Development of thermodynamic optimum searching (TOS) to improve the prediction accuracy of flux balance analysis

Flux balance analysis (FBA) has been widely used in calculating steady‐state flux distributions that provide important information for metabolic engineering. Several thermodynamics‐based methods, for example, quantitative assignment of reaction directionality and energy balance analysis have been developed to improve the prediction accuracy of FBA. However, these methods can only generate a thermodynamically feasible range, rather than the most thermodynamically favorable solution. We therefore developed a novel optimization method termed as thermodynamic optimum searching (TOS) to calculate the thermodynamically optimal solution, based on the second law of thermodynamics, the minimum magnitude of the Gibbs free energy change and the maximum entropy production principle (MEPP). Then, TOS was applied to five physiological conditions of Escherichia coli to evaluate its effectiveness. The resulting prediction accuracy was found significantly improved (10.7–48.5%) by comparing with the ¹³C‐fluxome data, indicating that TOS can be considered an advanced calculation and prediction tool in metabolic engineering. Biotechnol. Bioeng. 2013; 110: 914–923. © 2012 Wiley Periodicals, Inc.     

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures—Systems biology‐based interpretation using genome‐scale metabolic flux balance model and multivariate data analysis

Genome‐scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745–753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome‐scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163–1173, 2016     

Biosynthesis of novel polyhydroxyalkanoate containing 3-hydroxy-4-methylvalerate by Chromobacterium sp. USM2

Aims: Polyhydroxyalkanoate (PHA) with enhanced physicochemical properties will be ideal for a wide range of practical applications. The incorporation of 3‐hydroxy‐4‐methylvalerate (3H4MV) into the polymer backbone is known to improve the overall properties of the resulting polymer. However, the most suitable micro‐organism and PHA synthase that can synthesize this monomer efficiently still remain unknown at present. Therefore, we evaluated the abilities of a locally isolated Chromobacterium sp. USM2 to produce PHA containing 3H4MV. Methods and Results: The ability of Chromobacterium sp. USM2 to synthesize poly(3‐hydroxybutyrate‐co‐3‐hydroxy‐4‐methylvalerate) [P(3HB‐co‐3H4MV)] was evaluated under different culture conditions. It was found that Chromobacterium sp. USM2 can synthesize P(3HB‐co‐3H4MV) when glucose and isocaproic acid were fed as carbon source. However, the highest molar fraction of 3H4MV, 22 mol% was detected in Chromobacterium sp. USM2 when isocaproic acid was provided as the sole carbon source. In addition, aeration was identified as a crucial factor in initiating the accumulation of high 3H4MV molar fractions. Conclusions: Chromobacterium sp. USM2 was able to synthesize broad comonomer compositional distribution of P(3HB‐co‐3H4MV). Significance and Impact of the Study: Compared with Cupriavidus necator and Burkholderia sp., Chromobacterium sp. USM2 was found to have better ability to bioconvert isocaproic acid to form 3H4MV unit.     

MapMaker and PathTracer for tracking carbon in genome‐scale metabolic models

Constraint‐based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome‐scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint‐based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO₂ fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint‐based models to identify feasible, active, and high flux paths between metabolites.     

Production of medium-chain-length PHA in octanoate-fed enrichments dominated by Sphaerotilus sp.

Medium-chain-length polyhydroxyalkanoate (mcl-PHA) production by using microbial enrichments is a promising but largely unexplored approach to obtain elastomeric biomaterials from secondary resources. In this study, several enrichment strategies were tested to select a community with a high mcl-PHA storage capacity when feeding octanoate. On the basis of analysis of the metabolic pathways, the hypothesis was formulated that mcl-PHA production is more favorable under oxygen-limited conditions than short-chain-length PHA (scl-PHA). This hypothesis was confirmed by bioreactor experiments showing that oxygen limitation during the PHA accumulation experiments resulted in a higher fraction of mcl-PHA over scl-PHA (i.e., a PHA content of 76 wt% with an mcl fraction of 0.79 with oxygen limitation, compared to a PHA content of 72 wt% with an mcl-fraction of 0.62 without oxygen limitation). Physicochemical analysis revealed that the extracted PHA could be separated efficiently into a hydroxybutyrate-rich fraction with a higher M_w and a hydroxyhexanoate/hydroxyoctanoate-rich fraction with a lower M_w. The ratio between the two fractions could be adjusted by changing the environmental conditions, such as oxygen availability and pH. Almost all enrichments were dominated by Sphaerotilus sp. This is the first scientific report that links this genus to mcl-PHA production, demonstrating that microbial enrichments can be a powerful tool to explore mcl-PHA biodiversity and to discover novel industrially relevant strains.     

