonstrate how a rational approach to strain engineering, including carefully designed diagnostic experiments, provided critical insights about pathway bottlenecks, byproducts, expression balancing, and commercial robustness, leading to a superior BDO production strain and process.

Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol.

1,4-Butanediol (BDO) is an important commodity chemical used to manufacture over 2.5 million tons annually of valuable polymers, and it is currently produced exclusively through feedstocks derived from oil and natural gas. Herein we report what are to our knowledge the first direct biocatalytic routes to BDO from renewable carbohydrate feedstocks, leading to a strain of Escherichia coli capable of producing 18 g l(-1) of this highly reduced, non-natural chemical. A pathway-identification algorithm elucidated multiple pathways for the biosynthesis of BDO from common metabolic intermediates. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic operation of the oxidative tricarboxylic acid cycle, thereby generating reducing power to drive the BDO pathway. The organism produced BDO from glucose, xylose, sucrose and biomass-derived mixed sugar streams. This work demonstrates a systems-based metabolic engineering approach to strain design and development that can enable new bioprocesses for commodity chemicals that are not naturally produced by living cells.

Staphylococcus aureus TargetArray: comprehensive differential essential gene expression as a mechanistic tool to profile antibacterials.

The widespread emergence of antibiotic-resistant bacteria and a lack of new pharmaceutical development have catalyzed a need for new and innovative approaches for antibiotic drug discovery. One bottleneck in antibiotic discovery is the lack of a rapid and comprehensive method to identify compound mode of action (MOA). Since a hallmark of antibiotic action is as an inhibitor of essential cellular targets and processes, we identify a set of 308 essential genes in the clinically important pathogen Staphylococcus aureus. A total of 446 strains differentially expressing these genes were constructed in a comprehensive platform of sensitized and resistant strains. A subset of strains allows either target underexpression or target overexpression by heterologous promoter replacements with a suite of tetracycline-regulatable promoters. A further subset of 236 antisense RNA-expressing clones allows knockdown expression of cognate targets. Knockdown expression confers selective antibiotic hypersensitivity, while target overexpression confers resistance. The antisense strains were configured into a TargetArray in which pools of sensitized strains were challenged in fitness tests. A rapid detection method measures strain responses toward antibiotics. The TargetArray antibiotic fitness test results show mechanistically informative biological fingerprints that allow MOA elucidation.

Multiple pathways of mitochondrial-nuclear communication in yeast: intergenomic signaling involves ABF1 and affects a different set of genes than retrograde regulation.

Mitochondrial-nuclear communication is taking on increased importance in models of oxygen sensing, oxidative stress, aging, and disease. The deletion of the mitochondrial genome (mtDNA) and, hence, the ability to respire, affects expression of several nuclear genes through at least two different mitochondrial-nuclear communication pathways. One of the pathways, retrograde regulation, is activated by a reduction in respiration, while another, intergenomic signaling, is unaffected by respiration but requires mtDNA. Using DNA microarrays, we identify here a set of nuclear genes in Saccharomyces cerevisiae that are targets of intergenomic signaling. These nuclear genes are down-regulated in rho degrees cells that lack mtDNA but not in nuclear pet mutant rho(+)cells that possess mtDNA but lack respiration. Many of these nuclear genes encode mitochondrial proteins, implying that intergenomic signaling functions in coordinating mitochondrial and nuclear gene expression. In addition, analyses of deletion and linker scanning mutations in the promoter of the COX6 gene, a nuclear gene affected by intergenomic signaling, suggest an involvement of Abf1p transcription factor in intergenomic signaling. Together, these findings indicate that intergenomic signaling is distinct from retrograde regulation both in the nuclear genes that it regulates and in the way in which it affects their expression.

Use of constraint-based modeling for the prediction and validation of antimicrobial targets.

The overall process of antimicrobial drug discovery and development seems simple, to cure infectious disease by identifying suitable antibiotic drugs. However, this goal has been difficult to fulfill in recent years. Despite the promise of the high-throughput innovations sparked by the genomics revolution, discovery, and development of new antibiotics has lagged in recent years exacerbating the already serious problem of evolution of antibiotic resistance. Therefore, both new antimicrobials are desperately needed as are improvements to speed up or improve nearly all steps in the process of discovering novel antibiotics and bringing these to clinical use. Another product of the genomic revolution is the modeling of metabolism using computational methodologies. Genomic-scale networks of metabolic reactions based on stoichiometry, thermodynamics and other physico-chemical constraints that emulate microbial metabolism have been developed into valuable research tools in metabolic engineering and other fields. This constraint-based modeling is predictive in identifying critical reactions, metabolites, and genes in metabolism. This is extremely useful in determining and rationalizing cellular metabolic requirements. In turn, these methods can be used to predict potential metabolic targets for antimicrobial research especially if used to increase the confidence in prioritization of metabolic targets. The many different capacities of constraint-based modeling also enable prediction of cellular response to specific inhibitors such as antibiotics and this may, ultimately find a role in drug discovery and development. Herein, we describe the principles of metabolic modeling and how they might initially be applied to antimicrobial research.

Genome-wide operon prediction in Staphylococcus aureus.

Identification of operon structure is critical to understanding gene regulation and function, and pathogenesis, and for identifying targets towards the development of new antibiotics in bacteria. Recently, the complete genome sequences of a large number of important human bacterial pathogens have become available for computational analysis, including the major human Gram-positive pathogen Staphylococcus aureus. By annotating the predicted operon structure of the S.aureus genome, we hope to facilitate the exploration of the unique biology of this organism as well as the comparative genomics across a broad range of bacteria. We have integrated several operon prediction methods and developed a consensus approach to score the likelihood of each adjacent gene pair to be co-transcribed. Gene pairs were separated into distinct operons when scores were equal to or below an empirical threshold. Using this approach, we have generated a S.aureus genome map with scores annotated at the intersections of every adjacent gene pair. This approach predicted about 864 monocistronic transcripts and 533 polycistronic operons from the protein-encoding genes in the S.aureus strain Mu50 genome. When compared with a set of experimentally determined S.aureus operons from literature sources, this method successfully predicted at least 91% of gene pairs. At the transcription unit level, this approach correctly identified at least 92% of complete operons in this dataset. This consensus approach has enabled us to predict operons with high accuracy from a genome where limited experimental evidence for operon structure is available.

A genome-wide strategy for the identification of essential genes in Staphylococcus aureus.

To address the need for new approaches to antibiotic drug development, we have identified a large number of essential genes for the bacterial pathogen, Staphylococcus aureus, using a rapid shotgun antisense RNA method. Staphylococcus aureus chromosomal DNA fragments were cloned into a xylose-inducible expression plasmid and transformed into S. aureus. Homology comparisons between 658 S. aureus genes identified in this particular antisense screen and the Mycoplasma genitalium genome, which contains 517 genes in total, yielded 168 conserved genes, many of which appear to be essential in M. genitalium and other bacteria. Examples are presented in which expression of an antisense RNA specifically reduces its cognate mRNA. A cell-based, drug-screening assay is also described, wherein expression of an antisense RNA confers specific sensitivity to compounds targeting that gene product. This approach enables facile assay development for high throughput screening for any essential gene, independent of its biochemical function, thereby greatly facilitating the search for new antibiotics.