Interaction between lichen secondary metabolites and antibiotics against clinical isolates methicillin-resistant Staphylococcus aureus strains.
The in vitro antimicrobial activities of five compounds isolated from lichens, collected in several Southern regions of Chile (including the Chilean Antarctic Territory), were evaluated alone and in combination with five therapeutically available antibiotics, using checkerboard microdilution assay against methicillin-resistant clinical isolates strains of Staphylococcus aureus. [MIC.sub.90], [MIC.sub.50], as well as [MBC.sub.50] and [MBC.sub.50], for the lichen compounds were evaluated. The [MIC.sub.90] was ranging from 32 [micro]g/ml for perlatolic acid to 128 [micro]g/ml for [alpha]- collatolic acid. [MBC.sub.90] was ranging from onefold up to twofold the [MIC.sub.90] for each compound. A synergistic action was observed in combination with gentamicin, whilst antagonism was observed for some lichen compounds in combination with levofloxacin. All combinations with erythromycin were indifferent, whilst variability was observed for clindamycin and oxacillin combinations. Data from checkerboard assay were analysed and interpreted using the fractional inhibitory concentration index and the response surface approach using the [DELTA]E model. Discrepancies were found between both methods for some combinations. These could mainly be explained by the failure of FIC approach, being too much subjective and sensitive to experimental errors. These findings suggest, however, that the natural compounds from lichens are good candidates for the individuation of novel templates for the development of new antimicrobial agents or combinations of drugs for chemotherapy.
Lichen secondary metabolites
Lichens are symbiotic organisms derived by the close cellular union of a fungal (mycobiont) and an algal and/or cyanobacteria! (phytobiont) partner, comprising about 20,000 known species. They produce a variety of secondary compounds that typically arise from the fungal component secondary metabolism, many of them, exclusive of the lichen production. Chemotaxonomic studies have shown that the most unique lichen metabolites belong to the chemical classes of depsides, depsidones and dibenzofurans. The almost 800 known lichen secondary metabolites can be classified according to the classic biosynthetic pathways: the poliketidic path (monocyclic phenols, depsides, depsidones, depsones, dibenzofurans, xanthones, naphtaquinones anthraquinones, macrocyclic lactones, aliphatic acids, etc.), the mevalonic acid path (steroids, carotenoids, etc.) and the shikimic acid path (amino acid derivatives, cyclopeptides, etc.) (Huneck 1999).
Although medicinal plants have been used for centuries as sources of therapeutic agents worldwide, they cannot be classified as pure and efficient antimicrobial agents. However, in spite of the fact that plant-derived antibacterial compounds show a general low degree of activity, most plants, indeed, are successful in fighting infections (Hemaiswarya et al. 2008). Plants, in fact adopt "synergy" as their peculiar different paradigm to fight pathogenic microorganisms. Several studies have demonstrated that a number of natural products, which failed as antimicrobials, are able to dramatically increase the effectiveness of chemotherapeutic agents against Gram-negative and Gram-positive bacteria (Gibbons and Udo 2000; Tegos et al. 2002; Stavri et al. 2007; Hemaiswarya et al. 2008; Celenza et al. 2012; Segatore et al. 2012).
Antimicrobial resistance has emerged among pathogenic bacteria since the beginning of the antibiotic era. Resistance potentially extends to the entire repertoire of available therapeutic agents. Nowadays, bacteria expressing multidrug resistant (MDR), extensively drug resistant (XDR) and pandrug resistant (PDR) phenotypes are amongst the most important cause of infections in nosocomial and community settings and new drugs are urgently needed.
As a result of its intrinsic ability to overcome antibiotic chemotherapy, Staphylococcus aureus continuously expands its ecological niche. It is resistant to many adverse environmental conditions, so that MRSA strains are mainly associated with hospital acquired infections (HA-MRSA). The rate of mortality of septicemia caused by VISA raised from 30% for MRSA, to almost 80% (Hiramatsu et al. 1997; Burnie et al. 2000; Fridkin et al. 2003). Thus, the emergence of resistant S. aureus bacteria has serious consequences both in terms of therapeutic failures and impact on Health Care System.
To meet the growing challenge of S. aureus, the identification of novel targets for small molecules is one of the most important approach to face the problem (Garcia-Lara et al. 2005; Wright and Sutherland 2007; Gibbons 2008; Silver 2011).
To overcome antibiotic-mediated resistance, a valuable alternative would be the use of combination of drugs. Thus, substances that can increase susceptibility to currently licensed agents would be a very attractive and valuable option (Wagner and Ulrich-Merzenich 2009).
In this paper the authors analyse five selected compounds from Chilean lichens for their antimicrobial activity against MRSA clinical isolated strains, tested alone and in combination with five therapeutically available antibiotics. Data from checkerboard assay were interpreted by Loewe additivity-based model and Bliss independence-based model.
Materials and methods
Twenty methicillin-resistant S. aureus strains were used in this study. The organisms were collected during a 4 years period, from 2006 to 2010, at the University Hospital "San Salvatore" of l'Aquila, Italy. They were isolated from hospitalized patients, from wounds, surgical wounds, vascular and urinary catheters, blood, respiratory tract. They were identified as MRSA organisms by Phoenix System (Becton Dickinson). The methicillin-resistantS. aureus from the American Type Culture Collection, ATCC 43300 was used as control. Four strains, namely, AQ004, AQ006, AQ007 and AQ012 clinical isolates and the reference strain ATCC 43300 were used for the drug interaction assay. All those strains were resistant to clindamycin, erythromycin, gentamicin, levofloxacin, oxacillin, with the exception of ATCC 43300 that was sensible to levofloxacin.
