74 research outputs found
Predicting human genetic interactions from cancer genome evolution
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148880.pdf (Publisher’s version ) (Open Access
Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival
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149260.pdf (Publisher’s version ) (Open Access)Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing
Underground metabolism as a rich reservoir for pathway engineering
No full textMOTIVATION: Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level ‘underground’ enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering. RESULTS: Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development. AVAILABILITY AND IMPLEMENTATION: The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
CRISPR-Directed Microbiome Manipulation across the Food Supply Chain
No full textThe advent of CRISPR-based technologies has revolutionized genetics over the past decade, and genome editing is now widely implemented for diverse medical and agricultural applications, such as correcting genetic disorders and improving crop and livestock breeding. CRISPR-based technologies are also of great potential to alter the genetic content of food bacteria in order to control the composition and activity of microbial populations across the food supply chain, from the farm to consumer products. Advancing the food supply chain is of great societal importance as it involves optimizing fermentation processes to enhance taste and sensory properties of food products, as well as improving food quality and safety by controlling spoilage bacteria and pathogens. Here, we discuss the various CRISPR technologies that can alter bacterial functionalities and modulate the composition of microbial communities in foods. We illustrate how these applications can be harnessed along the food supply chain to manipulate microbiomes that encompass spoilage and pathogenic bacteria as well as desirable starter cultures and health-promoting probiotics
Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation
No full textIn the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether longterm adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy.BiotechnologyApplied Science
Use of genome-scale metabolic models in evolutionary systems biology.
No full textItem does not contain fulltextOne of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology
CRISPR-Cas genome engineering of esterase activity in Saccharomyces cerevisiae steers aroma formation
No full textOBJECTIVE: Saccharomyces cerevisiae is used worldwide for the production of ale-type beers. This yeast is responsible for the production of the characteristic fruity aroma compounds. Esters constitute an important group of aroma active secondary metabolites produced by S. cerevisiae. Previous work suggests that esterase activity, which results in ester degradation, may be the key factor determining the abundance of fruity aroma compounds. Here, we test this hypothesis by deletion of two S. cerevisiae esterases, IAH1 and TIP1, using CRISPR-Cas9 genome editing and by studying the effect of these deletions on esterase activity and extracellular ester pools.RESULTS: Saccharomyces cerevisiae mutants were constructed lacking esterase IAH1 and/or TIP1 using CRISPR-Cas9 genome editing. Esterase activity using 5-(6)-carboxyfluorescein diacetate (cFDA) as substrate was found to be significantly lower for ΔIAH1 and ΔIAH1ΔTIP1 mutants compared to wild type (WT) activity (P < 0.05 and P < 0.001, respectively). As expected, we observed an increase in relative abundance of acetate and ethyl esters and an increase in ethyl esters in ΔIAH1 and ΔTIP1, respectively. Interestingly, the double gene disruption mutant ΔIAH1ΔTIP1 showed an aroma profile comparable to WT levels, suggesting the existence and activation of a complex regulatory mechanism to compensate multiple genomic alterations in aroma metabolism.</p
Systems-biology approaches for predicting genomic evolution.
No full textItem does not contain fulltextIs evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic.01 september 201
A critical view of metabolic network adaptations.
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81470teusink.pdf (Publisher’s version ) (Open Access
Inferring metabolic states in uncharacterized environments using gene-expression measurements
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118653.pdf (Publisher’s version ) (Open Access)The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production
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