Large‐scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi‐level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi‐level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low‐complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.     

The effect of decreasing alkalinity on microbial community dynamics in a sulfate-reducing bioreactor as analyzed by PCR-SSCP

Alkalinity refers to the substance that can react with acid in an aqueous system, which measures its capac-ity to neutralize hydrogen iron (H ). Alkalinity influ-ences greatly the stability and treatment ability of an-aerobic bioreactor. It has been well …     

The effect of decreasing alkalinity on microbial community dynamics in a sulfate-reducing bioreactor as analyzed by PCR-SSCP

PCR-single-strand conformation polymorphism (SSCP) and Southern blotting techniques were adopted to investigate microbial community dynamics in a sulfate-reducing bioreactor caused by decreasing influent alkalinity. Experimental results indicated that the sulfate-removal rate approached 87% in 25 d under the conditions of influent alkalinity of 4000 mg/L (as CaCO₃) and sulfate-loading rate of 4.8 g/(L·d), which indicated that the bioreactor started up successfully. The analysis of microbial community structure in this stage showed that Lactococcus sp., Anaerofilum sp. and Kluyvera sp. were dominant populations. It was found that when influent alkalinity reduced to 1000 mg/L, sulfate-removal rate decreased rapidly to 35% in 3 d. Then influent alkalinity was increased to 3000 mg/L, the sulfate-removal rate rose to 55%. Under these conditions, the populations of Dysgonomonas sp., Sporobacte sp., Obesumbacterium sp. and Clostridium sp. got to rich, which predominated in the community together with Lactococcus sp., Anaerofilum sp. and Kluyvera sp. However, when the alkalinity was decreased to 1500 mg/L, the sulfate-removal rate rose to and kept stable at 70% and populations of Dysgonomonas sp., Sporobacter sp. and Obesumbacterium sp. died out, while some strains of Desulfovibrio sp. and Clostridium sp. increased in concentration. In order to determine the minimum alkalinity value that the system could tolerate, the influent alkalinity was decreased from 1500 to 400 mg/L secondly. This resulted in the sulfate-removal rate, pH value and effluent alkalinity dropping quickly. The amount of Petrotoga sp., Prevotella sp., Kluyvera sp. and Neisseria sp. reduced obviously. The result data from Southern blotting indicated that the amount of sulfate-reducing bacteria (SRBs) decreased with influent alkalinity dropping. Analysis of the microbial community structure and diversity showed that the SRBs populations were very abundant in the inoculated activated sludge and the alkalinity decrease caused the reduction of the populations noted. Most of resident populations in the bioreactor were fermentative acidogenic bacteria (FABs), among which the phylum Firmicute was in the majority, but SRBs were very few. This community structure demonstrates the cooperation between SRBs and FABs, which sustains the system’s high sulfate-removal and operation stability.     

Recovery of poly(3‐hydroxybutyrate‐co‐3‐hydroxyhexanoate) from Ralstonia eutropha cultures with non‐halogenated solvents

Reduced downstream costs, together with high purity recovery of polyhydroxyalkanoate (PHA), will accelerate the commercialization of high quality PHA‐based products. In this work, a process was designed for effective recovery of the copolymer poly(hydroxybutyrate‐co‐hydroxyhexanoate) (P(HB‐co‐HHx)) containing high levels of HHx (>15 mol%) from Ralstonia eutropha biomass using non‐halogenated solvents. Several non‐halogenated solvents (methyl isobutyl ketone, methyl ethyl ketone, and butyl acetate and ethyl acetate) were found to effectively dissolve the polymer. Isoamyl alcohol was found to be not suitable for extraction of polymer. All PHA extractions were performed from both dry and wet cells at volumes ranging from 2 mL to 3 L using a PHA to solvent ratio of 2% (w/v). Ethyl acetate showed both high recovery levels and high product purities (up to 99%) when using dry cells as starting material. Recovery from wet cells, however, eliminates a biomass drying step during the downstream process, potentially saving time and cost. When wet cells were used, methyl isobutyl ketone (MIBK) was shown to be the most favorable solvent for PHA recovery. Purities of up to 99% and total recovery yields of up to 84% from wet cells were reached. During polymer recovery with either MIBK or butyl acetate, fractionation of the extracted PHA occurred, based on the HHx content of the polymer. PHA with higher HHx content (17–30 mol%) remained completely in solution, while polymer with a lower HHx content (11–16 mol%) formed a gel‐like phase. All PHA in solution could be precipitated by addition of threefold volumes of n‐hexane or n‐heptane to unfiltered PHA solutions. Effective recycling of the solvents in this system is predicted due to the large differences in the boiling points between solvent and precipitant. Our findings show that two non‐halogenated solvents are good candidates to replace halogenated solvents like chloroform for recovery of high quality PHA. Biotechnol. Bioeng. 2013; 110: 461–470. © 2012 Wiley Periodicals, Inc.     