All tested antibiotics, clindamycin (CL1), erythromycin (ERY), gentamicin (GEN), levofloxacin (LVX), oxacillin (OXA), were from SigmaAldrich (Milan, Italy).
Secondary metabolites from lichens
The lichen secondary metabolites used in this study are: a-collatolic acid (COL), epiforellic acid (EPI), lobaric acid (LOB), perlatolic acid (PER) and (+)-protolichesterinic acid (PRO), whose structures are reported in Fig. 1 and Table 1. These compounds were isolated and structurally determined as previously reported (Fiedler et al. 1986; Piovano et al. 1989). The degree of purity for the compounds was >98% as determined by thin layer chromatography (TLC) and 1H NMR analyses.
In vitro susceptibility tests
The antimicrobial susceptibility pattern of the organisms used in this study was determined in accordance with the CLSI guidelines (CLSI 2010) by microdilution test performed in a 96 microwell plates with an inoculum of 5 x [10.sup.5] CFU/ml.
Bactericidal tests were performed as previously described by Pearson et al. (1980) and Taylor et al. (1983).
Checkerboard microdilution assay
The in vitro interactions between the antibiotics and the compounds from lichens were investigated by a two-dimensional checkerboard microdilution assay, using a 96-well microtitration plates as previously described (Segatore et al. 2012; Celenza et al. 2012). Briefly, in each well of the microplate 25 [micro]l of microbial growth medium were added. An aliquot of 25 [micro]l of a fourfold concentrated antibiotic was added to column 12. Then a twofold dilution was made from column 12 to column 2. A 25 [micro]l aliquot of each drug concentration of the compound was added to rows A to G. Row H contained only the antibiotic whilst column 1 only the compound. Well H1 was the drug free well used as growth control. Finally, 50 [micro]l of a saline solution (0.9% of NaCl) containing bacteria were added to each well of the microplate in order to obtain a final inoculum of 5 x [10.sup.5] CFU/ml. The microtitre plates were incubated at 37[degrees]C for 18 h. The growth in each well was quantified spectrophotometrically at 595 nm by a microplate reader iMark, BioRad (Milan, Italy). The percentage of growth in each well was calculated as:
[OD.sub.drug combination well] - [OD.sub.background]/ [OD.sub.drug free well] - [OD.sub.background]
where the background was obtained from the microorganism-free plates, processed as the inoculated plates. The MICs for each combination of drugs were defined as the concentration of drug that reduced growth by 80% compared to that of organisms grown in the absence of drug. All experiments were performed in triplicate.
Drug interaction models
In order to assess the nature of the in vitro interactions between the lichen compounds and antibiotics against each S. aureus, the data obtained from the checkerboard assay were analysed by nonparametric models based on the Loewe additivity model (LA) and the Bliss independence (BI) theory (Greco et al. 1995).
Loewe additivity-based model
The Loewe additivity model, based on the idea that an agent cannot interact with itself, is expressed by the following equation:
1 = [D.sub.1]/[ID.sub.x.1] + [D.sub.2]/[ID.sub.x.2]
where [ID.sub.x.1] and [ID.sub.x.2] are the concentrations of the drugs to result in X% inhibition for each respective drug alone. [D.sub.1] and [D.sub.2] are concentrations of each drug in the mixture that yield X% inhibition.
The interaction index as define by Berenbaum (1977) is expressed by the equation:
I = [D.sub.1]/[ID.sub.x.1] + [D.sub.2]/[ID.sub.x.2]
When I > 1, Loewe antagonism is claimed, when I < 1, Loewe synergism is claimed.
The interaction index as define by Berenbaum, can be adapted to calculate the fractional inhibitory concentration index (FICI), that is the mathematical expression of the effect of the combination of antibacterial agents expressed as:
[DELTA]FIC = [FIC.sub.A] + [FIC.sub.B] = [MIC.sub.AB]/[MIC.sub.A] + [MIC.sub.BA]/[MIC.sub.B]
where [MIC.sub.A] and [MIC.sub.B] are the MICs of drugs A and B when acting alone and [MIC.sub.AB] and [MIC.sub.BA] are the MICs of drugs A and B when acting in combination. Among all [SIGMA]FICs calculated for each microplate, the FICI was determined as the lowest [SIGMA]FIC ([SIGMA][FIC.sub.min]) when synergy is supposed, or the highest [SIGMA]FIC ([SIGMA][FIC.sub.max]) when antagonism is evident.
Since in MIC determination, the variation in a single result places a MIC value in a three-dilution range ([+ or -]1 dilution), therefore, the reproducibility errors in MIC checkerboard assays are considerable.
For that reasons, the interpretation of FICI data should be done taking into consideration values well below or above the theoretical cut-off (1.0) defined by Berenbaum. Synergy was, therefore, defined when FICI [less than or equal to] 0.5, while antagonism was defined when FICI > 4. A FIC index between 0.5 and 4 (0.5 < FICi [less than or equal to] 4) was considered indifferent (Odds 2003).
