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

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

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

Antibody engineering of a cytotoxic monoclonal antibody 84 against human embryonic stem cells: Investigating the effects of multivalency on cytotoxicity.

Antibody fragments have shown targeted specificity to their antigens, but only modest tissue retention times in vivo and in vitro. Multimerization has been used as a protein engineering tool to increase the number of binding units and thereby enhance the efficacy and retention time of antibody fragments. In this work, we explored the effects of valency using a series of self-assembling polypeptides based on the GCN4 leucine zipper multimerization domain fused to a single-chain variable fragment via an antibody upper hinge sequence. Four engineered antibody fragments with a valency from one to four antigen-binding units of a cytotoxic monoclonal antibody 84 against human embryonic stem cells (hESC) were constructed. We hypothesized that higher cytotoxicity would be observed for fragments with increased valency. Flow cytometry analysis revealed that the trimeric and tetrameric engineered antibody fragments resulted in the highest degree of cytotoxicity to the undifferentiated hESC, while the engineered antibody fragments were observed to have improved tissue penetration into cell clusters. Thus, a trade off was made for the trimeric versus tetrameric fragment due to improved tissue penetration. These results have direct implications for antibody-mediated removal of undifferentiated hESC during regenerative medicine and cell therapy.

Mechanistic elements and critical factors of cellular reprogramming revealed by stepwise global gene expression analyses.

A better understanding of the cellular and molecular mechanisms involved in the reprogramming of somatic cells is essential for further improvement of induced pluripotent stem (iPS) cell technology. In this study, we enriched for cells actively undergoing reprogramming at different time points by sorting the cells stained with a stem cell-selective fluorescent chemical probe CDy1 for their global gene expression analysis. Day-to-day comparison of differentially expressed genes showed highly dynamic and transient gene expressions during reprogramming, which were largely distinct from those of fully-reprogrammed cells. An unbiased analysis of functional regulation indicated robust modulation of concurrent programs at critical junctures. Globally, transcriptional programs involved in cell proliferation, morphology and signal transduction were instantly triggered as early as 3 days-post-infection to prepare the cell for reprogramming but became somewhat muted in the final iPS cells. On the other hand, the highly coordinated metabolic reprogramming process was more gradually activated. Subsequent network analysis of differentially expressed genes indicated PDGF-BB as a core player in reprogramming which was verified by our gain- and loss-of-function experiments. As such, our study has revealed previously-unknown insights into the mechanisms of cellular reprogramming.

Development of a chemically defined minimal medium for the exponential growth of Leuconostoc mesenteroides ATCC8293.

Leuconostoc mesenteroides is a heterofermentative Grampositive bacterium that plays key roles in fermentation of foods such as kimchi, sauerkraut, and milk, leading to the production of various organic acids and aromatic compounds. To study the microbiological and genomic characteristics of L. mesenteroides, we have developed a new chemically defined minimal medium by using the single omission technique. During the exponential cell growth, this species required glutamine, methionine, valine, and nicotinic acid as essential nutrients and 8 amino acids (arginine, cysteine, histidine, leucine, phenylalanine, proline, threonine, and tryptophan), 5 vitamins (ascorbic acid, folic acid, inosine, calcium panthothenate, and thiamine), and others (manganese, magnesium, adenine, uracil, and Tween 80) as supplemental nutrients. This medium is useful to study the metabolic characteristics of L. mesenteroides and to explain its role in food fermentation.

LC-MS-based metabolic characterization of high monoclonal antibody-producing Chinese hamster ovary cells.

