Multilevel interactions between native and ectopic isoprenoid pathways affect global metabolism in rice.

Isoprenoids are natural products derived from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). In plants, these precursors are synthesized via the cytosolic mevalonate (MVA) and plastidial methylerythritol phosphate (MEP) pathways. The regulation of these pathways must therefore be understood in detail to develop effective strategies for isoprenoid metabolic engineering. We hypothesized that the strict regulation of the native MVA pathway could be circumvented by expressing an ectopic plastidial MVA pathway that increases the accumulation of IPP and DMAPP in plastids. We therefore introduced genes encoding the plastid-targeted enzymes HMGS, tHMGR, MK, PMK and MVD and the nuclear-targeted transcription factor WR1 into rice and evaluated the impact of their endosperm-specific expression on (1) endogenous metabolism at the transcriptomic and metabolomic levels, (2) the synthesis of phytohormones, carbohydrates and fatty acids, and (3) the macroscopic phenotype including seed morphology. We found that the ectopic plastidial MVA pathway enhanced the expression of endogenous cytosolic MVA pathway genes while suppressing the native plastidial MEP pathway, increasing the production of certain sterols and tocopherols. Plants carrying the ectopic MVA pathway only survived if WR1 was also expressed to replenish the plastid acetyl-CoA pool. The transgenic plants produced higher levels of fatty acids, abscisic acid, gibberellins and lutein, reflecting crosstalk between phytohormones and secondary metabolism.

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.

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.

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.

Surrogate endpoints in phase III randomized trials of advanced gastroesophageal cancer: A systematic review and meta-analysis.

Overall survival (OS) is the most meaningful endpoint in clinical trials. However, owing to their limitations, surrogate endpoints are commonly used and validation studies are required to assess their reliability. Analysis of phase III randomized controlled trials (RCTs) of advanced gastroesophageal cancer (AGC) with > 100 patients, correlation coefficients (r), and determination coefficients (R²) between OS and surrogates were evaluated through meta-analyses. Progression-free survival (PFS), time to progression (TTP), and objective response rate (ORR) were examined to determine their correlations with OS. Analysis of 65 phase III RCTs (29,766 subjects) showed a moderate correlation between PFS/TTP and OS (r = 0.77, R² = 0.59), while ORR correlation was low (r = 0.56, R² = 0.31). Excluding immunotherapy trials improved the PFS/TTP and OS correlations (r = 0.83, R² = 0.70). These findings suggest the potential use of PFS/TTP in AGC phase III investigations, disregarding the use of ORR as a surrogate endpoint.

Hybrid assembly and comparative genomics unveil insights into the evolution and biology of the red-legged partridge.

The red-legged partridge Alectoris rufa plays a crucial role in the ecosystem of southwestern Europe, and understanding its genetics is vital for conservation and management. Here we sequence, assemble, and annotate a highly contiguous and nearly complete version of its genome. This assembly encompasses 96.9% of the avian genes flagged as essential in the BUSCO aves_odb10 dataset. Moreover, we pinpointed RNA and protein-coding genes, 95% of which had functional annotations. Notably, we observed significant chromosome rearrangements in comparison to quail (Coturnix japonica) and chicken (Gallus gallus). In addition, a comparative phylogenetic analysis of these genomes suggests that A. rufa and C. japonica diverged roughly 20 million years ago and that their common ancestor diverged from G. gallus 35 million years ago. Our assembly represents a significant advancement towards a complete reference genome for A. rufa, facilitating comparative avian genomics, and providing a valuable resource for future research and conservation efforts for the red-legged partridge.

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.

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.

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.

CAM Models: Lessons and Implications for CAM Evolution.

The evolution of Crassulacean acid metabolism (CAM) by plants has been one of the most successful strategies in response to aridity. On the onset of climate change, expanding the use of water efficient crops and engineering higher water use efficiency into C3 and C4 crops constitute a plausible solution for the problems of agriculture in hotter and drier environments. A firm understanding of CAM is thus crucial for the development of agricultural responses to climate change. Computational models on CAM can contribute significantly to this understanding. Two types of models have been used so far. Early CAM models based on ordinary differential equations (ODE) reproduced the typical diel CAM features with a minimal set of components and investigated endogenous day/night rhythmicity. This line of research brought to light the preponderant role of vacuolar malate accumulation in diel rhythms. A second wave of CAM models used flux balance analysis (FBA) to better understand the role of CO2 uptake in flux distribution. They showed that flux distributions resembling CAM metabolism emerge upon constraining CO2 uptake by the system. We discuss the evolutionary implications of this and also how CAM components from unrelated pathways could have integrated along evolution.

Colonic Microbiota Profile Characterization of the Responsiveness to Dietary Fibre Treatment in Hypercholesterolemia.