Bliss independence-based model
In the Bliss models, the combined effects of the drugs calculated from the effect of the individual drugs, are compared with those obtained experimentally. The BI theory is described by the equation: [E.sub.i] = [E.sub.A] x [E.sub.B], where [E.sub.i] is the calculated percentage of growth based on the theoretical non-interactive combination of drug A and B, and [E.sub.A] and [E.sub.B] are the experimental percentages of growth of each drug acting alone.
The experimental dose-response surface (Fig. 2A) is subtracted from the calculated theoretical surface to reveal any significant deviation from the zero-plane. The interaction is described by the difference ([DELTA]E) between the predicted and measured percentages of growth with drugs at various concentrations ([DELTA]E = [E.sub.predicted] - [E.sub.measured]). To determine the significance of differences between the experimental and calculated additive effects, the upper and lower 95% confidence limits of the experimental data were compared to the calculated additive effects. If the lower confidence limit of a point was greater than the calculated additivity, the observed synergy was considered to be significant. Similarly, if the upper confidence limit was lower than the calculated additivity, the observed antagonism was considered to be significant (Deminie et al. 1996; Prichard and Shipman 1990; Prichard et al. 1991). The [DELTA]E values calculated on a point-by-point basis were subsequently plotted on the z axis (Fig. 2B). Points of the difference surface above zero (positive) indicate synergy, below zero (negative) antagonism. In order to summarize the interaction surface, the Bliss synergy and antagonism differences and all their combinations were added up to yield a summary measure, respectively of Bliss synergy ([SIGMA]SYN) and Bliss antagonism ([SIGMA]ANT). Interactions <100% were considered weak, interactions between 100% and 200% were considered moderate, whilst interaction >200% were considered strong (Meletiadis et al. 2005).
In vitro susceptibility test and interaction of drugs
The in vitro antibacterial effects of lichen compounds is reported in Table 2. For all the 20 MRSA clinical isolates and the ATCC4300, [MIC.sub.50] and [MIC.sub.90] were calculated, as well as [MBC.sub.50] and [MBC.sub.90]. The minimum inhibitory activity required to inhibit the growth of 90% of the organisms was ranging from 32 [micro]g/ml for PER to 128 [micro]g/ml for COL. The [MBC.sub.90] measured was one- or twofold higher than the calculated [MIC.sub.90].
Amongst the compounds the most active was PER with [MIC.sub.50] and [MIC.sub.90] values 16 [micro]g/ml and 32 [micro]g/ml, respectively.
All 20 S. aureus clinical isolates and the reference strain ATCC 43300 were tested for their susceptibility to clindamycin, erythromycin, gentamicin, levofloxacin and oxacillin by microdilution test (data not shown). Amongst these strains, four (namely AQ004, AQ006, AQ007 and AQ012) and the reference strain ATCC 4330 were chosen for the interaction assay, since they were resistant to all tested antibiotics (Table 3), with the exception of the control strains which was sensible to levofloxacin. The MICs values of the lichen compounds versus the selected strains are reported in Table 4.
Clindamycin, erythromycin, gentamicin, levofloxacin and oxacillin were chosen for the drug-interaction assay, since they belong to different classes of antimicrobial agents, respectively, lincosamides, macrolides, aminoglycosides, quinolones and [beta]-lactams.
Tables 5-9 summarize the results of the broth microdilution checkerboard analysis interpreted by the F1CI and [DELTA]E methods of the five MRSA strains for the combination of lichen compounds and antibiotics. Results for each combination of antibiotics and lichen compounds are reported below.
According to FIC1 interpretation, synergism was found only in S. aureus AQ006 in combination with LOB (F1C1 = 0.3125), PER (FIC1 = 0.3125) and PRO (FICI = 0.3125). Indifference (FIG > 0.5) was observed in all other combinations. [DELTA]E model interpretation for clindamycin confirmed synergism for those combinations. Moreover, most of the combinations, which FIG was approximately 0.5 and reported as indifference, were interpreted by the [DELTA]E model as synergism (Table 5).
For the combination of erythromycin with lichen compounds, no synergism was found. In all combinations, FICI and [DELTA]E model were in accordance defining indifference (Table 6).
For the gentamicin combination, FICI synergism was observed in all lichen compounds and strains. The lowest FICIs were observed in PRO combination, with FIC indexes ranging from 0.1563 for ATCC 43300 and AQ007, to 0.2813 for AQ012 (Table 7).
For the combination of levofloxacin with lichen compounds, no synergism was found. FIC index for each combination was reported as indifference. [DELTA]E model confirmed FICI interpretation only in the combination with EPI and PRO. For all other combinations, antagonism was reported (Table 8).
According to FICI interpretation, synergism was found in only in two MRSA strains in oxacillin combination with COL (ATCC 4330, FICI = 0.5; AQ004, FICI = 0.3125), one with EPI (ATCC 4330, FICI = 0.5) and four with PRO (ATCC 43300, AQ004, AQ007 and AQ012, FICI = 0.5). Indifference (FICI > 0.5) was observed in all other combinations. [DELTA]E model interpretation for oxacillin confirmed synergism for those combinations. Moreover, most of the combinations, which FICI was approximately 0.5 and reported as indifference, were interpreted by the [DELTA]E model as synergism (Table 9).
The aim of this paper is to investigate the potential antimicrobial activity of several selected natural compounds from lichens alone, and in combination with commercially available antibiotics.