Cybernetic models based on lumped elementary modes accurately predict strain‐specific metabolic function

In a recent article, Song and Ramkrishna (Song and Ramkrishna [2010]. Biotechnol Bioeng 106(2):271–284) proposed a lumped hybrid cybernetic model (L‐HCM) towards extracting maximum information about metabolic function from a minimum of data. This approach views the total uptake flux as distributed among lumped elementary modes (L‐EMs) so as to maximize a prescribed metabolic objective such as growth or uptake rate. L‐EM is computed as a weighted average of EMs where the weights are related to the yields of vital products (i.e., biomass and ATP). In this article, we further enhance the predictive power of L‐HCMs through modifications in lumping weights with additional parameters that can be tuned with data viewed to be critical. The resulting model is able to make predictions of diverse metabolic behaviors varying greatly with strain types as evidenced from case studies of anaerobic growth of various Escherichia coli strains. Incorporation of the new lumping formula into L‐HCM remarkably improves model predictions with a few critical data, thus presenting L‐HCM as a dynamic tool as being not only qualitatively correct but also quantitatively accurate. Biotechnol. Bioeng. 2011; 108:127–140. © 2010 Wiley Periodicals, Inc.     

Investigation of microcystin removal from eutrophic surface water by aquatic vegetable bed

In southern China, many freshwater ecosystems, including lakes, rivers and reservoirs, are eutrophic. The nutrient loading coupled with year-round warm weather favors the growth of cyanobacteria, several of which can produce cyanotoxins, especially the potent liver toxins called microcystins, which are often detected in eutrophic drinking water sources. For purifying raw water used as source of drinking water treatment plants, an aquatic vegetable bed (AVB) experiment had been carried out in a hypertrophic waterfront of Lake Taihu, China, since October 2002. AVB was a simplification of the nutrient film technique (NFT) used to produce vegetables, which requires large quantities of water and nutrients. The average removal rates of total microcystin-RR and microcystin-LR were 63.0% and 66.7%, respectively. This study indicated that Ipomoea aquatica was able to absorb microcystins by using enzyme-linked immunosorbent assay (ELISA), and that the roots absorbed more toxins than leaves and stems. We used fluorescence in situ hybridization (FISH) to analyze the density of microcystin-degrading bacteria in AVB sediment. Two species of microcystin-degrading bacteria were detected, which indicated that microcystin bio-degradation processes did occur in AVB. Protozoa and metazoa were abundant in the rhizosphere. Aspidisca sp., Vorticella sp., Philodina sp., and Lecane sp. were the dominant species. The predation function of protozoa and metazoa had a positive effect on removal of cyanobacteria and microcystins.     

Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

Constraint-based modeling methods, such as Flux Balance Analysis (FBA), have been extensively used to decipher complex, information rich -omics datasets to elicit system-wide behavioral patterns of cellular metabolism. FBA has been successfully used to gain insight in a wide range of applications, such as range of substrate utilization, product yields and to design metabolic engineering strategies to improve bioprocess performance. A well-known challenge associated with large genome-scale metabolic networks is that they result in underdetermined problem formulations. Consequently, rather than unique solutions, FBA and related methods examine ranges of reaction flux values that are consistent with the studied physiological conditions. The wider the reported flux ranges, the higher the uncertainty in the determination of basic reaction properties, limiting interpretability of and confidence in the results. Herein, we propose a new, computationally efficient approach that refines flux range predictions by constraining reaction fluxes on the basis of the elemental balance of carbon. We compared carbon constraint FBA (ccFBA) against experimentally-measured intracellular fluxes using the latest CHO GEM (iCHO1766) and were able to substantially improve the accuracy of predicted flux values compared with FBA. ccFBA can be used as a stand-alone method but is also compatible with and complimentary to other constraint-based approaches.     

ΔFBA—Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.     

