Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

The Role of Endoplasmic Reticulum Stress on Reducing Recombinant Protein Production in Mammalian Cells

Therapeutic recombinant protein production relies on industrial scale culture of mammalian cells to produce active proteins in quantities sufficient for clinical use. The combination of stresses from industrial cell culture environment and recombinant protein production can overwhelm the protein synthesis machinery in the endoplasmic reticulum (ER). This leads to a buildup of improperly folded proteins which induces ER stress. Cells respond to ER stress by activating the Unfolded Protein Response (UPR). To restore proteostasis, ER sensor proteins reduce global protein synthesis and increase chaperone protein synthesis, and if that is insufficient the proteins are degraded. If proteostasis is still not restored, apoptosis is initiated. Increasing evidence suggests crosstalk between ER proteostasis and DNA damage repair (DDR) pathways. External factors (e.g., metabolites) from the cellular environment as well as internal factors (e.g., transgene copy number) can impact genome stability. Failure to maintain genome integrity reduces cell viability and in turn protein production. This review focuses on the association between ER stress and processes that affect protein production and secretion. The processes mediated by ER stress, including inhibition of global protein translation, chaperone protein production, degradation of misfolded proteins, DNA repair, and protein secretion, impact recombinant protein production. Recombinant protein production can be reduced by ER stress through increased autophagy and protein degradation, reduced protein secretion, and reduced DDR response.

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.

CRISPR-Cas9 knockout screen informs efficient reduction of the Komagataella phaffii secretome

BACKGROUND

The yeast Komagataella phaffii is widely used for manufacturing recombinant proteins, but secreted titers of recombinant proteins could be improved by genetic engineering. In this study, we hypothesized that cellular resources could be redirected from production of endogenous proteins to production of recombinant proteins by deleting unneeded endogenous proteins. In non-model microorganisms such as K. phaffii, however, genetic engineering is limited by lack gene annotation and knowledge of gene essentiality.

RESULTS

We identified a set of endogenous secreted proteins in K. phaffii by mass spectrometry and signal peptide prediction. Our efforts to disrupt these genes were hindered by limited annotation of essential genes. To predict essential genes, therefore, we designed, transformed, and sequenced a pooled library of guide RNAs for CRISPR-Cas9-mediated knockout of all endogenous secreted proteins. We then used predicted gene essentiality to guide iterative disruptions of up to 11 non-essential genes. Engineered strains exhibited a ~20× increase in the production of human serum albumin and a twofold increase in the production of a monoclonal antibody.

CONCLUSIONS

We demonstrated that disruption of as few as six genes can increase production of recombinant proteins. Further reduction of the endogenous proteome of K. phaffii may further improve strain performance. The pooled library of secretome-targeted guides for CRISPR-Cas9 and knowledge of gene essentiality reported here will facilitate future efforts to engineer K. phaffii for production of other recombinant proteins and enzymes.

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-producer 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.

Animal-free production of hen egg ovalbumin in engineered Saccharomyces cerevisiae via precision fermentation

To enable the sustainable production of ovalbumin (OVA) without relying on animal sources, the generally recognized as safe (GRAS) host Saccharomyces cerevisiae was used for the precision fermentation-based production of recombinant OVA. For this purpose, a signal peptide derived from EPX1, the most abundant extracellular protein produced by Pichia pastoris, was identified as a novel signal peptide for the efficient secretion of OVA in S. cerevisiae. To improve OVA secretion and cell growth, three helper proteins (PDI1, KAR2, and HAC1) present in the endoplasmic reticulum were expressed individually or in combination. Notably, the +P1/K2 strain coexpressing PDI1 and KAR2 with OVA produced 2 mg/L of OVA in the medium fraction; this value was 2.6-fold higher than the corresponding value for the control strain without helper proteins. Finally, a glucose-limited fed-batch fermentation process using the +P1/K2 strain yielded 132 mg/L of total OVA with 8 mg/L of extracellular OVA.

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Bacterial symbionts in oral niche use type VI secretion nanomachinery for fitness increase against pathobionts

Microbial ecosystems experience spatial and nutrient restrictions leading to the coevolution of cooperation and competition among cohabiting species. To increase their fitness for survival, bacteria exploit machinery to antagonizing rival species upon close contact. As such, the bacterial type VI secretion system (T6SS) nanomachinery, typically expressed by pathobionts, can transport proteins directly into eukaryotic or prokaryotic cells, consequently killing cohabiting competitors. Here, we demonstrate for the first time that oral symbiont Aggregatibacter aphrophilus possesses a T6SS and can eliminate its close relative oral pathobiont Aggregatibacter actinomycetemcomitans using its T6SS. These findings bring nearer the anti-bacterial prospects of symbionts against cohabiting pathobionts while introducing the presence of an active T6SS in the oral cavity.

