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. 2019 Mar 3;24(5):890.
doi: 10.3390/molecules24050890.

Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species

Alexandros Patsilinakos  1   2 , Marco Artini  3 , Rosanna Papa  4 , Manuela Sabatino  5 , Mijat Božović  6 , Stefania Garzoli  7 , Gianluca Vrenna  8 , Raissa Buzzi  9 , Stefano Manfredini  10 , Laura Selan  11 , Rino Ragno  12   13
Affiliations

Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species

Alexandros Patsilinakos et al. Molecules. .

Abstract

Biofilm resistance to antimicrobials is a complex phenomenon, driven not only by genetic mutation induced resistance, but also by means of increased microbial cell density that supports horizontal gene transfer across cells. The prevention of biofilm formation and the treatment of existing biofilms is currently a difficult challenge; therefore, the discovery of new multi-targeted or combinatorial therapies is growing. The development of anti-biofilm agents is considered of major interest and represents a key strategy as non-biocidal molecules are highly valuable to avoid the rapid appearance of escape mutants. Among bacteria, staphylococci are predominant causes of biofilm-associated infections. Staphylococci, especially Staphylococcus aureus (S. aureus) is an extraordinarily versatile pathogen that can survive in hostile environmental conditions, colonize mucous membranes and skin, and can cause severe, non-purulent, toxin-mediated diseases or invasive pyogenic infections in humans. Staphylococcus epidermidis (S. epidermidis) has also emerged as an important opportunistic pathogen in infections associated with medical devices (such as urinary and intravascular catheters, orthopaedic implants, etc.), causing approximately from 30% to 43% of joint prosthesis infections. The scientific community is continuously looking for new agents endowed of anti-biofilm capabilities to fight S. aureus and S epidermidis infections. Interestingly, several reports indicated in vitro efficacy of non-biocidal essential oils (EOs) as promising treatment to reduce bacterial biofilm production and prevent the inducing of drug resistance. In this report were analyzed 89 EOs with the objective of investigating their ability to modulate bacterial biofilm production of different S. aureus and S. epidermidis strains. Results showed the assayed EOs to modulated the biofilm production with unpredictable results for each strain. In particular, many EOs acted mainly as biofilm inhibitors in the case of S. epidermidis strains, while for S. aureus strains, EOs induced either no effect or stimulate biofilm production. In order to elucidate the obtained experimental results, machine learning (ML) algorithms were applied to the EOs' chemical compositions and the determined associated anti-biofilm potencies. Statistically robust ML models were developed, and their analysis in term of feature importance and partial dependence plots led to indicating those chemical components mainly responsible for biofilm production, inhibition or stimulation for each studied strain, respectively.

Keywords: Staphylococcus species; antimicrobial; biofilm; essential oil; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest

Figures

Figure 1
Figure 1
Percentages of biofilm production after treatment at two concentrations (3.125 mg/mL and 0.0488 mg/mL) for RSEOs against the four strains S. aureus 6538P (A) and 25923 (B), S. epidermidis RP62A (C) and O-47 (D, respectively). In the ordinate axis are reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 1
Figure 1
Percentages of biofilm production after treatment at two concentrations (3.125 mg/mL and 0.0488 mg/mL) for RSEOs against the four strains S. aureus 6538P (A) and 25923 (B), S. epidermidis RP62A (C) and O-47 (D, respectively). In the ordinate axis are reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 2
Figure 2
Percentages of biofilm production after treatment at two concentrations (3.125 mg/mL and 0.0488 mg/mL) for FVEOs against the four strains S. aureus 6538P (A) and 25923 (B), S. epidermidis RP62A (C) and O-47 (D, respectively). In the ordinate axis are is reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 2
Figure 2
Percentages of biofilm production after treatment at two concentrations (3.125 mg/mL and 0.0488 mg/mL) for FVEOs against the four strains S. aureus 6538P (A) and 25923 (B), S. epidermidis RP62A (C) and O-47 (D, respectively). In the ordinate axis are is reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 3
Figure 3
Percentages of biofilm production after treatment at two concentrations (3.125 mg/mL and 0.0488 mg/mL) for CGEOs against the four strains S. aureus 6538P (A) and 25923 (B), S. epidermidis RP62A (C) and O-47 (D), respectively). In the ordinate axis are is reported the percentage of bacterial biofilm production. The abscissa axis is centered at 100% biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 3
Figure 3
Percentages of biofilm production after treatment at two concentrations (3.125 mg/mL and 0.0488 mg/mL) for CGEOs against the four strains S. aureus 6538P (A) and 25923 (B), S. epidermidis RP62A (C) and O-47 (D), respectively). In the ordinate axis are is reported the percentage of bacterial biofilm production. The abscissa axis is centered at 100% biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 4
Figure 4
Antibiofilm effect of selected RSEOs on RP62A and on O-47 strains. In the ordinate axis is reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least in three replicates.
Figure 5
Figure 5
Antibiofilm effect of selected FVEOs on RP62A and on O-47 strains. In the ordinate axis is reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 6
Figure 6
Antibiofilm effect of selected CGEOs on RP62A and on O-47 strains. In the ordinate axis is reported the percentage of bacterial biofilm production. Data are reported as percentage of residual biofilm after the treatment in comparison with the untreated one. Each data point is composed of 4 independent experiments each performed with at least three replicates.
Figure 7
Figure 7
Feature importance plot for the 6538P/activation model defined at 3.125 mg/mL.
Figure 8
Figure 8
Feature importance plot for the 25923/activation model defined at 3.125 mg/mL.
Figure 9
Figure 9
Feature importance plot for the RP62A/inhibition model defined at 3.125 mg/mL.
Figure 10
Figure 10
Feature importance plot for the O-47/inhibition model defined at 3.125 mg/mL.
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