The MIC values calculated for these compounds versus MRSA clinical isolates were comparable with those generally found in many antibiotics used in clinical therapy. Since, the MBCs calculated were close to the MICs, we hypothesize that those compounds, at least against MRSA, act as bactericidal. Synergy is ideally defined as the interaction of two or more substances, to produce a combined effect greater than the sum of their separate effects. In addition, it is important to emphasize that even the models used to analyse drug-drug interaction might, somehow, affect the interpretation of the data. There are many published methods for assessing drug interactions, most of them are summarized and compared in the review of Greco (Greco et al. 1995). For instance we used two models, the FIG model based on Loewe additivity theory and [DELTA]E model based on Bliss independence theory.
The percentage of agreement in the interpretation of the FIC index and the response surface approach was highly variable amongst the combinations. It was ranging from 0% to 100% for the levofloxacin combinations (Table 8) to 100% for the erythromycin combinations (Table 6).
The discrepancy between the results of the two methods is the natural consequence of the high variability intrinsic in the FICI model (Cappelletty and Rybak 1996; Mackay et al. 2000; Sun et al. 2008; TeDorsthorst et al. 2002). The results obtained by this method are strongly dependent on the MIC endpoints and the cutoff values used to define synergism and antagonism. For instance, most of the combinations interpreted as indifference by F1C index model, which value is more than 0.5 but less than 1, are interpreted as synergic using the [DELTA]E model. The same is found in levofloxacin combinations, in which, the interpretation of FIC index model as indifference (FICI > 1 but <4) is, for some lichen compounds combinations, interpreted by the [DELTA]E model as antagonism.
In comparison with the FIC index model, the response surface approach, as determined by the [DELTA]E model, allows an objective analysis of the experimental data which are compared with a theoretical model. Fitting of the theoretical model to the whole data surface allows the use of all available data from the checkerboard assay, thereby indicating the statistical significance of the interaction.
Interestingly, levofloxacin, in accordance to the [DELTA]E model interpretation, acts antagonistically in combination with COL, LOB and PER (Table 8). Although, generally, fluoroquinolones rarely show antagonism in combination with other antibiotics, our data seem to agree with several studies in which fluoroquinolones both Grampositive and Gram-negative bacteria, in combination with ertapenem, rifampin, fusidic acid and linezolid act antagonistically (Hosgor-Limoncu et al. 2008; Murillo et al. 2008; Neu 1991; Sahuquillo et al. 2006; Sweeney and Zurenko 2003; Uri 1993).
A few data about the biological activities of the tested compounds are reported. The cytotoxic activity of lobaric acid and protolichesterinic acid has been assessed in a previous study (Brisdelli et al. 2013), where protolichesterinic acid has been demonstrated to show antiproliferative activity on several human cancer cell lines. Its cytotoxic effect has been related to the ability to induce apoptosis through a caspase-dependent pathway in HeLa cells.
Amongst the tested compounds, only for protolichesterinic acid it is possible to hypothesize a mechanism of action. The acyl-itaconic acid derivative protolichesterinic acid resembles, from a structural stand-point, the well-known anticancerogenic C75 (Kuhajda et al. 2000). They differ from each other for the length of the acyl-moiety, which is respectively Cl 2 and C8. C75 is a novel, potent synthetic inhibitor of the beta-ketoacyl synthase moiety of the eukaryotic fatty acid synthase (FAS type 1).
Although several differences can be retrieved between FAS type 1 and FAS type II (Archea and Eubacteria), FAS systems are quite conserved. It is plausible that protolichesterinic acid, acts as inhibitor of the beta-ketoacyl-acyl carrier protein synthase III (FabH) from S. aureus.
Bacterial pathogens are increasingly becoming resistant even to the most recently approved antibiotics (Cottarel and Wierzbowski 2007). There is a crucial and urgent need for the developing of new classes of antibiotics or for revitalizing the already used ones. The emergence of MDR, XDR and PDR bacteria has serious consequences both in terms of therapeutic failures and impact on Health Care System. Thus, substances able to increase the susceptibility to currently licensed agents, would be a very attractive and valuable option. Preserving the efficacy of currently available antibiotics, therefore, remains a major goal in particular if we consider that pharmaceutical companies seem to be no more interested in developing new classes of antimicrobial agents. Furthermore, the investigation of the potential antimicrobial activity of natural molecules from the secondary metabolism of living wild organisms could provide us, as happened in the past, new templates for the synthesis of more efficient and potent antimicrobial agents.
We can conclude that the compounds used in this study show a good antimicrobial activity against multi-drug resistant MRSA clinical isolates. The combination of these compounds with clyndamicin, erythromycin, levofloxacin, oxacillin and gentamicin, is highly synergic only for gentamicin, and, on the contrary, the combination with levofloxacin is not advantageous.
We chose to analyse the checkerboard assay data with two models, the FIC index and the response surface approach. However, although the former methods is popular among bacteriologists, and historically important, it is subjective, sensitive to experimental errors and often provides approximated results and variable conclusions.
At this purpose, the response surface approach is a good alternative for determining the interaction between drugs with antimicrobial activity.
Finally, it is important to emphasize that the results obtained in in vitro studies do not necessarily correlate with clinical outcome. However, they are a first step for the individuation of novel molecules or templates for the development of new antimicrobial agents or combinations of drugs for chemotherapy.
