Unravelling the essential elements for recombinant adeno-associated virus (rAAV) production in animal cell-based platforms.

Recombinant adeno-associated viruses (rAAVs) stand at the forefront of gene therapy applications, holding immense significance for their safe and efficient gene delivery capabilities. The constantly increasing and unmet demand for rAAVs underscores the need for a more comprehensive understanding of AAV biology and its impact on rAAV production. In this literature review, we delved into AAV biology and rAAV manufacturing bioprocesses, unravelling the functions and essentiality of proteins involved in rAAV production. We discuss the interconnections between these proteins and how they affect the choice of rAAV production platform. By addressing existing inconsistencies, literature gaps and limitations, this review aims to define a minimal set of genes that are essential for rAAV production, providing the potential to advance rAAV biomanufacturing, with a focus on minimizing the genetic load within rAAV-producing cells.

Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ.

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.

LooplessFluxSampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models.

BACKGROUND: Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible to avoid thermodynamically infeasible loops in flux samples when using convex samplers on large metabolic models. Current strategies for randomly sampling the non-convex loopless flux space display limited efficiency and lack theoretical guarantees. RESULTS: Here, we present LooplessFluxSampler, an efficient algorithm for exploring the loopless mass-balanced flux solution space of metabolic models, based on an Adaptive Directions Sampling on a Box (ADSB) algorithm. ADSB is rooted in the general Adaptive Direction Sampling (ADS) framework, specifically the Parallel ADS, for which theoretical convergence and irreducibility results are available for sampling from arbitrary distributions. By sampling directions that adapt to the target distribution, ADSB traverses more efficiently the sample space achieving faster mixing than other methods. Importantly, the presented algorithm is guaranteed to target the uniform distribution over convex regions, and it provably converges on the latter distribution over more general (non-convex) regions provided the sample can have full support. CONCLUSIONS: LooplessFluxSampler enables scalable statistical inference of the loopless mass-balanced solution space of large metabolic models. Grounded in a theoretically sound framework, this toolbox provides not only efficient but also reliable results for exploring the properties of the almost surely non-convex loopless flux space. Finally, LooplessFluxSampler includes a Markov Chain diagnostics suite for assessing the quality of the final sample and the performance of the algorithm.

Harnessing metabolic plasticity in CHO cells for enhanced perfusion cultivation.

Chinese Hamster Ovary (CHO) cells have rapidly become a cornerstone in biopharmaceutical production. Recently, a reinvigoration of perfusion culture mode in CHO cell cultivation has been observed. However, most cell lines currently in use have been engineered and adapted for fed-batch culture methods, and may not perform optimally under perfusion conditions. To improve the cell's resilience and viability during perfusion culture, we cultured a triple knockout CHO cell line, deficient in three apoptosis related genes BAX, BAK, and BOK in a perfusion system. After 20 days of culture, the cells exhibited a halt in cell proliferation. Interestingly, following this phase of growth arrest, the cells entered a second growth phase. During this phase, the cell numbers nearly doubled, but cell specific productivity decreased. We performed a proteomics investigation, elucidating a distinct correlation between growth arrest and cell cycle arrest and showing an upregulation of the central carbon metabolism and oxidative phosphorylation. The upregulation was partially reverted during the second growth phase, likely caused by intragenerational adaptations to stresses encountered. A phase-dependent response to oxidative stress was noted, indicating glutathione has only a secondary role during cell cycle arrest. Our data provides evidence of metabolic regulation under high cell density culturing conditions and demonstrates that cell growth arrest can be overcome. The acquired insights have the potential to not only enhance our understanding of cellular metabolism but also contribute to the development of superior cell lines for perfusion cultivation.

CRISPR-Cas12a-integrated transgenes in genomic safe harbors retain high expression in human hematopoietic iPSC-derived lineages and primary cells.

Discovery of genomic safe harbor sites (SHSs) is fundamental for multiple transgene integrations, such as reporter genes, chimeric antigen receptors (CARs), and safety switches, which are required for safe cell products for regenerative cell therapies and immunotherapies. Here we identified and characterized potential SHS in human cells. Using the CRISPR-MAD7 system, we integrated transgenes at these sites in induced pluripotent stem cells (iPSCs), primary T and natural killer (NK) cells, and Jurkat cell line, and demonstrated efficient and stable expression at these loci. Subsequently, we validated the differentiation potential of engineered iPSC toward CD34+ hematopoietic stem and progenitor cells (HSPCs), lymphoid progenitor cells (LPCs), and NK cells and showed that transgene expression was perpetuated in these lineages. Finally, we demonstrated that engineered iPSC-derived NK cells retained expression of a non-virally integrated anti-CD19 CAR, suggesting that several of the investigated SHSs can be used to engineer cells for adoptive immunotherapies.

