bioensayo miel de manuka y otros

8/10/2019 Bioensayo Miel de Manuka y Otros

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Original article

High-throughput microbial bioassays to screen potential New

Zealand functional food ingredients intended to manage the growth

of probiotic and pathogenic gut bacteria

Douglas I. Rosendale,1,2* Ian S. Maddox,2 Michelle C. Miles,1 Maroussia Rodier,1 Margot Skinner3 &

Juliet Sutherland4

1 Nutrition and Health Group, New Zealand Institute for Crop and Food Research Limited, Mt Albert Research Centre, 120 Mt Albert Road,

Sandringham, Auckland 1025, New Zealand

2 College of Sciences, Massey University, Albany Campus, Albany, Auckland 1311, New Zealand

3 The Horticulture and Food Research Institute of New Zealand Limited, Mt Albert Research Centre, 120 Mt Albert Road, Sandringham,

Auckland 1025, New Zealand

4 Nutrition and Health Group, New Zealand Institute for Crop and Food Research Limited, Palmerston North Research Centre, Batchelar Road,

Palmerston North 4474, New Zealand

(Received 23 September 2008; Accepted in revised form 24 September 2008)

Summary A spectrophotometric bioassay was used to screen selected food ingredients intended for development of

functional foods designed to influence the growth of gut bacteria. Doseresponse profiles displaying Dgrowth,

the magnitude of deviation from growth of controls, were generated for probiotics Lactobacillus reuteri,

Lactobacillus rhamnosus,Bifidobacterium lactisand pathogensEscherichia coli,SalmonellaTyphimurium and

Staphylococcus aureus. Ingredients were manuka honey UMF20+(dose-dependently increased probiotics

and decreased pathogens); bee pollen (biphasic growth effects against all); Rosehips and BroccoSprouts

(increased all dose-dependently); blackcurrant oil (little effect) and propolis (inhibited all strains). Ingredients

were also bioassayed in pairs to assess desirable or undesirable synergistic interactions. Observed synergies

included manuka honey (predominantly desirable); rosehips or BroccoSprouts(desirable and undesirable);

blackcurrant oil (desirable) and propolis (tended towards synergies reinforcing its antimicrobial effects),

collectively revealing a complex web of interactions which varied by ingredient and bacterial strain. Manuka

honey was particularly effective at influencing gut bacteria. The surprising frequency of undesirable

synergistic interactions illustrates the importance of pre-testing potential ingredient combinations intended

for use in functional foods.

Keywords Bioassay, functional food, New Zealand, pathogen, probiotic, screening, synergies.

Introduction

The gastrointestinal (GI) tract is the bodys largest tissueboundary, essentially continuous with the outside of thebody, which interacts with nutrients, exogenous com-pounds and gut microflora in a complex interplay ofenvironmental factors and genetic elements. The host

and complex microbial communities coexist, interact andcompete in conditions that are often far from optimal.

Microorganisms colonise the GI tract heavily, pri-marily in the distal gut, to the extent that they vastlyoutnumber the cells forming the human body (Madara,2004). These resident bacteria form complex ecosystems

with enormous diversity, and more than 50% arebelieved to be unculturable by conventional techniques(Tlaskalova-Hogenova et al., 2004). The commensal(normal, indigenous) microflora coexists with their hostin a mutually beneficial arrangement and although theyare able to exert pathological effects, it is rare for themto do so. Pathogenic organisms, including members of

the genera Escherichia, Salmonella and Staphylococcus,are generally believed to be transient inhabitants,although some pathogens (such as Helicobacter) areable to form stable infections, where they colonise thehost over a longer term without the manifestation ofsymptoms associated with the presence of the organism(Acheson & Luccioli, 2004).

Commensal organisms are one of the main factorsthat prevent the establishment of pathogenic organisms

*Correspondent: Fax: +64 9 845 9029;

e-mail: [email protected]

International Journal of Food Science and Technology 2008, 43, 22572267 2

doi:10.1111/j.1365-2621.2008.01863.x

2008 The Authors. Journal compilation 2008 Institute of Food Science and Technology


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in the gut, via a number of mechanisms including theproduction of antimicrobial compounds, competitionfor food or excluding the pathogens from environmentalniches (Rolfe, 2000; Reid & Burton, 2002; Saxelin et al.,2005). The study of the interactions between commensalorganisms, pathogenic organisms and the host is a

complex and growing research field, particularly interms of probiotic organisms defined strains ofcommensal bacteria such as members of the genusLactobacillus and Bifidobacteria, that show specific,beneficial functions in gut health, with positive influ-ences on the gut microbiota and anti-pathogenic activityas well as influencing human health and nutritionalstatus (Collins & Gibson, 1999; FAOWHO, 2001;Rusch, 2002; Sanders, 2003; Tannock, 2004; Rastallet al., 2005; Martin et al., 2008).

