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. The effectiveness of our method 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 grow 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 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 the complex dynamics of microbial communities. Moreover, the 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 macroorganisms, such as plants and animals, as well as microorganisms.

Driving towards digital biomanufacturing by CHO genome-scale models.

Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.

Decoding cellular mechanism of recombinant adeno-associated virus (rAAV) and engineering host-cell factories toward intensified viral vector manufacturing.

Recombinant adeno-associated virus (rAAV) is one of the prominent gene delivery vehicles that has opened promising opportunities for novel gene therapeutic approaches. However, the current major viral vector production platform, triple transfection in mammalian cells, may not meet the increasing demand. Thus, it is highly required to understand production bottlenecks from the host cell perspective and engineer the cells to be more favorable and tolerant to viral vector production, thereby effectively enhancing rAAV manufacturing. In this review, we provided a comprehensive exploration of the intricate cellular process involved in rAAV production, encompassing various stages such as plasmid entry to the cytoplasm, plasmid trafficking and nuclear delivery, rAAV structural/non-structural protein expression, viral capsid assembly, genome replication, genome packaging, and rAAV release/secretion. The knowledge in the fundamental biology of host cells supporting viral replication as manufacturing factories or exhibiting defending behaviors against viral production is summarized for each stage. The control strategies from the perspectives of host cell and materials (e.g., AAV plasmids) are proposed as our insights based on the characterization of molecular features and our existing knowledge of the AAV viral life cycle, rAAV and other viral vector production in the Human embryonic kidney (HEK) cells.

Evaluating oleaginous yeasts for enhanced microbial lipid production using sweetwater as a sustainable feedstock.

BACKGROUND: Yeasts exhibit promising potential for the microbial conversion of crude glycerol, owing to their versatility in delivering a wide range of value-added products, particularly lipids. Sweetwater, a methanol-free by-product of the fat splitting process, has emerged as a promising alternative feedstock for the microbial utilization of crude glycerol. To further optimize sweetwater utilization, we compared the growth and lipid production capabilities of 21 oleaginous yeast strains under different conditions with various glycerol concentrations, sweetwater types and pH. RESULTS: We 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. Subsequently, to decipher the underlying mechanism, the transcriptomic changes of R. toruloides 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. CONCLUSION: Our comparative study successfully demonstrated sweetwater as a cost-effective feedstock while identifying R. toluroides NRRL Y-6987 as a highly promising microbial oil producer. Furthermore, we also suggested potential sweetwater type and strain engineering targets that could potentially enhance microbial lipid production.

Exploring metabolic effects of dipeptide feed media on CHO cell cultures by in silico model-guided flux analysis.

There is a growing interest in perfusion or continuous processes to achieve higher productivity of biopharmaceuticals in mammalian cell culture, specifically Chinese hamster ovary (CHO) cells, towards advanced biomanufacturing. These intensified bioprocesses highly require concentrated feed media in order to counteract their dilution effects. However, designing such condensed media formulation poses several challenges, particularly regarding the stability and solubility of specific amino acids. To address the difficulty and complexity in relevant media development, the biopharmaceutical industry has recently suggested forming dipeptides by combining one from problematic amino acids with selected pairs to compensate for limitations. In this study, we combined one of the lead amino acids, L-tyrosine, which is known for its poor solubility in water due to its aromatic ring and hydroxyl group, with glycine as the partner, thus forming glycyl-L-tyrosine (GY) dipeptide. Subsequently, we investigated the utilization of GY dipeptide during fed-batch cultures of IgG-producing CHO cells, by changing its concentrations (0.125 × , 0.25 × , 0.5 × , 1.0 × , and 2.0 ×). Multivariate statistical analysis of culture profiles was then conducted to identify and correlate the most significant nutrients with the production, followed by in silico model-guided analysis to systematically evaluate their effects on the culture performance, and elucidate metabolic states and cellular behaviors. As such, it allowed us to explain how the cells can more efficiently utilize GY dipeptide with respect to the balance of cofactor regeneration and energy distribution for the required biomass and protein synthesis. For example, our analysis results uncovered specific amino acids (Asn and Gln) and the 0.5 × GY dipeptide in the feed medium synergistically alleviated the metabolic bottleneck, resulting in enhanced IgG titer and productivity. In the validation experiments, we tested and observed that lower levels of Asn and Gln led to decreased secretion of toxic metabolites, enhanced longevity, and elevated specific cell growth and titer. KEY POINTS: • Explored the optimal Tyr dipeptide for the enhanced CHO cell culture performance • Systematically analyzed effects of dipeptide media by model-guided approach • Uncovered synergistic metabolic utilization of amino acids with dipeptide.

