Biology subjects

Lee, D.-Y.

Publications and source records attributed to Lee, D.-Y..

18 recordsLinked to original sources

bioRxiv2026

Metabolic signals regulate resuscitation speed of antibiotic persister bacteria during infection

All living organisms adjust their metabolism in response to environmental changes. Under unfavorable conditions, organisms enter a state of dormancy by halting metabolism, enabling survival. Dormant bacteria become highly tolerant to antibiotics-a phenomenon called persistence. Here, we demonstrate that selective metabolic reprogramming controls the resuscitation speed of persister after antibiotic exposure. Using multi-omics and in silico modeling, we found that dormant bacteria reprogram metabolic pathways to modulate persister awakening. Accumulation of L-serine and reduction of arginine drive rapid resuscitation. L-serine promotes cysteine biosynthesis and motility while reducing energy metabolism to facilitate rapid resuscitation. In contrast, arginine slows regrowth from dormancy by enhancing ethanol-aldehyde and energy metabolism. L-serine and arginine can, respectively, promote or inhibit the regrowth of antibiotic persister cells in macrophages and mouse models, and regulate the awakening speed of Salmonella, E. coli, and methicillin-resistant Staphylococcus aureus (MRSA). These findings suggest new strategies to target chronic bacterial infections. TeaserL-serine speeds and arginine slows the awakening of antibiotic persisters, revealing targets for chronic infection.

microbiology↗

bioRxiv2025

Development of male-sterile lines of Setaria viridis to accelerate C4 model plant genetics

Setaria viridis is a diploid C4 grass in the Poaceae family, notable for its rapid life cycle of 6-8 weeks from sowing to seed--much shorter than the 4-5 months required by crops such as Zea mays and Sorghum bicolor. This fast growth makes S. viridis a valuable model for C4 crop research. Genetic crosses are essential for studying gene function, but manual crossing is labor-intensive and time-consuming. To address this, we developed a male-sterile line by targeting the S. viridis ortholog of Setaria italica NO POLLEN 1 (SiNP1), which encodes a glucose-methanol-choline oxidoreductase required for pollen exine formation. Using Cas9 and TREX2-mediated genome editing, we generated SiNP1 knockouts in both the S. viridis ME034V and A10.1 backgrounds that were fully male-sterile. Backcrossing T0 male-sterile plants to ME034V yielded a stable line homozygous for a 59 bp deletion, easily genotyped by PCR. Using this line, we developed a simple and efficient crossing protocol that eliminates the need for emasculation. This method enables a single person to perform up to 100 crosses per day--compared to 15 using traditional methods--and yields 20-32 F1 hybrid seeds per panicle with 100% genetic purity. We also quantified pollen flow and outcrossing frequencies under greenhouse conditions to develop optimal bagging strategies and prevent unintended pollination.This resource accelerates genetic research in S. viridis, enhancing its utility as a premier C4 model for mapping and functional genomics.

plant biology↗

bioRxiv2025

Remote sensing of endogenous pigmentation by inducible synthetic circuits in grasses

Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built, and tested in plants to demonstrate proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterization method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pathways, stimulating plants to overproduce secondary metabolites with value to human health. Applying inducible pigmentation coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems. SummaryO_ST_ABSSynbio tools for C4 grass modelC_ST_ABS[bullet] Advantage of synthetic switches as tools for biopharming and functional genomics [bullet]Our workflow to optimize the gene circuits from a transient system to a stable transgenic [bullet]Testing and taming golden gate elements in monocot system, S. viridis

plant biology↗

bioRxiv2025

Characterization of Human Anterior Neural Organoids as a Model for Investigating Cohen Syndrome

Neural organoids display three-dimensional (3D) structures that resemble in vivo neural architectures. Previously, we developed a novel two-dimensional (2D) neural induction-based protocol for culturing spinal cord organoids, enabling size control and recapitulating neural tube morphogenesis. In this study, we evaluated the application of this concept to induce the anterior regions of the brain and generate human anterior neural organoids (hANOs). By inducing neuroepithelial (NE) cells in 2D and re-aggregating them led to tube-forming morphogenesis similar to that posterior spinal cord induction. The transcriptome profiles of these hANOs resembled the frontal cortex of 20 weeks post-conception (PCW) human embryos. Using this hANOs protocol, we investigated microcephaly phenotypes associated with Cohen syndrome (CS), caused by biallelic loss-of-function variants in VPS13B gene. Deleting VPS13B in human pluripotent stem cells resulted in Golgi dispersion and growth retardation onset in mutant hANOs, akin to CS patients with postnatal microcephaly. This delay is partly linked to reduced neuronal growth. Additionally, mature CS organoids showed enhanced hyper-excitability associated with an excitatory/inhibitory imbalance. In conclusion, this protocol is suitable for studying microcephaly phenotypes from human genetic mutations due to its simplicity and scalability.

neuroscience↗

bioRxiv2025

A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present iCHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, iCHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used iCHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging iCHO3Ks structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, iCHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

systems biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2024

The mitochondrial thiolase ACAT1 regulates monocyte/macrophage type I interferon via epigenetic control.