There is an interest in traditional and non-conven-tional medicines either to treat imbalances or to main-tain a healthy balance of complex microflorahostinteractions in the gut. This includes the use of

functional foods, foods which provide more thannutritional benefits to the consumer (Roberfroid,2000). We are developing potential functional foodingredients that modulate the gut microflora for thepromotion and maintenance of healthy GI microbialhomeostasis. Such modulation may take the form ofprebiotics; and food ingredients (usually oligosaccha-rides) indigestible by the host that are utilised by andencourage the growth of probiotic organisms andbeneficially modify the health of the host (Gibson &Roberfroid, 1995; Collins & Gibson, 1999; Schrezenmeir& de Vrese, 2001; Rastall et al., 2005; Parracho &Gibson, 2007). Focusing on prebiotics avoids viability

issues with probiotic supplementations, whilst ensuringthat organisms native to the gut are targeted (Gibson &Roberfroid, 1995). Other food ingredients may actthrough antimicrobial compounds which prevent ordiscourage the growth or establishment of pathogens.Food ingredients, or ingredient combinations, are likelyto have multiple modes of action, so their use may carrythe additional benefit that the development of microbialresistance to them is unlikely. Collectively, these effectscould be a potential alternative to pharmaceuticalintervention for people with pathogen-related disordersas well as preventatives for symptom-free people.

This work describes high-throughput bioassay screen-ing of the potential antimicrobial andor prebiotic

effects of six potential functional food ingredients ofNew Zealand origin; manuka honey UMF 20+,propolis, bee pollen, BroccoSprouts, rosehips andblackcurrant seed oil. These ingredients were selectedas trial ingredients for the development of a rapidselection system for use as either a stand-alone foodingredient or as part of a combination with otheringredients, on the basis of potential or known ability toimpact upon microbial growth. We regard the testing of

ingredients in combination with each other as impor-tant, if not more so, than the ability to act alone, giventhat ingredients will be consumed as a part of a complexfood matrix. Furthermore, a simple process for theidentification of synergistic interactions between ingre-dients, whereupon a combination generates an effect

greater than the sum of the individual ingredients,would be of value to the food industry and to theconsumer looking to maximise the benefits of functionalfood for the purposes of managing the gut ecosystem.Finally, we aimed to determine whether unwantedinteractions or antagonisms could result from themixing of individually efficacious ingredients. Suchknowledge would impact heavily upon the selection ofingredient combinations made by both the food industryand the consumer.

To identify and effectively communicate combinationsuseful for the development of concept functional foods,the combinations of ingredients were to be categorisedon the basis of how they performed relative to the

efficacy of the ingredients tested independently, againstany given bacteria. Two categories were established,termed desirable combinations and undesirable com-binations. A desirable combination was defined as anincrease in probiotic growth or a decrease in pathogengrowth compared to the growth achieved using eitherextract independently, whilst an undesirable combina-tion was defined as decreased or increased probiotic orpathogen growth, respectively.

There are a variety of techniques available for measur-ing the antimicrobial activity of natural compoundsoutlined by Patton et al. (2006) who investigated manukahoney antimicrobial activity. These researchers outline

the advantages and disadvantages of the three primarymethods: disc diffusion, well diffusion and spectrophoto-metric analysis, with the demonstrable conclusion thatthe latter method was more accurate, sensitive, reproduc-ible, faster and cheaper. The possibility for extensivekinetic studies with lower concentrations than possiblewith well diffusion assays was claimed (Patton et al.,2006). Thus, a spectroscopic microplate assay method issuitable for measuring changes in microbial growthduring a screening program of potentially antimicrobialingredients, where the format (microplate layout), num-ber of replicates and control well designs could beoptimised for rapid, accurate and consistent high-throughput screening and for ease of subsequent statis-

tical analyses. In addition, the spectrophotometric mea-surement of optical density (OD) has the added benefit ofbeing a more suitable index of final microbial biomassthan use of viable cell counts (Krist et al., 1998), as viablecell counts does not necessarily represent biomass due todifferences in cell mass and shape, where same biomasscan be contained within several smaller cells or few largercells. OD closely correlates with biomass except inextreme cases (Kristet al.,1998).

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Thus the two aims of this work could be summarisedas follows:

Firstly, to establish a rapid spectrophotometricmicrobial growth assay and a manner of expressingthe efficacy of the functional food ingredients. Thesesimple tests may be applied during subsequent investi-

gation of the efficacy of other potential functional foodingredients intended to manage the gut microflora. Thusthis investigation was initiated by determining the pro-or antimicrobial doseresponse profiles of the selectedingredients in detail, and by identifying which ingredientcombinations are capable of generating synergistic (orunwanted antagonistic) effects against a panel of bothprobiotic and pathogenic bacteria.

