Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.     

Real time phase detection based online monitoring of batch fermentation processes

Industrial fermentations conducted in a batch or semi-batch mode demonstrate significant batch-to-batch variability. Current batch process monitoring strategies involve manual interpretation of highly informative but low frequency offline measurements such as concentrations of products, biomass and substrates. Fermentors are also fitted with computer interfaced instrumentation, enabling high frequency online measurements of several variables and automated techniques which can utilize this data would be desirable. Evolution of a batch fermentation, which typically uses complex medium, can be conceptualized as a sequence of several distinct metabolic phases. Monitoring of batch processes can then be achieved by detecting the phase change events, also termed as singular points (SP). In this work, we propose a novel moving window based real-time monitoring strategy for SP detection based only on online measurements. The key hypothesis of the strategy is that the statistical properties of the online data undergo a significant change around an SP. The strategy is easily implementable and does not require past data or prior knowledge of the number or time of occurrence of SPs. The efficacy of the proposed approach has been demonstrated to be superior compared to that of reported techniques for industrially relevant model organisms. The proposed approach can be used to decide offline sampling timings in real time.     

Quantification of metabolically active biomass using Methylene Blue dye Reduction Test (MBRT): measurement of CFU in about 200 s

Quantification of viable cells is a critical step in almost all biological experiments. Despite its importance, the methods developed so far to differentiate between viable and non-viable cells suffer from major limitations such as being time intensive, inaccurate and expensive. Here, we present a method to quantify viable cells based on reduction of methylene blue dye in cell cultures. Although the methylene blue reduction method is well known to check the bacterial load in milk, its application in the quantification of viable cells has not been reported. We have developed and standardized this method by monitoring the dye reduction rate at each time point for growth of Escherichia coli. The standard growth curve was monitored using this technique. The Methylene Blue dye Reduction Test (MBRT) correlates very well with Colony Forming Units (CFU) up to a 800 live cells as established by plating. The test developed is simple, accurate and fast (200 s) as compared to available techniques. We demonstrate the utility of the developed assay to monitor CFU rapidly and accurately for E. coli, Bacillus subtilis and a mixed culture of E. coli and B. subtilis. This assay, thus, has a wide applicability to all types of aerobic organisms.     

Metabolic flux analysis of Cyanothece sp. ATCC 51142 under mixotrophic conditions

Cyanobacteria are a group of photosynthetic prokaryotes capable of utilizing solar energy to fix atmospheric carbon dioxide to biomass. Despite several “proof of principle” studies, low product yield is an impediment in commercialization of cyanobacteria-derived biofuels. Estimation of intracellular reaction rates by ¹³C metabolic flux analysis (¹³C-MFA) would be a step toward enhancing biofuel yield via metabolic engineering. We report ¹³C-MFA for Cyanothece sp. ATCC 51142, a unicellular nitrogen-fixing cyanobacterium, known for enhanced hydrogen yield under mixotrophic conditions. Rates of reactions in the central carbon metabolism under nitrogen-fixing and -non-fixing conditions were estimated by monitoring the competitive incorporation of ¹²C and ¹³C from unlabeled CO₂ and uniformly labeled glycerol, respectively, into terminal metabolites such as amino acids. The observed labeling patterns suggest mixotrophic growth under both the conditions, with a larger fraction of unlabeled carbon in nitrate-sufficient cultures asserting a greater contribution of carbon fixation by photosynthesis and an anaplerotic pathway. Indeed, flux analysis complements the higher growth observed under nitrate-sufficient conditions. On the other hand, the flux through the oxidative pentose phosphate pathway and tricarboxylic acid cycle was greater in nitrate-deficient conditions, possibly to supply the precursors and reducing equivalents needed for nitrogen fixation. In addition, an enhanced flux through fructose-6-phosphate phosphoketolase possibly suggests the organism’s preferred mode under nitrogen-fixing conditions. The ¹³C-MFA results complement the reported predictions by flux balance analysis and provide quantitative insight into the organism’s distinct metabolic features under nitrogen-fixing and -non-fixing conditions.     

