JOURNAL ARTICLE
Optimization and scaling up of extracellular polysaccharide production by submerged culture of Ganoderma lucidum on starch-containing medium using response surface methodology and laboratory bioreactors of various designs.
Published In: Letters in Applied Microbiology, 2024, v. 77, n. 12. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Tikhomirova, Tatyana S; Taraskevich, Maxim R; Lepekhin, Yuriy A; Shevelyova, Marina P; Nemashkalov, Vitaliy A 3 of 3
Abstract
This article focuses on optimizing the production of exopolysaccharides (EPS) by the medicinal fungus Ganoderma lucidum using crude wheat starch as a renewable carbon source. Employing response surface methodology (RSM), the study identified an optimal medium composition—15 g⋅l⁻¹ wheat starch, 0.375 g⋅l⁻¹ ammonium chloride (NH4Cl), and 0.75 g⋅l⁻¹ calcium chloride (CaCl2) with a carbon-to-nitrogen (C/N) ratio of 40—that significantly increased EPS concentration and starch degradation during submerged fermentation. Scale-up experiments in stirred tank and column bioreactors demonstrated that cultivation conditions, particularly agitation intensity, influenced EPS yield and substrate consumption, with low-shear stress favoring starch degradation and higher agitation promoting EPS release. The findings highlight the potential for sustainable EPS production from inexpensive plant-based substrates without costly pretreatment, providing a basis for further research on stress factors and bioreactor design in fungal polysaccharide biosynthesis.
Additional Information
- Source:Letters in Applied Microbiology. 2024/12, Vol. 77, Issue 12, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:0266-8254
- DOI:10.1093/lambio/ovae115
- Accession Number:182092502
- Copyright Statement:Copyright of Letters in Applied Microbiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.