Editorial: Microbial biosurfactants: updates on their biosynthesis, production and applications
Bibliographic record
Abstract
Traditional and widely used natural surfactants include lecithins, saponins (e.g., from the soap nut, Sapindus sp.) or surfactants derived from renewable raw materials such as soap (vegetable or animal fatty acid salts), betaine surfactants and alkyl polyglycosides (APGs). In contrast, microbial biosurfactants are derived from fermentation based on renewable raw materials. Currently, however, there is still a limited choice of widely available microbial biosurfactants, mostly represented by three glycolipids: sophorolipids, rhamnolipids and mannosylerythritol lipids. As the demand for fully biodegradable ingredients from renewable plant sources will certainly continue to increase, especially regarding the replacement of sulfate-based surfactants, sustainable alternatives that respond to consumer ethical concerns are needed; microbial biosurfactants precisely meet these requirements. For these reasons, research and development into biosurfactants is particularly important at the moment. Correspondingly the utilization of renewable substrates and challenges of sustainability are the common thread running through this Research Topic, which presents selected state-of-the-art contributions in the area of biosurfactants. It is linked to the second Biosurfactants International Conference, which took place from September 28th -30th, 2022 in Stuttgart Hohenheim, Germany.This research topic deals specifically with the characterization, analysis, biosynthesis, purification and life cycle assessment of glycolipid and lipopeptide biosurfactants. A first thematic focus of the 15 Research Topic articles is represented by the glycolipids. Nakamichi et al. provide structural insight into the catalytic domain of an acyltransferase involved in MEL biosynthesis by the basidiomycetous yeast Pseudozyma tsukubaensis. Bippus et al. evaluated the environmental impact of MEL production through a life cycle assessment (LCA) analysis and reported that the substrates, energy requirements for bioreactor aeration and solvents used for purification are the main contributors to the environmental impact. Eras-Muñoz et al. report on the use of industrial wastes as alternative feedstocks for the sustainable production of sophorolipids via solid-state fermentation. Most microbial biosurfactants naturally display a specific but limited structural variability, which restricts their properties and areas of application. Pala et al. have addressed this challenge by chemically modifying microbially produced sophorolipids to produce a series of amines and hydrogenated derivatives, then testing their antimicrobial properties. Glycine glucolipid, a little-studied biosurfactant from Alcanivorax borkumensis, has been studied by Karmainski et al. and they report on achieving improved growth rates and production kinetics. Another little-studied glycolipid class was addressed by Haala et al. who developed and optimized a minimal medium for the production of liamocins, polyol lipids biosurfactant produced by the yeast-like fungus Aureobasidium pullulans. Kumar et al. report on their study on a novel Starmerella species capable to produce sophorolipids, also able to reduce naphthyl ketones to their corresponding alcohols. Finally, studies of general interest for biosurfactant research are also presented in a couple of articles: Jimoh et al. review the use of biosurfactants as anti-biofilm agents in industrial water systems through their biocidal and dispersant properties, and as anti-fouling and anti-corrosion agents, while Sass et al. present a simple method for quantification of anionic biosurfactants in aqueous solutions.Overall, we are very pleased by the quality, diversity, and originality of scientific contributions compiled in this Research Topic. We believe it provides a representative cross-section of current research topics on microbial biosurfactants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".