Functional sites in protein families uncovered via an objective and automated graph theoretic approach

We report a method for detection of recurring side-chain patterns (DRESPAT) using an unbiased and automated graph theoretic approach. We first list all structural patterns as sub-graphs where the protein is represented as a graph. The patterns from proteins are compared pair-wise to detect patterns common to a protein pair based on content and geometry criteria. The recurring pattern is then detected using an automated search algorithm from the all-against-all pair-wise comparison data of proteins. Intra-protein pattern comparison data are used to enable detection of patterns recurring within a protein. A method has been proposed for empirical calculation of statistical significance of recurring pattern. The method was tested on 17 protein sets of varying size, composed of non-redundant representatives from SCOP superfamilies. Recurring patterns in serine proteases, cysteine proteases, lipases, cupredoxin, ferredoxin, ferritin, cytochrome c, aspartoyl proteases, peroxidases, phospholipase A2, endonuclease, SH3 domain, EF-hand and lectins show additional residues conserved in the vicinity of the known functional sites. On the basis of the recurring patterns in ferritin, EF-hand and lectins, we could separate proteins or domains that are structurally similar yet different in metal ion-binding characteristics. In addition, novel recurring patterns were observed in glutathione-S-transferase, phospholipase A2 and ferredoxin with potential structural/functional roles. The results are discussed in relation to the known functional sites in each family. Between 2000 and 50,000 patterns were enumerated from each protein with between ten and 500 patterns detected as common to an evolutionarily related protein pair. Our results show that unbiased extraction of functional site pattern is not feasible from an evolutionarily related protein pair but is feasible from protein sets comprising five or more proteins. The DRESPAT method does not require a user-defined pattern, size or location of the pattern and therefore, has the potential to uncover new functional sites in protein families.     

Effect of high CO<Subscript>2</Subscript> concentrations on the growth and macromolecular composition of a heat- and high-light-tolerant microalga

A green microalga, Acutodesmus sp., a close relative of Acutodesmus deserticola, was isolated from the wastewater discharges of an oil refinery in India. This study examined the effects of light intensity, temperature, pH, and high-CO₂ treatments (up to 20 %) on the growth of the alga and investigated the effects of different CO₂ treatments on its macromolecular composition (protein, carbohydrate, and lipids). Under controlled laboratory conditions, the alga showed high growth rates over a wide range of light (up to 700 μmol photons m^?2 s^?1), temperature (up to 40 °C), and pH (5–10) conditions. In the stationary phase, the highest protein and carbohydrate content was found to be 71.52 and 40.72 % of dry weight at 5 and 15 % CO₂, respectively. After 5 days of cultivation, the maximum dry weight biomass attained in these cultures was 1.149, 1.99, 1.75, and 1.65 g L^?1 at 5, 10, 15, and 20 % CO₂, respectively, indicating that this strain has significant tolerance to CO₂. These results indicate that this strain is a promising candidate for use in biofixation of CO₂ from the flue gases emitted by industries, and it also has a strong potential as a feedstock for value-added substances.     

Substrate uptake,phosphorus repression,and effect of seed culture on glycopeptide antibiotic production: Process model development and experimental validation

Actinomycetes, the soil borne bacteria which exhibit filamentous growth, are known for their ability to produce a variety of secondary metabolites including antibiotics. Industrial scale production of such antibiotics is typically carried out in a multi‐substrate medium where the product formation may experience catabolite repression by one or more of the substrates. Availability of reliable process models is a key bottleneck in optimization of such processes. Here we present a structured kinetic model to describe the growth, substrate uptake and product formation for the glycopeptide antibiotic producer strain Amycolatopsis balhimycina DSM5908. The model is based on the premise that the organism is an optimal strategist and that the various metabolic pathways are regulated via key rate limiting enzymes. Further, the model accounts for substrate inhibition and catabolite repression. The model is also able to predict key phenomena such as simultaneous uptake of glucose and glycerol but with different specific uptake rates, and inhibition of glycopeptide production by high intracellular phosphate levels. The model is successfully applied to both production and seed medium with varying compositions and hence has good predictive ability over a variety of operating conditions. The model parameters are estimated via a well‐designed experimental plan. Adequacy of the proposed model was established via checking the model sensitivity to its parameters and confidence interval calculations. The model may have applications in optimizing seed transfer, medium composition, and feeding strategy for maximizing production. Biotechnol. Bioeng. 2010;105: 109–120. © 2009 Wiley Periodicals, Inc.