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 along with their feeding are a key strategy to maximize culture performance in biopharmaceutical processes. However, the sensitivity of mammalian cells to their culture environment necessitates specific nutritional requirements for their growth and the production of high-quality proteins such as antibodies, depending on the cell lines and operational conditions employed. In this regard, previously we developed a 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 and reformulate basal media. To expand our exploration for media development research, we evaluated two chemically defined feed media, A and B, using a monoclonal antibody-producing CHO-K1 cell line in ambr15 bioreactor runs. We observed a significant impact of the feed media on various aspects of cell culture, including growth, longevity, viability, productivity, and the production of toxic metabolites. Specifically, the concentrated feed A was inadequate in sustaining prolonged cell culture and achieving high titers when compared to feed B. Within our framework, we systematically investigated the major metabolic bottlenecks in the tricarboxylic acid cycle and relevant amino acid transferase reactions. This analysis identified target components that play a crucial role in alleviating bottlenecks and designing highly productive cell cultures, specifically the addition of glutamate to feed A and asparagine to feed B. Based on our findings, we reformulated the feeds by adjusting the amounts of the targeted amino acids and successfully validated the effectiveness of the strategy in promoting cell growth, life span, and/or titer.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations.

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 and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, 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 RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes.

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

Strategies for targeted gene delivery using lipid nanoparticles and cell-derived nanovesicles.

Gene therapy is a promising approach for the treatment of many diseases. However, the effective delivery of the cargo without degradation in vivo is one of the major hurdles. With the advent of lipid nanoparticles (LNPs) and cell-derived nanovesicles (CDNs), gene delivery holds a very promising future. The targeting of these nanosystems is a prerequisite for effective transfection with minimal side-effects. In this review, we highlight the emerging strategies utilized for the effective targeting of LNPs and CDNs, and we summarize the preparation methodologies for LNPs and CDNs. We have also highlighted the non-ligand targeting of LNPs toward certain organs based on their composition. It is highly expected that continuing the developments in the targeting approaches of LNPs and CDNs for the delivery system will further promote them in clinical translation.

Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins.

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP-producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI-based data-driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in-line data sets during mAb-producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.

Enhanced cell growth, production, and mAb quality produced in Chinese hamster ovary-K1 cells by supplementing polyamine in the media.

Polyamines such as putrescine (PUT), spermidine (SPD), and spermine (SPM) are amine group-containing biomolecules that regulate multiple intracellular functions such as proliferation, differentiation, and stress response in mammalian cells. Although these biomolecules can be generated intracellularly, lack of polyamine-synthesizing activity has occasionally been reported in a few mammalian cell lines such as Chinese hamster ovary (CHO)-K1; thus, polyamine supplementation in serum-free media is required to support cell growth and production. In the present study, the effects of biogenic polyamines PUT, SPD, and SPM in media on cell growth, production, metabolism, and antibody quality were explored in cultures of antibody-producing CHO-K1 cells. Polyamine withdrawal from media significantly suppressed cell growth and production. On the other hand, enhanced culture performance was achieved in polyamine-containing media conditions in a dose-dependent manner regardless of polyamine type. In addition, in polyamine-deprived medium, distinguishing metabolic features, such as enriched glycolysis and suppressed amino acid consumption, were observed and accompanied by higher heterogeneity of antibody quality compared with the optimal concentration of polyamines. Furthermore, an excessive concentration of polyamines negatively affected culture performance as well as antibody quality. Hence, the results suggest that polyamine-related metabolism needs to be further investigated and polyamines in cell growth media should be optimized as a controllable parameter in CHO cell culture bioprocessing. KEY POINTS: • Polyamine supplementation enhanced cell growth and production in a dose-dependent manner • Polyamine type and concentration in the media affected mAb quality • Optimizing polyamines in the media is suggested in CHO cell bioprocessing.

Liver's influence on the brain through the action of bile acids.

The liver partakes as a sensor and effector of peripheral metabolic changes and a regulator of systemic blood and nutrient circulation. As such, abnormalities arising from liver dysfunction can influence the brain in multiple ways, owing to direct and indirect bilateral communication between the liver and the brain. Interestingly, altered bile acid composition resulting from perturbed liver cholesterol metabolism influences systemic inflammatory responses, blood-brain barrier permeability, and neuron synaptic functions. Furthermore, bile acids produced by specific bacterial species may provide a causal link between dysregulated gut flora and neurodegenerative disease pathology through the gut-brain axis. This review will cover the role of bile acids-an often-overlooked category of active metabolites-in the development of neurological disorders associated with neurodegeneration. Further studies into bile acid signaling in the brain may provide insights into novel treatments against neurological disorders.

