scholarly journals Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 331 ◽  
Author(s):  
Zhuangrong Huang ◽  
Seongkyu Yoon
Keyword(s):  
Cell Lines ◽  
Chinese Hamster ◽  
Parental Cell ◽  
Process Conditions ◽  
Parental Cell Line ◽  
Specific Cell ◽  
Scale Models ◽  
Metabolic Models ◽  
Genome Scale ◽  
The Impact

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

The Cisplatin, 5-fluorouracil, Irinotecan, and Gemcitabine Treatment in Resistant 2D and 3D Model Triple Negative Breast Cancer Cell Line: ABCG2 Expression Data

2021 ◽  
Vol 21 ◽  
Author(s):  
Fatma Kubra Ata ◽  
Serap Yalcin
Keyword(s):  
Breast Cancer ◽  
Cell Culture ◽  
Cell Line ◽  
Cell Lines ◽  
Expression Profile ◽  
Three Dimensional ◽  
Parental Cell ◽  
Parental Cell Line ◽  
Xtt Assay ◽  
Gene And Protein Expression

Background: Chemotherapeutics have been commonly used in cancer treatment. Objective: In this study, the effects of Cisplatin, 5-fluorouracil, Irinotecan, and Gemcitabine have been evaluated on two-dimensional (2D) (sensitive and resistance) cell lines and three dimensional (3D) spheroid structure of MDA-MB-231. The 2D cell culture lacks a natural tissue-like structural so, using 3D cell culture has an important role in the development of effective drug testing models. Furthermore, we analyzed the ATP Binding Cassette Subfamily G Member 2 (ABCG2) gene and protein expression profile in this study. We aimed to establish a 3D breast cancer model that can mimic the in vivo 3D breast cancer microenvironment. Methods: The 3D spheroid structures were multiplied (globally) using the three-dimensional hanging drop method. The cultures of the parental cell line MDA-MB-231 served as the controls. After adding the drugs in different amounts we observed a clear and well-differentiated spheroid formation for 24 h. The viability and proliferation capacity of 2D (sensitive and resistant) cell lines and 3D spheroid cell treatment were assessed by the XTT assay. Results: Cisplatin, Irinotecan, 5-Fu, and Gemcitabine-resistant MDA-MB-231 cells were observed to begin to disintegrate in a three-dimensional clustered structure at 24 hours. Additionally, RT-PCR and protein assay showed overexpression of ABCG2 when compared to the parental cell line. Moreover, MDA-MB-231 cells grown in 3D showed decreased sensitivity to chemotherapeutics treatment. Conclusion: More resistance to chemotherapeutics and altered gene expression profile was shown in 3D cell cultures when compared with the 2D cells. These results might play an important role to evaluate the efficacy of anticancer drugs, explore mechanisms of MDR in the 3D spheroid forms.

scholarly journals Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
N. T. Devika ◽  
Karthik Raman
Keyword(s):  
Short Chain Fatty Acids ◽  
Acetate Production ◽  
Scale Models ◽  
Powerful Approach ◽  
Metabolic Models ◽  
Commercial Applications ◽  
Different Strains ◽  
Genome Scale ◽  
Modelling Approach

AbstractBifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

scholarly journals BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

2019 ◽  
Author(s):  
Charles J Norsigian ◽  
Neha Pusarla ◽  
John Luke McConn ◽  
James T Yurkovich ◽  
Andreas Dräger ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Knowledge Base ◽  
The Past ◽  
Scale Models ◽  
Metabolic Models ◽  
New Community ◽  
Genome Annotations ◽  
Genome Scale ◽  
High Quality Genome ◽  
Strain Genome

Abstract The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

scholarly journals Towards scaling elementary flux mode computation

2019 ◽  
Vol 21 (6) ◽  
pp. 1875-1885
Author(s):  
Ehsan Ullah ◽  
Mona Yosafshahi ◽  
Soha Hassoun
Keyword(s):  
Pathway Analysis ◽  
Alternative Pathway ◽  
Solution Space ◽  
Computational Technique ◽  
Elementary Flux Mode ◽  
Scale Models ◽  
Network Compression ◽  
Genome Scale ◽  
The Impact ◽  
Flux Mode