Nature as blueprint: Global phenotype engineering of CHO production cells based on a multi-omics comparison with plasma cells

Especially for the production of artificial, difficult to express molecules a further development of the CHO production cell line is required to keep pace with the continuously increasing demands. However, the identification of novel targets for cell line engineering to improve CHO cells is a time and cost intensive process. Since plasma cells are evolutionary optimized for a high antibody expression in mammals, we performed a comprehensive multi-omics comparison between CHO and plasma cells to exploit optimized cellular production traits. Comparing the transcriptome, proteome, miRNome, surfaceome and secretome of both cell lines identified key differences including 392 potential overexpression targets for CHO cell engineering categorized in 15 functional classes like transcription factors, protein processing or secretory pathway. In addition, 3 protein classes including 209 potential knock-down/out targets for CHO engineering were determined likely to affect aggregation or proteolysis. For production phenotype engineering, several of these novel targets were successfully applied to transient and transposase mediated overexpression or knock-down strategies to efficiently improve productivity of CHO cells. Thus, substantial improvement of CHO productivity was achieved by taking nature as a blueprint for cell line engineering.

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.

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.

Size matters: the biochemical logic of ligand type in endocrine crosstalk

The endocrine system is a fundamental type of long-range cell-cell communication that is important for maintaining metabolism, physiology, and other aspects of organismal homeostasis. Endocrine signaling is mediated by diverse blood-borne ligands, also called hormones, including metabolites, lipids, steroids, peptides, and proteins. The size and structure of these hormones are fine-tuned to make them bioactive, responsive, and adaptable to meet the demands of changing environments. Why has nature selected such diverse ligand types to mediate communication in the endocrine system? What is the chemical, signaling, or physiologic logic of these ligands? What fundamental principles from our knowledge of endocrine communication can be applied as we continue as a field to uncover additional new circulating molecules that are claimed to mediate long-range cell and tissue crosstalk? This review provides a framework based on the biochemical logic behind this crosstalk with respect to their chemistry, temporal regulation in physiology, specificity, signaling actions, and evolutionary development.

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.

Directed evolution of biomass intensive CHO cells by adaptation to sub-physiological temperature

We report a simple and effective means to increase the biosynthetic capacity of host CHO cells. Lonza proprietary CHOK1SV® cells were evolved by serial sub-culture for over 150 generations at 32 °C. During this period the specific proliferation rate of hypothermic cells gradually recovered to become comparable to that of cells routinely maintained at 37 °C. Cold-adapted cell populations exhibited (1) a significantly increased volume and biomass content (exemplified by total RNA and protein), (2) increased mitochondrial function, (3) an increased antioxidant capacity, (4) altered central metabolism, (5) increased transient and stable productivity of a model IgG4 monoclonal antibody and Fc-fusion protein, and (6) unaffected recombinant protein N-glycan processing. This phenotypic transformation was associated with significant genome-scale changes in both karyotype and the relative abundance of thousands of cellular mRNAs across numerous functional groups. Taken together, these observations provide evidence of coordinated cellular adaptations to sub-physiological temperature. These data reveal the extreme genomic/functional plasticity of CHO cells, and that directed evolution is a viable genome-scale cell engineering strategy that can be exploited to create host cells with an increased cellular capacity for recombinant protein production.

Secretome Analysis: Reading Cellular Sign Language to Understand Intercellular Communication

A significant portion of mammalian proteomes is secreted to the extracellular space to fulfill crucial roles in cell-to-cell communication. To best recapitulate the intricate and multi-faceted crosstalk between cells in a live organism, there is an ever-increasing need for methods to study protein secretion in model systems that include multiple cell types. In addition, posttranslational modifications further expand the complexity and versatility of cellular communication. This review aims to summarize recent strategies and model systems that employ cellular coculture, chemical biology tools, protein enrichment, and proteomic methods to characterize the composition and function of cellular secretomes. This is all geared towards gaining better understanding of organismal biology in vivo mediated by secretory signaling.

Secretory Conservation in Insulin Producing Cells: Is There a System-Level Law of Mass Action in Biology?