A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism.

Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells.

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.

Endothelial Cell Phenotypes Demonstrate Different Metabolic Patterns and Predict Mortality in Trauma Patients.

In trauma patients, shock-induced endotheliopathy (SHINE) is associated with a poor prognosis. We have previously identified four metabolic phenotypes in a small cohort of trauma patients (N = 20) and displayed the intracellular metabolic profile of the endothelial cell by integrating quantified plasma metabolomic profiles into a genome-scale metabolic model (iEC-GEM). A retrospective observational study of 99 trauma patients admitted to a Level 1 Trauma Center. Mass spectrometry was conducted on admission samples of plasma metabolites. Quantified metabolites were analyzed by computational network analysis of the iEC-GEM. Four plasma metabolic phenotypes (A-D) were identified, of which phenotype D was associated with an increased injury severity score (p < 0.001); 90% (91.6%) of the patients who died within 72 h possessed this phenotype. The inferred EC metabolic patterns were found to be different between phenotype A and D. Phenotype D was unable to maintain adequate redox homeostasis. We confirm that trauma patients presented four metabolic phenotypes at admission. Phenotype D was associated with increased mortality. Different EC metabolic patterns were identified between phenotypes A and D, and the inability to maintain adequate redox balance may be linked to the high mortality.

Enhancing CHO cell productivity through a dual selection system using Aspg and Gs in glutamine free medium.

The dominant method for generating Chinese hamster ovary (CHO) cell lines that produce high titers of biotherapeutic proteins utilizes selectable markers such as dihydrofolate reductase (Dhfr) or glutamine synthetase (Gs), alongside inhibitory compounds like methotrexate or methionine sulfoximine, respectively. Recent work has shown the importance of asparaginase (Aspg) for growth in media lacking glutamine-the selection medium for Gs-based selection systems. We generated a Gs/Aspg double knockout CHO cell line and evaluated its utility as a novel dual selectable system via co-transfection of Gs-Enbrel and Aspg-Enbrel plasmids. Using the same selection conditions as the standard Gs system, the resulting cells from the Gs/Aspg dual selection showed substantially improved specific productivity and titer compared to the standard Gs selection method, however, with reduced growth rate and viability. Following adaptation in the selection medium, the cells improved viability and growth while still achieving ~5-fold higher specific productivity and ~3-fold higher titer than Gs selection alone. We anticipate that with further optimization of culture medium and selection conditions, this approach would serve as an effective addition to workflows for the industrial production of recombinant biotherapeutics.

The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility.

The topology of metabolic networks is recognisably modular with modules weakly connected apart from sharing a pool of currency metabolites. Here, we defined modules as sets of reversible reactions isolated from the rest of metabolism by irreversible reactions except for the exchange of currency metabolites. Our approach identifies topologically independent modules under specific conditions associated with different metabolic functions. As case studies, the E.coli iJO1366 and Human Recon 2.2 genome-scale metabolic models were split in 103 and 321 modules respectively, displaying significant correlation patterns in expression data. Finally, we addressed a fundamental question about the metabolic flexibility conferred by reversible reactions: "Of all Directed Topologies (DTs) defined by fixing directions to all reversible reactions, how many are capable of carrying flux through all reactions?". Enumeration of the DTs for iJO1366 model was performed using an efficient depth-first search algorithm, rejecting infeasible DTs based on mass-imbalanced and loopy flux patterns. We found the direction of 79% of reversible reactions must be defined before all directions in the network can be fixed, granting a high degree of flexibility.

Engineering death resistance in CHO cells for improved perfusion culture.

The reliable and cost-efficient manufacturing of monoclonal antibodies (mAbs) is essential to fulfil their ever-growing demand. Cell death in bioreactors reduces productivity and product quality, and is largely attributed to apoptosis. In perfusion bioreactors, this leads to the necessity of a bleed stream, which negatively affects the overall process economy. To combat this limitation, death-resistant Chinese hamster ovary cell lines were developed by simultaneously knocking out the apoptosis effector proteins Bak1, Bax, and Bok with CRISPR technology. These cell lines were cultured in fed-batch and perfusion bioreactors and compared to an unmodified control cell line. In fed-batch, the death-resistant cell lines showed higher cell densities and longer culture durations, lasting nearly a month under standard culture conditions. In perfusion, the death-resistant cell lines showed slower drops in viability and displayed an arrest in cell division after which cell size increased instead. Pertinently, the death-resistant cell lines demonstrated the ability to be cultured for several weeks without bleed, and achieved similar volumetric productivities at lower cell densities than that of the control cell line. Perfusion culture reduced fragmentation of the mAb produced, and the death-resistant cell lines showed increased glycosylation in the light chain in both bioreactor modes. These data demonstrate that rationally engineered death-resistant cell lines are ideal for mAb production in perfusion culture, negating the need to bleed the bioreactor whilst maintaining product quantity and quality.

Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions.

The human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient's lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.

Recycling carbon for sustainable protein production using gas fermentation.

Current food production practices contribute significantly to climate change. To transition into a sustainable future, a combination of new food habits and a radical food production innovation must occur. Single-cell protein from microbial fermentation can profoundly impact sustainability. This review paper explores opportunities offered by gas fermentation to completely replace our reliance on fossil fuels for the production of food. Together with synthetic biology, designed microbial proteins from gas fermentation have the potential to reduce our dependence on fossil fuels and make food production more sustainable.

Analytical tools for unravelling the metabolism of gas-fermenting Clostridia.

Acetogens harness the Wood-Ljungdahl Pathway, a unique metabolic pathway for C1 capture close to the thermodynamic limit. Gas fermentation using acetogens is already used for CO-to-ethanol conversion at industrial-scale and has the potential to valorise a range of C1 and waste substrates to short-chain and medium-chain carboxylic acids and alcohols. Advances in analytical quantification and metabolic modelling have helped guide industrial gas fermentation designs. Further advances in the measurements of difficult to measure metabolites are required to improve kinetic modelling and understand the regulation of acetogen metabolism. This will help guide future synthetic biology designs needed to realise the full potential of gas fermentation in stimulating a circular bioeconomy.

Modeling apoptosis resistance in CHO cells with CRISPR-mediated knockouts 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.

Multi-omic characterisation of Streptomyces hygroscopicus NRRL 30439: detailed assessment of its secondary metabolic potential.

The emergence of multidrug-resistant pathogenic bacteria creates a demand for novel antibiotics with distinct mechanisms of action. Advances in next-generation genome sequencing promised a paradigm shift in the quest to find new bioactive secondary metabolites. Genome mining has proven successful for predicting putative biosynthetic elements in secondary metabolite superproducers such as Streptomycetes. However, genome mining approaches do not inform whether biosynthetic gene clusters are dormant or active under given culture conditions. Here we show that using a multi-omics approach in combination with antiSMASH, it is possible to assess the secondary metabolic potential of a Streptomyces strain capable of producing mannopeptimycin, an important cyclic peptide effective against Gram-positive infections. The genome of Streptomyces hygroscopicus NRRL 30439 was first sequenced using PacBio RSII to obtain a closed genome. A chemically defined medium was then used to elicit a nutrient stress response in S. hygroscopicus NRRL 30439. Detailed extracellular metabolomics and intracellular proteomics were used to profile and segregate primary and secondary metabolism. Our results demonstrate that the combination of genomics, proteomics and metabolomics enables rapid evaluation of a strain's performance in bioreactors for industrial production of secondary metabolites.

Soft-sensors application for automated feeding control in high-throughput mammalian cell cultures.

The ever-increasing demand for biopharmaceuticals has created the need for improving the overall productivity of culture processes. One such operational concept that is considered is fed-batch operations as opposed to batch operations. However, optimal fed-batch operations require complete knowledge of the cell culture to optimize the culture conditions and the nutrients feeding. For example, when using high-throughput small-scale bioreactors to test multiple clones that do not behave the same, depletion or overfeeding of some key components can occur if the feeding strategy is not individually optimized. Over the recent years, various solutions for real-time measuring of the main cell culture metabolites have been proposed. Still, the complexity in the implementation of these techniques has limited their use. Soft-sensors present an opportunity to overcome these limitations by indirectly estimating these variables in real-time. This manuscript details the development of a new soft-sensor-based fed-batch strategy to maintain substrate concentration (glucose and glutamine) at optimal levels in small-scale multiparallel Chinese Hamster Ovary Cells cultures. Two alternatives to the standard feeding strategy were tested: an OUR soft-sensor-based strategy for glucose and glutamine (Strategy 1) and a dual OUR for glutamine and CO2 /alkali addition for glucose soft-sensor strategy (Strategy 2). The results demonstrated the applicability of the OUR soft-sensor-based strategy to optimize glucose and glutamine feedings, which yielded a 21% increase in final viable cell density (VCD) and a 31% in erythropoietin titer compared with the reference one. However, CO2 /alkali addition soft-sensor suffered from insufficient data to relate alkali addition with glucose consumption. As a result, the culture was overfed with glucose resulting in a 4% increase on final VCD, but a 9% decrease in final titer compared with the Reference Strategy.

GRASP: a computational platform for building kinetic models of cellular metabolism.

