Changing biosynthesis of terpenoid percursors in rice through synthetic biology.

Many highly valued chemicals in the pharmaceutical, biotechnological, cosmetic, and biomedical industries belong to the terpenoid family. Biosynthesis of these chemicals relies on polymerization of Isopentenyl di-phosphate (IPP) and/or dimethylallyl diphosphate (DMAPP) monomers, which plants synthesize using two alternative pathways: a cytosolic mevalonic acid (MVA) pathway and a plastidic methyleritritol-4-phosphate (MEP) pathway. As such, developing plants for use as a platform to use IPP/DMAPP and produce high value terpenoids is an important biotechnological goal. Still, IPP/DMAPP are the precursors to many plant developmental hormones. This creates severe challenges in redirecting IPP/DMAPP towards production of non-cognate plant metabolites. A potential solution to this problem is increasing the IPP/DMAPP production flux in planta. Here, we aimed at discovering, understanding, and predicting the effects of increasing IPP/DMAPP production in plants through modelling. We used synthetic biology to create rice lines containing an additional ectopic MVA biosynthetic pathway for producing IPP/DMAPP. The rice lines express three alternative versions of the additional MVA pathway in the plastid, in addition to the normal endogenous pathways. We collected data for changes in macroscopic and molecular phenotypes, gene expression, isoprenoid content, and hormone abundance in those lines. To integrate the molecular and macroscopic data and develop a more in depth understanding of the effects of engineering the exogenous pathway in the mutant rice lines, we developed and analyzed data-centric, line-specific, multilevel mathematical models. These models connect the effects of variations in hormones and gene expression to changes in macroscopic plant phenotype and metabolite concentrations within the MVA and MEP pathways of WT and mutant rice lines. Our models allow us to predict how an exogenous IPP/DMAPP biosynthetic pathway affects the flux of terpenoid precursors. We also quantify the long-term effect of plant hormones on the dynamic behavior of IPP/DMAPP biosynthetic pathways in seeds, and predict plant characteristics, such as plant height, leaf size, and chlorophyll content from molecular data. In addition, our models are a tool that can be used in the future to help in prioritizing re-engineering strategies for the exogenous pathway in order to achieve specific metabolic goals.

Modeling the effects of strigolactone levels on maize root system architecture.

Maize is the most in-demand staple crop globally. Its production relies strongly on the use of fertilizers for the supply of nitrogen, phosphorus, and potassium, which the plant absorbs through its roots, together with water. The architecture of maize roots is determinant in modulating how the plant interacts with the microbiome and extracts nutrients and water from the soil. As such, attempts to use synthetic biology and modulate that architecture to make the plant more resilient to drought and parasitic plants are underway. These attempts often try to modulate the biosynthesis of hormones that determine root architecture and growth. Experiments are laborious and time-consuming, creating the need for simulation platforms that can integrate metabolic models and 3D root growth models and predict the effects of synthetic biology interventions on both, hormone levels and root system architectures. Here, we present an example of such a platform that is built using Mathematica. First, we develop a root model, and use it to simulate the growth of many unique 3D maize root system architectures (RSAs). Then, we couple this model to a metabolic model that simulates the biosynthesis of strigolactones, hormones that modulate root growth and development. The coupling allows us to simulate the effect of changing strigolactone levels on the architecture of the roots. We then integrate the two models in a simulation platform, where we also add the functionality to analyze the effect of strigolactone levels on root phenotype. Finally, using in silico experiments, we show that our models can reproduce both the phenotype of wild type maize, and the effect that varying strigolactone levels have on changing the architecture of maize roots.

A mathematical model for strigolactone biosynthesis in plants.

Strigolactones mediate plant development, trigger symbiosis with arbuscular mycorrhizal fungi, are abundant in 80% of the plant kingdom and help plants gain resistance to environmental stressors. They also induce germination of parasitic plant seeds that are endemic to various continents, such as Orobanche in Europe or Asia and Striga in Africa. The genes involved in the early stages of strigolactones biosynthesis are known in several plants. The regulatory structure and the latter parts of the pathway, where flux branching occurs to produce alternative strigolactones, are less well-understood. Here we present a computational study that collects the available experimental evidence and proposes alternative biosynthetic pathways that are consistent with that evidence. Then, we test the alternative pathways through in silico simulation experiments and compare those experiments to experimental information. Our results predict the differences in dynamic behavior between alternative pathway designs. Independent of design, the analysis suggests that feedback regulation is unlikely to exist in strigolactone biosynthesis. In addition, our experiments suggest that engineering the pathway to modulate the production of strigolactones could be most easily achieved by increasing the flux of ß-carotenes going into the biosynthetic pathway. Finally, we find that changing the ratio of alternative strigolactones produced by the pathway can be done by changing the activity of the enzymes after the flux branching points.