Secondly, this work was to form a first step inconfirming the viability of New Zealand functional foodingredients as an alternative, non-pharmaceutical ap-proach to maintaining human gut health and wellbeingthrough promotion of gut microbial homeostasis.

Methods

Microbial methods

Organisms used in this study included Lactobacillusreuteri DPC16, Lactobacillus rhamnosus HN001(DR20), Bifidobacterium lactis HN019 (DR10),Salmonella enterica serovar Typhimurium ATCC 1772,Escherichia coli O157:H7 strain 2988 and Staphylococ-cus aureus ATCC 25932. Lactobacillus reuteri, S.Typhimurium and E. coli were supplied by BioactivesResearch New Zealand Ltd, Auckland, New Zealand.Lactobacillus rhamnosus and B. lactis were from The

New Zealand Dairy Research Institute, PalmerstonNorth, New Zealand. Staphylococcus aureus belongedto the Functional Microbiology Laboratory collection,The New Zealand Institute for Crop and Food ResearchLtd, Auckland, New Zealand.

For this work we confined the choice of bacterialisolates used for the testing to a select few. Probioticorganisms selected were L. reuteri, L. rhamnosus andB. lactis. Pathogenic organisms selected were E. coli,S. Typhimurium and S. aureus. These probiotics andpathogens grew under similar respective culture condi-tions, which were deemed important because isolatesrequiring different growth conditions or significantlydiffering nutrient supplementation or growth rates (e.g.

H. pylori) would hinder high-throughput analysis ofgrowth. The strains were relevant to the purposes of theinvestigation based upon their ability to contribute to(probiotic strains) or impacting negatively upon (path-ogenic strains) the intestinal wellbeing of the normalhealthy individual (cf. Listeria monocytogenes).

Aerobic and anaerobic organisms were stored at)80 C in broth containing 15% (vv) glycerol. Frozencultures were revived by scraping a loop across the

frozen stock and streaking on an agar plate to producesingle colonies upon overnight incubation under condi-tions of appropriate aerobicity. Purity was assessedmicroscopically by Gram staining. Single colonies wereused to inoculate broth. Broth cultures were routinelygrown or passaged by inoculating a loopful (10 lL) into

glass screwcap tubes (10 mL). Caps were not fullytightened, to ensure exposure to the correct atmosphere.Cultures were grown at 37 C in a Contherm DigitalSeries Incubator. A minimum of four subcultures onbroth, performed daily, were used to ensure organismswere fully adapted to the media prior to use for anymicrobial experiments.

The aerobesS. Typhimurium and E. coliwere grownon tryptic soy broth (TSB), whilst S. aureus was grownon brain heart infusion (BHI) broth. The anaerobicbacteria L. reuteri, L. rhamnosus and B. lactis weregrown on de Mann Rogosa Sharpe (MRS) brothsupplemented with 005% (wv) cysteine in Gas-PakEZ Incubation Chambers containing Gas-Pak EZ Gas

Generating Sachets. Where necessary, vessels weremonitored with anaerobic indicator strips.

Bacterial inoculums for the microbial assays wereprepared by estimating the culture density with ahaemocytometer and adjusting to that required for theassay (102 cells mL)1 for L. reuteri, 103 cells mL)1 forall others). Viable counts of the inoculum were routinelyperformed by spread plating and calculating colonyforming units (CFU) mL)1 to confirm the haemo-cytometer counts.

Food ingredient preparation

Manuka honey UMF 20+, bee pollen granules (mixedfloral source) and propolis (80% tincture) were suppliedby the manufacturer (Comvita New Zealand Ltd, Bay ofPlenty, New Zealand). Blackcurrant seed oil was pur-chased from Nutrizeal Ltd (Nelson, New Zealand) andsupplied sealed under N2gas, and rosehips (Sweet Briar,Rosa rubiginosa) and BroccoSprouts were purchasedfrom local suppliers (Christchurch, New Zealand) andfreeze-dried within 5 days of purchase.

All samples except propolis and blackcurrant oil weresolubilised using 25 mm sodium phosphate buffer pH74. Bee pollen samples also contained 02% DMSO inthe buffer to aid solubilisation. These suspensions werehomogenised, filtered through Whatman No. 4 filter

paper, centrifuged to remove particulate matter, andsterilised through a series of 08, 04 and 02 lm filters.Propolis and blackcurrant oil were solubilised in ethanolor DMSO, respectively, and then diluted in deionisedwater for use. To establish the concentration of solublematerial in the ingredient solution an aliquot of thesolution was dispensed (05 mL) into pre-weighed tubesimmediately after filtration, lyophilised and the dryweight of the soluble material calculated, including

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2008 The Authors. Journal compilation 2008 Institute of Food Science and Technology International Journal of Food Science and Technology 2008


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corrections for the buffer salt weight. The ingredientsand their concentrations used in the microbial growthassay (below) are given in Table 1.