Lipid-derived acetyl-CoA is shown to be the major carbon source for histone acetylation. However, there is no direct evidence demonstrating lipid metabolic pathway contribututions to this process. Mitochondrial acetyl-CoA acetyltransferase 1 (ACAT1) catalyzes the final step of {beta}-oxidation, the aerobic process catabolizing fatty acids (FA) into acetyl-CoA. To investigate this in the context of immunometabolism, we generated macrophage cell line lacking ACAT1. 13C-carbon tracing combined with mass spectrometry confirmed incorporation of FA-derived carbons into histone H3 and this incorporation was reduced in ACAT1 KO macrophage cells. RNA-seq identified a subset of genes downregulated in ACAT1 KO cells including STAT1/2 and interferon stimulated genes (ISGs). CHIP analysis demonstrated reduced acetyl-H3 binding to STAT1 promoter/enhancer regions. Increasing histone acetylation rescued STAT1/2 expression in ACAT1 KO cells. Concomitantly, ligand triggered IFN{beta} release was blunted in ACAT1 KO cells and rescued by reconstitution of ACAT1. Furthermore, ACAT1 promotes FA-mediated histone acetylation in an acetylcarnitine shuttle-dependent manner. In patients with obesity, levels of ACAT1 and histone acetylation are abnormally elevated. Thus, our study identified a novel link between ACAT1 mediated FA metabolism and epigenetic modification on STAT1/2 that uncovers a regulatory role of lipid metabolism in innate immune signaling and opens novel avenues for interventions in human diseases such as obesity.

immunology↗

bioRxiv2023

Sweetwater: an underrated crude glycerol for sustainable lipid production in non-conventional yeasts

Sweetwater, a methanol-free by-product of the fat splitting process, is an emerging alternative feedstock for microbial utilization of crude glycerol. Yeasts are promising candidates in this context due to their versatility in delivering a wide range of value-added products including lipids. To enhance sweetwater utilization, we investigated the growth and lipid production of 21 oleaginous yeast strains in sweetwater and found that nutrient limitation and the unique carbon composition of sweetwater boosted significant lipid accumulation in several strains, in particular Rhodosporidium toruloides NRRL Y-6987. To decipher the underlying mechanism, the transcriptomic changes of R. toluroides NRRL Y-6987 were further analyzed, indicating potential sugars and oligopeptides in sweetwater supporting growth and lipid accumulation as well as exogenous fatty acid uptake leading to the enhanced lipid accumulation. Our comparative study successfully demonstrated sweetwater as a cost-effective feedstock and suggested potential sweetwater type and strain engineering targets for increasing microbial lipid production. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/558176v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1ad5738org.highwire.dtl.DTLVardef@139227eorg.highwire.dtl.DTLVardef@7a8e1org.highwire.dtl.DTLVardef@e072c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Debottlenecking and reformulating feed media for improved CHO cell growth and titer by data-driven and model-guided analyses

Designing and selecting cell culture media and feed are a key strategy to maximize culture performance in industrial biopharmaceutical processes. However, this is a major challenge for therapeutic proteins production since mammalian cells are very sensitive to their culture environment and require specific nutritional needs to grow and produce high-quality proteins such as antibodies. In this regard, in our previous study, we developed data-driven and in-silico model-guided systematic framework to investigate the effect of growth media on Chinese hamster ovary (CHO) cell culture performance, allowing us to design a new media formulation. To expand our exploration to feed, in this study, we evaluated two chemically defined feed media, A and B, in Ambr15 bioreactor runs using a monoclonal antibody-producing CHO K1 cell line. The feeds had a significant impact on cell growth, longevity, viability, and productivity and toxic metabolites production. Specifically, concentrated feed A was not sufficient to support prolonged cell culture and high titer compared to feed B. The framework systematically characterized the major metabolic bottlenecks in the TCA cycle and its related amino acid transferase reactions, and identified key design components, such as asparagine, aspartate, and glutamate, needed for highly productive cell cultures. From our results, we designed three new feeds by adjusting the levels of those amino acids and successfully validated their effectiveness in promoting cell growth and/or titer.

bioengineering↗

bioRxiv2023

Macrodomain Mac1 of SARS-CoV-2 Nonstructural Protein 3 Hydrolyzes Diverse ADP-ribosylated Substrates