This study aimed to determine how the microbiota profile might be predisposed to a better response in blood lipid profiles due to dietary fibre supplementation. A three-arm intervention study that included three different fibre types (mainly insoluble, soluble, and antioxidant fibre) supplemented (19.2 g/day) during 2 months in individuals with hypercholesterolemia was developed. Changes in faecal microbiota and blood lipid profile after fibre supplementation were determined. In all volunteers, regardless of fibre type, an increase in the abundance of Bifidobacterium was observed, and similarly, an inverse relationship between faecal propionic acid and blood LDL-cholesterol, LDL particle size, and LDL/HDL particle ratio (p-values 0.0067, 0.0002, and 0.0067, respectively) was observed. However, not all volunteers presented an improvement in lipid profile. The non-responders to fibre treatment showed a decrease in microbiota diversity (Shannon and Simpson diversity index p-values of 0.0110 and 0.0255, respectively) after the intervention; where the reduction in short-chain fatty acids (SCFAs) producing bacterial genera such as Clostridium XIVa and Ruminococcus after dietary fibre treatment was the main difference. It was concluded that the non-responsiveness to dietary fibre treatment might be mediated by the lack of ability to maintain a stable SCFA producing bacteria diversity and composition after extra fibre intake.

Cost-effectiveness of early detection of breast cancer in Catalonia (Spain).

BACKGROUND: Breast cancer (BC) causes more deaths than any other cancer among women in Catalonia. Early detection has contributed to the observed decline in BC mortality. However, there is debate on the optimal screening strategy. We performed an economic evaluation of 20 screening strategies taking into account the cost over time of screening and subsequent medical costs, including diagnostic confirmation, initial treatment, follow-up and advanced care. METHODS: We used a probabilistic model to estimate the effect and costs over time of each scenario. The effect was measured as years of life (YL), quality-adjusted life years (QALY), and lives extended (LE). Costs of screening and treatment were obtained from the Early Detection Program and hospital databases of the IMAS-Hospital del Mar in Barcelona. The incremental cost-effectiveness ratio (ICER) was used to compare the relative costs and outcomes of different scenarios. RESULTS: Strategies that start at ages 40 or 45 and end at 69 predominate when the effect is measured as YL or QALYs. Biennial strategies 50-69, 45-69 or annual 45-69, 40-69 and 40-74 were selected as cost-effective for both effect measures (YL or QALYs). The ICER increases considerably when moving from biennial to annual scenarios. Moving from no screening to biennial 50-69 years represented an ICER of 4,469€ per QALY. CONCLUSIONS: A reduced number of screening strategies have been selected for consideration by researchers, decision makers and policy planners. Mathematical models are useful to assess the impact and costs of BC screening in a specific geographical area.

Minimization of biosynthetic costs in adaptive gene expression responses of yeast to environmental changes.

Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses.

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.

Breast cancer incidence and overdiagnosis in Catalonia (Spain).

INTRODUCTION: Early detection of breast cancer (BC) with mammography may cause overdiagnosis and overtreatment, detecting tumors which would remain undiagnosed during a lifetime. The aims of this study were: first, to model invasive BC incidence trends in Catalonia (Spain) taking into account reproductive and screening data; and second, to quantify the extent of BC overdiagnosis. METHODS: We modeled the incidence of invasive BC using a Poisson regression model. Explanatory variables were: age at diagnosis and cohort characteristics (completed fertility rate, percentage of women that use mammography at age 50, and year of birth). This model also was used to estimate the background incidence in the absence of screening. We used a probabilistic model to estimate the expected BC incidence if women in the population used mammography as reported in health surveys. The difference between the observed and expected cumulative incidences provided an estimate of overdiagnosis. RESULTS: Incidence of invasive BC increased, especially in cohorts born from 1940 to 1955. The biggest increase was observed in these cohorts between the ages of 50 to 65 years, where the final BC incidence rates more than doubled the initial ones. Dissemination of mammography was significantly associated with BC incidence and overdiagnosis. Our estimates of overdiagnosis ranged from 0.4% to 46.6%, for women born around 1935 and 1950, respectively. CONCLUSIONS: Our results support the existence of overdiagnosis in Catalonia attributed to mammography usage, and the limited malignant potential of some tumors may play an important role. Women should be better informed about this risk. Research should be oriented towards personalized screening and risk assessment tools.

Evolution based on domain combinations: the case of glutaredoxins.

BACKGROUND: Protein domains represent the basic units in the evolution of proteins. Domain duplication and shuffling by recombination and fusion, followed by divergence are the most common mechanisms in this process. Such domain fusion and recombination events are predicted to occur only once for a given multidomain architecture. However, other scenarios may be relevant in the evolution of specific proteins, such as convergent evolution of multidomain architectures. With this in mind, we study glutaredoxin (GRX) domains, because these domains of approximately one hundred amino acids are widespread in archaea, bacteria and eukaryotes and participate in fusion proteins. GRXs are responsible for the reduction of protein disulfides or glutathione-protein mixed disulfides and are involved in cellular redox regulation, although their specific roles and targets are often unclear. RESULTS: In this work we analyze the distribution and evolution of GRX proteins in archaea, bacteria and eukaryotes. We study over one thousand GRX proteins, each containing at least one GRX domain, from hundreds of different organisms and trace the origin and evolution of the GRX domain within the tree of life. CONCLUSION: Our results suggest that single domain GRX proteins of the CGFS and CPYC classes have, each, evolved through duplication and divergence from one initial gene that was present in the last common ancestor of all organisms. Remarkably, we identify a case of convergent evolution in domain architecture that involves the GRX domain. Two independent recombination events of a TRX domain to a GRX domain are likely to have occurred, which is an exception to the dominant mechanism of domain architecture evolution.