Altered secretion of insulin from pancreatic β-cells can manifest into disorders. For example, a lack of endogenously produced and/or secreted insulin results in Type 1 diabetes (and other associated subtypes). Pancreatic β-cells are the endocrine secretory cells that promote insulin secretion in response to glucose stimulation. Secretion in response to extracellular triggers is an interplay among various signaling pathways, transcription factors, and molecular mechanisms. The Mouse Insulinoma 6 (MIN6) cell line serves as a model system for gaining mechanistic insights into pancreatic β-cell functions. It is obvious that higher glucose consumption and increased insulin secretion are correlated. However, it has been reported that intracellular ATP levels remain ∼ constant beyond the extracellular glucose (EG) concentration of 10 mM. Therefore, any cause-effect relationship between glucose consumption (GC) and enhanced insulin secretion (eIS) remains unclear. We also found that total cellular protein, as well as total protein content in the culture "supernatant," remains constant regardless of varying EG concentrations. This indicated that eIS may be at the cost of (a) intracellular synthesis of other proteins and (b) secretion of other secretory proteins, or both (a) and (b), somehow coupled with GC by cells. To gain insights into the above, we carried out a transcriptome study of MIN6 cells exposed to hypoglycemic (HoG = 2.8 mM EG) and hyperglycemic (HyG = 25 mM EG) conditions. Expression of transcripts was analyzed in terms of Fragments Per Kilobase of transcript per Million mapped reads and Transcripts Per Million (FPKM and TPM) as well as values obtained by normalizing w.r.t. "∑(FPKM)" and "∑(TPM)." We report that HyG extracellular conditions lead to an ∼2-fold increase in insulin secretion compared to HoG measured by the enzyme-linked immunosorbent assay (ELISA) and transcripts of secreted proteins as well as their isoforms decreased in HyG conditions compared to HoG. Our results show for the first time that eIS in HyG conditions is at the cost of reduced transcription of other secreted proteins and is coupled with higher GC. The higher GC at increased extracellular glucose also indicates a yet undiscovered role of glucose molecules enhancing insulin secretion, since ATP levels resulting from glucose metabolism have been reported to be constant above an EG concentration of 10 mM. While extrapolation of our results to clinical implications is ambitious at best, this work reports novel cellular level aspects that seem relevant in some clinical observations pertaining to Type 1 diabetes. In addition, the conservatory nature of cellular secretions in insulin-secreting cells, discovered here, may be a general feature in cell biology.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome-scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy robust, nonlinear optimization and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.

Overexpression of peroxisome proliferator-activated receptor γ co-activator-1⍺ (PGC-1⍺) in Chinese hamster ovary cells increases oxidative metabolism and IgG productivity

Chinese hamster ovary (CHO) cells are used extensively to produce protein therapeutics, such as monoclonal antibodies (mAbs), in the biopharmaceutical industry. MAbs are large proteins that are energetically demanding to synthesize and secrete; therefore, high-producing CHO cell lines that are engineered for maximum metabolic efficiency are needed to meet increasing demands for mAb production. Previous studies have identified that high-producing cell lines possess a distinct metabolic phenotype when compared to low-producing cell lines. In particular, it was found that high mAb production is correlated to lactate consumption and elevated TCA cycle flux. We hypothesized that enhancing flux through the mitochondrial TCA cycle and oxidative phosphorylation would lead to increased mAb productivities and final titers. To test this hypothesis, we overexpressed peroxisome proliferator-activated receptor γ co-activator-1⍺ (PGC-1⍺), a gene that promotes mitochondrial metabolism, in an IgG-producing parental CHO cell line. Stable cell pools overexpressing PGC-1⍺ exhibited increased oxygen consumption, indicating increased mitochondrial metabolism, as well as increased mAb specific productivity compared to the parental line. We also performed 13C metabolic flux analysis (MFA) to quantify how PGC-1⍺ overexpression alters intracellular metabolic fluxes, revealing not only increased TCA cycle flux, but global upregulation of cellular metabolic activity. This study demonstrates the potential of rationally engineering the metabolism of industrial cell lines to improve overall mAb productivity and to increase the abundance of high-producing clones in stable cell pools.

How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?

Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13 C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements.