Abstract While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.

scholarly journals cis Expression of DC-SIGN Allows for More Efficient Entry of Human and Simian Immunodeficiency Viruses via CD4 and a Coreceptor

2001 ◽  
Vol 75 (24) ◽  
pp. 12028-12038 ◽  
Author(s):  
Benhur Lee ◽  
George Leslie ◽  
Elizabeth Soilleux ◽  
Una O'Doherty ◽  
Sarah Baik ◽  
...  
Keyword(s):  
Cell Lines ◽  
Viral Entry ◽  
Parental Cell ◽  
Jurkat Cells ◽  
Target Cells ◽  
Parental Cell Line ◽  
Dependent Manner ◽  
Immunodeficiency Virus ◽  
T Cell Lines

ABSTRACT DC-SIGN is a C-type lectin expressed on dendritic cells and restricted macrophage populations in vivo that binds gp120 and acts intrans to enable efficient infection of T cells by human immunodeficiency virus type 1 (HIV-1). We report here that DC-SIGN, when expressed in cis with CD4 and coreceptors, allowed more efficient infection by both HIV and simian immunodeficiency virus (SIV) strains, although the extent varied from 2- to 40-fold, depending on the virus strain. Expression of DC-SIGN on target cells did not alleviate the requirement for CD4 or coreceptor for viral entry. Stable expression of DC-SIGN on multiple lymphoid lines enabled more efficient entry and replication of R5X4 and X4 viruses. Thus, 10- and 100-fold less 89.6 (R5/X4) and NL4–3 (X4), respectively, were required to achieve productive replication in DC-SIGN-transduced Jurkat cells when compared to the parental cell line. In addition, DC-SIGN expression on T-cell lines that express very low levels of CCR5 enabled entry and replication of R5 viruses in a CCR5-dependent manner, a property not exhibited by the parental cell lines. Therefore, DC-SIGN expression can boost virus infection in cis and can expand viral tropism without affecting coreceptor preference. In addition, coexpression of DC-SIGN enabled some viruses to use alternate coreceptors like STRL33 to infect cells, whereas in its absence, infection was not observed. Immunohistochemical and confocal microscopy data indicated that DC-SIGN was coexpressed and colocalized with CD4 and CCR5 on alveolar macrophages, underscoring the physiological significance of these cis enhancement effects.

A Novel Microarray Gene Expression Analysis of Murine AML Cell Lines Provides Clues to the Development of Drug Resistance to Ara-C

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 5017-5017
Author(s):  
Susan K Rathe ◽  
David Largaespada
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Cell Lines ◽  
Ribosomal Subunit ◽  
Nuclear Factor Κb ◽  
Parental Cell ◽  
Parental Cell Line ◽  
Drug Resistant ◽  
Microarray Gene Expression ◽  
Expression Microarrays

Abstract Acute myeloid leukemia (AML) has the ability to evade cell death in the presence of chemotherapeutic cocktails containing cytosine arabinoside (Ara-C). This lab previously developed two highly resistant murine AML cell lines, B117H and B140H, by introducing increasing concentrations of Ara-C to their parental cell lines, B117P and B140P, respectively. B117H and B140H can tolerate Ara-C concentrations ~1000X that of their drug sensitive parental cell lines. mRNA from all four cell lines were used in gene expression microarrays for the purpose of comparing Ara-C drug resistant murine AML cell lines with their Ara-C drug sensitive parental lines. A novel algorithm was developed to evaluate the changes in gene expression between the drug resistant and drug sensitive cells. The algorithm differed from more conventional algorithms in two key ways. First, the detection data was normalized by using ribosomal subunit 9 (Rsp9) as the normalization gene, and secondly it calculated fold change by comparing the minimum value of one population to the maximum value of the other population. The output of this algorithm was a list of genes with significant gene expression changes. These genes were next submitted to the Ingenuity Pathway Analysis (IPA) process. IPA implicated nuclear factor-κB (NFκB) in the Ara-C resistance process. Cell growth assays confirmed that the Ara-C drug resistant B117H cell line was significantly more sensitive to NFκB inhibition than its Ara-C sensitive parental cell line. This leads us to believe that the selection of Ara-C resistance may also concomitantly make some AML cells highly sensitive to killing by NFκB inhibition. This theory is being tested further through the use of drug combination assays, to determine if a synergistic or antagonistic relationship exists between Ara-C and various drugs that affect the NFκB pathway.

