EPathOptimize: A Combinatorial Approach for Transcriptional Balancing of Metabolic Pathways.
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ABSTRACT:
SUBMITTER: Jones JA
PROVIDER: S-EPMC4650668 | biostudies-literature | 2015 Jun
REPOSITORIES: biostudies-literature
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Project description:The heart of a newborn mouse has an exceptional capacity to regenerate from myocardial injury that is lost within the first week of its life. In order to elucidate the molecular mechanisms taking place in the mouse heart during this critical period we applied an untargeted combinatory multiomics approach using large-scale mass spectrometry-based quantitative proteomics, metabolomics and mRNA sequencing on hearts from 1-day-old and 7-day-old mice. As a result, we quantified 1.937 proteins (366 differentially expressed), 612 metabolites (263 differentially regulated) and revealed 2.586 differentially expressed gene loci (2.175 annotated genes). The analyses pinpointed the fructose-induced glycolysis-pathway to be markedly active in 1-day-old neonatal mice. Integrated analysis of the data convincingly demonstrated cardiac metabolic reprogramming from glycolysis to oxidative phosphorylation in 7-days old mice, with increases of key enzymes and metabolites in fatty acid transport (acylcarnitines) and β-oxidation. An upsurge in the formation of reactive oxygen species and an increase in oxidative stress markers, e.g., lipid peroxidation, altered sphingolipid and plasmalogen metabolism were also evident in 7-days mice. In vitro maintenance of physiological fetal hypoxic conditions retained the proliferative capacity of cardiomyocytes isolated from newborn mice hearts. In summary, we provide here a holistic, multiomics view toward early postnatal changes associated with loss of a tissue regenerative capacity in the neonatal mouse heart. These results may provide insight into mechanisms of human cardiac diseases associated with tissue regenerative incapacity at the molecular level, and offer a prospect to discovery of novel therapeutic targets.
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Project description:The heart of new-born mouse has exceptional capacity to regenerate from myocardial injury. However, the mouse heart loses this capacity within the first week of its life. In order to elucidate the molecular mechanisms taking place in the mouse heart during the loss of its regenerative potential, a systematic comparison of 1-day and 7-day-old mouse hearts was performed at the proteomic, metabolomic, and transcriptomic levels.
2018-12-01 | GSE107760 | GEO
Project description:Cyanobacteria are simple, efficient, genetically-tractable photosynthetic microorganisms which in principle represent ideal biocatalysts for CO2 capture and conversion. However, in practice, genetic instability and low productivity are key, linked problems in engineered cyanobacteria. We took a massively parallel approach, generating and characterising libraries of synthetic promoters and RBSs for the cyanobacterium Synechocystis sp. PCC 6803, and assembling a sparse combinatorial library of millions of metabolic pathway-encoding construct variants. Genetic instability was observed for some variants, which is expected when variants cause metabolic burden. Surprisingly however, in a single combinatorial round without iterative optimisation, 80% of variants chosen at random and cultured photoautotrophically over many generations accumulated the target terpenoid lycopene from atmospheric CO2, apparently overcoming genetic instability. This large-scale parallel metabolic engineering of cyanobacteria provides a new platform for development of genetically stable cyanobacterial biocatalysts for sustainable light-driven production of valuable products directly from CO2, avoiding fossil carbon or competition with food production.
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Project description:BackgroundDiabetes, as a significant disease affecting public health, requires early detection for effective management and intervention. However, imbalanced datasets pose a challenge to accurate diabetes prediction. This imbalance often results in models performing poorly in predicting minority classes, affecting overall diagnostic performance.ObjectivesTo address this issue, this study employs a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS) for data balancing and uses Optuna for hyperparameter optimization of machine learning models. This approach aims to fill the gap in current research concerning data balancing and model optimization, thereby improving prediction accuracy and computational efficiency.MethodsFirst, the study uses SMOTE and RUS methods to process the imbalanced diabetes dataset, balancing the data distribution. Then, Optuna is utilized to optimize the hyperparameters of the LightGBM model to enhance its performance. During the experiment, the effectiveness of the proposed methods is evaluated by comparing the training results of the dataset before and after balancing.ResultsThe experimental results show that the enhanced LightGBM-Optuna model improves the accuracy from 97.07% to 97.11%, and the precision from 97.17% to 98.99%. The time required for a single search is only 2.5 seconds. These results demonstrate the superiority of the proposed method in handling imbalanced datasets and optimizing model performance.ConclusionsThe study indicates that combining SMOTE and RUS data balancing algorithms with Optuna for hyperparameter optimization can effectively enhance machine learning models, especially in dealing with imbalanced datasets for diabetes prediction.