Cellular allostatic load is linked to increased energy expenditure and accelerated biological aging

Stress triggers anticipatory physiological responses that promote survival, a phenomenon termed allostasis. However, the chronic activation of energy-dependent allostatic responses results in allostatic load, a dysregulated state that predicts functional decline, accelerates aging, and increases mortality in humans. The energetic cost and cellular basis for the damaging effects of allostatic load have not been defined. Here, by longitudinally profiling three unrelated primary human fibroblast lines across their lifespan, we find that chronic glucocorticoid exposure increases cellular energy expenditure by ∼60%, along with a metabolic shift from glycolysis to mitochondrial oxidative phosphorylation (OxPhos). This state of stress-induced hypermetabolism is linked to mtDNA instability, non-linearly affects age-related cytokines secretion, and accelerates cellular aging based on DNA methylation clocks, telomere shortening rate, and reduced lifespan. Pharmacologically normalizing OxPhos activity while further increasing energy expenditure exacerbates the accelerated aging phenotype, pointing to total energy expenditure as a potential driver of aging dynamics. Together, our findings define bioenergetic and multi-omic recalibrations of stress adaptation, underscoring increased energy expenditure and accelerated cellular aging as interrelated features of cellular allostatic load.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in the biotechnology industry 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 protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to 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.

Targeting SARS-CoV-2 by synthetic dual-acting thiol compounds that inhibit Spike/ACE2 interaction and viral protein production

The SARS-CoV-2 life cycle is strictly dependent on the environmental redox state that influences both virus entry and replication. A reducing environment impairs the binding of the spike protein (S) to the angiotensin-converting enzyme 2 receptor (ACE2), while a highly oxidizing environment is thought to favor S interaction with ACE2. Moreover, SARS-CoV-2 interferes with redox homeostasis in infected cells to promote the oxidative folding of its own proteins. Here we demonstrate that synthetic low molecular weight (LMW) monothiol and dithiol compounds induce a redox switch in the S protein receptor binding domain (RBD) toward a more reduced state. Reactive cysteine residue profiling revealed that all the disulfides present in RBD are targets of the thiol compounds. The reduction of disulfides in RBD decreases the binding to ACE2 in a cell-free system as demonstrated by enzyme-linked immunosorbent and surface plasmon resonance (SPR) assays. Moreover, LMW thiols interfere with protein oxidative folding and the production of newly synthesized polypeptides in HEK293 cells expressing the S1 and RBD domain, respectively. Based on these results, we hypothesize that these thiol compounds impair both the binding of S protein to its cellular receptor during the early stage of viral infection, as well as viral protein folding/maturation and thus the formation of new viral mature particles. Indeed, all the tested molecules, although at different concentrations, efficiently inhibit both SARS-CoV-2 entry and replication in Vero E6 cells. LMW thiols may represent innovative anti-SARS-CoV-2 therapeutics acting directly on viral targets and indirectly by inhibiting cellular functions mandatory for viral replication.

Proprotein convertases regulate trafficking and maturation of key proteins within the secretory pathway

Proprotein Convertases (PCs) are serine endoproteases that regulate the homeostasis of protein substrates in the cell. The PCs family counts 9 members-PC1/3, PC2, PC4, PACE4, PC5/6, PC7, Furin, SKI-1/S1P, and PCSK9. The first seven PCs are known as Basic Proprotein Convertases due to their propensity to cleave after polybasic clusters. SKI-1/S1P requires the additional presence of hydrophobic residues for processing, whereas PCSK9 is catalytically dead after autoactivation and exerts its functions using mechanisms alternative to direct cleavage. All PCs traffic through the canonical secretory pathway, reaching different compartments where the various substrates reside. Despite PCs members do not share the same subcellular localization, most of the cellular organelles count one or more Proprotein Convertases, including ER, Golgi stack, endosomes, secretory granules, and plasma membranes. The widespread expression of these enzymes at the systemic level speaks for their importance in the homeostasis of a large number of biological functions. Among others, PCs cleave precursors of hormones and growth factors and activate receptors and transcription factors. Notably, dysregulation of the enzymatic activity of Proprotein Convertases is associated to major human pathologies, such as cardiovascular diseases, cancer, diabetes, infections, inflammation, autoimmunity diseases, and Parkinson. In the current COVID-19 pandemic, Furin has further attracted the attention as a key player for conferring high pathogenicity to SARS-CoV-2. Here, we review the Proprotein Convertases family and their most important substrates along the secretory pathway. Knowledge about the complex functions of PCs is important to identify potential drug strategies targeting this class of enzymes.

