Fermentation Strategies for Production of Pharmaceuticals and Food Ingredients

A special issue of Fermentation (ISSN 2311-5637). This special issue belongs to the section "Industrial Fermentation".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5046

Special Issue Editors


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Guest Editor
Curia. Parque Tecnológico de León, C/Andrés Suárez s/n, 24009 León, Spain
Interests: industrial fermentation; downstream process; bacteria; fungi; antibiotics; carotenoids; immunosuppressant; steroids; cannabinoids; synthetic biology

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Guest Editor
Área de Bioquímica y Biología Molecular, Departamento de Biología Molecular, Universidad de León, 24007 León, Spain
Interests: secondary metabolites; microorganisms; proteomics; plastics; actinobacteria; fungi; carotenoids; steroids; immunosuppressors; antibiotics
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Special Issue Information

Dear Colleagues,

Fermentation of microorganisms allows the production of industrially relevant compounds, including active pharmaceutical ingredients (APIs), food and feed supplements, products for cosmetics, agriculture, etc. The fermentative and the downstream processes have reached amazing levels of efficiency, being commercially competitive compared to isolation of products from plants and animals or to chemical synthesis.

Thus, products from microbes are incredibly diverse, ranging from very large molecules, such as proteins, monoclonal antibodies, nucleic acids, carbohydrate polymers, or even cells, to small molecules that are usually divided into primary metabolites, which are essential for vegetative growth, and secondary metabolites, i.e., those nonessentials for microbial growth.

Evolution of fermentative methodologies has achieved impressive production processes. For example, the classic production of beta-lactam antibiotics such as penicillin by Penicillium chrysogenum or cephalosporin by Acremonium chrysogenum, has given way to the production of new antibiotic molecules, including daptomycin, pristinamycin, dalbavancin, oritavancin, etc. In the same way, fermentative production of food ingredients, which includes amino acids, is a very well-known process mainly based on Corynebacterium glutamicum strains, as well as carotenoids (beta-carotene, lycopene, astaxanthin, zeaxanthin, canthaxanthin, etc.), or enzymes (beta-galactosidase, renin, chymosin, etc.).

This Special Issue will comprise current studies addressing problems and solutions for environmental, industrial, technical, and consumer challenges of “Microbial Fermentation” in the 21st century. Hence, the goal is to compile both recent innovative research results, as well as reviews on the production of microbial metabolites with interest in clinical (antibiotics), industrial (enzymes), agriculture (biopesticides), food and feed (carotenoids), or farming (pest antagonists) areas of application. Thus, manuscripts on industrial strain development, scale-up and scale-down methodologies, and downstream processes are welcomed.

Dr. José Luis Barredo
Dr. Carlos Barreiro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fermentation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microbial fermentation
  • APIs
  • antibiotics
  • steroids
  • immunosuppressant
  • carotenoids
  • amino acids
  • secondary metabolites
  • primary metabolites

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Published Papers (2 papers)

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Research

13 pages, 1353 KiB  
Article
Silkworm Pupae Coupled with Glucose Control pH Mediates GABA Hyperproduction by Lactobacillus hilgardii
by Luchan Gong, Tingting Li, Shuyi Lv, Xiaozhou Zou, Jun Wang and Bowen Wang
Fermentation 2023, 9(7), 691; https://doi.org/10.3390/fermentation9070691 - 24 Jul 2023
Cited by 1 | Viewed by 1688
Abstract
γ-Aminobutyric acid (GABA) is a ubiquitous nonprotein amino acid that has multiple physiological functions and has received significant attention in the pharmaceutical and food industries. Although there are many GABA-producing bacteria, the high cost of strain cultivation limits its food additive and pharmaceutical [...] Read more.
γ-Aminobutyric acid (GABA) is a ubiquitous nonprotein amino acid that has multiple physiological functions and has received significant attention in the pharmaceutical and food industries. Although there are many GABA-producing bacteria, the high cost of strain cultivation limits its food additive and pharmaceutical raw material application. In our study, Lactobacillus hilgardii GZ2, a novel GABA-producing strain, was investigated. We attempted to replace nitrogen sources with silkworm pupae, the waste resource of the silk reeling industry, in GYP complex medium. The GABA titer reached 33.2 g/L by using 10 g/L silkworm pupae meal instead of tryptone. Meanwhile, the pH of fermentation was automatically controlled by adjusting the addition of glucose and monosodium glutamate. Finally, the highest GABA yield and productivity were 229.3 g/L and 3.2 g/L/h in L. hilgardii when silkworm pupae meal was replaced with tryptone combined with glucose and monosodium glutamate feeding. By utilizing the waste resource to reduce the cost of the nitrogen source and automatically controlling the pH in L. hilgardii, a hyper titer and productivity of GABA was generated for applications in the food and pharmaceutical industries. Full article
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15 pages, 2335 KiB  
Article
Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA)
by Shehab Abdulhabib Alzaeemi, Efaq Ali Noman, Muhanna Mohammed Al-shaibani, Adel Al-Gheethi, Radin Maya Saphira Radin Mohamed, Reyad Almoheer, Mubarak Seif, Kim Gaik Tay, Noraziah Mohamad Zin and Hesham Ali El Enshasy
Fermentation 2023, 9(3), 200; https://doi.org/10.3390/fermentation9030200 - 21 Feb 2023
Cited by 7 | Viewed by 2528
Abstract
The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (x1 [...] Read more.
The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (x1), pH (x2), incubation time (x3), and soybean concentration (x4). The coefficient of the predicted model using the Box–Behnken design (BBD) was R2 = 0.9079 (p < 0.05); however, the lack of fit was significant indicating that independent factors are not fitted with the quadratic model. These results were confirmed during the optimization process, which revealed that the standard error (SE) of the predicted model was 11.65 while the coefficient was 0.9799, at which 145.35 and 124.54 IU mL−1 of the actual and predicted enzyme production was recorded at 34 °C, pH 8.5, after 7 days and with 10 g L−1 of organic soybean powder concentrations. Compared to the RBFNN-GA, the results revealed that the investigated factors had benefits and effects on L-asparaginase, with a correlation coefficient of R = 0.935484, and can classify 91.666667% of the test data samples with a better degree of precision; the actual values are higher than the predicted values for the L-asparaginase data. Full article
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