Strain-to-strain variability among Staphylococcus aureus causing prosthetic joint infection drives heterogeneity in response to levofloxacin and rifampicin.

INTRODUCTION: Levofloxacin and rifampicin are the preferred treatment for prosthetic joint infection (PJI) caused by Staphylococcus aureus, especially when managed with implant retention (DAIR). However, a significant variability of success has been reported, which could be related to intrinsic characteristics of the microorganism. Our aim was to evaluate the variability in the anti-biofilm response to levofloxacin and rifampicin in a clinical collection of S. aureus. MATERIAL AND METHODS: Eleven levofloxacin- and rifampicin-susceptible S. aureus isolates causing PJI managed with DAIR were included. Levofloxacin, rifampicin and levofloxacin + rifampicin were tested in an in vitro static biofilm model in microtitre plates, where 48 h biofilms were challenged with antimicrobials during 24 h. Additionally, two genetically similar strains were tested in the CDC Biofilm Reactor, where 48 h biofilms were treated during 56 h. Antimicrobial activity was assessed by viable biofilm-embedded cells recount, and by crystal violet staining. RESULTS: All antimicrobial regimens showed significant anti-biofilm activity, but a notable scattering in the response was observed across all strains (inter-strain coefficient of variation for levofloxacin, rifampicin and levofloxacin + rifampicin of 22.8%, 35.8% and 34.5%, respectively). This variability was tempered with the combination regimen when tested in the biofilm reactor. No correlation was observed between the minimal biofilm eradicative concentration and the antimicrobial activity. Recurrent S. aureus isolates exhibited higher biofilm-forming ability compared with strains from resolved infections (7.6 log10 cfu/cm2±0.50 versus 9.0 log10 cfu±0.07). CONCLUSIONS: Significant variability may be expected in response to levofloxacin and rifampicin among biofilm-embedded S. aureus. A response in the lower range, together with other factors of bad prognosis, could be responsible of treatment failure.

Hydrolytic Enzymes as Potentiators of Antimicrobials against an Inter-Kingdom Biofilm Model.

Biofilms are recalcitrant to antimicrobials, partly due to the barrier effect of their matrix. The use of hydrolytic enzymes capable to degrade matrix constituents has been proposed as an alternative strategy against biofilm-related infections. This study aimed to determine whether hydrolytic enzymes could potentiate the activity of antimicrobials against hard-to-treat interkingdom biofilms comprising two bacteria and one fungus. We studied the activity of a series of enzymes alone or in combination, followed or not by antimicrobial treatment, against single-, dual- or three-species biofilms of Staphylococcus aureus, Escherichia coli, and Candida albicans, by measuring their residual biomass or culturable cells. Two hydrolytic enzymes, subtilisin A and lyticase, were identified as the most effective to reduce the biomass of C. albicans biofilm. When targeting interkingdom biofilms, subtilisin A alone was the most effective enzyme to reduce biomass of all biofilms, followed by lyticase combined with an enzymatic cocktail composed of cellulase, denarase, and dispersin B that proved previously active against bacterial biofilms. The subsequent incubation with antimicrobials further reduced the biomass. Enzymes alone did not reduce culturable cells in most cases and did not interfere with the cidal effects of antimicrobials. Therefore, this work highlights the potential interest of pre-exposing interkingdom biofilms to hydrolytic enzymes to reduce their biomass besides the number of culturable cells, which was not achieved when using antimicrobials alone. IMPORTANCE Biofilms are recalcitrant to antimicrobial treatments. This problem is even more critical when dealing with polymicrobial, interkingdom biofilms, including both bacteria and fungi, as these microorganisms cooperate to strengthen the biofilm and produce a complex matrix. Here, we demonstrate that the protease subtilisin A used alone, or a cocktail containing lyticase, cellulase, denarase, and dispersin B markedly reduce the biomass of interkingdom biofilms and cooperate with antimicrobials to act upon these recalcitrant forms of infection. This work may open perspectives for the development of novel adjuvant therapies against biofilm-related infections.

Pharmacodynamics of Moxifloxacin, Meropenem, Caspofungin, and Their Combinations against In Vitro Polymicrobial Interkingdom Biofilms.