The selection of suitable mammalian cell lines with high specific productivities is a crucial aspect of large-scale recombinant protein production. This study utilizes a metabolomics approach to elucidate the key characteristics of Chinese hamster ovary (CHO) cells with high monoclonal antibody productivities (q(mAb)). Liquid chromatography-mass spectrometry (LC-MS)-based intracellular metabolite profiles of eight single cell clones with high and low q(mAb) were obtained at the mid-exponential phase during shake flask batch cultures. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) subsequently revealed key differences between the high and low q(mAb) clones, as indicated by the variable importance for projection (VIP) scores. The mass peaks were further examined for their potential association with q(mAb) across all clones using Pearson's correlation analysis. Lastly, the identities of metabolites with high VIP and correlation scores were confirmed by comparison with standards through LC-MS-MS. A total of seven metabolites were identified-NADH, FAD, reduced and oxidized glutathione, and three activated sugar precursors. These metabolites are involved in key cellular pathways of citric acid cycle, oxidative phosphorylation, glutathione metabolism, and protein glycosylation. To our knowledge, this is the first study to identify metabolites that are associated closely with q(mAb). The results suggest that the high producers had elevated levels of specific metabolites to better regulate their redox status. This is likely to facilitate the generation of energy and activated sugar precursors to meet the demands of producing more glycosylated recombinant monoclonal antibodies.

Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture.

The increasing demand for recombinant therapeutic proteins highlights the need to constantly improve the efficiency and yield of these biopharmaceutical products from mammalian cells, which is fully achievable only through proper understanding of cellular functioning. Towards this end, the current study exploited a combined metabolomics and in silico modeling approach to gain a deeper insight into the cellular mechanisms of Chinese hamster ovary (CHO) fed-batch cultures. Initially, extracellular and intracellular metabolite profiling analysis shortlisted key metabolites associated with cell growth limitation within the energy, glutathione, and glycerophospholipid pathways that have distinct changes at the exponential-stationary transition phase of the cultures. In addition, biomass compositional analysis newly revealed different amino acid content in the CHO cells from other mammalian cells, indicating the significance of accurate protein composition data in metabolite balancing across required nutrient assimilation, metabolic utilization, and cell growth. Subsequent in silico modeling of CHO cells characterized internal metabolic behaviors attaining physiological changes during growth and non-growth phases, thereby allowing us to explore relevant pathways to growth limitation and identify major growth-limiting factors including the oxidative stress and depletion of lipid metabolites. Such key information on growth-related mechanisms derived from the current approach can potentially guide the development of new strategies to enhance CHO culture performance.

Enhanced production of neuroprogenitors, dopaminergic neurons, and identification of target genes by overexpression of sonic hedgehog in human embryonic stem cells.

Molecular and cellular signaling pathways are involved in the process of neural differentiation from human embryonic stem cells (hESC) to terminally differentiated neurons. The Sonic hedgehog (SHH) morphogen is required to direct the differentiation of hESC to several neural subtypes, for example, dopaminergic (DA) or motor neurons. However, the roles of SHH signaling and the pathway target genes that regulate the diversity of cellular responses arising from SHH activation during neurogenesis of hESC have yet to be elucidated. In this study, we report that overexpression of SHH in hESC promotes the derivation of neuroprogenitors (NP), increases proliferation of NP, and subsequently increases the yield of DA neurons. Next, gene expression changes resulting from the overexpression of SHH in hESC-derived NP were examined by genome-wide transcriptional profiling. Categorizing the differentially expressed genes according to the Gene Ontology biological processes showed that they are involved in numerous cellular processes, including neural development, NP proliferation, and neural specification. In silico GLI-binding sites analysis of the differentially expressed genes also identified a set of putative novel direct target genes of SHH in hESC-derived NP, which are involved in nervous system development. Electrophoretic mobility shift assays and promoter-luciferase assays confirmed that GLI1 binds to the promoter region and activates transcription of HEY2, a NOTCH signaling target gene. Taken together, our data provide evidence for the first time that there is cross-talk between the NOTCH and SHH signaling pathways in hESC-derived NP and also provide significant new insights into transcriptional targets in SHH-mediated neural differentiation of hESC.

NADPH-dependent pgi-gene knockout Escherichia coli metabolism producing shikimate on different carbon sources.