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

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

Data-driven and model-guided systematic framework for media development in CHO cell culture.

Proposed herein is a systematic media design framework that combines multivariate statistical approaches with in silico analysis of a genome-scale metabolic model of Chinese hamster ovary cell. The framework comprises sequential modules including cell culture and metabolite data collection, multivariate data analysis, in silico modeling and flux prediction, and knowledge-based identification of target media components. Two monoclonal antibody-producing cell lines under two different media conditions were used to demonstrate the applicability of the framework. First, the cell culture and metabolite profiles from all conditions were generated, and then statistically and mechanistically analyzed to explore combinatorial effects of cell line and media on intracellular metabolism. As a result, we found a metabolic bottleneck via a redox imbalance in the TCA cycle in the poorest growth condition, plausibly due to inefficient coenzyme q10-q10h2 recycling. Subsequent in silico simulation allowed us to suggest q10 supplementation to debottleneck the imbalance for the enhanced cellular energy state and TCA cycle activity. Finally, experimental validation was successfully conducted by adding q10 in the media, resulting in increased cell growth. Taken together, the proposed framework rationally identified target nutrients for cell line-specific media design and reformulation, which could greatly improve cell culture performance.

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of Chinese hamster ovary cell cultures.

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS-R) with the flux balance analysis of a genome-scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha-1 antitrypsin producing CHO-DG44 cells grown in one commercial and another two in-house media under development. At the onset, PLS-R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found suboptimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modeling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells.

Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics.

The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics.

Combination of red and blue light induces anthocyanin and other secondary metabolite biosynthesis pathways in an age-dependent manner in Batavia lettuce.

Lettuce is commonly consumed around the world, spurring the cultivation of green- and red-leaf varieties in indoor farms. One common consideration for indoor cultivation is the light wavelengths/spectrum, which is an important factor for regulating growth, development, and the accumulation of metabolites. Here, we show that Batavia lettuce (Lactuca sativa cv. "Batavia") grown under a combination of red (R) and blue (B) light (RB, R:B = 3:1) displayed better growth and accumulated more anthocyanin than lettuce grown under fluorescent light (FL). Anthocyanin concentration was also higher in mature stage than early stage lettuce. By performing a comparative transcriptome analysis of early and mature stage lettuce grown under RB or FL (RB or FL-lettuce), we found that RB induced the expression of genes related to oxidation-reduction reaction and secondary metabolite biosynthesis. Furthermore, plant age affected the transcriptome response to RB, as mature RB-lettuce had six times more differentially expressed genes than early RB-lettuce. Also, genes related to the accumulation of secondary metabolites such as flavonoids and anthocyanins were more induced in mature RB-lettuce. A detailed analysis of the anthocyanin biosynthesis pathway revealed key genes that were up-regulated in mature RB-lettuce. Concurrently, branching pathways for flavonol and lignin precursors were down-regulated.

Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer, and product quality.

A robust monoclonal antibody (mAb) bioprocess requires physiological parameters such as temperature, pH, or dissolved oxygen to be well-controlled as even small variations in them could potentially impact the final product quality. For instance, pH substantially affects N-glycosylation, protein aggregation, and charge variant profiles, as well as mAb productivity. However, relatively less is known about how pH jointly influences product quality and titer. In this study, we investigated the effect of pH on culture performance, product titer, and quality profiles by applying longitudinal multi-omics profiling, including transcriptomics, proteomics, metabolomics, and glycomics, at three different culture pH set points. The subsequent systematic analysis of multi-omics data showed that pH set points differentially regulated various intracellular pathways including intracellular vesicular trafficking, cell cycle, and apoptosis, thereby resulting in differences in specific productivity, product titer, and quality profiles. In addition, a time-dependent variation in mAb N-glycosylation profiles, independent of pH, was identified to be mainly due to the accumulation of mAb proteins in the endoplasmic reticulum disrupting cellular homeostasis over culture time. Overall, this multi-omics-based study provides an in-depth understanding of the intracellular processes in mAb-producing CHO cell line under varied pH conditions, and could serve as a baseline for enabling the quality optimization and control of mAb production.