Construction of a bacterial artificial chromosome library of Endoclita excrescens as a tool for comparative gene mapping in Lepidoptera

Karyotype evolution in one of the most diverse and species‐rich group of insects, moths and butterflies (Lepidoptera), has interesting features that remain to be resolved. Recent studies showed that fluorescence in situ hybridization using bacterial artificial chromosome clones (BAC‐FISH) is an efficient cytogenetic method for identification and gene mapping of lepidopteran chromosomes. Using comparative mapping by BAC‐FISH, extensive synteny of genes was revealed between chromosomes of different lepidopteran species based on Bombyx mori genomic information. However, this comparative mapping has been done only in representatives of advanced groups of Lepidoptera. Here we constructed a BAC library of Endoclita excrescens, which belongs to the primitive lepidopteran family Hepialidae. High molecular weight DNA for the library construction was prepared from the pupae by using a rapid nuclear isolation method known in plants. The BAC clones of E. excrescens contain 66.6 kb inserts on average. The successful application of BAC‐FISH showed that the BAC library of E. excrescens is a useful tool for comparative gene mapping on chromosomes of this species.     

Essential‐Oil Variability of Juniperus deltoides R.P.Adams along the East Adriatic Coast – How Many Chemotypes Are There?

The composition of the essential oils isolated from twigs of ten Juniperus deltoides R.P . Adams populations from the east Adriatic coast was determined by GC‐FID and GC/MS analyses. Altogether, 169 compounds were identified, representing 95.6–98.4% of the total oil composition. The oils were dominated by monoterpenes (average content of 61.6%), which are characteristic oil components of species of the Juniperus section. Two monoterpenes, α‐pinene and limonene, were the dominant constituents, comprising on average 46.78% of the essential oils. Statistical methods were deployed to determine the diversity of the terpene classes and the common terpenes between the investigated populations. These statistical analyses revealed the existence of three chemotypes within all populations, i.e., a α‐pinene, limonene, and limonene/α‐pinene type.     

The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum

The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a ¹³C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600–800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate.     

Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

Background  

In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.     

Variability of n‐Alkanes and Nonacosan‐10‐ol in Natural Populations of Picea omorika

This is the first report of population variability of the contents of n‐alkanes and nonacosan‐10‐ol in the needle epicuticular waxes of Serbian spruce (Picea omorika). The hexane extracts of needle samples originated from three natural populations in Serbia (Vranjak, Zmajeva?ki potok, and Mile?evka Canyon) were investigated by GC and GC/MS analyses. The amount of nonacosan‐10‐ol varied individually from 50.05 to 74.42% (65.74% in average), but the differences between the three investigated populations were not statistically confirmed. The results exhibited variability of the composition of n‐alkanes in the epicuticular waxes with their size ranging from C₁₈ to C₃₅. The most abundant n‐alkanes were C₂₉, C₃₁, and C₂₇ (35.22, 13.77, and 12.28% in average, resp.). The carbon preference index of all the n‐alkanes (CPI_total) of the P. omorika populations (average of populations I–III) ranged from 3.3 to 11.5 (mean of 5.9), while the average chain length (ACL) ranged from 26.6 to 29.2. The principal component and cluster analyses of the contents of nine n‐alkanes showed the greatest difference for the population growing in the Mile?evka Canyon. The obtained results were compared with previous literature data given for other Picea species, and this comparison was briefly discussed.     

Chemodiversity of Volatile Oils in Thapsia garganica L. (Apiaceae)

The chemical composition of the volatile oils obtained from the roots, leaves, flowers, and stems of Thapsia garganica of Tunisian origin was investigated by GC‐FID and GC/MS analyses. Sesquiterpene hydrocarbons and oxygenated monoterpenes were predominant in the oils of all plant parts. Bicyclogermacrene (21.59–35.09%) was the main component in the former compound class, whereas geranial (3.31–14.84%) and linalool (0.81–10.9%) were the most prominent ones in the latter compound class. Principal‐component (PCA) and hierarchical‐cluster (HCA) analyses revealed some common constituents, but also significant variability amongst the oils of the different plant parts. This organ‐specific oil composition was discussed in relation to their biological and ecological functions. For the evaluation of the intraspecific chemical variability in T. garganica, the composition of the flower volatile oils from four wild populations was investigated. Bicyclogermacrene, linalool, and geranial were predominant in the oils of three populations, whereas epicubenol, β‐sesquiphellandrene, and cadina‐1,4‐diene were the most prominent components of the oil of one population. PCA and HCA allowed the separation of the flower oils into three distinct groups, however, no relationship was found between the volatile‐oil composition and the geographical distribution and pedoclimatic conditions of the studied populations.