We explored the physiological and metabolic effects of different carbon sources (glucose, fructose, and glucose/fructose mixture) in phosphoglucose isomerase (pgi) knockout Escherichia coli mutant producing shikimic acid (SA). It was observed that the pgi(-) mutant grown on glucose exhibited significantly lower cell growth compared with the pgi(+) strain and its mixed glucose/fructose fermentation grew well. Interestingly, when fructose was used as a carbon source, the pgi(-) mutant showed the enhanced SA production compared with the pgi(+) strain. In silico analysis of a genome-scale E. coli model was then conducted to characterize the cellular metabolism and quantify NAPDH regeneration, which allowed us to understand such experimentally observed attenuated cell growth and enhanced SA production in glucose- and fructose-consuming pgi(-) mutant, respectively with respect to cofactor regeneration.

Integrated transcriptome and binding sites analysis implicates E2F in the regulation of self-renewal in human pluripotent stem cells.

Rapid cellular growth and multiplication, limited replicative senescence, calibrated sensitivity to apoptosis, and a capacity to differentiate into almost any cell type are major properties that underline the self-renewal capabilities of human pluripotent stem cells (hPSCs). We developed an integrated bioinformatics pipeline to understand the gene regulation and functions involved in maintaining such self-renewal properties of hPSCs compared to matched fibroblasts. An initial genome-wide screening of transcription factor activity using in silico binding-site and gene expression microarray data newly identified E2F as one of major candidate factors, revealing their significant regulation of the transcriptome. This is underscored by an elevated level of its transcription factor activity and expression in all tested pluripotent stem cell lines. Subsequent analysis of functional gene groups demonstrated the importance of the TFs to self-renewal in the pluripotency-coupled context; E2F directly targets the global signaling (e.g. self-renewal associated WNT and FGF pathways) and metabolic network (e.g. energy generation pathways, molecular transports and fatty acid metabolism) to promote its canonical functions that are driving the self-renewal of hPSCs. In addition, we proposed a core self-renewal module of regulatory interplay between E2F and, WNT and FGF pathways in these cells. Thus, we conclude that E2F plays a significant role in influencing the self-renewal capabilities of hPSCs.

Reconstruction of a genome-scale metabolic network of Rhodococcus erythropolis for desulfurization studies.

The remarkable catabolic diversity of Rhodococcus erythropolis makes it an interesting organism for bioremediation and fuel desulfurization. However, a model that can describe and explain the combined influence of various intracellular metabolic activities on its desulfurizing capabilities is missing from the literature. Such a model can greatly aid the development of R. erythropolis as an effective desulfurizing biocatalyst. This work reports the reconstruction of the first genome-scale metabolic model for R. erythropolis using the available genomic, experimental, and biochemical information. We have validated our in silico model by successfully predicting cell growth results and explaining several experimental observations in the literature on biodesulfurization using dibenzothiophene. We report several in silico experiments and flux balance analyses to propose minimal media, determine gene and reaction essentiality, and compare effectiveness of carbon, nitrogen, and sulfur sources. We demonstrate the usefulness of our model by studying a few in silico mutants of R. erythropolis for improved biodesulfurization, and comparing the desulfurization abilities of R. erythropolis with an in silico mutant of E. coli.

Mathematical modeling and sensitivity analysis of the integrated TNFα-mediated apoptotic pathway for identifying key regulators.

TNFα-mediated apoptosis is one of the complex and tightly regulated cellular processes as it involves the activation of both pro- and anti-apoptotic signaling pathways. Thus, it is important to elucidate the molecular players of this process and their dynamics in order to gain an in-depth understanding of the mechanisms underlying apoptosis. To this end, we proposed an integrated model of TNFα-mediated apoptosis pathway in Type I cells, formulated based on the principles of mass action kinetics. The model includes major apoptotic modules-the extrinsic and intrinsic pathways, the NFκB survival signaling and various regulatory mechanisms. We performed simulations and sensitivity analyses to study the role of NFκB pathway in regulating apoptosis, and identified IAP as one of the more potent regulators of apoptosis.

Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media.