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Project description:BackgroundCritical illness causes a shift away from mitochondrial metabolism towards a greater dependence on glycolysis. This metabolic shift is thought to be associated with lactic acidosis, organ dysfunction and poor clinical outcomes. The current paradigm is that low oxygen supply causes regional hypoxia, which in turn drives such a metabolic shift. In this study, we evaluated whether the shift towards glycolysis can also occur in cells where oxygen supply is plentiful.MethodsWe used circulating blood cells from non-hypoxic critically ill patients (n = 47) as a model to study cellular metabolism in a normal oxygen milieu. We measured the transcriptomic profiles of canonical metabolic pathways in these cells and compared them to cells obtained from healthy controls (n = 18).ResultsTranscriptomic profiling revealed a significant reprogramming of metabolic pathways during critical illness. In well-oxygenated cells, there was a reduced expression of tricarboxylic acid cycle genes and genes associated with pyruvate entry into the mitochondria suggesting decreased mitochondrial oxidation. In contrast, glycolysis was accelerated, as reflected by an up-regulation of genes coding for enzymes of early and late glycolytic pathway that were associated with increased lactate production. The pentose phosphate pathway genes for NADPH production were also up-regulated suggesting enhanced antioxidant production during increased oxidative stress.ConclusionsContrary to the established paradigm, aerobic glycolysis does occur in non-hypoxic cells during critical illness and its occurrence may represent an adaptive strategy common to cells under increased oxidative stress. Further study of this previously under-recognized metabolic phenomenon might identify novel drug target for antioxidant therapy.
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Project description:Photoperiod length induces in temperate plants major changes in growth rates, morphology and metabolism with, for example, modifications in the partitioning of photosynthates to avoid starvation at the end of long nights. However, this has never been studied for a tropical perennial species adapted to grow in a natural photoperiod close to 12 h/12 h all year long. We grew Coffea arabica L., an understorey perennial evergreen tropical species in its natural 12 h/12 h and in a short 8 h/16 h photoperiod, and we investigated its responses at the physiological, metabolic and transcriptomic levels. The expression pattern of rhythmic genes, including core clock genes, was affected by changes in photoperiod. Overall, we identified 2859 rhythmic genes, of which 89% were also rhythmic in Arabidopsis thaliana L. Under short-days, plant growth was reduced, and leaves were thinner with lower chlorophyll content. In addition, secondary metabolism was also affected with chlorogenic acid and epicatechin levels decreasing, and in agreement, the genes involved in lignin synthesis were overexpressed and those involved in the flavanol pathway were underexpressed. Our results show that the 8 h/16 h photoperiod induces drastic changes in morphology, metabolites and gene expression, and the responses for gene expression are similar to those observed in the temperate annual A. thaliana species. Short photoperiod induces drastic changes in gene expression, metabolites and leaf structure, some of these responses being similar to those observed in A. thaliana.
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Project description:BackgroundAmyrin is an important triterpenoid and precursor to a wide range of cosmetic, pharmaceutical and nutraceutical products. In this study, we metabolically engineered the oleaginous yeast, Yarrowia lipolytica to produce α- and β-amyrin on simple sugar and waste cooking oil.ResultsWe first validated the in vivo enzymatic activity of a multi-functional amyrin synthase (CrMAS) from Catharanthus roseus, by expressing its codon-optimized gene in Y. lipolytica and assayed for amyrins. To increase yield, prevailing genes in the mevalonate pathway, namely HMG1, ERG20, ERG9 and ERG1, were overexpressed singly and in combination to direct flux towards amyrin biosynthesis. By means of a semi-rational protein engineering approach, we augmented the catalytic activity of CrMAS and attained ~ 10-folds higher production level on glucose. When applied together, protein engineering with enhanced precursor supplies resulted in more than 20-folds increase in total amyrins. We also investigated the effects of different fermentation conditions in flask cultures, including temperature, volumetric oxygen mass transfer coefficient and carbon source types. The optimized fermentation condition attained titers of at least 100 mg/L α-amyrin and 20 mg/L β-amyrin.ConclusionsThe design workflow demonstrated herein is simple and remarkably effective in amplifying triterpenoid biosynthesis in the yeast Y. lipolytica.
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Project description:BACKGROUND: During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. RESULTS: In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. CONCLUSIONS: The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.
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