1
|
Mohamed H, Naz T, Liu Q, Li S, Wang X, Song Y. Fed-batch fermentation of Mucor circinelloides reveals significant improvement in biomass and lipid accumulation through performance evaluation, chemical analysis, and expression profiling. BIORESOURCE TECHNOLOGY 2024; 398:130540. [PMID: 38452954 DOI: 10.1016/j.biortech.2024.130540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/27/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
This study aimed to improve the lipid and biomass yields of Mucor circinelloides WJ11 by implementing four different fed-batch fermentation strategies, varied in time and glucose concentration (S1-S4). The S1 fermentation strategy yielded the highest biomass, lipid, and fatty acid content (22 ± 0.7 g/L, 53 ± 1.2 %, and 28 ± 1.6 %) after 120 and 144 h, respectively. The γ-linolenic acid titer of 0.75 ± 0.0 g/L was greatest in S3 after 48 h. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to analyze the transcription of key genes involved in lipid accumulation. The glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, and ATP-citrate lyase genes showed increased expression levels. Fourier-transform infrared (FTIR) spectroscopy was used to analyze the biochemical profile during fermentation strategies. Optimal abiotic factors for production efficiency included pH 6.5, 25-26 °C, 15 % (v/v) inoculum, 500 rpm, 20 %-30 % dissolved oxygen, and 120 h fermentation. Glucose co-feeding offers valuable insights to develop effective fermentation strategies for lipid production.
Collapse
Affiliation(s)
- Hassan Mohamed
- Colin Ratledge Center of Microbial Lipids, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China; Department of Botany and Microbiology, Faculty of Science, Al-Azhar University, Assiut 71524, Egypt
| | - Tahira Naz
- Colin Ratledge Center of Microbial Lipids, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
| | - Qing Liu
- Colin Ratledge Center of Microbial Lipids, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
| | - Shaoqi Li
- Colin Ratledge Center of Microbial Lipids, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
| | - Xiuwen Wang
- Colin Ratledge Center of Microbial Lipids, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
| | - Yuanda Song
- Colin Ratledge Center of Microbial Lipids, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| |
Collapse
|
2
|
Prajapati N, Patel J, Singh S, Yadav VK, Joshi C, Patani A, Prajapati D, Sahoo DK, Patel A. Postbiotic production: harnessing the power of microbial metabolites for health applications. Front Microbiol 2023; 14:1306192. [PMID: 38169918 PMCID: PMC10758465 DOI: 10.3389/fmicb.2023.1306192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
Postbiotics, which are bioactive substances derived from the metabolic processes of beneficial microbes, have received considerable attention in the field of microbiome science in recent years, presenting a promising path for exploration and innovation. This comprehensive analysis looks into the multidimensional terrain of postbiotic production, including an extensive examination of diverse postbiotic classes, revealing their sophisticated mechanisms of action and highlighting future applications that might significantly affect human health. The authors thoroughly investigate the various mechanisms that support postbiotic production, ranging from conventional fermentation procedures to cutting-edge enzyme conversion and synthetic biology approaches. The review, as an acknowledgment of the field's developing nature, not only highlights current achievements but also navigates through the problems inherent in postbiotic production. In order to successfully include postbiotics in therapeutic interventions and the production of functional food ingredients, emphasis is given to critical elements, including improving yields, bolstering stability, and assuring safety. The knowledge presented herein sheds light on the expanding field of postbiotics and their potential to revolutionize the development of novel therapeutics and functional food ingredients.
Collapse
Affiliation(s)
- Nidhi Prajapati
- Department of Biotechnology, Smt. S. S. Patel Nootan Science and Commerce College, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Jinil Patel
- Department of Microbiology, Smt. S. S. Patel Nootan Science and Commerce College, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Sachidanand Singh
- Department of Biotechnology, Smt. S. S. Patel Nootan Science and Commerce College, Sankalchand Patel University, Visnagar, Gujarat, India
- Department of Biotechnology, School of Energy and Technology, Pandit Deendayal Energy University, Knowledge Corridor, Gandhinagar, Gujarat, India
| | - Virendra Kumar Yadav
- Department of Life Sciences, Hemchandracharya North Gujarat University, Patan, Gujarat, India
| | - Chinmayi Joshi
- Department of Biotechnology, Smt. S. S. Patel Nootan Science and Commerce College, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Anil Patani
- Department of Biotechnology, Smt. S. S. Patel Nootan Science and Commerce College, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Dharmendra Prajapati
- Department of Biotechnology, Smt. S. S. Patel Nootan Science and Commerce College, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Dipak Kumar Sahoo
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
| | - Ashish Patel
- Department of Life Sciences, Hemchandracharya North Gujarat University, Patan, Gujarat, India
| |
Collapse
|
3
|
Stephenson A, Lastra L, Nguyen B, Chen YJ, Nivala J, Ceze L, Strauss K. Physical Laboratory Automation in Synthetic Biology. ACS Synth Biol 2023; 12:3156-3169. [PMID: 37935025 DOI: 10.1021/acssynbio.3c00345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.
