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. 2022 Apr 5;10(4):779.
doi: 10.3390/microorganisms10040779.

Surfactin Shows Relatively Low Antimicrobial Activity against Bacillus subtilis and Other Bacterial Model Organisms in the Absence of Synergistic Metabolites

Lars Lilge  1 , Nadine Ersig  1 , Philipp Hubel  2 , Moritz Aschern  1 , Evelina Pillai  3 , Peter Klausmann  1 , Jens Pfannstiel  2 , Marius Henkel  1 , Kambiz Morabbi Heravi  1 , Rudolf Hausmann  1
Affiliations

Surfactin Shows Relatively Low Antimicrobial Activity against Bacillus subtilis and Other Bacterial Model Organisms in the Absence of Synergistic Metabolites

Lars Lilge et al. Microorganisms. .

Abstract

Surfactin is described as a powerful biosurfactant and is natively produced by Bacillus subtilis in notable quantities. Among other industrially relevant characteristics, antimicrobial properties have been attributed to surfactin-producing Bacillus isolates. To investigate this property, stress approaches were carried out with biotechnologically established strains of Corynebacterium glutamicum, Bacillus subtilis, Escherichia coli and Pseudomonas putida with the highest possible amounts of surfactin. Contrary to the popular opinion, the highest growth-reducing effects were detectable in B. subtilis and E. coli after surfactin treatment of 100 g/L with 35 and 33%, respectively, while P. putida showed no growth-specific response. In contrast, other antimicrobial biosurfactants, like rhamnolipids and sophorolipids, showed significantly stronger effects on bacterial growth. Since the addition of high amounts of surfactin in defined mineral salt medium reduced the cell growth of B. subtilis by about 40%, the initial stress response at the protein level was analyzed by mass spectrometry, showing induction of stress proteins under control of alternative sigma factors σB and σW as well as the activation of LiaRS two-component system. Overall, although surfactin is associated with antimicrobial properties, relatively low growth-reducing effects could be demonstrated after the surfactin addition, challenging the general claim of the antimicrobial properties of surfactin.

Keywords: Bacillus subtilis; biosurfactants; secondary metabolites; stress response; surfactin.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Surfactin stress approaches with biotechnologically established strains. Cell growth of B. subtilis KM0 (A), C. glutamicum ATCC13032 (B), E. coli BL21 (DE3) (C) and P. putida KT2440 (D) was monitored in LB medium. When the cultures reached an OD600 of approximately 1 (black crosses), equal volumes of cell culture were mixed with increasing concentrations of surfactin: 0 g/L (black squares), 50 g/L (red dots) and 100 g/L (blue triangles). Cell growth was monitored for further 6 h after stress induction. All stress approaches were performed in biological triplicates.
Figure 2
Figure 2
Stress approaches with biotechnologically established strains using rhamnolipids and sophorolipids. Cell growth of B. subtilis KM0 (A,E), C. glutamicum ATCC13032 (B,F), E. coli BL21 (DE3) (C,G) and P. putida KT2440 (D,H) was monitored in LB medium. After reaching an OD600 of approximately 1 (black crosses), increasing concentrations of rhamnolipids (A–D) or sophorolipids (E–H) were added to equal volumes of the cell culture. Cell growth was monitored for further 6 h after stress induction. All stress approaches were performed in biological triplicates.
Figure 2
Figure 2
Stress approaches with biotechnologically established strains using rhamnolipids and sophorolipids. Cell growth of B. subtilis KM0 (A,E), C. glutamicum ATCC13032 (B,F), E. coli BL21 (DE3) (C,G) and P. putida KT2440 (D,H) was monitored in LB medium. After reaching an OD600 of approximately 1 (black crosses), increasing concentrations of rhamnolipids (A–D) or sophorolipids (E–H) were added to equal volumes of the cell culture. Cell growth was monitored for further 6 h after stress induction. All stress approaches were performed in biological triplicates.
Figure 3
Figure 3
Effect of surfactin on B. subtilis during cultivation in defined mineral salt medium. (A) The B. subtilis KM0 strain was cultivated in mineral salt medium containing 8 g/L glucose. In addition, different surfactin concentrations of 0 (black squares), 10 (red dots), 30 (blue triangles), 50 (green inverted triangles) and 70 g/L (violet diamonds) were added to the cultivation medium. Cell growth was monitored hourly. A polynomial curve fit of the order of 9 was integrated using the Origin graphing tool. (B) The effect of the surfactin present during cultivation was determined using the overall growth rates calculated for the exponential growth phase (white bars) and the maximum specific growth rates during the cultivation process (grey bars). All cultivations were performed in biological triplicates.
Figure 4
Figure 4
Identification and classification of proteins with altered abundance after surfactin treatment. (A) B. subtilis KM0 was cultivated in mineral salt medium (black crosses). An equal volume of the cell suspension was then mixed with either fresh mineral salt medium (black squares) or with surfactin solution (red dots). The cultivations and stress approaches were performed in biological triplicates. (B) Differences between protein abundances identified before and 10 min after surfactin treatment were mapped into a Volcano plot using R package proteus (v. 1.6.14.0). Non-significant proteins (marked in grey) are below the p-value range, while significantly reduced proteins were highlighted in blue and induced proteins in red. The x-axis shows the log2 fold-changes of the identified proteins between control and surfactin treatment, while the y-axis demonstrates the -log10 of the adjusted p-value. (C) All proteins with significantly changed abundances after surfactin treatment were classified according to information from the SubtiWiki database [55]. Proteins with increased presence after surfactin treatment were colored in red, while reduced proteins were highlighted in blue. The specific colouring was based on the calculated log2 fold-changes. The size of the polygon was determined based on the occurrence of each protein in the dataset.
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