Estimation of age- and stage-specific Catalan breast cancer survival functions using US and Catalan survival data.

BACKGROUND: During the last part of the 1990s the chance of surviving breast cancer increased. Changes in survival functions reflect a mixture of effects. Both, the introduction of adjuvant treatments and early screening with mammography played a role in the decline in mortality. Evaluating the contribution of these interventions using mathematical models requires survival functions before and after their introduction. Furthermore, required survival functions may be different by age groups and are related to disease stage at diagnosis. Sometimes detailed information is not available, as was the case for the region of Catalonia (Spain). Then one may derive the functions using information from other geographical areas. This work presents the methodology used to estimate age- and stage-specific Catalan breast cancer survival functions from scarce Catalan survival data by adapting the age- and stage-specific US functions. METHODS: Cubic splines were used to smooth data and obtain continuous hazard rate functions. After, we fitted a Poisson model to derive hazard ratios. The model included time as a covariate. Then the hazard ratios were applied to US survival functions detailed by age and stage to obtain Catalan estimations. RESULTS: We started estimating the hazard ratios for Catalonia versus the USA before and after the introduction of screening. The hazard ratios were then multiplied by the age- and stage-specific breast cancer hazard rates from the USA to obtain the Catalan hazard rates. We also compared breast cancer survival in Catalonia and the USA in two time periods, before cancer control interventions (USA 1975-79, Catalonia 1980-89) and after (USA and Catalonia 1990-2001). Survival in Catalonia in the 1980-89 period was worse than in the USA during 1975-79, but the differences disappeared in 1990-2001. CONCLUSION: Our results suggest that access to better treatments and quality of care contributed to large improvements in survival in Catalonia. On the other hand, we obtained detailed breast cancer survival functions that will be used for modeling the effect of screening and adjuvant treatments in Catalonia.

Effectiveness of early detection on breast cancer mortality reduction in Catalonia (Spain).

BACKGROUND: At present, it is complicated to use screening trials to determine the optimal age intervals and periodicities of breast cancer early detection. Mathematical models are an alternative that has been widely used. The aim of this study was to estimate the effect of different breast cancer early detection strategies in Catalonia (Spain), in terms of breast cancer mortality reduction (MR) and years of life gained (YLG), using the stochastic models developed by Lee and Zelen (LZ). METHODS: We used the LZ model to estimate the cumulative probability of death for a cohort exposed to different screening strategies after T years of follow-up. We also obtained the cumulative probability of death for a cohort with no screening. These probabilities were used to estimate the possible breast cancer MR and YLG by age, period and cohort of birth. The inputs of the model were: incidence of, mortality from and survival after breast cancer, mortality from other causes, distribution of breast cancer stages at diagnosis and sensitivity of mammography. The outputs were relative breast cancer MR and YLG. RESULTS: Relative breast cancer MR varied from 20% for biennial exams in the 50 to 69 age interval to 30% for annual exams in the 40 to 74 age interval. When strategies differ in periodicity but not in the age interval of exams, biennial screening achieved almost 80% of the annual screening MR. In contrast to MR, the effect on YLG of extending screening from 69 to 74 years of age was smaller than the effect of extending the screening from 50 to 45 or 40 years. CONCLUSION: In this study we have obtained a measure of the effect of breast cancer screening in terms of mortality and years of life gained. The Lee and Zelen mathematical models have been very useful for assessing the impact of different modalities of early detection on MR and YLG in Catalonia (Spain).

Pathways to defense metabolites and evading fruit bitterness in genus Solanum evolved through 2-oxoglutarate-dependent dioxygenases.

The genus Solanum comprises three food crops (potato, tomato, and eggplant), which are consumed on daily basis worldwide and also producers of notorious anti-nutritional steroidal glycoalkaloids (SGAs). Hydroxylated SGAs (i.e. leptinines) serve as precursors for leptines that act as defenses against Colorado Potato Beetle (Leptinotarsa decemlineata Say), an important pest of potato worldwide. However, SGA hydroxylating enzymes remain unknown. Here, we discover that 2-OXOGLUTARATE-DEPENDENT-DIOXYGENASE (2-ODD) enzymes catalyze SGA-hydroxylation across various Solanum species. In contrast to cultivated potato, Solanum chacoense, a widespread wild potato species, has evolved a 2-ODD enzyme leading to the formation of leptinines. Furthermore, we find a related 2-ODD in tomato that catalyzes the hydroxylation of the bitter α-tomatine to hydroxytomatine, the first committed step in the chemical shift towards downstream ripening-associated non-bitter SGAs (e.g. esculeoside A). This 2-ODD enzyme prevents bitterness in ripe tomato fruit consumed today which otherwise would remain unpleasant in taste and more toxic.