Dong-Yup Lee

Ginseng Genome Database: an open-access platform for genomics of Panax ginseng

BackgroundThe ginseng (Panax ginseng C.A. Meyer) is a perennial herbaceous plant that has been used in traditional oriental medicine for thousands of years. Ginsenosides, which have significant pharmacological effects on human health, are the foremost bioactive constituents in this plant. Having realized the importance of this plant to humans, an integrated omics resource becomes indispensable to facilitate genomic research, molecular breeding and pharmacological study of this herb.DescriptionThe first draft genome sequences of P. ginseng cultivar “Chunpoong” were reported recently. Here, using the draft genome, transcriptome, and functional annotation datasets of P. ginseng, we have constructed the Ginseng Genome Database http://ginsengdb.snu.ac.kr/, the first open-access platform to provide comprehensive genomic resources of P. ginseng. The current version of this database provides the most up-to-date draft genome sequence (of approximately 3000 Mbp of scaffold sequences) along with the structural and functional annotations for 59,352 genes and digital expression of genes based on transcriptome data from different tissues, growth stages and treatments. In addition, tools for visualization and the genomic data from various analyses are provided. All data in the database were manually curated and integrated within a user-friendly query page.ConclusionThis database provides valuable resources for a range of research fields related to P. ginseng and other species belonging to the Apiales order as well as for plant research communities in general. Ginseng genome database can be accessed at http://ginsengdb.snu.ac.kr/.

Genome and evolution of the shade‐requiring medicinal herb Panax ginseng

Summary Panax ginseng C. A. Meyer, reputed as the king of medicinal herbs, has slow growth, long generation time, low seed production and complicated genome structure that hamper its study. Here, we unveil the genomic architecture of tetraploid P. ginseng by de novo genome assembly, representing 2.98 Gbp with 59 352 annotated genes. Resequencing data indicated that diploid Panax species diverged in association with global warming in Southern Asia, and two North American species evolved via two intercontinental migrations. Two whole genome duplications (WGD) occurred in the family Araliaceae (including Panax) after divergence with the Apiaceae, the more recent one contributing to the ability of P. ginseng to overwinter, enabling it to spread broadly through the Northern Hemisphere. Functional and evolutionary analyses suggest that production of pharmacologically important dammarane‐type ginsenosides originated in Panax and are produced largely in shoot tissues and transported to roots; that newly evolved P. ginseng fatty acid desaturases increase freezing tolerance; and that unprecedented retention of chlorophyll a/b binding protein genes enables efficient photosynthesis under low light. A genome‐scale metabolic network provides a holistic view of Panax ginsenoside biosynthesis. This study provides valuable resources for improving medicinal values of ginseng either through genomics‐assisted breeding or metabolic engineering.

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection. Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Improving catalytic activity of the Baeyer–Villiger monooxygenase-based Escherichia coli biocatalysts for the overproduction of (Z)-11-(heptanoyloxy)undec-9-enoic acid from ricinoleic acid

Baeyer–Villiger monooxygenases (BVMOs) can be used for the biosynthesis of lactones and esters from ketones. However, the BVMO-based biocatalysts are not so stable under process conditions. Thereby, this study focused on enhancing stability of the BVMO-based biocatalysts. The biotransformation of ricinoleic acid into (Z)-11-(heptanoyloxy)undec-9-enoic acid by the recombinant Escherichia coli expressing the BVMO from Pseudomonas putida and an alcohol dehydrogenase from Micrococcus luteus was used as a model system. After thorough investigation of the key factors to influence stability of the BVMO, Cys302 was identified as an engineering target. The substitution of Cys302 to Leu enabled the engineered enzyme (i.e., E6BVMOC302L) to become more stable toward oxidative and thermal stresses. The catalytic activity of E6BVMOC302L-based E. coli biocatalysts was also greater than the E6BVMO-based biocatalysts. Another factor to influence biocatalytic performance of the BVMO-based whole-cell biocatalysts was availability of carbon and energy source during biotransformations. Glucose feeding into the reaction medium led to a marked increase of final product concentrations. Overall, the bioprocess engineering to improve metabolic stability of host cells in addition to the BVMO engineering allowed us to produce (Z)-11-(heptanoyloxy)undec-9-enoic acid to a concentration of 132u2009mM (41u2009g/L) from 150u2009mM ricinoleic acid within 8u2009h.

In silico model-guided identification of transcriptional regulator targets for efficient strain design

BackgroundCellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.ResultsWe developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.ConclusionsIn silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.

An LC–MS-based lipidomics pre-processing framework underpins rapid hypothesis generation towards CHO systems biotechnology

IntroductionGiven a raw LC–MS dataset, it is often required to rapidly generate initial hypotheses, in conjunction with other ‘omics’ datasets, without time-consuming lipid verifications. Furthermore, for meta-analysis of many datasets, it may be impractical to conduct exhaustive confirmatory analyses. In other cases, samples for validation may be difficult to obtain, replicate or maintain. Thus, it is critical that the computational identification of lipids is of appropriate accuracy, coverage, and unbiased by a researcher’s experience and prior knowledge.ObjectivesWe aim to prescribe a systematic framework for lipid identifications, without usage of their characteristic retention-time by fully exploiting their underlying mass features.ResultsInitially, a hybrid technique, for deducing both common and distinctive daughter ions, is used to infer parent lipids from deconvoluted spectra. This is followed by parent confirmation using basic knowledge of their preferred product ions. Using the framework, we could achieve an accuracy of ~u200980% by correctly identified 101 species from 18 classes in Chinese hamster ovary (CHO) cells. The resulting inferences could explain the recombinant-producing capability of CHO-SH87 cells, compared to non-producing CHO-K1 cells. For comparison, a XCMS-based study of the same dataset, guided by a user’s ad-hoc knowledge, identified less than 60 species of 12 classes from thousands of possibilities.ConclusionWe describe a systematic LC–MS-based framework that identifies lipids for rapid hypothesis generation.

Comparative phenotypic analysis of CHO clones and culture media for lactate shift

We explored the effects of media and clonal variation on the lactate shift which can be considered as one of the desirable features in CHO cell culture. Various culture profiles with the specific growth and antibody production rates under three different media conditions in two CHO producing clones were evaluated by resorting to multivariate statistical analysis. In most cases, glutamine depletion coincided with lactate consumption, suggesting that glutaminolysis rather than glycolysis was the preferred pathway for the pyruvate supply toward lactate production. With respect to the lactate shift, high performing medium showed higher glutamate uptake, higher aspartate secretion and lower serine uptake compared to other media conditions. In addition, clone itself exhibited the desired lactate consumption more consistently accompanying with distinguishing phenotype. The clone exhibiting lactate shift produced lesser lactate in exponential phase but two-fold higher non-toxic alanine, thus leading to better culture environment. Thus, we understand the balanced selection of clone and media composition enables cells to utilize the metabolic pathways for the desired lactate shift.