NAD+ supplementation improves mAb productivity in CHO cells via a glucose metabolic shift

Aerobic glycolysis and its by-product lactate accumulation are usually associated with adverse culture phenotypes such as poor cell viability and productivity. Due to the lack of knowledge on underlying mechanisms and accompanying biological processes, the regulation of aerobic glycolysis has been an ongoing challenge in culture process development for therapeutic protein productivity. Nicotinamide adenine dinucleotide (NAD⁺ ), a coenzyme and co-substrate in energy metabolism, promotes the conversion of inefficient glycolysis into an efficient oxidative phosphorylation (OXPHOS) pathway. However, the effect of NAD⁺ on Chinese hamster ovary (CHO) cells for biopharmaceutical production has not been reported yet. In this work, we aimed to elucidate the influence of NAD⁺ on cell culture performance by examining metabolic shifts and mAb productivity. The supplementation of NAD⁺ increased the intracellular concentration of NAD⁺ and promoted SIRT3 expression. Antibody titer and the specific productivity in the growth phase were improved by up to 1.82- and 1.88-fold, respectively, with marginal restrictions on cell growth. NAD⁺ significantly reduced the accumulation of reactive oxygen species (ROS) and the lactate yield from glucose, determined by lactate accumulation versus glucose consumption (Y_LAC/GLC ). In contrast, OXPHOS capacity and amino acid consumption rate increased substantially. Collectively, these results suggest that NAD⁺ contributes to improving therapeutic protein productivity in bioprocessing via inducing an energy metabolic shift.

OxPhos defects cause hypermetabolism and reduce lifespan in cells and in patients with mitochondrial diseases

Patients with primary mitochondrial oxidative phosphorylation (OxPhos) defects present with fatigue and multi-system disorders, are often lean, and die prematurely, but the mechanistic basis for this clinical picture remains unclear. By integrating data from 17 cohorts of patients with mitochondrial diseases (n = 690) we find evidence that these disorders increase resting energy expenditure, a state termed hypermetabolism. We examine this phenomenon longitudinally in patient-derived fibroblasts from multiple donors. Genetically or pharmacologically disrupting OxPhos approximately doubles cellular energy expenditure. This cell-autonomous state of hypermetabolism occurs despite near-normal OxPhos coupling efficiency, excluding uncoupling as a general mechanism. Instead, hypermetabolism is associated with mitochondrial DNA instability, activation of the integrated stress response (ISR), and increased extracellular secretion of age-related cytokines and metabokines including GDF15. In parallel, OxPhos defects accelerate telomere erosion and epigenetic aging per cell division, consistent with evidence that excess energy expenditure accelerates biological aging. To explore potential mechanisms for these effects, we generate a longitudinal RNASeq and DNA methylation resource dataset, which reveals conserved, energetically demanding, genome-wide recalibrations. Taken together, these findings highlight the need to understand how OxPhos defects influence the energetic cost of living, and the link between hypermetabolism and aging in cells and patients with mitochondrial diseases.

Improving bone morphogenetic protein (BMP) production in CHO cells through understanding of BMP synthesis, signaling and endocytosis

Bone morphogenetic proteins (BMPs) are a group of growth factors with the clinical potential to regulate cartilage and bone formation. Functionally active mature recombinant human BMPs (rhBMPs), produced primarily in Chinese hamster ovary (CHO) cells for clinical applications, are considered difficult to express because they undergo maturation processes, signaling pathways, or endocytosis. Although BMPs are a family of proteins with similar mature domain sequence identities, their individual properties are diverse. Thus, understanding the properties of individual rhBMPs is essential to improve rhBMP production in CHO cells. In this review, we discuss various approaches to improve rhBMP production in CHO cells by understanding the overall maturation process, signaling pathways and endocytosis of individual rhBMPs.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells

Recombinant protein production can cause severe stress on cellular metabolism, resulting in limited titer and product quality. To investigate cellular and metabolic characteristics associated with these limitations, we compare HEK293 clones producing either erythropoietin (EPO) (secretory) or GFP (non-secretory) protein at different rates. Transcriptomic and functional analyses indicate significantly higher metabolism and oxidative phosphorylation in EPO producers compared with parental and GFP cells. In addition, ribosomal genes exhibit specific expression patterns depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased EPO secretion, we detect higher gene expression related to negative regulation of endoplasmic reticulum (ER) stress, including upregulation of ATF6B, which aids EPO production in a subset of clones by overexpression or small interfering RNA (siRNA) knockdown. Our results offer potential target pathways and genes for further development of the secretory power in mammalian cell factories.

Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints

The concept of metabolic models with resource allocation constraints has been around for over a decade and has clear advantages even when implementation is relatively rudimentary. Nonetheless, the number of organisms for which such a model is reconstructed is low. Various approaches exist, from coarse-grained consideration of enzyme usage to fine-grained description of protein translation. These approaches are reviewed here, with a particular focus on user-friendly solutions that can introduce resource allocation constraints to metabolic models of any organism. The availability of k_cat data is a major hurdle, where recent advances might help to fill in the numerous gaps that exist for this data, especially for nonmodel organisms.

Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production.

Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.

The secretory pathway - the key for unlocking the potential of Chinese hamster ovary cell factories for manufacturing therapeutic proteins

Mammalian cell factories (in particular the CHO cell system) have been crucial in the rise of biopharmaceuticals. Mammalian cells have compartmentalized organelles where intricate networks of proteins manufacture highly sophisticated biopharmaceuticals in a specialized production pipeline - the secretory pathway. In the bioproduction context, the secretory pathway functioning is key for the effectiveness of cell factories to manufacture these life-changing medicines. This review describes the molecular components and events involved in the secretory pathway, and provides a comprehensive summary of the intracellular steps limiting the production of therapeutic proteins as well as the achievements in engineering CHO cell secretory machinery. We also consider antibody-producing plasma cells (so called "professional" secretory cells) to explore the mechanisms underpinning their unique secretory function/features. Such understandings offer the potential to further enhancement of the current CHO cell production platforms for manufacturing next generation of biopharmaceuticals.

Harnessing secretory pathway differences between HEK293 and CHO to rescue production of difficult to express proteins

Biologics represent the fastest growing group of therapeutics, but many advanced recombinant protein moieties remain difficult to produce. Here, we identify metabolic engineering targets limiting expression of recombinant human proteins through a systems biology analysis of the transcriptomes of CHO and HEK293 during recombinant expression. In an expression comparison of 24 difficult to express proteins, one third of the challenging human proteins displayed improved secretion upon host cell swapping from CHO to HEK293. Guided by a comprehensive transcriptomics comparison between cell lines, especially highlighting differences in secretory pathway utilization, a co-expression screening of 21 secretory pathway components validated ATF4, SRP9, JUN, PDIA3 and HSPA8 as productivity boosters in CHO. Moreover, more heavily glycosylated products benefitted more from the elevated activities of the N- and O-glycosyltransferases found in HEK293. Collectively, our results demonstrate the utilization of HEK293 for expression rescue of human proteins and suggest a methodology for identification of secretory pathway components for metabolic engineering of HEK293 and CHO.

Modelling Apoptosis Resistance in CHO cells with CRISPR-Mediated Knock-outs of Bak1, Bax, and Bok

Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been genetically engineered to resist cell death. However, the kinetics that governs cell fate in bioreactors are confounded by many variables associated with batch processes. Here, we used CRISPR‐Cas9 to create combinatorial knockouts of the three known BCL‐2 family effector proteins: Bak1, Bax, and Bok. To assess the response to apoptotic stimuli, cell lines were cultured in the presence of four cytotoxic compounds with different mechanisms of action. A population‐based model was developed to describe the behavior of the resulting viable cell dynamics as a function of genotype and treatment. Our results validated the synergistic antiapoptotic nature of Bak1 and Bax, while the deletion of Bok had no significant impact. Importantly, the uniform application of apoptotic stresses permitted direct observation and quantification of a delay in the onset of cell death through Bayesian inference of meaningful model parameters. In addition to the classical death rate, a delay function was found to be essential in the accurate modeling of the cell death response. These findings represent an important bridge between cell line engineering strategies and biological modeling in a bioprocess context.

Leveraging rAAV bioprocess understanding and next generation bioanalytics development

Recombinant adeno-associated (rAAV) vector-based gene therapy has been the focus of intense research driven by the safety profile and several recent clinical breakthroughs. As of April 2021, there are two rAAV-based gene therapies approved and more than two-hundred active clinical trials (approximately thirty in Phase III). However, the expected increase in demand for rAAV vectors still poses several challenges. Discussed herein are key aspects related to R&D needs and Chemistry, Manufacturing and Control (CMC) efforts required to attend this growing demand. Authors provide their perspective on strategic topics for rAAV-based therapies success: scalability and productivity; improved safety; increased process understanding combined with development of orthogonal bioanalytics that are able to identify, monitor and control Critical Quality Attributes (CQAs) during bioprocessing.

A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.