Ingredient solutions were dispensed in aliquots andfrozen at )80 C, and aliquots used only once, toprevent any freezethaw-induced denaturing of active

components. The blackcurrant seed oil extract aliquotswere sealed under N2 gas to minimise oxidation andthen frozen. Ingredients were thawed to room temper-ature and diluted as required immediately prior to eachassay. All ingredients used during this work weresolubilised as part of a single batch, so that batch-specific variation could be avoided.

Microbial growth assays

A ninety-six-well microplate growth bioassay measuringoptical density (OD) was used throughout this work. Tocompare ingredient effects on a group of diversebacterial strains which achieve quite different ODs, a

standardised value expressing the change in growth ofcultures in the presence of ingredient(s) relative to thegrowth of unsupplemented control cultures, or DGrowth,was calculated and used to represent the magnitude ofeffect. This value was calculated by the simple method ofconverting the OD to a percentage of the control ODand then subtracting 100, effectively normalising thecontrol growth to a baseline value of zero (eqn 1).

DGrowth Extract OD blank OD 100

Control OD blank OD

100

1

This represents the magnitude of deviation of thegrowth of a culture from the growth of the controlculture, which had not been supplemented with ingre-dient. This resulted in a positive or negative Dgrowthvalue representing increased or decreased growth,respectively, where the magnitude of deviation fromthe control Dgrowth value was a measure of the ingredi-ents relative efficacy. This was found to be the bestmethod of graphically comparing the effects of varying

concentrations of the same ingredient against multiplebacterial strains.

Single ingredient assay

For single ingredient analyses, the first column of a

ninety-six-well microplate was filled with the appropri-ate bacterial growth medium supplemented with aningredient. Multiple microplates were prepared, onemicroplate per strain of bacteria, and with separatemicroplates for separate ingredients. A range of ingre-dient concentrations was examined by conducting atwo-fold dilution series across the microplate, movingleft to right from the first column, containing the highestconcentration (Table 1). This resulted in eleven dilutionseach containing eight replicate wells (50 lL), eachdilution half the concentration of the previous one.Eight replicate control wells containing medium withoutingredient were always included in the last column of theninety-six-well microplate.

Microplates were inoculated with an equal volume(50 lL) of bacterial inoculum, and the optical density(OD) of the microplate was immediately measured at awavelength of 620 nm using a Thermo Multiscan EXninety-six-well plate reader to determine the blank (zerogrowth) value. Microplates were incubated at 37 C for16 h, and then the OD was determined to measure thegrowth of the cultures at late log phase-early stationaryphase growth of the organisms. The 0-h reading wassubtracted from the 16-h end-point reading to eliminateall changes in optical density not due to growth.Therefore, potential variations in plate density, mediacolour or any other unknown factors could be

accounted for. Furthermore, un-inoculated ingredientcontrols were routinely included to confirm the sterilityof the ingredients.

Data were subjected to statistical analysis using theGenstat program (Genstat Release 82, Lawes Agricul-tural Trust, Rothamsted Experimental Station, UK)because it allowed pooling of results for comparisonacross ingredients, and across bacterial strain, frommultiple determinations, by determining analysis ofvariance using the two-tailed anova function. The leastsignificant difference (LSD) of the means (n = 8), at the95% confidence interval (P < 005) for all of the strainstested for a particular ingredient, was shown on a singlegraph per ingredient. This allowed simple comparison of

the effects of an ingredient on multiple organisms, whichwas particularly useful when comparing the effects onprobiotics vs. pathogens.

The pH of the culture medium, before and afterincubation with bacteria, was measured using an ISFETKS701 pH meter (Shindengen, Tokyo, Japan), both inthe presence of the maximum ingredient concentrationused, and in the control cultures. These measurementswere to exclude either changes in pH, or buffering of the

Table 1 Ingredients and concentrations at the highest dose for the

single ingredient assay

Ingredient Concentration (mg mL)1)

Manuka honey UMF 20+ 200

Bee pollena 50

Rosehips 25

BroccoSprouts 175

Blackcurrant seed oilb 021

Propolisb 06

aIncludes DMSO.bIncludes ethanol.




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media, as reasons for any observed increases ordecreases in bacterial growth.

To estimate the bacterial viability counts wherenecessary, ingredients were assayed for antimicrobialactivity exactly as described above, except that resultingcultures were serially diluted in medium and spread

plated on agar plates. Colonies were counted and theCFU mL)1 of the original well culture was determinedand compared to the control (unsupplemented) cultures.

Combined ingredient assay

Combined ingredient assays were performed exactly asdescribed for single ingredient assays above, exceptmaintaining the same assay volume meant that ingredi-ents were present at half the concentration of the highestdose used when tested singly.

