Rhodococcus strain 124 is able to convert indene into indandiol via the actions of at least two dioxygenase systems and a putative monooxygenase system. We have identified a cosmid clone from 124 genomic DNA that is able to confer the ability to convert indene to indandiol upon Rhodococcus erythropolis SQ1, a strain that normally can not convert or metabolize indene. HPLC analysis reveals that the transformed SQ1 strain produces cis-(1R,2S)-indandiol, suggesting that the cosmid clone encodes a naphthalenetype dioxygenase. DNA sequence analysis of a portion of this clone confirmed the presence of genes for the dioxygenase as well as genes encoding a dehydrogenase and putative aldolase. These genes will be useful for manipulating indene bioconversion in Rhodococcus strain 124.

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We demonstrate a general approach for metabolic engineering of biocatalytic systems comprising the uses of a chemostat for strain improvement and radioisotopic tracers for the quantification of pathway fluxes. Flux determination allows the identification of target pathways for modification as validated by subsequent overexpression of the corresponding gene. We demonstrate this method in the indene bioconversion network of Rhodococcus modified for the overproduction of 1,2-indandiol, a key precursor for the AIDS drug Crixivan. C omplex metabolic and bioconversion pathways containing parallel, branching, and͞or reversible reactions can be studied quantitatively under the framework of metabolic engineering, which uses steady-state fluxes as fundamental determinants of cell physiology (1, 2). It is necessary to use these methods to distinguish the relative importance of competing metabolic reactions to guide target selection for the improvement of biological production of secondary metabolites or small molecules important for pharmaceutical and materials applications (3). To date, applications of metabolic engineering have been limited to primarily linear pathways and cases in which the relevant biochemistry and associated genetics are well established. In many cases, efforts focusing on transformation of cells by ad hoc methods have failed where genes are introduced based on conclusions derived in the absence of quantitative analysis of pathways. Consequently, an approach that considers the systemic properties of a bioconversion to identify rational targets is valuable. Such an approach can be based on determination of fluxes in bioconversion networks, which has been a focus of metabolic engineering for the past 10 years.We have developed and applied a general framework for the optimization of bioconversion systems in the context of the directed biocatalytic production of trans-(1R,2R)-indandiol suitable for the synthesis of the HIV protease inhibitor Crixivan (Merck). Chartrain et al. (4) isolated Rhodococcus sp. I24, which possesses the required oxygenase enzyme activities for converting indene to (2R)-indandiol (Fig. 1). The Crixivan chiral precursor (Ϫ),-cis-(1S,2R)-1-aminoindan-2-ol [(Ϫ)-CAI] can then be synthesized from (2R)-indandiol through a Ritter reaction (5, 6). However, besides the desired (2R)-indandiol product, several other side-products are secreted also in a Rhodococcus sp. I24 fermentation that reduce the desired product yield and selectivity. Therefore, it is of interest to modify I24 genetically to eliminate undesirable reactions and enhance the productforming pathway. Because of the poorly characterized nature of I24 genetics a priori, it is imperative that an approach be developed to prioritize network targets for modification in light of the current state of knowledge of the given biological system.The general framework described here is comprised of five essential steps: (i) establishment of an experimental system for strain selection and metabolic network analysis, (ii) definition of the ...

Radiolabeled tracers can provide valuable information about the structure of and flux distributions in biocatalytic reaction networks. This method derives from prior studies of glucose metabolism in mammalian systems and is implemented by pulsing a culture with a radiolabeled metabolite that can be transported into the cells and subsequently measuring the radioactivity of all network metabolites following separation by liquid chromatography. Intracellular fluxes can be directly determined from the transient radioactivity count data by tracking the depletion of the radiolabeled metabolite and/or the accompanying accumulation of any products formed. This technique differs from previous methods in that it is applied within a systems approach to the problem of flux determination. It has been used for the investigation of the indene bioconversion network expressed in Rhodococcus sp. KY1. Flux estimates obtained by radioactive tracers were confirmed by macroscopic metabolite balancing and showed that indene oxidation in steady state chemostat cultures proceeds primarily through a monooxygenase activity forming (1S,2R)-indan oxide, with no dehydrogenation of trans-(1R,2R)-indandiol. These results confirmed the significance of indan oxide formation and identified the hydrolysis of indan oxide as a key step in maximizing the production of (2R)-indandiol, a chiral precursor of the HIV protease inhibitor, Crixivanw.Keywords: metabolic flux; radiolabeled tracers; indene; Rhodococcus; metabolic engineering.Rational improvement of cellular properties through metabolic engineering requires detailed knowledge of cell physiology, in particular as expressed by fluxes and flux distributions through key intracellular pathways. Metabolic flux analysis provides a framework for determining pathway fluxes from the stoichiometry of the pathway reactions in combination with extracellular metabolite measurements [1][2][3]. Rates of metabolite consumption and production, conveniently measured in chemostat experiments, usually provide the necessary information to calculate the fluxes. Batch and fed-batch systems have also been used occasionally. This framework has been applied to many biological systems, such as lysine production in Corynebacterium glutamicum [4,5], and central carbon metabolism in Escherichia coli [6 -9], yeast [10,11], hybridoma cells [12][13][14], and organ tissue [15], among others.Metabolic flux analysis can be further enhanced with the inclusion of additional measurement techniques providing more detailed flux information than that embedded in typical extracellular metabolite data. Such information is particularly important for the study of underdetermined systems, which occur when the number of unknown fluxes exceeds the number of available flux measurements and metabolite balances [2]. This is usually the case with highly interconnected networks or networks comprising metabolic cycles [16]. The basis of such measurement methods is the use of isotope labels. For example, 13 C-labeling can yield flux information from ...