A liquid chromatography-mass spectrometry (LC-MS) based metabolomics platform was previously established to identify and profile extracellular metabolites in culture media of mammalian cells. This presented an opportunity to isolate novel apoptosis-inducing metabolites accumulating in the media of antibody-producing Chinese hamster ovary (CHO mAb) fed-batch bioreactor cultures. Media from triplicate cultures were collected daily for the metabolomics analysis. Concurrently, cell pellets were obtained for determination of intracellular caspase activity. Metabolite profiles from the LC-MS data were subsequently examined for their degree of correlation with the caspase activity. A panel of extracellular metabolites, the majority of which were nucleotides/nucleosides and amino acid derivatives, exhibited good (R² > 0.8) and reproducible correlation. Some of these metabolites, such as oxidized glutathione, AMP and GMP, were later shown to induce apoptosis when introduced to fresh CHO mAb cultures. Finally, metabolic engineering targets were proposed to potentially counter the harmful effects of these metabolites.

Cervical spondylolysis: three cases and a review of the current literature.

STUDY DESIGN: Case report. OBJECTIVES: To describe a rare case of cervical spondylolysis with an adjacent secondary dysplastic change, and to review the current literature regarding cervical spondylolysis. SUMMARY OF BACKGROUND DATA: Three patients presented with minor trauma history and radiographical C6 level spondylolysis. METHODS: Cervical spines were analyzed with plain radiography, multidetector computerized tomography, and magnetic resonance imaging. RESULTS: In all 3 patients, plain radiographs revealed a bilateral cleft of the C6 articular mass. The patients presented with long-term minimal discomfort of the posterior neck. In 2 patients, a trauma event increased the pain and produced neurologic deficits. In addition, an adjacent dysplastic change was present on imaging studying in 2 of the patients, 1 of whom also presented with a cord signal change above the spondylolytic level. CONCLUSION: Early diagnosis and appropriate management of cases of spondylolysis are important. In addition, surgical plans for cervical spondylolysis should be considered if the adjacent levels are unstable or fragile.

Elucidation of metabolism in hybridoma cells grown in fed-batch culture by genome-scale modeling.

Genome-scale modeling of mouse hybridoma cells producing monoclonal antibodies (mAb) was performed to elucidate their physiological and metabolic states during fed-batch cell culture. Initially, feed media nutrients were monitored to identify key components among carbon sources and amino acids with significant impact on the desired outcome, for example, cell growth and antibody production. The monitored profiles indicated rapid assimilation of glucose and glutamine during the exponential growth phase. Significant increase in mAb concentration was also observed when glutamine concentration was controlled at 0.5 mM as a feeding strategy. Based on the reconstructed genome-scale metabolic network of mouse hybridoma cells and fed-batch profiles, flux analysis was then implemented to investigate the cellular behavior and changes in internal fluxes during the cell culture. The simulated profile of the cell growth was consistent with experimentally measured specific growth rate. The in silico simulation results indicated (i) predominant utilization of glycolytic pathway for ATP production, (ii) importance of pyruvate node in metabolic shifting, and (iii) characteristic pattern in lactate to glucose ratio during the exponential phase. In future, experimental and in silico analyses can serve as a promising approach to identifying optimal feeding strategies and potential cell engineering targets as well as facilitate media optimization for the enhanced production of mAb or recombinant proteins in mammalian cells.

Colored Petri net modeling and simulation of signal transduction pathways.

Presented herein is a methodology for quantitatively analyzing the complex signaling network by resorting to colored Petri nets (CPN). The mathematical as well as Petri net models for two basic reaction types were established, followed by the extension to a large signal transduction system stimulated by epidermal growth factor (EGF) in an application study. The CPN models based on the Petri net representation and the conservation and kinetic equations were used to examine the dynamic behavior of the EGF signaling pathway. The usefulness of Petri nets is demonstrated for the quantitative analysis of the signal transduction pathway. Moreover, the trade-offs between modeling capability and simulation efficiency of this pathway are explored, suggesting that the Petri net model can be invaluable in the initial stage of building a dynamic model.