Multiplex genome editing eliminates the Warburg Effect without impacting growth rate in mammalian cells.

The Warburg effect is ubiquitous in proliferative mammalian cells, including cancer cells, but poses challenges for biopharmaceutical production, as lactate accumulation inhibits cell growth and protein production. Previous efforts to eliminate lactate production via knockout have failed in mammalian bioprocessing since lactate dehydrogenase has proven essential. However, here we eliminated the Warburg effect in Chinese hamster ovary (CHO) and HEK293 cells by simultaneously knocking out lactate dehydrogenase and regulators involved in a negative feedback loop that typically inhibits pyruvate conversion to acetyl-CoA. In contrast to long-standing assumptions about the role of aerobic glycolysis, Warburg-null cells maintain wildtype growth rate while producing negligible lactate. Further characterization of Warburg-null CHO cells showed a compensatory increase in oxygen consumption, a near total reliance on oxidative metabolism, and higher cell densities in fed-batch cell culture. These cells remained amenable for production of diverse biotherapeutic proteins, reaching industrially relevant titers and maintaining product glycosylation. Thus, the ability to eliminate the Warburg effect is an important development for biotherapeutic production and provides a tool for investigating a near-universal metabolic phenomenon.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

iLipidome: enhancing statistical power and interpretability using hidden biosynthetic interdependencies in the lipidome.

Numerous biological processes and diseases are influenced by lipid composition. Advances in lipidomics are elucidating their roles, but analyzing and interpreting lipidomics data at the systems level remain challenging. To address this, we present iLipidome, a method for analyzing lipidomics data in the context of the lipid biosynthetic network, thus accounting for the interdependence of measured lipids. iLipidome enhances statistical power, enables reliable clustering and lipid enrichment analysis, and links lipidomic changes to their genetic origins. We applied iLipidome to investigate mechanisms driving changes in cellular lipidomes following supplementation of docosahexaenoic acid (DHA) and successfully identified the genetic causes of alterations. We further demonstrated how iLipidome can disclose enzyme-substrate specificity and pinpoint prospective glioblastoma therapeutic targets. Finally, iLipidome enabled us to explore underlying mechanisms of cardiovascular disease and could guide the discovery of early lipid biomarkers. Thus, iLipidome can assist researchers studying the essence of lipidomic data and advance the field of lipid biology.

Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network.

Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.

Boltzmann Model Predicts Glycan Structures from Lectin Binding.

Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

Glycoengineered recombinant alpha1-antitrypsin results in comparable in vitro and in vivo activities to human plasma-derived protein.

Alpha-1-antitrypsin (A1AT) is a multifunctional, clinically important, high value therapeutic glycoprotein that can be used for the treatment of many diseases such as alpha-1-antitrypsin deficiency, diabetes, graft-versus-host-disease, cystic fibrosis and various viral infections. Currently, the only FDA-approved treatment for A1AT disorders is intravenous augmentation therapy with human plasma-derived A1AT. In addition to its limited supply, this approach poses a risk of infection transmission, since it uses therapeutic A1AT harvested from donors. To address these issues, we sought to generate recombinant human A1AT (rhA1AT) that is chemically and biologically indistinguishable from its plasma-derived counterpart using glycoengineered Chinese Hamster Ovary (geCHO-L) cells. By deleting nine key genes that are part of the CHO glycosylation machinery and expressing the human ST6GAL1 and A1AT genes, we obtained stable, high producing geCHO-L lines that produced rhA1AT having an identical glycoprofile to plasma-derived A1AT (pdA1AT). Additionally, the rhA1AT demonstrated in vitro activity and in vivo half-life comparable to commercial pdA1AT. Thus, we anticipate that this platform will help produce human-like recombinant plasma proteins, thereby providing a more sustainable and reliable source of therapeutics that are cost-effective and better-controlled with regard to purity, clinical safety and quality.

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

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.

LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework.

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

Mechanotransduction-induced interplay between phospholamban and yes-activated protein induces smooth muscle cell hypertrophy.

The gastrointestinal system is a hollow organ affected by fibrostenotic diseases that cause volumetric compromise of the lumen via smooth muscle hypertrophy and fibrosis. Many of the driving mechanisms remain unclear. Yes-associated protein-1 (YAP) is a critical mechanosensory transcriptional regulator that mediates cell hypertrophy in response to elevated extracellular rigidity. In the type 2 inflammatory disorder, eosinophilic esophagitis (EoE), phospholamban (PLN) can induce smooth muscle cell hypertrophy. We used EoE as a disease model for understanding a mechanistic pathway in which PLN and YAP interact in response to rigid extracellular substrate to induce smooth muscle cell hypertrophy. PLN-induced YAP nuclear sequestration in a feed-forward loop caused increased cell size in response to a rigid substrate. This mechanism of rigidity sensing may have previously unappreciated clinical implications for PLN-expressing hollow systems such as the esophagus and heart.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

The diversification of methods for studying cell-cell interactions and communication.

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Resource allocation in mammalian systems.

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Predicting pathways for old and new metabolites through clustering.

The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While the discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB (Human Metabolome Database), only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.

A Boltzmann model predicts glycan structures from lectin binding.

Glycans are complex oligosaccharides involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese Hamster Ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment.

Complex networks of cell-cell interactions (CCIs) within the tumor microenvironment (TME) play a crucial role in cancer persistence. These communication axes represent prime targets for therapeutic intervention, but our incomplete understanding of the cellular heterogeneity and interacting partners within the TME remains a stubborn barrier to complete drug responses. This review outlines recent advances in the study of CCIs that leverage single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies that can clarify TME dynamics. We anticipate that these strategies will promote discovery of CCIs critical to the tumor-immune interface and will, by extension, expand the repertoire of druggable tumor biomarkers.

Inferring secretory and metabolic pathway activity from omic data with secCellFie.

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Pathogen-driven degradation of endogenous and therapeutic antibodies during streptococcal infections.

Group A streptococcus (GAS) is a major bacterial pathogen responsible for both local and systemic infections in humans. The molecular mechanisms that contribute to disease heterogeneity remain poorly understood. Here we show that the transition from a local to a systemic GAS infection is paralleled by pathogen-driven alterations in IgG homeostasis. Using animal models and a combination of sensitive proteomics and glycoproteomics readouts, we documented the progressive accumulation of IgG cleavage products in plasma, due to extensive enzymatic degradation triggered by GAS infection in vivo. The level of IgG degradation was modulated by the route of pathogen inoculation, and mechanistically linked to the combined activities of the bacterial protease IdeS and the endoglycosidase EndoS, upregulated during infection. Importantly, we show that these virulence factors can alter the structure and function of exogenous therapeutic IgG in vivo. These results shed light on the role of bacterial virulence factors in shaping GAS pathogenesis, and potentially blunting the efficacy of antimicrobial therapies.