Biofilms colonize medical devices and are often recalcitrant to antibiotics. Interkingdom biofilms, where at least a bacterium and a fungus are present, increase the likelihood of therapeutic failures. In this work, a three-species in vitro biofilm model including Staphylococcus aureus, Escherichia coli, and Candida albicans was used to study the activity of the antibiotics moxifloxacin and meropenem, the antifungal caspofungin, and combinations of them against interkingdom biofilms. The culturable cells and total biomass were evaluated to determine the pharmacodynamic parameters of the drug response for the incubation with the drugs alone. The synergic or antagonistic effects (increased/decreased effects) of the combination of drugs were analyzed with the highest-single-agent method. Biofilms were imaged in confocal microscopy after live/dead staining. The drugs had limited activity when used alone against single-, dual-, and three-species biofilms. When used in combination, additive effects against single- and dual-species biofilms and increased effects (synergy) against biomass of three-species biofilms were observed. In addition, the two antibiotics showed different patterns, moxifloxacin being more active when targeting S. aureus and meropenem when targeting E. coli. All these observations were confirmed by confocal microscopy images. Our findings highlight the interest in combining caspofungin with antibiotics against interkingdom biofilms.

In vitro polymicrobial inter-kingdom three-species biofilm model: influence of hyphae on biofilm formation and bacterial physiology.

Biofilms are an important medical burden, notably for patients with orthopaedic device-related infections. When polymicrobial, these infections are more lethal and recalcitrant. Inter-kingdom biofilm infections are poorly understood and challenging to treat. Here, an in vitro three-species model including Staphylococcus aureus, Escherichia coli and Candida albicans was developed, to represent part of the diversity observed in orthopaedic infections or other clinical contexts. The importance of fungal hyphae for biofilm formation and virulence factor expression was explored. Two protocols were set up, allowing, or not, for hyphal formation. Culturable cells and biomass were characterised in both models, and biofilms were imaged in bright-field, confocal and electron microscopes. The expression of genes related to virulence, adhesion, exopolysaccharide synthesis and stress response was analysed in early-stage and mature biofilms. It was found that biofilms enriched in hyphae had larger biomass and showed higher expression levels of genes related to bacterial virulence or exopolysaccharides synthesis.

Maximization of information transmission influences selection of native phosphorelay architectures.

Phosphorelays are signal transduction circuits that sense environmental changes and adjust cellular metabolism. Five different circuit architectures account for 99% of all phosphorelay operons annotated in over 9,000 fully sequenced genomes. Here we asked what biological design principles, if any, could explain selection among those architectures in nature. We began by studying kinetically well characterized phosphorelays (Spo0 of Bacillus subtilis and Sln1 of Saccharomyces cerevisiae). We find that natural circuit architecture maximizes information transmission in both cases. We use mathematical models to compare information transmission among the architectures for a realistic range of concentration and parameter values. Mapping experimentally determined phosphorelay protein concentrations onto that range reveals that the native architecture maximizes information transmission in sixteen out of seventeen analyzed phosphorelays. These results suggest that maximization of information transmission is important in the selection of native phosphorelay architectures, parameter values and protein concentrations.

In Vitro Study of the Synergistic Effect of an Enzyme Cocktail and Antibiotics against Biofilms in a Prosthetic Joint Infection Model.

Prosthetic joint infections (PJI) are frequent complications of arthroplasties. Their treatment is made complex by the rapid formation of bacterial biofilms, limiting the effectiveness of antibiotic therapy. In this study, we explore the effect of a tri-enzymatic cocktail (TEC) consisting of an endo-1,4-ß-d-glucanase, a ß-1,6-hexosaminidase, and an RNA/DNA nonspecific endonuclease combined with antibiotics of different classes against biofilms of Staphylococcus aureus, Staphylococcus epidermidis, and Escherichia coli grown on Ti-6Al-4V substrates. Biofilms were grown in Trypticase soy broth (TSB) with 10 g/liter glucose and 20 g/liter NaCl (TGN). Mature biofilms were assigned to a control group or treated with the TEC for 30 min and then either analyzed or reincubated for 24 h in TGN or TGN with antibiotics. The cytotoxicity of the TEC was assayed against MG-63 osteoblasts, primary murine fibroblasts, and J-774 macrophages using the lactate dehydrogenase (LDH) release test. The TEC dispersed 80.3 to 95.2% of the biofilms' biomass after 30 min. The reincubation of the treated biofilms with antibiotics resulted in a synergistic reduction of the total culturable bacterial count (CFU) compared to that of biofilms treated with antibiotics alone in the three tested species (additional reduction from 2 to more than 3 log10 CFU). No toxicity of the TEC was observed against the tested cell lines after 24 h of incubation. The combination of pretreatment with TEC followed by 24 h of incubation with antibiotics had a synergistic effect against biofilms of S. aureus, S. epidermidis, and E. coli Further studies should assess the potential of the TEC as an adjuvant therapy in in vivo models of PJI.