However, to compare the efficacy of these combina-tions with those obtained using the extracts indepen-dently, and to mine the data for the most efficacious

combinations, additional processing of the data wasrequired. This would serve to identify potentialsynergistic or unwanted antagonistic interactions in arapid and simple manner consistent with the aims of thiswork: to identify those combinations worth pursuingfurther, and to recognise combinations to be avoided.

Two simple comparisons were performed, in order toassign the terms of effect (desirable, good, poor,undesirable) introduced previously, and calculatedaccording to the following methods:

Comparison 1: Combination Dgrowth values werenoted that significantly (P < 005) deviated furtherfrom the control value (zero) than the theoretical sum

of theD

growthvalues of the individual extracts (eqn 2).Does DGrowthAB exceedDGrowthA DGrowthB? 2

This immediately identified Dgrowthvalues which werepotentially synergistically acting to exert prebiotic orantimicrobial effects, as the activity of the combinationwas more than the sum of the effect of the ingredientsacting independently. The term exceed (eqn 2) is usedbecause this equation is designed to compare like valueswith like (positive Dgrowth with positive Dgrowth, negativeDgrowth with negative Dgrowth). This simplistic compar-ison does not take into account the effect of addingunlike values, i.e. a weak positive growth value added toa strong negative growth value, or vice versa, in effect

cancelling out a part of the apparent effect of thestronger contributor. Thus a second comparison wasintroduced.

Comparison 2: Combination Dgrowth values werenoted that significantly (P < 005) deviated furtherfrom the control (zero) value than the most extreme ofthe Dgrowth values from either one of the ingredientsindependently (eqn 3).

Does DGrowthAB exceed either DGrowthA or DGrowthB?

3

This identified ingredients displaying an apparentlyincreased efficacy despite an artificial lowering of thesum of the activities obtained from eqn 2. For example,

an ingredient providing an extreme Dgrowth such as a100% increase in growth over the control culture (aDgrowth value of 100) combined with an ingredientinhibiting the growth to only 80% of the growth of thecontrol (a Dgrowthvalue of 20) would have an apparentDgrowthABvalue of 80 using eqn 2. Should the growth ofthe combination of ingredients yield a number such as a90% increase in growth (Dgrowthvalue of 90) then eqn 2would identify a potentially synergistic effect, whichwas, in fact, less than the activity of the first ingredientacting independently. However, should the combinationyield, for example, a 120% increase, or Dgrowth of 120,then that would satisfy the comparisons drawn fromboth eqns 2 and 3 and represent a synergistic combina-

tion.Ingredient combinations which met Comparison 1

were termed good, that is, the combination performedbetter than mathematically predicted. Those which didnot meet Comparison 1 were termed poor. Again, goodcombinations did not necessarily outperform singleingredients acting independently.

Ingredient combinations which met both require-ments, that is, had an effect greater than either ingre-dient alone or predicted in combination (synergistic),were termed desirable or undesirable, based upontheir activity relative to the bacteria (probiotic orpathogen) being tested, as described in the introduction

to this paper.

Results and discussion

Single ingredient assay

A total of thirty-six doseresponse curves were gener-ated. Figure 1 displays the effects of the six ingredients,each against six bacterial species. Limitations on thequantity of two of the ingredients, blackcurrant oil andpropolis, meant that only preliminary dose-responsescreening of these ingredients against S. aureus wasperformed (data not shown).

Manuka honey (Fig. 1). increased probiotic growth

and decreased pathogen growth in a dose-dependentmanner. There was a clear difference in the effect on thepathogens and probiotics, validating both the means ofexpressing the data (Dgrowth values) on the same chartfor immediate visual recognition, and in the choice ofmodel ingredient to trial the system.

The decrease in pathogen growth was expected.This result can be explained by a contribution of




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factors: Manuka honey, derived from Leptospermumspp., has known wound-healing and antimicrobialproperties (Molan, 2001). Honey antimicrobial proper-ties have been largely attributed to the presence ofresidual peroxide (White et al., 1963) arising from bee-derived glucose oxidase upon dilution. Honey alsocontains a high sugar content, sufficient to lower awenough to prevent microbial growth through osmoticshock. Other factors include acidic pH and the presenceof plant-derived phenolic compounds (Molan, 1992).Manuka honey also has non-peroxide activity (Molan &

Russel, 1998), the Unique Manuka Factor (UMF),suggested to include very high levels of the 1,2-dicarbonyl compound methylglyoxal (MGO) (Weigelet al., 2004; Adams et al., 2008; Mavric et al., 2008).Subsequent data (D. Rosendale, unpublished results)suggests that the inhibition of pathogen growth shownin this work is almost entirely due to the honey sugarslowering the aw of the assay solution. The honey wasused at a dose containing MGO at less than 0 3 mm.

