A cybernetic model to predict the effect of freely available nitrogen substrate on rifamycin B production in complex media

Metabolic Engineering of Main Transcription Factors in Carbon, Nitrogen, and Phosphorus Metabolisms for Enhanced Production of Bacitracin in Bacillus licheniformis

Primary metabolism plays a key role in the synthesis of secondary metabolite. In this study, the main transcription factors in carbon, nitrogen, and phosphorus metabolisms (CcpA, CcpC, CcpN, CodY, TnrA, GlnR, and PhoP) were engineered to improve bacitracin yield in Bacillus licheniformis DW2, an industrial strain for bacitracin production. First, our results demonstrated that deletions of ccpC and ccpN improved ATP and NADPH supplies, and the bacitracin yields were respectively increased by 14.02% and 16.06% compared with that of DW2, while it was decreased significantly in ccpA deficient strain DW2ΔccpA. Second, excessive branched chain amino acids (BCAAs) were accumulated in codY, tnrA, and glnR deletion strains DW2ΔcodY, DW2ΔtnrA, and DW2ΔglnR, which resulted in the nitrogen catabolite repressions and reductions of bacitracin yields. Moreover, overexpression of these regulators improved intracellular BCAA supplies, and further enhanced bacitracin yields by 14.17%, 12.98%, and 16.20%, respectively. Furthermore, our results confirmed that phosphate addition reduced bacitracin synthesis capability, and bacitracin yield was improved by 15.71% in gene phop deletion strain. On the contrary, overexpression of PhoP led to a 19.40% decrease of bacitracin yield. Finally, a combinatorial engineering of these above metabolic manipulations was applied, and bacitracin yield produced by the final strain DW2-CNCTGP (Simultaneously deleting ccpC, ccpN, phop and overexpressing glnR, codY, and tnrA in DW2) reached 1014.38 U/mL, increased by 35.72% compared to DW2, and this yield was the highest bacitracin yield currently reported. Taken together, this study implied that metabolic engineering of carbon, nitrogen, and phosphorus metabolism regulators is an efficient strategy to enhance bacitracin production, and provided a promising B. licheniformis strain for industrial production of bacitracin.

Preferential utilization of intracellular nutrients supports microalgal growth under nutrient starvation: multi-nutrient mechanistic model and experimental validation

Microalgae are able to grow even under exhaustion of some key nutrients such as nitrogen and phosphorous. Here, we report a multi-nutrient mechanistic model to predict heterotrophic growth of Chlorella sp. FC2 IITG over two sequential phases of fermentation: nutrient sufficient condition to nutrient starved condition. The model assumes that the growth of the microorganism takes place via sequential utilization of extracellular nutrients (ECN) under nutrient replete condition followed by intracellular stored nutrients under exhaustion of limiting nutrients. Further, intracellular nutrient was assumed to be in three different forms: structural form of nutrient (SFN), readily utilizable nutrient (RUN) and non-readily utilizable nutrient (Non-RUN). After the exhaustion of ECN, microorganism switches to RUN followed by Non-RUN to continue its growth, which was experimentally validated by extracting intracellular nitrate and phosphate compounds. The model also incorporates variability in yield coefficients for nitrate and phosphate utilizations.

Development of balanced medium composition for improved rifamycin B production by isolated Amycolatopsis sp. RSP-3

AIM

To develop optimum fermentation environment for enhanced rifamycin B production by isolated Amycolatopsis sp. RSP-3.

METHODS AND RESULTS

The impact of different fermentation parameters on rifamycin B production by isolated Amycolatopsis sp. RSP-3 was investigated using Taguchi methodology. Controlling fermentation factors were selected based on one variable at a time methodology. The isolated strain revealed more than 25% higher production compared to literature reports. Five different nutritional components (soyabean meal, glucose, potassium nitrate, calcium carbonate and barbital) and inoculum concentration showed impact on rifamycin B production at individual and interactive level. At optimized environment, 65% contribution was observed from selected fermentation parameters.

CONCLUSIONS

Soyabean meal and calcium carbonate were the most significant factors among the selected factors followed by barbital and potassium nitrate. Glucose, however, showed the least significance on rifamycin B production with this strain. A maximum of 5.12 g l(-1) rifamycin B production was achieved with optimized medium containing (g l(-1)) soyabean meal, 27; glucose, 100; potassium nitrate, 4; calcium carbonate, 3 and barbital, 1.2.

SIGNIFICANCE AND IMPACT OF THE STUDY

The present study signifies identification of balanced medium component concentrations for improved rifamycin B production by isolated Amycolatopsis sp. RSP-3. This strain requires organic and inorganic nitrogen sources for effective product yield. Yet at individual level, organic nitrogen source has c. nine-fold higher influence compared to inorganic one.