scholarly journals Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

2020 ◽  
Author(s):  
Song-Min Schinn ◽  
Carly Morrison ◽  
Wei Wei ◽  
Lin Zhang ◽  
Nathan E. Lewis
Keyword(s):  
Mammalian Cells ◽  
Performance Metrics ◽  
Chinese Hamster ◽  
Nutrient Utilization ◽  
Ovary Cell ◽  
Systematic Evaluation ◽  
Model Parameters ◽  
Metabolic Models ◽  
Genome Scale ◽  
Parameter Values

AbstractGenome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells – including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

scholarly journals Activation of the erythropoietin receptor by the Friend spleen focus- forming virus gp55 glycoprotein induces constitutive protein tyrosine phosphorylation

Blood ◽  
1992 ◽  
Vol 80 (12) ◽  
pp. 3070-3078 ◽  
Author(s):  
MO Showers ◽  
JF Moreau ◽  
D Linnekin ◽  
B Druker ◽  
AD D'Andrea
Keyword(s):  
Growth Factor ◽  
Tyrosine Kinase ◽  
Tyrosine Phosphorylation ◽  
Cell Lines ◽  
Kinase Activity ◽  
Parental Cell ◽  
Erythropoietin Receptor ◽  
Parental Cell Line ◽  
Tyrosine Kinase Activity ◽  
Murine Cell

The erythropoietin receptor (EPO-R) can be activated to signal cell growth by binding either EPO or gp55, the Friend spleen focus-forming virus (SFFV) glycoprotein. EPO binding induces tyrosine kinase activity and rapid tyrosine phosphorylation of several cellular substrates. To test for gp55-induced tyrosine kinase activity, we performed immunoblots on two murine cell lines that stably express EPO-R and gp55. Stimulation of the parental cell line, Ba/F3, with murine interleukin-3 (IL-3) resulted in rapid, dose-dependent tyrosine phosphorylation of a 97-Kd substrate. Stimulation with IL-3 or EPO of the Ba/F3 cells expressing the recombinant EPO-R (Ba/F3-EPO-R) resulted in tyrosine phosphorylation of the same p97 substrate. These latter cells, when transformed to growth factor-independence by the Friend gp55 glycoprotein, exhibited constitutive tyrosine phosphorylation of the 97-Kd substrate. Other growth factor-independent Ba/F3 subclones, transformed with either the oncoprotein, v-abl, or with a constitutively activated EPO-R, also had constitutive phosphorylation of a 97-Kd substrate. In CTLL-2-EPO-R cells, a T-lymphocyte line stably transfected with the EPO-R, the 97-Kd substrate was tyrosine- phosphorylated in response to IL-2 or EPO. The 97-Kd protein was constitutively phosphorylated in CTLL-2-EPO-R-gp55 cells. In conclusion, a 97-Kd protein found in two murine cell lines is tyrosine-phosphorylated in response to multiple growth factors and viral oncoproteins, and appears to be a central phosphoprotein in signal transduction.

scholarly journals Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

2018 ◽  
Author(s):  
Daniel Machado ◽  
Sergej Andrejev ◽  
Melanie Tramontano ◽  
Kiran Raosaheb Patil
Keyword(s):  
Microbial Communities ◽  
Single Species ◽  
Model Organisms ◽  
Universal Model ◽  
Microbial Species ◽  
Scale Models ◽  
Metabolic Models ◽  
User Friendly ◽  
Genome Scale ◽  
Automated Tool

AbstractGenome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool – CarveMe – for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

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