This concentration was below the limit required toinhibit E. coli growth (Ferguson et al., 1996).

Increases in probiotic growth, a prebiotic effectattributed to manuka honey, have not been reportedin the literature. This is a significant finding, and themechanisms responsible for this outcome remain to beelucidated. Similar results against other organisms havebeen reported for honey such as against the yeastCandida albicans in the literature (Patton et al., 2006),but the mechanism is currently unknown. Whilst honeyis predominantly sugar, and could conceivably contrib-

ute to increased growth of the honey-supplementedcultures due to greater nutrient than present in themedia of the control cultures, the probiotic growthmedia MRS is rich in glucose, thus lack of sugar limitingthe growth of the control cultures relative to honeysamples is unlikely.

Changes in the medium pH might be expected toaffect bacterial growth, whereas buffering of the mediacould increase probiotic growth relative to the assay

0

100

50

50

100

150

0.01 0.1 1 10

growth

Ingredientconcentration (mg mL1

)

Manuka honey extract

growth


)

50

0

50

0.001 0.01 0.1 1 10

Bee pollen

growth

0.001 0.01 0.1 1 10

50

0

50

Rosehip


) Ingredient concentration (mg mL1

)

growth

50

0

50

100

0.001 0.01 0.1 1 10

BroccoSprout

growth

Ingredient concentration (mg mL1

)

50

0

50

0.0001 0.001 0.01 0.1 1

Blackcurrant oil

growth

Ingredient concentration (mg mL1

)

0.0001 0.001 0.01 0.1 1

50

0

50Propolis

Figure 1 Dgrowth values from bacterial cul-

tures supplemented with increasing doses of

functional food ingredients. Probiotic bacter-

ia: Lactobacillus reuteristrain DPC16 (),

Lactobacillus rhamnosusstrain DR20 (m) and

Bifidobacterium lactis strain DR10 (n).

Pathogenic bacteria: Staphylococcus aureus

(), Escherichia colistrain O157:H7 (+) and

Salmonella Typhimurium (). Data points

show the mean (n=24) 16 h growth at 37 C,

obtained over 3 separate experiments with 8

replicates per experiment. The bar displaysthe Least Significant Difference at P < 0.05.




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control wells. This is because without buffering, accu-mulating acidic byproducts in the media inhibit growth.No ingredient-induced pH changes were observed (datanot shown). In the case of the probiotic organisms, theexpected lowering of pH after growth, induced by theacidic by-product, was observed. Essentially, growth

(OD) was proportional to the pH, and thus proportionalto the production of acidic by-products. Thus, theobserved increases in probiotic growth could be attrib-uted to factors other than the ingredients influencing thepH value of the medium.

Preliminary experiments suggest that sub-lethal(


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could reflect the presence of the same or a similar activecompound(s).

OD data were compared to viable counts where it wasfound that increases or decreases in OD were accompa-nied by increases or decreases in plate counts (data notshown). Whilst statistical analyses were not performed,

this assessment was considered sufficient to discountpotential factors such as cell clumping or settling or,alternatively, major changes in cell morphology factors which might have respectively increased ordecreased the incident light reaching the photomultiplierand thus given apparent OD changes unrelated togrowth or biomass.

Consistency of OD across experiments was assured byconducting initial experiments to optimise the processinvolved examining the microplate reader outputs forreproducibility and to ensure potential plate reader hotspots (where wells of the ninety-six-well microplate ormicroplate reader might consistently read higher orlower than the surrounding wells) were accounted for.

The same brand of ninety-six-well microplate (Costar,Corning, NY, USA) was used throughout to eliminateinter-brand microplate variation. After ensuring thespectrophotometer operated appropriately, the bacterialgrowth conditions have been tested and optimised toensure the growth media, culture revival and handling,inoculum concentrations and incubation times could bestandardised to consistently generate optical densitiesthat would potentially allow deviations from the theo-retical control value (i.e. cultures unsupplemented byextracts) to still fall well within the plate readers usefulabsorbance range (an absorbance of 0110) at late logphase growth under standard handling conditions (data

not shown). These conditions were adopted for allexperiments to maintain consistency and allow compar-ison with assays conducted at different times.

We elected not to calculate minimum inhibitoryconcentrations (MICs) of the ingredients from the ODdata. Firstly, where food ingredients have unknown orpoorly defined active components, presenting the data asDgrowth for a given amount of solubilised ingredientmaterial would not misrepresent our knowledge of thequality and quantity of active components. Secondly,perhaps the use of MICs is more suitable for compoundsintended for pharmaceutical-based eradication proce-dures than management of gut microorganisms bydietary intervention. Finally, the expression of dose

response curves might shed some light on the nature ofthe Dgrowthvalues observed. Thus, we could speculate onthe presence and effects of putative active componentswhilst the chemical determination and quantitativeanalyses required to establish their presence and con-centration, and thus determine useful MICs, remainsoutside the scope of this paper. That is not to say thatfindings from this work would not be used to promptsuch work in the future.