Conflict of interest
There was no conflict of interest.
Received 16 June 2014
Revised 6 October 2014
Accepted 14 December 2014
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Alessia Sabatini (a), Fabrizia Brisdelli (a), Marisa Piovano (b), Marcello Nicoletti (c), Gianfranco Amicosante (a), Mariagrazia Perilli (a), Giuseppe Celenza (a),*
(a) Department of Biotechnological and Applied Clinical Sciences, University of l'Aquila, L'Aquila, Italy
(b) Department of Chemistry, Universidad Tecnica F. Santa Maria, Casilla 110 V, Valparaiso, 6, Chile
(c) Department of Enviromental Biology, University Sapienza, Rome, Italy
* Corresponding author at: Department of Biotechnological and Applied Clinical Sciences, University of l'Aquila, Via Vetoio, 1,67100 l'Aquila, Italy. Tel.: +39 0862433444.
E-mail address: celenza@>univaq.it (G. Celenza).
Table 1 Selected constituents tested for antimicrobial activity, their lichen species and Chilean regions of collection. Compound Species [alpha]-Collatolic acid Lecanora atra (Hudson) Acharius Epiphorellic acid Comicularia epiphorella (Nyl.) Du Rietz Lobaric acid Stereocaulon alpinum Laurer ex Funck Perlatolic acid Stereocaulon sp. Protolichesterinic acid Comicularia aculeata (Schreb.) Ach. Compound Chilean geographic origin [alpha]-Collatolic acid Robert Island, Shetland del Sur, Antarctica Epiphorellic acid Conguillio National Park, Region de la Araucania Lobaric acid Ardley Cove, King George island, Shetland del Sur, Antarctica Perlatolic acid Parque Nacional Puyehue, Region de Los Lagos, Chile Protolichesterinic acid Ardley Cove, King George Island, Shetland del Sur, Antarctica Compound Reference [alpha]-Collatolic acid Sweeney and Zurenko (2003) Epiphorellic acid Gibbons (2008) Lobaric acid Sweeney and Zurenko (2003) Perlatolic acid Not published Protolichesterinic acid Sweeney and Zurenko (2003) Table 2 MIC and MBC calculated for all S. aureus strains. Compound MIC ([micro]g/ml) Range [MIC.sub.50] [MIC.sub.90] COL 32-128 128 128 EPI 8-32 32 32 LOB 32-128 32 64 PER 4-64 16 32 PRO 4-64 32 64 Compound MBC ([micro]g/ml) Range [MIC.sub.50] [MIC.sub.90] COL 128-512 256 512 EPI 16-128 64 128 LOB 32-256 64 128 PER 16-64 32 64 PRO 32-128 64 128 Table 3 MIC of antibiotics for the strains of S. aureus used in the checkerboard assay. Strain Median MIC ([micro]g/ml) (range) Antibiotic CLI ERY GEN ATCC43300 8192 (512-1024)1024 (64-256)256 AQ004 8192 (512-1024)1024 (16-32)32 AQ006 (64-128)128 512 (128-256)128 AQ007 (32-64)32 (16-64)64 128 AQ012 16,384 (1024-2048)1024 (32-64)64 Strain Median MIC ([micro]g/ml) (range) Antibiotic LVX OXA ATCC43300 [less than or equal to] 0.5 (8-16)8 AQ004 (16-64)16 (128-256)128 AQ006 (8-32)8 2048 AQ007 (16-64)16 (256-512)256 AQ012 32 (256-512)512 Table 4 MIC of lichen compounds for the strains of S. aureus used in the checkerboard assay. Strain Median MIC ([micro]g/ml) (range) Compound COL EP1 LOB ATCC43300 (16-32) 32 (8-16) 8 32 AQ004 (32-64) 32 (8-16) 16 64 AQ006 (32-128) 128 (16-32) 16 (64-128) 64 AQ007 (64-128) 128 (16-32) 32 64 AQ012 (32-128) 64 (8-32) 16 (64-128) 64 Strain Median MIC ([micro]g/ml) (range) Compound PER PRO ATCC43300 (8-32) 32 (8-16) 16 AQ004 (32-64) 32 (4-16) 16 AQ006 (8-32) 16 (32-64) 32 AQ007 (4-32) 16 32 AQ012 (8-32) 16 (16-32) 32 Table 5 In vitro interaction between clindamycin and secondary metabolites from lichens as determined by non-parametric FIC1 and the [DELTA]E model (a). Compound Strain FICI Median (range) INT COL ATCC43300 (1.0625-1.125) 1.25 IND AQ004 (1.0625-1.125) 1.125 IND AQ006 (0.625-0.75) 0.625 IND AQ007 1 IND AQ012 (1.0625-1.125) 1.125 IND EPI ATCC43300 (0.5156-0.5313) 0.5156 IND AQ004 (0.5625-0.625) 0.625 IND AQ006 (0.5313-05625) 0.5313 IND AQ007 (0.5313-0.5625) 0.5625 IND AQ012 (1.0313-1.0625) 1.0625 IND LOB ATCC43300 (1.0625-1.125) 1.0625 IND AQ004 (0.5020-0.0539) 0.502 IND AQ006 (0.2813-0.3125) 0.3125 SYN AQ007 (0.625-0.75) 0.75 IND AQ012 (1.0625-1.125) 1.125 IND PER ATCC43300 (1.0039-1.0078) 1.0039 IND AQ004 (0.5-0.501) 0.501 IND AQ006 (0.2813-0.3125) 0.3125 SYN AQ007 (0.5313-0.625) 0.625 IND AQ012 (0.5-0.5002) 0.5002 IND PRO ATCC43300 (0.5078-0.5313) 0.5313 IND AQ004 (0.5-0.501) 0.501 IND AQ006 (0.3125-0.375) 0.3125 SYN AQ007 (0.5313-0.5625) 0.5625 IND AQ012 (1.0625-1.125) 1.0625 IND Compound Strain [DELTA]E model (b) [SIGMA]SYN (n) [SIGMA]ANT (n) INT COL ATCC43300 62.7 (28) -30.7 (30) IND AQ004 42.7 (29) -38.1 (35) IND AQ006 98.1 (26) -96.8 (43) IND AQ007 87.5 (18) -67.6 (20) IND AQ012 43.1 (12) -89.3 (18) IND EPI ATCC43300 1285.9 (30) -85.9 (8) SYN AQ004 119.4 (20) -82.5 (11) SYN AQ006 179.0 (23) -46.0 (7) SYN AQ007 780.4 (41) -43.2 (9) SYN AQ012 73.1 (15) -91.5 (25) IND LOB ATCC43300 69.8 (36) -92.2 (11) IND AQ004 199.6 (51) -40.0 (13) SYN AQ006 401.2 (23) -65.4 (34) SYN AQ007 82.3 (16) -65.7 (14) IND AQ012 83.4 (15) -94.0 (19) IND PER ATCC43300 22.8 (31) -99.6 (27) IND AQ004 164.1 (46) -50.1 (25) SYN AQ006 1057.2 (41) -52.3 (24) SYN AQ007 88.9 (44) -41 (26) IND AQ012 692,5 (30) -71.4 (25) SYN PRO ATCC43300 149.2 (50) -4.5 (1) SYN AQ004 154.6 (56) -32.6 (6) SYN AQ006 1340.3 (38) -65.6 (25) SYN AQ007 795.1 (40) 95.1 (14) SYN AQ012 75.2 (17) -79.9 (16) IND (a) INT, interpretation; IND, indifference; SYN, synergy; ANT, antagonism. Synergy was defined as an FICI of <0.5, antagonism was defined as an FICI of >4, and indifference was defined as an FICI >0.5 and <4. (b) n, number of drug combinations (among the 77 drug combinations for each strain) with statistically significant synergy or antagonism. Table 6 In vitro interaction between erythromycin and secondary metabolites from lichens as determined by nonparametric FIC1 and the [DELTA]E model (a). Compound Strain FICI Median (range) INT COL ATCC43300 (0.5625-0.625) 0.625 IND AQ004 (0.5625-0.625) 0.625 IND AQ006 (0.5625-0.625) 0.5625 IND AQ007 (0.625-0.75) 0.75 IND AQ012 (0.625-1) 0.75 IND EP1 ATCC43300 (0.625-075) 0.75 IND AQ004 (0.5625-0.625) 0.625 IND AQ006 (0.625-0.75) 0.75 IND AQ007 (0.625-0.75) 0.75 IND AQ012 (1.25-1.5) 1.25 IND LOB ATCC43300 (1.25-1.5) 1.25 IND AQ004 (0.625-0.75) 0.75 IND AQ006 (0.5625-0.625) 0.625 IND AQ007 1 IND AQ012 (1.25-1.5) 1.25 IND PER ATCC43300 (1.25-1.5) 1.5 IND AQ004 (0.75-1) 0.75 IND AQ006 (0.5625-0.625) 0.625 IND AQ007 (0.625-0.75) 0.75 IND AQ012 (1.0625-1.125) 1.125 IND PRO ATCC43300 (1.25-1.5) 1.25 IND AQ004 (0.625-075) 0.75 IND AQ006 (0.5625-0.625) 0.625 IND AQ007 (0.625-0.75) 0.75 IND AQ012 (0.625-0.75) 0.75 IND Compound Strain [DELTA]E model (b) [SIGMA]SYN (n) [SIGMA]ANT(n) INT COL ATCC43300 64.5 (9) -99.2 (9) IND AQ004 99.8 (13) -83.6 (11) IND AQ006 23.8 (4) -96.8 (19) IND AQ007 13.2 (2) -86.9 (2) IND AQ012 13.1 (2) -94.8 (18) IND EP1 ATCC43300 68.3 (11) -97.7 (22) IND AQ004 56.5 (9) -94.6 (21) IND AQ006 45.7 (7) -90.4 (21) IND AQ007 46.3 (6) -92.8 (17) IND AQ012 20.7 (5) -84.6 (15) IND LOB ATCC43300 29.6 (5) -28.4 (5) IND AQ004 13.5 (3) -68.1 (13) IND AQ006 42.0 (7) -89.3 (19) IND AQ007 41.7 (6) -13.1 (2) IND AQ012 11.4 (2) -18.6 (4) IND