Sequential utilization of substrates by Pseudomonas putida CSV86: Signatures of intermediate metabolites and online measurements

Pseudomonas putida CSV86 preferentially utilizes aromatics over glucose and co-metabolizes them with organic acids. On aromatics plus glucose, CSV86 utilized aromatics first with concomitant appearance of transient metabolites such as salicylate, benzaldehyde and benzoate. Citrate was the main extracellular metabolite observed during glucose uptake. The strain showed simultaneous utilization of organic acids and aromatic compounds. Based on the metabolite analysis and growth profiles, we hypothesize that the repression of glucose utilization could be due to organic acid intermediates generated from aromatic compound metabolism. The online measurements indicate the instantaneous metabolic state of the culture. For example, the CO(2) evolution and agitation speed show peak values during the two growth phases in the diauxic growth while dissolved oxygen values show decrease at the corresponding durations. These measurements correlated well with the offline measurements but provided a better time resolution of the process.

Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: A case study with rifamycin B fermentation

Industrial production of antibiotics, biopharmaceuticals and enzymes is typically carried out via a batch or fed-batch fermentation process. These processes go through various phases based on sequential substrate uptake, growth and product formation, which require monitoring due to the potential batch-to-batch variability. The phase shifts can be identified directly by measuring the concentrations of substrates and products or by morphological examinations under microscope. However, such measurements are cumbersome to obtain. We present a method to identify phase transitions in batch fermentation using readily available online measurements. Our approach is based on Dynamic Principal Component Analysis (DPCA), a multivariate statistical approach that can model the dynamics of non-stationary processes. Phase-transitions in fermentation produce distinct patterns in the DPCA scores, which can be identified as singular points. We illustrate the application of the method to detect transitions such as the onset of exponential growth phase, substrate exhaustion and substrate switching for rifamycin B fermentation batches. Further, we analyze the loading vectors of DPCA model to illustrate the mechanism by which the statistical model accounts for process dynamics. The approach can be readily applied to other industrially important processes and may have implications in online monitoring of fermentation batches in a production facility.

Phase shifts in the stoichiometry of rifamycin B fermentation and correlation with the trends in the parameters measured online

Antibiotic fermentation processes are raw material cost intensive and the profitability is greatly dependent on the product yield per unit substrate consumed. In order to reduce costs, industrial processes use organic nitrogen substrates (ONS) such as corn steep liquor and yeast extract. Thus, although the stoichiometric analysis is the first logical step in process development, it is often difficult to achieve due to the ill-defined nature of the medium. Here, we present a black-box stoichiometric model for rifamycin B production via Amycolatopsis mediterranei S699 fermentation in complex multi-substrate medium. The stoichiometric coefficients have been experimentally evaluated for nine different media compositions. The ONS was quantified in terms of the amino acid content that it provides. Note that the black box stoichiometric model is an overall result of the metabolic reactions that occur during growth. Hence, the observed stoichiometric coefficients are liable to change during the batch cycle. To capture the shifts in stoichiometry, we carried out the stoichiometric analysis over short intervals of 8-16 h in a batch cycle of 100-200 h. An error analysis shows that there are no systematic errors in the measurements and that there are no unaccounted products in the process. The growth stoichiometry shows a shift from one substrate combination to another during the batch cycle. The shifts were observed to correlate well with the shifts in the trends of pH and exit carbon dioxide profiles. To exemplify, the ammonia uptake and nitrate uptake phases were marked by a decreasing pH trend and an increasing pH trend, respectively. Further, we find the product yield per unit carbon substrate to be greatly dependent on the nature of the nitrogen substrate. The analysis presented here can be readily applied to other fermentation systems that employ multi-substrate complex media.

Hierarchical amino acid utilization and its influence on fermentation dynamics: rifamycin B fermentation using Amycolatopsis mediterranei S699, a case study

Background

Industrial fermentation typically uses complex nitrogen substrates which consist of mixture of amino acids. The uptake of amino acids is known to be mediated by several amino acid transporters with certain preferences. However, models to predict this preferential uptake are not available. We present the stoichiometry for the utilization of amino acids as a sole carbon and nitrogen substrate or along with glucose as an additional carbon source. In the former case, the excess nitrogen provided by the amino acids is excreted by the organism in the form of ammonia. We have developed a cybernetic model to predict the sequence and kinetics of uptake of amino acids. The model is based on the assumption that the growth on a specific substrate is dependent on key enzyme(s) responsible for the uptake and assimilation of the substrates. These enzymes may be regulated by mechanisms of nitrogen catabolite repression. The model hypothesizes that the organism is an optimal strategist and invests resources for the uptake of a substrate that are proportional to the returns.

Results

Stoichiometric coefficients and kinetic parameters of the model were estimated experimentally for Amycolatopsis mediterranei S699, a rifamycin B overproducer. The model was then used to predict the uptake kinetics in a medium containing cas amino acids. In contrast to the other amino acids, the uptake of proline was not affected by the carbon or nitrogen catabolite repression in this strain. The model accurately predicted simultaneous uptake of amino acids at low cas concentrations and sequential uptake at high cas concentrations. The simulated profile of the key enzymes implies the presence of specific transporters for small groups of amino acids.

Conclusion

The work demonstrates utility of the cybernetic model in predicting the sequence and kinetics of amino acid uptake in a case study involving Amycolatopsis mediterranei, an industrially important organism. This work also throws some light on amino acid transporters and their regulation in A. mediterranei .Further, cybernetic model based experimental strategy unravels formation and utilization of ammonia as well as its inhibitory role during amino acid uptake. Our results have implications for model based optimization and monitoring of other industrial fermentation processes involving complex nitrogen substrate.