Combined ingredients assay

The effects of the combinations of ingredients on thegrowth of the bacteria are shown in Table 2. Combina-tions were noted that fulfilled the criteria desirable,undesirable, good or poor.

Desirable combinations included manuka honey com-bined with bee pollen (suppressed S. Typhimurium),with rosehips (suppressed S. Typhimurium, promotedL. rhamnosus), with BroccoSprouts (suppressedS. aureus, promoted B. lactis), with blackcurrant oil(suppressed S. Typhimurium, promoted B. lactis) andwith propolis (suppressed S. Typhimurium andS. aureus).

Undesirable combinations, in which combined activ-ities promoting pathogen growth or suppressing probi-otic growth exceeded the contribution from thecomponent ingredients, included manuka honey com-bined with rosehip (suppressed B. lactis) or propolis(suppressed all three probiotic strains), or propolis or

blackcurrant oil combined with rosehip or BroccoSpr-outs.

Some ingredient combinations, such as manuka honeycombined with propolis, or propolis combined withBroccoSprouts, yielded both desirable and undesirableeffects, depending upon which strain of bacteria wasused. Thus, the response of the bacteria to any givencombination of ingredients was often strain-specific.Mathematical analysis failed to show any significantrelationship or pattern involving combination, strainand effect (D. Hedderley, personal communication).This is not unexpected, given the complex mixtures ofpotentially bioactive compounds present in the ingredi-

ents used in this study.Manuka honey was particularly effective at increas-ing probiotic growth and reducing pathogen growth,both alone and in combination with other foodingredients.

The less soluble compounds, propolis and blackcur-rant oil, and the plant ingredients rosehips and Brocco-Sprouts, tended to generate the most interesting effectsin combinations, and thus appear suitable for use asadjuvants or mitigants to moderate the effects of otheractive ingredients. It is feasible that this may beattributable to fatty acid or phenolic compound(s)perturbing bacterial membranes. Plant compounds(phenolics, polyphenolics, flavones, flavanoids, tannins,

coumarins, terpenes and alkaloids) are known antimi-crobial agents with a variety of mechanisms of actionincluding reacting with proteins or perturbing mem-branes thereby increasing permeability, depending onthe lipophilicity of the compounds [reviewed by Cowan(1999)]. The generation of synergistic responses maybe a consequence of the low concentration of theingredients, where at higher concentration they mayhave exerted direct antimicrobial activity. Some plant




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compounds have demonstrated synergistic effects incombination [for example, essential oils and flavanoidshave been shown to contribute to synergies (Williamson,2001)]. The ability of the rosehip to synergise with otheringredients is interesting in regard to the synergisticactivities of the rose-derived Tellimagrandin I (Shiotaet al., 2000) mentioned earlier.

Conclusions

The high-throughput spectrophotometric microbial bio-assay refined and used during the course of this workhas proven to be simple, robust, sensitive, accurate,highly reproducible, and easily amenable to use withmultiple potential active compounds at a variety of

Table 2 Microbial combined ingredient assay results displaying changes in growth from combinations of ingredients

Dgrowth

Strain

Ingredient

(mg mL-1)

Manuka

honey 1000

Bee pollen

2500

Rosehip

1750

Brocco-Sprouts

0525

Black-currant

oil 0105

Propolis

0300

DPC16 Manuka honey +603 +887d +526c +922d +491d )882b,d

DR10 +296 +741d +34b,d +872a,c +496a,c )645b,d

DR20 +204 +575 +279a,c +544d +146d )890b,d

2988 )434 )352d )430c )411 )390d )476c

1772 )355 )424a,d )450a,d )473 )417a,d )505a,d

25932 )61 +240 )168c )10a +48 )490a,d

DPC16 Bee pollen +800 +413d +716d +479d +128d

DR10 +767 +371d +653d +558d )20d

DR20 +373 +480d +189d +238d )231d

2988 +102 +207 +29d +72d )18

1772 +225 +252d +183 +263 +100d

25932 +300 +404 +300 +146d )45a,d

DPC16 Rosehip +171 +576 +311 a,c )304 b,d

DR10 +281 +299d +264 c )155 d

DR20 +128 +480d )106 d )439 d

2988 +12

6 +6

8

d

+5

5

d)

5

21772 +144 +121c +153 d +35 d

25932 +54 +229b,c +138 )867 a,d

DPC 16 Bro cc o- Spr ou ts +481 +298d +210

DR10 +201 +348a,c +319a,c

DR20 +456 +453 +108c

2988 +01 +12d +60b,c

1772 )85 +59c +115b,c

25932 +69 +61 )07c

DPC16 Black- currant oil +63 )136

DR10 )24 )171

DR20 +61 )374

2988 +69 )820

1772 +83 +60

25932 +63 )136

DPC16 Propolis )