Synergistic Effects of Pulsed Lavage and Antimicrobial Therapy Against Staphylococcus aureus Biofilms in an in-vitro Model.

Background: Prosthetic joint infections (PJI) are difficult to treat complications of joint arthroplasty. Debridement with implant retention is a common treatment strategy and frequently involves the use of pulsed lavage (PL). However, PL effects on biofilms and antibiotic activity have been scarcely studied in-vitro. We report the effects of PL, vancomycin or flucloxacillin used independently or in combination against Staphylococcus aureus biofilms. Methods: Biofilms of 3 methicillin-susceptible (MSSA) and of 3 methicillin-resistant (MRSA) S. aureus were grown on Ti6Al4V coupons in TGN (TSB + 1%glucose + 2%NaCl). After 24 h, PL was applied to half of the samples (50 mL saline from 5 cm). Samples were either reincubated for 24 h in TGN or TGN + flucloxacillin or vancomycin. Analyses included CFUs counts, biomass assays or fluorescence microscopy. Results: PL transiently reduced bacterial counts by 3-4 Log10 CFU/coupon, but bacterial regrowth to baseline levels was seen after 24 h. At 20 mg/L, flucloxacillin reduced both the CFU counts (3 Log10 CFU/coupon) and biomass (-70%) in one MSSA only, while vancomycin had no effects against MRSA. PL combined with a 24 h reincubation with vancomycin or flucloxacillin at 20 mg/L was synergistic (-5 to 6.5 Log10 CFU/coupon; 81-100% biomass reduction). Fluorescence microscopy confirmed that PL removed most of the biofilm and that subsequent antibiotic treatment partially killed bacteria. Conclusions: While PL only transiently reduces the bacterial load and antibiotics at clinically relevant concentrations show no or limited activity on biofilms, their combination is synergistic against MRSA and MSSA biofilms. These results highlight the need for thorough PL before antibiotic administration in PJI.

Automatic Methods for Carotid Contrast-Enhanced Ultrasound Imaging Quantification of Adventitial Vasa Vasorum.

Adventitial vasa vasorum are physiologic microvessels that nourish artery walls. In the presence of cardiovascular risk factors, these microvessels proliferate abnormally. Studies have reported that they are the first stage of atheromatous disease. Contrast-enhanced ultrasound (CEUS) of the carotid allows direct, quantitative and non-invasive visualization of the adventitial vasa vasorum. Hence, the development of computer-assisted methods that speed image analysis and eliminate user subjectivity is important. We developed methods for automatic analyses and quantification of vasa vasorum neovascularization in CEUS and tested these methods in a cohort of 186 individuals, 63 of whom were healthy volunteers. We implemented alternative automatic strategies for using the images to stratify patients according to their risk group and compare the strategies with respect to diagnostic performance. An automatic single-parameter strategy performs less effectively than the corresponding Arcidiacono method based on manual interpretation of the images (68 < area under the receiver operating characteristic curve [AUROC] for the manual Arcidiacono method < 82; 60 < AUROC for the automatic single-parameter strategy < 63). However, by use of additional image parameters, an automatic multiparameter strategy has significantly improved performance with respect to the manual Arcidiacono method (78 < AUROC < 90). The automatic multiparameter strategy is a valuable alternative to the manual Arcidiacono method, improving both diagnostic speed and diagnostic accuracy.

Quantitative Operating Principles of Yeast Metabolism during Adaptation to Heat Stress.

Microorganisms evolved adaptive responses to survive stressful challenges in ever-changing environments. Understanding the relationships between the physiological/metabolic adjustments allowing cellular stress adaptation and gene expression changes being used by organisms to achieve such adjustments may significantly impact our ability to understand and/or guide evolution. Here, we studied those relationships during adaptation to various stress challenges in Saccharomyces cerevisiae, focusing on heat stress responses. We combined dozens of independent experiments measuring whole-genome gene expression changes during stress responses with a simplified kinetic model of central metabolism. We identified alternative quantitative ranges for a set of physiological variables in the model (production of ATP, trehalose, NADH, etc.) that are specific for adaptation to either heat stress or desiccation/rehydration. Our approach is scalable to other adaptive responses and could assist in developing biotechnological applications to manipulate cells for medical, biotechnological, or synthetic biology purposes.

Computer-assisted initial diagnosis of rare diseases.

Introduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype. Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient's symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis. Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms. Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.

Identification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modeling.

Plant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated ß-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed.

A survey of HK, HPt, and RR domains and their organization in two-component systems and phosphorelay proteins of organisms with fully sequenced genomes.