Improving the secretory capacity of CHO producer cells: The effect of controlled Blimp1 expression, a master transcription factor for plasma cells

With the advent of novel therapeutic proteins with complex structures, cellular bottlenecks in secretory pathways have hampered the high-yield production of difficult-to-express (DTE) proteins in CHO cells. To mitigate their limited secretory capacity, recombinant CHO (rCHO) cells were engineered to express Blimp1, a master regulator orchestrating B cell differentiation into professional secretory plasma cells, using the streamlined CRISPR/Cas9-based recombinase-mediated cassette exchange landing pad platform. The expression of Blimp1α or Blimp1β in rCHO cells producing DTE recombinant human bone morphogenetic protein-4 (rhBMP-4) increased specific rhBMP-4 productivity (q_rhBMP-4). However, since Blimp1α expression suppressed cell growth more significantly than Blimp1β expression, only Blimp1β expression enhanced rhBMP-4 yield. In serum-free suspension culture, Blimp1β expression significantly increased the rhBMP-4 concentration (>3-fold) and q_rhBMP-4 (>4-fold) without significant increase in hBMP-4 transcript levels. In addition, Blimp1β expression facilitated mature rhBMP-4 secretion by active proteolytic cleavage in the secretory pathway. Transcriptomic profiling (RNA-seq) revealed global changes in gene expression patterns that promote protein processing in secretory organelles. In-depth integrative analysis of the current RNA-seq data, public epigenome/RNA-seq data, and in silico analysis identified 45 potential key regulators of Blimp1 that are consistently up- or down-regulated in Blimp1β expressing rCHO cells and plasma cells. Blimp1β expression also enhanced the production of easy-to-express monoclonal antibodies (mAbs) and modulated the expression of key regulators in rCHO cells producing mAb. Taken together, the results show that controlled expression of Blimp1β improves the production capacity of rCHO cells by regulating secretory machinery and suggest new opportunities for engineering promising targets that are resting in CHO cells.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation

Amyloid disorders such as Alzheimer's disease (AD) involve the aggregation of secreted proteins. However, it is largely unclear how secretory-pathway proteins contribute to amyloid formation. We developed a systems biology framework integrating expression data with protein-protein interaction networks to estimate a tissue's fitness for producing specific secreted proteins and analyzed the fitness of the secretory pathway of various brain regions and cell types for synthesizing the AD-associated amyloid precursor protein (APP). While key amyloidogenic pathway components were not differentially expressed in AD brains, we found Aβ deposition correlates with systemic down- and upregulation of the secretory-pathway components proximal to APP and amyloidogenic secretases, respectively, in AD. Our analyses suggest that perturbations from three AD risk loci cascade through the APP secretory-support network and into the endocytosis pathway, connecting amyloidogenesis to dysregulation of secretory-pathway components supporting APP and suggesting novel therapeutic targets for AD. A record of this paper's transparent peer review process is included in the supplemental information.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Transcriptomic Analysis of Salivary Glands of Ornithodoros brasiliensis Aragão, 1923, the Agent of a Neotropical Tick-Toxicosis Syndrome in Humans

Tick salivary glands produce and secrete a variety of compounds that modulate host responses and ensure a successful blood meal. Despite great progress made in the identification of ticks salivary compounds in recent years, there is still a paucity of information concerning salivary molecules of Neotropical argasid ticks. Among this group of ticks, considering the number of human cases of parasitism, including severe syndromes and hospitalization, Ornithodoros brasiliensis can be considered one of the major Neotropical argasid species with impact in public health. Here, we describe the transcriptome analysis of O. brasiliensis salivary glands (ObSG). The transcriptome yielded ~14,957 putative contigs. A total of 368 contigs were attributed to secreted proteins (SP), which represent approximately 2.5% of transcripts but ~53% expression coverage transcripts per million. Lipocalins are the major protein family among the most expressed SP, accounting for ~16% of the secretory transcripts and 51% of secretory protein abundance. The most expressed transcript is an ortholog of TSGP4 (tick salivary gland protein 4), a lipocalin first identified in Ornithodoros kalahariensis that functions as a leukotriene C₄ scavenger. A total of 55 lipocalin transcripts were identified in ObSG. Other transcripts potentially involved in tick-host interaction included as: basic/acid tail secretory proteins (second most abundant expressed group), serine protease inhibitors (including Kunitz inhibitors), 5' nucleotidases (tick apyrases), phospholipase A₂, 7 disulfide bond domain, cystatins, and tick antimicrobial peptides. Another abundant group of proteins in ObSG is metalloproteases. Analysis of these major protein groups suggests that several duplication events after speciation were responsible for the abundance of redundant compounds in tick salivary glands. A full mitochondrial genome could be assembled from the transcriptome data and confirmed the close genetic identity of the tick strain sampled in the current study, to a tick strain previously implicated in tick toxicoses. This study provides novel information on the molecular composition of ObSG, a Brazilian endemic tick associated with several human cases of parasitism. These results could be helpful in the understanding of clinical findings observed in bitten patients, and also, could provide more information on the evolution of Neotropical argasids.

Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy production

Cancer is considered a high‐risk condition for severe illness resulting from COVID‐19. The interaction between severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) and human metabolism is key to elucidating the risk posed by COVID‐19 for cancer patients and identifying effective treatments, yet it is largely uncharacterised on a mechanistic level. We present a genome‐scale map of short‐term metabolic alterations triggered by SARS‐CoV‐2 infection of cancer cells. Through transcriptomic‐ and proteomic‐informed genome‐scale metabolic modelling, we characterise the role of RNA and fatty acid biosynthesis in conjunction with a rewiring in energy production pathways and enhanced cytokine secretion. These findings link together complementary aspects of viral invasion of cancer cells, while providing mechanistic insights that can inform the development of treatment strategies.

Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis

Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother’s fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.

Glycomics can uncover important molecular changes but measured glycans are highly interconnected and incompatible with common statistical methods, introducing pitfalls during analysis. Here, the authors develop an approach to identify glycan dependencies across samples to facilitate comparative glycomics.

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of ¹³C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

There is an increasing demand for protein pharmaceuticals, especially monoclonal antibodies. Chinese Hamster Ovary (CHO) are currently the leading production host due to their ability to perform human-like post-translational modifications. However, it typically takes several months of trial-and-error approaches to develop a high-producer cell line. Metabolic modelling has the potential to make cell line and process development faster and cheaper by predicting targeted modifications to the cell line genome, cultivation medium or bioprocess. In fact, genome-scale metabolic reconstructions of CHO are already available, and ready for use in cell line development. However, in order to successfully use these models, we need to make sure that they are able to accurately predict metabolic phenotypes. Here we use genome-scale metabolic models of CHO to evaluate the models’ ability to correctly predict intracellular flux distributions. We find that a crucial key ingredient for the correct estimation of central carbon fluxes is the non-growth associated maintenance energy (mATP). We estimated mATP computationally and confirmed it experimentally. Adding this single constraint leads to significantly better predictions of intracellular fluxes, especially in glycolysis and the tricarboxylic acid cycle.

From omics to Cellular mechanisms in mammalian cell factory development

Mammalian cells have been used widely as biopharmaceutical cell factories due to their ability to make complex biotherapeutic proteins with human-compatible modifications. However, their application for some products has been hampered by low protein yields. Numerous studies have aimed to characterize cellular bottlenecks in the hope of boosting protein productivity, but the complexity of the underlying pathways and the diversity of the modifications have complicated cell engineering when relying solely on traditional methodologies. Incorporating omics-based and systems approaches into cell engineering can provide valuable insights into desirable phenotypes of cell factories. Here, we discuss cell engineering strategies for enhancing protein productivity in mammalian cell factories, particularly CHO and HEK293, and the opportunities and limitations of the genome-wide screening and multi-omics approaches for guiding cell engineering. Systems biology strategies will also be discussed to show how they refine our understanding of the cellular mechanisms which will aid in effective engineering strategies.

Machine learning-based investigation of the cancer protein secretory pathway

Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.

The secretory pathway is a series of intracellular compartments and enzymes that process and export proteins from the cell to its surrounding environment. Dysfunction of the secretory pathway is associated with many diseases, including cancer, and therefore constitutes a potential target for novel therapeutic strategies. The large number of interacting components that comprise the secretory pathway pose a challenge when attempting to identify where the dysfunction originates or how to restore healthy function. To improve our understanding of how the secretory pathway is changed within tumors, we used gene expression data from normal tissue and tumor samples from thousands of individuals which included many different types of cancers. The data was analyzed using different machine learning algorithms which we trained to predict sample characteristics, such as disease severity. This training quantified the relative degree to which each gene was associated with the tumor characteristic, allowing us to predict which secretory pathway components were important for processes such as tumor progression—both within specific cancer types and across many different cancer types. The machine learning-based approach demonstrated excellent performance compared to traditional gene expression analysis methods and identified several secretory pathway components with strong evidence of involvement in tumor development.