Editorial: Microbial biosurfactants: updates on their biosynthesis, production and applications
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,033 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».