Two Component Systems and Phosphorelays (TCS/PR) are environmental signal transduction cascades in prokaryotes and, less frequently, in eukaryotes. The internal domain organization of proteins and the topology of TCS/PR cascades play an important role in shaping the responses of the circuits. It is thus important to maintain updated censuses of TCS/PR proteins in order to identify the various topologies used by nature and enable a systematic study of the dynamics associated with those topologies. To create such a census, we analyzed the proteomes of 7,609 organisms from all domains of life with fully sequenced and annotated genomes. To begin, we survey each proteome searching for proteins containing domains that are associated with internal signal transmission within TCS/PR: Histidine Kinase (HK), Response Regulator (RR) and Histidine Phosphotranfer (HPt) domains, and analyze how these domains are arranged in the individual proteins. Then, we find all types of operon organization and calculate how much more likely are proteins that contain TCS/PR domains to be coded by neighboring genes than one would expect from the genome background of each organism. Finally, we analyze if the fusion of domains into single TCS/PR proteins is more frequently observed than one might expect from the background of each proteome. We find 50 alternative ways in which the HK, HPt, and RR domains are observed to organize into single proteins. In prokaryotes, TCS/PR coding genes tend to be clustered in operons. 90% of all proteins identified in this study contain just one of the three domains, while 8% of the remaining proteins combine one copy of an HK, a RR, and/or an HPt domain. In eukaryotes, 25% of all TCS/PR proteins have more than one domain. These results might have implications for how signals are internally transmitted within TCS/PR cascades. These implications could explain the selection of the various designs in alternative circumstances.

An in vitro system for the rapid functional characterization of genes involved in carotenoid biosynthesis and accumulation.

We have developed an assay based on rice embryogenic callus for rapid functional characterization of metabolic genes. We validated the assay using a selection of well-characterized genes with known functions in the carotenoid biosynthesis pathway, allowing rapid visual screening of callus phenotypes based on tissue color. We then used the system to identify the functions of two uncharacterized genes: a chemically synthesized ß-carotene ketolase gene optimized for maize codon usage, and a wild-type Arabidopsis thaliana ortholog of the cauliflower Orange gene. In contrast to previous reports (Lopez, A.B., Van Eck, J., Conlin, B.J., Paolillo, D.J., O'Neill, J. and Li, L. () J. Exp. Bot. 59, 213-223; Lu, S., Van Eck, J., Zhou, X., Lopez, A.B., O'Halloran, D.M., Cosman, K.M., Conlin, B.J., Paolillo, D.J., Garvin, D.F., Vrebalov, J., Kochian, L.V., Küpper, H., Earle, E.D., Cao, J. and Li, L. () Plant Cell 18, 3594-3605), we found that the wild-type Orange allele was sufficient to induce chromoplast differentiation. We also found that chromoplast differentiation was induced by increasing the availability of precursors and thus driving flux through the pathway, even in the absence of Orange. Remarkably, we found that diverse endosperm-specific promoters were highly active in rice callus despite their restricted activity in mature plants. Our callus system provides a unique opportunity to predict the effect of metabolic engineering in complex pathways, and provides a starting point for quantitative modeling and the rational design of engineering strategies using synthetic biology. We discuss the impact of our data on analysis and engineering of the carotenoid biosynthesis pathway.

Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization.

BACKGROUND: Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. RESULTS: Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. CONCLUSION: The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters.

Targeted transcriptomic and metabolic profiling reveals temporal bottlenecks in the maize carotenoid pathway that may be addressed by multigene engineering.

Carotenoids are a diverse group of tetraterpenoid pigments found in plants, fungi, bacteria and some animals. They play vital roles in plants and provide important health benefits to mammals, including humans. We previously reported the creation of a diverse population of transgenic maize plants expressing various carotenogenic gene combinations and exhibiting distinct metabolic phenotypes. Here we performed an in-depth targeted mRNA and metabolomic analysis of the pathway to characterize the specific impact of five carotenogenic transgenes and their interactions with 12 endogenous genes in four transgenic lines representing distinct genotypes and phenotypes. We reconstructed the temporal profile of the carotenoid pathway during endosperm development at the mRNA and metabolic levels (for total and individual carotenoids), and investigated the impact of transgene expression on the endogenous pathway. These studies enabled us to investigate the extent of any interactions between the introduced transgenic and native partial carotenoid pathways during maize endosperm development. Importantly, we developed a theoretical model that explains these interactions, and our results suggest genetic intervention points that may allow the maize endosperm carotenoid pathway to be engineered in a more effective and predictable manner.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.