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Developing a knob-into-hole asymmetric bispecific IgG1 monoclonal antibody (mAb) poses manufacturing challenges due to the expression of chain pairing variants, also called mispaired species, in the desired product. The incorrect pairing of light and heavy chains could result in heterogeneous mispaired species of homodimers, heterodimers, light chain swapping, and low molecular weight species (LMWS). Standard chromatography, capillary electrophoretic, or spectroscopic methods poorly resolve these from the main variants. Here, we report a highly sensitive reverse-phase polyphenyl ultra-high-performance liquid chromatography (RP-UHPLC) method to accurately measure mispaired species of Duet mAb format, an asymmetric IgG1 bispecific mAb, for both process development and quality control analytical tests. Coupled with electrospray ionization mass spectrometry (ESI-MS), it enabled direct online characterization of mispaired species. This single direct assay detected diverse mispaired IgG-like species and LMWS. The method resolved eight disulfide bonds dissociated LMWS and three mispaired LMWS. It also resolved three different types of IgG-like mispaired species, including two homodimers and one heterodimer. The characterization and quantification simultaneously enabled the cell line selection that produces a lesser heterogeneity and lower levels of mispaired species with the desired correctly paired product. The biological activity assessment of samples with increased levels of these species quantified by the method exhibited a linear decline in potency with increasing levels of mispaired species in the desired product. We also demonstrated the utility of the technique for testing in-process intermediate materials to determine and assess downstream purification process capability in removing diverse mispaired IgG-like species and LMWS to a certain level during the downstream purification process. Our investigation demonstrates that adopting this method was vital in developing asymmetric bispecific mAb from the initial stage of cell line development to manufacturing process development. Therefore, this tool could be used in the control strategy to monitor and control mispaired species during manufacturing, thus improving the quality control of the final product.

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The current trend in industrial biotechnology is to move from batch or fed-batch fermentations to continuous operations. The success of this transition will require the development of genetically stable production strains, the use of strong constitutive promoters, and the development of new medium formulations that allow an appropriate balance between cell growth and product formation. We identified genes that showed high expression in Komagataella phaffii during different steady-state conditions and explored the utility of promoters of these genes (Chr1-4_0586 and FragB_0052) in optimizing the expression of two different r-proteins, human lysozyme (HuLy), and the anti-idiotypic antibody fragment, Fab-3H6, in comparison with the widely used glyceraldehyde-3-phosphate dehydrogenase promoter. Our results showed that the promoter strength was highly dependent on the cultivation conditions and thus constructs should be tested under a range of conditions to determine both the best performing clone and the ideal promoter for the expression of the protein of interest. An important benefit of continuous production is that it facilitates the use of the genome-scale metabolic models in the design of strains and cultivation media. In silico flux distributions showed that production of either protein increased the flux through aromatic amino acid biosynthesis. Tyrosine supplementation increased the productivity for both proteins, whereas tryptophan addition did not cause any significant change and, phenylalanine addition increased the expression of HuLy but decreased that of Fab-3H6. These results showed that a genome-scale metabolic model can be used to assess the metabolic burden imposed by the synthesis of a specific r-protein and then this information can be used to tailor a cultivation medium to increase production.

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Genome-scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene-reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome-scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation-reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.

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Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

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[This corrects the article DOI: 10.1099/mic.0.000477.].

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BACKGROUND: Iron and copper homeostatic pathways are tightly linked since copper is required as a cofactor for high affinity iron transport. Atx1p plays an important role in the intracellular copper transport as a copper chaperone transferring copper from the transporters to Ccc2p for its subsequent insertion into Fet3p, which is required for high affinity iron transport. RESULTS: In this study, genome-wide transcriptional landscape of ATX1 deletants grown in media either lacking copper or having excess copper was investigated. ATX1 deletants were allowed to recover full respiratory capacity in the presence of excess copper in growth environment. The present study revealed that iron ion homeostasis was not significantly affected by the absence of ATX1 either at the transcriptional or metabolic levels, suggesting other possible roles for Atx1p in addition to its function as a chaperone in copper-dependent iron absorption. The analysis of the transcriptomic response of atx1∆/atx1∆ and its integration with the genetic interaction network highlighted for the first time, the possible role of ATX1 in cell cycle regulation, likewise its mammalian counterpart ATOX1, which was reported to play an important role in the copper-stimulated proliferation of non-small lung cancer cells. CONCLUSIONS: The present finding revealed the dispensability of Atx1p for the transfer of copper ions to Ccc2p and highlighted its possible role in the cell cycle regulation. The results also showed the potential of Saccharomyces cerevisiae as a model organism in studying the capacity of ATOX1 as a therapeutic target for lung cancer therapy.

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Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

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MOTIVATION: Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets. RESULTS: We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications. AVAILABILITY AND IMPLEMENTATION: The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG. CONTACT: sgo24@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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A high degree of conservation of the copper homeostasis pathway between yeast and humans makes yeast an ideal model organism for studying copper-related disorders. In this study, a system based integrative approach was used to investigate the genome-wide effects of the deletion of the yeast ortholog of Wilson and Menkes diseases encoding a Cu(2+)-transporting P-type ATPase (CCC2) in different copper containing media and to compare with the wild type. The experimental design applied in this study enabled the observation of the effect of CCC2 deletion, extracellular copper levels and interactive effects of both factors in S. cerevisiae cells. The integrative analysis of the transcriptome with the interactome and regulome further elucidated the pathways affected by the disturbance of copper homeostasis. The results demonstrated that iron homeostasis is disturbed in the absence of CCC2 under copper deficient conditions and also revealed the importance of this gene in the maintenance of iron homeostasis under high copper conditions. NAD(+) metabolism was observed to be affected both by the deletion of CCC2 and the level of bio-available extracellular copper. The regulation of glucose transporters was also affected in the absence of CCC2 and a starvation-like response was observed in a copper level dependent manner. Alterations in the amino acid metabolism and specifically in the arginine metabolic process observed at the transcriptional level provided further support through the integration of the metabolomic data. This study also highlighted pyridoxine deficiency caused by the absence of CCC2. The observation of the improvement in the respiratory capacity of CCC2 deleted cells by supplementation with pyridoxine as well as with nicotinic acid may shed light on novel therapeutic interventions for Wilson and Menkes diseases.

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BACKGROUND: Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response. PRINCIPAL FINDINGS: In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in Saccharomyces cerevisiae was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (TPI1, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4, PDC1, RPS26A and ARF1) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes ACT1 and TDH3. The most stable reference genes from this pool were determined as TPI1, FBA1, CDC19 and ACT1 in response to a perturbation in the amount of available glucose and as FBA1, TDH3, CCW12 and ACT1 in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, HAP4 and MEP2, in response to relaxation from glucose and ammonium limitations, respectively. CONCLUSIONS: A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest.

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