A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Hendry, John I.; Dinh, Hoang V.; Sarkar, Debolina; Wang, Lin; Bandyopadhyay, Anindita; Pakrasi, Himadri B.; Maranas, Costas D.

doi:10.3390/metabo11030168

Cited by 6 publications

(4 citation statements)

References 74 publications

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“…ATCC 33047 methylase genes to allow successful conjugation (Bandyopadhyay et al, 2021). This first report of genetic modifications in this strain will most likely lead to additional strain engineering, which can be guided by this host's genomescale model (Hendry et al, 2021).…”

Section: Limited Genetic Tools Have Been Developed and Applied In Mar...mentioning

confidence: 92%

Synthetic biology in marine cyanobacteria: Advances and challenges

Bourgade

Stensjö

2022

Front. Microbiol.

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The current economic and environmental context requests an accelerating development of sustainable alternatives for the production of various target compounds. Biological processes offer viable solutions and have gained renewed interest in the recent years. For example, photosynthetic chassis organisms are particularly promising for bioprocesses, as they do not require biomass-derived carbon sources and contribute to atmospheric CO2 fixation, therefore supporting climate change mitigation. Marine cyanobacteria are of particular interest for biotechnology applications, thanks to their rich diversity, their robustness to environmental changes, and their metabolic capabilities with potential for therapeutics and chemicals production without requiring freshwater. The additional cyanobacterial properties, such as efficient photosynthesis, are also highly beneficial for biotechnological processes. Due to their capabilities, research efforts have developed several genetic tools for direct metabolic engineering applications. While progress toward a robust genetic toolkit is continuously achieved, further work is still needed to routinely modify these species and unlock their full potential for industrial applications. In contrast to the understudied marine cyanobacteria, genetic engineering and synthetic biology in freshwater cyanobacteria are currently more advanced with a variety of tools already optimized. This mini-review will explore the opportunities provided by marine cyanobacteria for a greener future. A short discussion will cover the advances and challenges regarding genetic engineering and synthetic biology in marine cyanobacteria, followed by a parallel with freshwater cyanobacteria and their current genetic availability to guide the prospect for marine species.

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Section: Limited Genetic Tools Have Been Developed and Applied In Mar...mentioning

confidence: 92%

Synthetic biology in marine cyanobacteria: Advances and challenges

Bourgade

Stensjö

2022

Front. Microbiol.

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“…5E). A reaction catalyzed by lactam synthase converting 5-aminopentanoate into valerolactam was identified (Hendry et al, 2021). These are several examples demonstrating the benefits of using EBPI for extracting reactions associated with target chemicals that are not covered by the presentative biological pathway databases.…”

Section: Extraction Of Biochemical Reaction Information Not Covered B...mentioning

confidence: 99%

“…(D) β-Ketoadipate to levulinic acid (Kim et al, 2023b). (E) 5-Aminopentanoate to valerolactam (Hendry et al, 2021). Abbreviations: D,L-DAP, D,L-diaminopimelate; HTPA, (2S,4S)-4-Hydroxy-2,3,4,5-tetrahydrodipicolinate; L-DOPA, 3,4-Dihydroxy-L-phenylalanine; L,L-DAP, L,L-diaminopimelate; L,L-SDAP, N-succinyl-L,L-2,6-diaminopimelate; and THDPA, 2,3,4,5-tetrahydrodipicolinic acid.…”

Section: Extraction Of Biochemical Reaction Information Not Covered B...mentioning

confidence: 99%

A machine learning framework for extracting information from biological pathway images in the literature

Kwon,

Lee,

Kim

2024

Preprint

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There have been significant advances in literature mining, allowing for the extraction of target information from the literature. However, biological literature often includes biological pathway images that are difficult to extract in an easily editable format. To address this challenge, this study aims to develop a machine learning framework called the “Extraction of Biological Pathway Information” (EBPI). The framework automates the search for relevant publications, extracts biological pathway information from images within the literature, including genes, enzymes, and metabolites, and generates the output in a tabular format. For this, this framework determines the direction of biochemical reactions, and detects and classifies texts within biological pathway images. Performance of EBPI was evaluated by comparing the extracted pathway information with manually curated pathway maps. EBPI will be useful for extracting biological pathway information from the literature in a high-throughput manner, and can be used for pathway studies, including metabolic engineering.

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“…Escherichia coli is a preferred host for the production of recombinant proteins due to the ease of genetic engineering, protein expression potential, and the availability of synthetic biology tools and high cell‐density cultivation protocols (Choi et al, 2006). In addition, its metabolism has been extensively studied and the reported genome‐scale metabolic models (GSMMs) are well curated, allowing their use in simulation of metabolic pathways (Hendry et al, 2021; Khodayari & Maranas, 2016; Reed et al, 2003). Combining the metabolite exchange rates measured during E. coli cultivation with a GSMM, using flux balance analysis (FBA) or metabolic flux analysis (MFA), enables the estimation of the intracellular reaction rates (Edwards & Palsson, 2000; McCloskey et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Dynamic flux balance analysis of high cell density fed‐batch culture of Escherichia coli BL21 (DE3) with mass spectrometry‐based spent media analysis

Dodia,

Mishra,

Nakrani

et al. 2024

Biotech & Bioengineering

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Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism‐specific genome‐scale metabolic models (GSMM) and by assuming instantaneous pseudo‐steady states for processes that are inherently dynamic. This technique is well‐suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed‐batch, high‐cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri‐carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

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A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Cited by 6 publications

References 74 publications

Synthetic biology in marine cyanobacteria: Advances and challenges

Synthetic biology in marine cyanobacteria: Advances and challenges

A machine learning framework for extracting information from biological pathway images in the literature

Dynamic flux balance analysis of high cell density fed‐batch culture of Escherichia coli BL21 (DE3) with mass spectrometry‐based spent media analysis

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