19

3DR10 )81

DR20 )454

2988 )135

1772 )36

25932 )193

Lactobacillus rhamnosus, DPC16; Bifidobacterium lactis, DR10;Lactobacillus rhamnosus, DR20; Escherichia coli, 2988; SalmonellaTyphimurium, 1772;

Staphylococcus aureus, 25932.aDesirable combined effect = increase (probiotic) or decrease (pathogen) of growth from the combination which (i) exceeds the sum (LSD P< 005) of

the contributing ingredient activities and (ii) exceeds the value (LSD P< 005) of the most extreme of the contributing ingredient activities.bUndesirable combined effect = increase (pathogen) or decrease (probiotic) of growth from the combination which (i) exceeds the sum (LSD P< 005)

of the contributing ingredient activities and (ii) exceeds the value (LSD P< 005) of the most extreme of the contributing ingredient activities.cGrowth exceeds the sum (LSD P< 005) of the contributing ingredient activities.dGrowth less than the sum (LSD P< 005) of the contributing ingredient activities.




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doses and against a number of bacterial species. Muchinformation can be derived from the data generated hereand, from a commercial food development perspective,it is fast and inexpensive, ensuring cost does not limituse in an industrial setting.

The manuka honey was the most promising candidate

for the inclusion into a concept functional food intendedto manage gut bacteria for the purposes of maintainingand increasing gut health. Although the mechanisms ofhoney on bacteria, both alone and in combination withother potentially bioactive ingredients are still to be fullyexplored, the antimicrobial data are entirely consistentwith the effects of manuka honey currently in theliterature. Further investigations on the beneficial effectsof this ingredient are currently being carried out by theauthors, both in vitro and in vivo.

As observed with the manuka honey in particular, thebee products in general, and to a lesser extent with therosehip solution, there appears to be a division betweenthe effects of the ingredients on probiotic organisms

compared with the effects on pathogens. The inclusionof the Gram positive pathogen Staphylococcus in someof the assays dispels the potential theory that the resultswere dictated by a Gram positive or Gram negative-specific elements. The role played by the differing mediarequirements, and the lactic acid production and anaer-obic respiration of the probiotics versus the aerobicpathogens, has not yet been explored. Furthermore,manuka honey or other ingredient-derived factors con-tributing to increased growth of the probiotic organismshave not been unequivocally identified, nor their mech-anisms established. Currently, this phenomenon ofincreased probiotic growth in the presence of food

ingredients (which are not conventional prebiotics suchas oligosaccharides) is not prominent in the literature,and is an exciting new development.

Collectively, thesein vitroinvestigations into potentialsynergistic interactions between ingredients illustrate apotential for combining food ingredients to modifycomponents of the gut flora to a degree not achieved bya single ingredient alone. Although combinations ofingredients yield unusual results, sometimes desirableand sometimes undesirable from a health perspective,specific combinations such as manuka honey extractcombined with BroccoSprouts or to a lesser extent,bee pollen, rosehip and blackcurrant oil, show immedi-ate potential as an ingredient combination in, for

example, a yoghurt containing B. lactis DR10, whichis specifically and synergistically encouraged to grow bythree of those four combinations. It is recognised thatthe effects of these ingredients or ingredient combina-tions may perform differently with mixed populations ofbacteria than with single strains tested in isolation. Inaddition, they may perform very differently in a morecomplex food matrix or as conditions change duringconsumption and digestion. Food synergy is the basis

for modern nutrition science. It is an extremely complexarea and is composed not only of interactions betweencompounds in ingredients but between them and thegeneral food matrix. Also, whilst some of these ingre-dients may well retain their efficacy when incorporatedinto foods, other factors such as safety, toxicity and

organoleptic impact would need to be considered. It isacknowledged that documented allergic responses havebeen observed with bee products (for example, referMenniti-Ippolito et al., 2008), which may limit theirusefulness to some manufacturers or potential consum-ers.

Finally, in regard to the adverse synergies observed, athorough screening programme should be considered asan essential part of functional food development toavoid any undesirable synergies between functional foodingredients.

Acknowledgments

The authors wish to thank Lynn McIntyre (Institute ofEnvironmental Science and Research Limited (ESR),Christchurch, New Zealand) for valuable advice andsupport, and Alison Wallace (New Zealand Institute forCrop and Food Research Limited Christchurch, NewZealand) for support and sourcing ingredients.

Douglas Rosendale is in receipt of a Crop and FoodResearch PhD studentship and this project is part of theFoods forH. pyloriProgramme (C02X0402) funded bythe Foundation for Research Science and TechnologyNew Zealand with co-investment from Comvita NewZealand Ltd.

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