Collapse
Affiliation(s)
- Ashley Stephenson
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Microsoft Research, Redmond, Washington 98052, United States
| | - Lauren Lastra
- Microsoft Research, Redmond, Washington 98052, United States
| | - Bichlien Nguyen
- Microsoft Research, Redmond, Washington 98052, United States
| | - Yuan-Jyue Chen
- Microsoft Research, Redmond, Washington 98052, United States
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Luis Ceze
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Karin Strauss
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Microsoft Research, Redmond, Washington 98052, United States
| |
Collapse
|
4
|
Dodia H, Sunder AV, Borkar Y, Wangikar PP. Precision fermentation with mass spectrometry-based spent media analysis. Biotechnol Bioeng 2023; 120:2809-2826. [PMID: 37272489 DOI: 10.1002/bit.28450] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/13/2023] [Accepted: 05/15/2023] [Indexed: 06/06/2023]
Abstract
Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.
Collapse
Affiliation(s)
- Hardik Dodia
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | | | - Yogen Borkar
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| |
Collapse
|
5
|
Gomez Romero S, Boyle N. Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction. Compr Rev Food Sci Food Saf 2023; 22:3422-3443. [PMID: 37306528 DOI: 10.1111/1541-4337.13193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/07/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023]
Abstract
The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.
Collapse
Affiliation(s)
- Sandra Gomez Romero
- Quantitative Biosciences and Engineering, Colorado School of Mines, Golden, Colorado, USA
| | - Nanette Boyle
- Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado, USA
| |
Collapse
|
6
|
Gasparek M, Steel H, Papachristodoulou A. Deciphering mechanisms of production of natural compounds using inducer-producer microbial consortia. Biotechnol Adv 2023; 64:108117. [PMID: 36813010 DOI: 10.1016/j.biotechadv.2023.108117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 02/22/2023]
Abstract
Living organisms produce a wide range of metabolites. Because of their potential antibacterial, antifungal, antiviral, or cytostatic properties, such natural molecules are of high interest to the pharmaceutical industry. In nature, these metabolites are often synthesized via secondary metabolic biosynthetic gene clusters that are silent under the typical culturing conditions. Among different techniques used to activate these silent gene clusters, co-culturing of "producer" species with specific "inducer" microbes is a particularly appealing approach due to its simplicity. Although several "inducer-producer" microbial consortia have been reported in the literature and hundreds of different secondary metabolites with attractive biopharmaceutical properties have been described as a result of co-cultivating inducer-producer consortia, less attention has been devoted to the understanding of the mechanisms and possible means of induction for production of secondary metabolites in co-cultures. This lack of understanding of fundamental biological functions and inter-species interactions significantly limits the diversity and yield of valuable compounds using biological engineering tools. In this review, we summarize and categorize the known physiological mechanisms of production of secondary metabolites in inducer-producer consortia, and then discuss approaches that could be exploited to optimize the discovery and production of secondary metabolites.
Collapse
Affiliation(s)
- Miroslav Gasparek
- Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom.
| | - Harrison Steel
- Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom
| | | |
Collapse
|
7
|
Yang CT, Kristiani E, Leong YK, Chang JS. Big data and machine learning driven bioprocessing - Recent trends and critical analysis. BIORESOURCE TECHNOLOGY 2023; 372:128625. [PMID: 36642201 DOI: 10.1016/j.biortech.2023.128625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
Collapse
Affiliation(s)
- Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan
| | - Endah Kristiani
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia
| | - Yoong Kit Leong
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan
| | - Jo-Shu Chang
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
| |
Collapse
|
8
|
Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation. FERMENTATION-BASEL 2023. [DOI: 10.3390/fermentation9030239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.
Collapse
|