Patterns of cross-resistance and collateral sensitivity between clinical antibiotics and natural antimicrobials

Abstract

Bacteria interact with a multitude of other organisms, many of which produce antimicrobials. Selection for resistance to these antimicrobials has the potential to result in resistance to clinical antibiotics when active compounds target the same bacterial pathways. The possibility of such cross-resistance between natural antimicrobials and antibiotics has to our knowledge received very little attention. The antimicrobial activity of extracts from seaweeds, known to be prolific producers of antimicrobials, is here tested against Staphylococcus aureus isolates with varied clinical antibiotic resistance profiles. An overall effect consistent with cross-resistance is demonstrated, with multidrug-resistant S. aureus strains being on average more resistant to seaweed extracts. This pattern could potentially indicate that evolution of resistance to antimicrobials in the natural environment could lead to resistance against clinical antibiotics. However, patterns of antimicrobial activity of individual seaweed extracts vary considerably and include collateral sensitivity, where increased resistance to a particular antibiotic is associated with decreased resistance to a particular seaweed extract. Our correlation-based methods allow the identification of antimicrobial extracts bearing most promise for downstream active compound identification and pharmacological testing.

Keywords: Antimicrobials; Staphylococcus aureus; antibiotic resistance; collateral sensitivity; cross‐resistance; seaweeds.

Figure 1

Correlation between clinical resistance (sum of 22 antibiotics assayed using VITEK technology) and seaweed resistance (sum of 27 methanolic extracts) for 28 S. aureus isolates (R ² = 0.21, p < 0.01).

Figure 2

Correlation between dilution factor and inhibition zone size for 27 extracts assayed on strain SA2934 (R ² = 0.21, p < 0.01). Dilution factor is inversely proportional to Minimal Inhibitory Concentration (MIC).

Figure 3

Correlation between resistance against clinical antibiotics (assayed using VITEK technology) and seaweed resistance as quantified by the sum of inhibition zone sizes of the 27 seaweed extracts able to inhibit all 28 S. aureus isolates (R ² = 0.08, p < 0.07).

Figure 4

Box‐plots of inhibition zone size standard deviations for a high and low variance seaweed antimicrobial activity group created by the k‐means algorithm with default settings

Figure 5

Pearson correlation coefficients between seaweed extract inhibition zone sizes and clinical antibiotic MICs assayed using VITEK technology generated on a test panel of 28 S. aureus isolates. Colour‐coded values range from −1 = perfect negative correlation (red) to 1 = perfect positive correlation (blue); the size of the data points co‐varies with colour intensity

Figure 6

Differences in anti‐S. aureus activity between three Cystoseira species: C. tamariscifolia (CT), C. baccata (CB) and C. nodicaulis (CN) based on the average of two independent replicates. (a) Heatmap showing inhibition zone size (yellow: small, red: large) for each of the three extracts on 28 S. aureus isolates clearly demarcates the CN extract as having differential activity. (b) A correlation matrix plotting inhibition zone sizes for pairs of extracts on the S. aureus panel

Figure 7

A maximum‐likelihood phylogenetic tree based on whole genome sequence data of 27 S. aureus genomes used in this study mapped to EMRSA15 reference genome HO 5,096 0,412 (not included in the tree) (2,832,299 bp). The panel on the right indicates susceptibility to 25 seaweed extracts, quantified by zone of inhibition (red = resistant, no inhibition; green = susceptible, high inhibition). Ceramium sp., and A. armata were excluded to aid visualization as they produced extremely high inhibition on the majority of the isolates. White cells in the figure indicate missing data. The phylogenetic tree was generated using a GTR model of nucleotide substitution and a GAMMA model of rate heterogeneity in RaxML