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for a global pandemic that resulted in more than 6-million deaths worldwide. The virus encodes several non-structural proteins (Nsps) that contain elements capable of disrupting cellular processes. Among these Nsp proteins, Nsp3 contains macrodomains, e.g., Mac1, Mac2, Mac3, with potential effects on host cells. Mac1 has been shown to increase SARS-CoV-2 virulence and disrupt ADP-ribosylation pathways in mammalian cells. ADP-ribosylation results from the transfer of the ADP-ribose moiety of NAD+ to various acceptors, e.g., proteins, DNA, RNA, contributing on a cells biological processes. ADP-ribosylation is the mechanism of action of bacterial toxins, e.g., Pseudomonas toxins, diphtheria toxin that disrupt protein biosynthetic and signaling pathways. On the other hand, some viral macrodomains cleavage ADP-ribose-acceptor bond, generating free ADP-ribose. By this reaction, the macrodomain-containing proteins interfere ADP-ribose homeostasis in host cells. Here, we examined potential hydrolytic activities of SARS-CoV-2 Mac1, 2, and 3 on substrates containing ADP-ribose. Mac1 cleaved -NAD+, but not {beta}-NAD+, consistent with stereospecificity at the C-1" bond. In contrast to ARH1 and ARH3, Mac1 did not require Mg2+ for optimal activity. Mac1 also hydrolyzed O-acetyl-ADP-ribose and ADP-ribose-1"-phosphat, but not Mac2 and Mac3. However, Mac1 did not cleave -ADP-ribose-(arginine) and ADP-ribose-(serine)-histone H3 peptide, suggesting that Mac1 hydrolyzes ADP-ribose attached to O- and N-linked functional groups, with specificity at the catalytic site in the ADP-ribose moiety. We conclude that SARS-CoV-2 Mac1 may exert anti-viral activity by reversing host-mediated ADP-ribosylation. New insights on Nsp3 activities may shed light on potential SARS-CoV-2 therapeutic targets. IMPORTANCESARS-CoV-2, the virus responsible for COVID-19, encodes 3 macrodomain-containing proteins, e.g., Mac1, Mac2, Mac3, within non-structural proteins 3 (Nsp3). Mac1 was shown previously to hydrolyze ADP-ribose-phosphate. Inactivation of Mac1 reduced viral proliferation. Here we report that Mac1, but not Mac2 and Mac3, has multiple activities, i.e., Mac1 hydrolyzed. -NAD+ and O-acetyl-ADP-ribose. However, Mac1 did not hydrolyze {beta}-NAD+, ADP-ribose-serine on a histone 3 peptide (aa1-21), and ADP-ribose-arginine, exhibiting substrate selectivity. These data suggest that Mac1 may have multi-function as a -NAD+ consumer for viral replication and a disruptor of host-mediated ADP-ribosylation pathways. Understanding Mac1s mechanisms of action is important to provide possible therapeutic targets for COVID-19.

biochemistry↗

bioRxiv2022

Improved Neutralization of Omicron BA.4/5, BA.4.6, BA.2.75.2, BQ.1.1, and XBB.1 with Bivalent BA.4/5 Vaccine

The BNT162b2 bivalent BA.4/5 COVID-19 vaccine has been authorized to mitigate COVID-19 due to current Omicron and potentially future variants. New sublineages of SARS-CoV-2 Omicron continue to emerge and have acquired additional mutations, particularly in the spike protein, that may lead to improved viral fitness and immune evasion. The present study characterized neutralization activities against new Omicron sublineages BA.4.6, BA.2.75.2, BQ.1.1, and XBB.1 after a 4th dose (following three doses of BNT162b2) of either the original monovalent BNT162b2 or the bivalent BA.4/5 booster in individuals >55 years of age. For all participants, the 4th dose of monovalent BNT162b2 vaccine induced a 3.0x, 2.9x, 2.3x, 2.1x, 1.8x, and 1.5x geometric mean neutralizing titer fold rise (GMFR) against USA/WA1-2020 (a strain isolated in January 2020), BA.4/5, BA.4.6, BA.2.75.2, BQ.1.1, and XBB.1, respectively; the bivalent vaccine induced 5.8x, 13.0x, 11.1x, 6.7x, 8.7x, and 4.8x GMFRs. For individuals without SARS-CoV-2 infection history, BNT162b2 monovalent induced 4.4x, 3.0x, 2.5x, 2.0x, 1.5x, and 1.3x GMFRs, respectively; the bivalent vaccine induced 9.9x, 26.4x, 22.2x, 8.4x, 12.6x, and 4.7x GMFRs. These data suggest the bivalent BA.4/5 vaccine is more immunogenic than the original BNT162b2 monovalent vaccine against circulating Omicron sublineages, including BQ.1.1 that is becoming prevalent globally.

microbiology↗

bioRxiv2022

Kinetics-based Inference of Environment-Dependent Microbial Interactions and Their Dynamic Variation

Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. This unique capability of our approach was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids), but competitively growing on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates, but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics. IMPORTANCEModeling of environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer complex dynamics of microbial communities. Moreover, robust prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among microorganisms such as plants and animals, as well as microorganisms.

bioinformatics↗

bioRxiv2021

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet-induced and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) naturally occur in animal and plant niches and are well-known to elicit several health benefits in humans. Yet, how they adapt their functional metabolic landscapes to diverse nutrient environments and synthesize relevant bioactive compounds remain unexplored across genera, species and strains. Hence, presented herein is a systematic framework for comprehensively characterizing the genome-wide metabolisms of six representative LAB by combining multi-omics data with in silico modeling. We analyse the differences in their growth and cellular fitness, biosynthetic capability of health-relevant compounds, i.e., postbiotics, and probable interactions with 15 common gut microbiota under 11 virtual dietary regimes, and show such attributes are diet- and species-specific. Particularly, some LAB exhibit a desirable balance between synthesis of beneficial postbiotic compounds, positive interactions with beneficial gut commensals, and the ability to colonize and persist in gut environment. We also observe that "high fat-low carb" diets likely lead to detrimental outcomes in most LAB. Our results clearly highlight that probiotics are not "one size fits all" commodities and need to be formulated in a personalised manner for their use as dietary supplements and live biotherapeutics. Overall, the proposed framework will systematize the probiotic administration and could also widen the strain repertoire.

systems biology↗

bioRxiv2020

NINJA: an inducible genetic model for creating neoantigens in vivo

Mouse models with inducible neoantigens have been historically difficult to generate because of leaky expression of antigens in the thymus, which causes central tolerance in developing CD8 and CD4 T cells. Attempts to resolve this problem using existing genetic tools have been unsuccessful. We developed the iNversion INducible Joined neoAntigen (NINJA) mouse model that uses RNA splicing, DNA recombination, and three levels of regulation to prevent neoantigen leakiness and allow tight control over the induction of neoantigen expression. We describe the development of these genetic tools and their use for obtaining tumor cell lines with inducible neoantigen expression. Moreover, we show that the genetic regulation in NINJA mice bypasses central and peripheral tolerance mechanisms and allows for robust endogenous CD8 and CD4 T cells responses upon neoantigen induction in peripheral tissues. Thus, NINJA fills a long-standing gap in the field and will enable studies of how T cells respond to defined neoantigens in the context of peripheral tolerance, autoimmune diseases, and developing tumors.

immunology↗

bioRxiv2019

Enzyme capacity-based genome scale modelling of CHO cells

Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.

systems biology↗

bioRxiv2019

On the impact of biomass composition in constraint-based flux analysis

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions; the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Thus, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as DNA, RNA, protein, and lipid composition vary notably, variations in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We further observed that while macromolecular compositions are similar across taxonomically closer species, certain monomers, especially fatty acids, vary substantially. Based on the analysis results, we subsequently propose a new extension to FBA, named "Flux Balance Analysis with Ensemble Biomass (FBAwEB)", to embrace the natural variation in selected components of the biomass equation. The current study clearly highlights that certain components of the biomass equation are very sensitive to different conditions, and the ensemble representation of biomass equation in the FBA framework enables us to account for such natural variations accurately during GEM-guided in silico simulations.

systems biology↗

bioRxiv2019

Multi-omics profiling of CHO parental hosts reveals cell line-specific variations in bioprocessing traits

Chinese hamster ovary (CHO) cells are the most prevalent mammalian cell factories for producing recombinant therapeutic proteins due to their ability to synthesize human-like post-translational modifications and ease of maintenance in suspension cultures. Currently, a wide variety of CHO host cell lines have been developed; substantial differences exist in their phenotypes even when transfected with the same target vector. However, relatively less is known about the influence of their inherited genetic heterogeneity on phenotypic traits and production potential from the bioprocessing point of view. Herein, we present a global transcriptome and proteome profiling of three commonly used parental cell lines (CHO-K1, CHO-DXB11 and CHO-DG44) in suspension cultures and further report their growth-related characteristics, and N- and O-glycosylation patterns of host cell proteins (HCPs). The comparative multi-omics analysis indicated that some physiological variations of CHO cells grown in the same media are possibly originated from the genetic deficits, particularly in the cell cycle progression. Moreover, the dihydrofolate reductase deficient DG44 and DXB11 possess relatively less active metabolism when compared to K1 cells. The protein processing abilities and the N- and O-glycosylation profiles also differ significantly across the host cell lines, suggesting the need to select host cells in a rational manner for the cell line development on the basis of recombinant protein being produced.

systems biology↗

bioRxiv2018

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.\n\nHere, 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 models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote 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.

systems biology↗