PER ATCC43300 17.5 (3) -66.9 (11) IND AQ004 80.4 (21) -36.1 (7) IND AQ006 49.8 (9) -63.6 (13) IND AQ007 14.8 (5) -33.9 (7) IND AQ012 58.7 (10) -74.0 (15) IND PRO ATCC43300 74.0 (13) -44.4 (9) IND AQ004 93.2 (20) -98.0 (25) IND AQ006 99.8 (21) -99.2 (26) IND AQ007 88.6 (17) -29.9 (8) IND AQ012 38.4 (7) -58.1 (10) IND (a) INT, interpretation; IND, indifference; SYN, synergy; ANT, antagonism. Synergy was defined as an FICI of [less than or equal to] 0.5, antagonism was defined as an FICI of >4, and indifference was defined as an F1C1 >0.5 and [less than or equal to] 4. (b) n, number of drug combinations (among the 77 drug combinations for each strain) with statistically significant synergy or antagonism. Table 7 In vitro interaction between gentamicin and secondary metabolites from lichens as determined by non-parametric FIC1 and the [DELTA]E model (a). Compound Strain FICI Median (range) INT COL ATCC43300 (0.375-0.5) 0.5 SYN AQ004 (0.1875-0.25) 0.1875 SYN AQ006 (0.1875-0.250) 0.25 SYN AQ007 (0.375-0.5) 0.375 SYN AQ012 0.5 SYN EPI ATCC43300 0.3125 SYN AQ004 (0.375-0.5) 0.5 SYN AQ006 (0.25-0.2578) 0.25 SYN AQ007 (0.25-0.2578) 0.25 SYN AQ012 (0.3125-0.375) 0.375 SYN LOB ATCC43300 (0.3125-0.375) 0.375 SYN AQ004 0.375 SYN AQ006 (0.5-0.5002) 0.5 SYN AQ007 (0.5-0.501) 0.5 SYN AQ012 (0.5-0.502) 0.5 SYN PER ATCC43300 (0.2813-0.375) 0.375 SYN AQ004 (0.3125-0.375) 0.375 SYN AQ006 (0.375-0.5) 0.375 SYN AQ007 (0.375-0.5) 0.375 SYN AQ012 (0.375-0.5) 0.5 SYN PRO ATCC43300 (0.1563-0.1875) 0.1563 SYN AQ004 (0.1875-0.25) 0.1875 SYN AQ006 (0.25-0.2578) 0.25 SYN AQ007 (0.1563-0.1875) 0.1563 SYN AQ012 (0.2813-0.25) 0.2813 SYN Compound Strain [DELTA]E model (b) [SIGMA]SYN (n) [SIGMA]ANT (n) INT COL ATCC43300 1182.1 (44) -66.4 (13) SYN AQ004 1819.2 (52) -95.2 (20) SYN AQ006 843.0 (37) -58.1 (12) SYN AQ007 1145.0 (47) -63.7 (12) SYN AQ012 473.0 (37) -91.8 (15) SYN EPI ATCC43300 929.8 (29) -76.4 (16) SYN AQ004 2192.0 (53) -26.0 (3) SYN AQ006 1631.1 (46) -97.6 (18) SYN AQ007 1237.8 (45) -99.3 (21) SYN AQ012 564.4 (30) -94.1 (10) SYN LOB ATCC43300 659.3 (21) -89.4 (15) SYN AQ004 2639.7 (51) -66.5 (13) SYN AQ006 734.2 (43) -91.9 (15) SYN AQ007 345.2 (20) -68.3 (12) SYN AQ012 335.0 (23) -76.2 (5) SYN PER ATCC43300 360.9 (21) -57.1 (10) SYN AQ004 534.7 (32) -88.5 (15) SYN AQ006 1058.5 (37) -74.1 (16) SYN AQ007 598.7 (32) -81.2 (16) SYN AQ012 595.7 (35) -67.0 (12) SYN PRO ATCC43300 1657.2 (44) -87.4 (19) SYN AQ004 3312.7 (55) -58.6 (6) SYN AQ006 1173.4 (48) -44.7 (9) SYN AQ007 2875.3 (54) -35.1 (5) SYN AQ012 664.0 (35) -97.3 (8) SYN (a) INT, interpretation; IND, indifference; SYN, synergy; ANT, antagonism. Synergy was defined as an FICI of [less than or equal to] 0.5, antagonism was defined as an FICI of >4, and indifference was defined as an FICI >0.5 and [less than or equal to] 4. (b) n, number of drug combinations (among the 77 drug combinations for each strain) with statistically significant synergy or antagonism. Table 8 In vitro interaction between levofloxacin and secondary metabolites from lichens as determined by nonparametric FIC1 and the [DELTA]E model (a). Compound Strain FICI Median (range) INT COL ATCC43300 ND AQ004 (1.5-2) 1.5 IND AQ006 1.5 IND AQ007 2 IND AQ012 (1.125-1.5) 1.5 IND EPI ATCC43300 ND AQ004 (1.0625-1.5) 1.5 IND AQ006 (1.0625-1.25) 1.25 IND AQ007 1.5 IND AQ012 1.5 IND LOB ATCC43300 ND AQ004 2.5 IND AQ006 2.5 IND AQ007 (2-2.5) 2.5 IND AQ012 2.25 IND PER ATCC43300 ND AQ004 2.5 IND AQ006 2.5 IND AQ007 2.5 IND AQ012 2.25 IND