Summary: Kinetic models of metabolism are crucial to understand the inner workings of cell metabolism. By taking into account enzyme regulation, detailed kinetic models can provide accurate predictions of metabolic fluxes. Comprehensive consideration of kinetic regulation requires highly parameterized non-linear models, which are challenging to build and fit using available modelling tools. Here, we present a computational package implementing the GRASP framework for building detailed kinetic models of cellular metabolism. By defining the mechanisms of enzyme regulation and a reference state described by reaction fluxes and their corresponding Gibbs free energy ranges, GRASP can efficiently sample an arbitrarily large population of thermodynamically feasible kinetic models. If additional experimental data are available (fluxes, enzyme and metabolite concentrations), these can be integrated to generate models that closely reproduce these observations using an approximate Bayesian computation fitting framework. Within the same framework, model selection tasks can be readily performed. Availability and implementation: GRASP is implemented as an open-source package in the MATLAB environment. The software runs in Windows, macOS and Linux, is documented (graspk.readthedocs.io) and unit-tested. GRASP is freely available at github.com/biosustain/GRASP. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Model-guided dynamic control of essential metabolic nodes boosts acetyl-coenzyme A-dependent bioproduction in rewired Pseudomonas putida.

Pseudomonas putida is evolutionarily endowed with features relevant for bioproduction, especially under harsh operating conditions. The rich metabolic versatility of this species, however, comes at the price of limited formation of acetyl-coenzyme A (CoA) from sugar substrates. Since acetyl-CoA is a key metabolic precursor for a number of added-value products, in this work we deployed an in silico-guided rewiring program of central carbon metabolism for upgrading P. putida as a host for acetyl-CoA-dependent bioproduction. An updated kinetic model, integrating fluxomics and metabolomics datasets in addition to manually-curated information of enzyme mechanisms, identified targets that would lead to increased acetyl-CoA levels. Based on these predictions, a set of plasmids based on clustered regularly interspaced short palindromic repeats (CRISPR) and dead CRISPR-associated protein 9 (dCas9) was constructed to silence genes by CRISPR interference (CRISPRi). Dynamic reduction of gene expression of two key targets (gltA, encoding citrate synthase, and the essential accA gene, encoding subunit A of the acetyl-CoA carboxylase complex) mediated an 8-fold increase in the acetyl-CoA content of rewired P. putida. Poly(3-hydroxybutyrate) (PHB) was adopted as a proxy of acetyl-CoA availability, and two synthetic pathways were engineered for biopolymer accumulation. By including cell morphology as an extra target for the CRISPRi approach, fully rewired P. putida strains programmed for PHB accumulation had a 5-fold increase in PHB titers in bioreactor cultures using glucose. Thus, the strategy described herein allowed for rationally redirecting metabolic fluxes in P. putida from central metabolism towards product biosynthesis-especially relevant when deletion of essential pathways is not an option.

Network mapping of primary CD34+ cells by Ampliseq based whole transcriptome targeted resequencing identifies unexplored differentiation regulatory relationships.

With the exception of a few master transcription factors, regulators of neutrophil maturation are poorly annotated in the intermediate phenotypes between the granulocyte-macrophage progenitor (GMP) and the mature neutrophil phenotype. Additional challenges in identifying gene expression regulators in differentiation pathways relate to challenges wherein starting cell populations are heterogeneous in lineage potential and development, are spread across various states of quiescence, as well as sample quality and input limitations. These factors contribute to data variability make it difficult to draw simple regulatory inferences. In response we have applied a multi-omics approach using primary blood progenitor cells primed for homogeneous proliferation and granulocyte differentiation states which combines whole transcriptome resequencing (Ampliseq RNA) supported by droplet digital PCR (ddPCR) validation and mass spectrometry-based proteomics in a hypothesis-generation study of neutrophil differentiation pathways. Primary CD34+ cells isolated from human cord blood were first precultured in non-lineage driving medium to achieve an active, proliferating phenotype from which a neutrophil primed progenitor was isolated and cultured in neutrophil lineage supportive medium. Samples were then taken at 24-hour intervals over 9 days and analysed by Ampliseq RNA and mass spectrometry. The Ampliseq dataset depth, breadth and quality allowed for several unexplored transcriptional regulators and ncRNAs to be identified using a combinatorial approach of hierarchical clustering, enriched transcription factor binding motifs, and network mapping. Network mapping in particular increased comprehension of neutrophil differentiation regulatory relationships by implicating ARNT, NHLH1, PLAG1, and 6 non-coding RNAs associated with PU.1 regulation as cell-engineering targets with the potential to increase total neutrophil culture output. Overall, this study develops and demonstrates an effective new hypothesis generation methodology for transcriptome profiling during differentiation, thereby enabling identification of novel gene targets for editing interventions.