PRO ATCC43300 ND AQ004 (2-2.125) 2 IND AQ006 (1.25-1.5) 1.5 IND AQ007 (1-1.5) 1.5 IND AQ012 1 IND Compound Strain [DELTA]E model (b) [SIGMA]SYN (n) [SIGMA]ANT (n) INT COL ATCC43300 ND ND AQ004 25.3 (5) -635.4 (59) ANT AQ006 24.9 (3) -459.8 (45) ANT AQ007 5.3 (2) -1601.8 (60) ANT AQ012 29.5 (6) -502.0 (52) ANT EPI ATCC43300 ND ND AQ004 72.3 (15) -45.1 (9) IND AQ006 98.1 (21) -62.3 (12) IND AQ007 56.8 (12) -90.9 (17) IND AQ012 64.3 (14) -87.9 (16) IND LOB ATCC43300 ND ND AQ004 99.7 (19) -485.4 (54) ANT AQ006 98.6 (20) -507.2 (41) ANT AQ007 0 (0) -998.5 (61) ANT AQ012 92.9 (12) -643.4 (40) ANT PER ATCC43300 ND ND AQ004 43 (8) -971.7 (36) ANT AQ006 14.5 (4) -483.0 (39) ANT AQ007 57.4 (20) -408.1 (28) ANT AQ012 75.1 (27) -272.8 (25) ANT PRO ATCC43300 ND ND AQ004 82.1 (19) -96.0 (10) IND AQ006 57.2 (10) -98.3 (15) IND AQ007 38.3 (5) -78.1 (39) IND AQ012 97.1 (19) -68.0 (12) IND (a) INT, interpretation; IND, indifference; SYN, synergy; ANT. antagonism. Synergy was defined as an FICI of [less than or equal to] 0.5, antagonism was defined as an FICI of >4, and indifference was defined as an FICI >0.5 and [less than or equal to] 4. ND. not detected. (b) n, number of drug combinations (among the 77 drug combinations for each strain) with statistically significant synergy or antagonism. Table 9 In vitro interaction between oxacillin and secondary metabolites from lichens as determined by nonparametric FICI and the [DELTA]E model (a). Compound Strain FICI Median (range) INT COL ATCC43300 (0.5-0.5002) 0.5 SYN AQ004 (0.3125-0.375) 0.3125 SYN AQ006 (0.5313-0.5625) 0.5625 IND AQ007 (0.625-0.75) 0.75 IND AQ012 (0.625-0.75) 0.75 IND EPI ATCC43300 (0.5-0.501) 0.5 SYN AQ004 (0.5625-0.6250) 0.625 IND AQ006 0.625 IND AQ007 (0.5078-0.5156) 0.5078 IND AQ012 (0.5078-0.5156) 0.5078 IND LOB ATCC43300 (1-1.0039) 1 IND AQ004 (1-1.002) 1.002 IND AQ006 (0.5313-0.5625) 0.5625 IND AQ007 (0.5625-0.625) 0.625 IND AQ012 (0.5625-0.625) 0.625 IND PER ATCC43300 0.75 IND AQ004 1 IND AQ006 (1.0039-1.25) 1.0039 IND AQ007 1 IND AQ012 (0.625-0.75) 0.75 IND PRO ATCC43300 (0.5-0.5002) 0.5 SYN AQ004 (0.5-0.5002) 0.5 SYN AQ006 (0.5039-0.5078) 0.5039 IND AQ007 (0.5-0.502) 0.5 SYN AQ012 (0.5-0.5002) 0.5 SYN Compound Strain [DELTA]E model (b) [SIGMA]SYN (n) [SIGMA]ANT (n) INT COL ATCC43300 645.8 (25) -99.2 (9) SYN AQ004 1075.3 (42) -83.6 (6) SYN AQ006 238.0 (19) -48.9 (9) SYN AQ007 132.7 (11) -86.9 (19) SYN AQ012 131.1 (13) -94.8 (22) SYN EPI ATCC43300 665.7 (30) -88.0 (17) SYN AQ004 495.5 (20) -99.1 (22) SYN AQ006 819.0 (43) -62.0 (19) SYN AQ007 1129.6 (34) -82.4 (12) SYN AQ012 804.1 (32) -47.3 (11) SYN LOB ATCC43300 92.5 (18) -77.6 (14) IND AQ004 89.1 (15) -85.0 (12) IND AQ006 274.3 (27) -94.1 (13) SYN AQ007 99.2 (17) -67.2 (8) IND AQ012 87.2 (13) -80.5 (10) IND PER ATCC43300 97.1 (23) -75.2 (12) IND AQ004 89.2 (16) -67.3 (12) IND AQ006 79.8 (18) -98.9 (21) IND AQ007 89.8 (17) -54.6 (9) IND AQ012 63.7 (9) -92.2 (11) IND PRO ATCC43300 426.6 (18) -31.5 (6) SYN AQ004 406.8 (37) -74.9 (15) SYN AQ006 380.2 (31) -99.2 (10) SYN AQ007 819.8 (27) -76.7 (15) SYN AQ012 1047.5 (35) -23.4 (4) SYN (a) INT, interpretation; IND, indifference; SYN, synergy; ANT, antagonism. Synergy was defined as an FICI of [less than or equal to] 0.5, antagonism was defined as an FICI of >4, and indifference was defined as an FICI >0.5 and [less than or equal to] 4. (b) n, number of drug combinations (among the 77 drug combinations for each strain) with statistically significant synergy or antagonism.