1 CSIRO Livestock Industries, St Lucia, Australia
2 Department of Animal Science, Faculty of Agricultural and Food Sciences, University of ManitobaWinnipeg, MB, Canada R3T 2N2
Correspondence
Denis Krause
Denis_Krause{at}umanitoba.ca
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ABSTRACT |
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INTRODUCTION |
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Acacia spp. are regarded as multi-purpose trees, and can be used as fodder plants (Hove et al., 2001) to supplement nitrogen in grazing ruminants (El-Hassan et al., 2000
). However, acacias contain at least two classes of toxic compounds: tannins (Smith et al., 2001
) and non-protein amino acids (McSweeney et al., 2000
; Odenyo et al., 1997
). Tannins are polyphenolic compounds that inhibit many ruminal bacteria and the amino acids may induce systemic toxicity in animals (Khanbabaee & van Ree, 2001
). The effects of multi-purpose trees on the ecology of the rumen are of interest, because of the potential of micro-organisms to metabolize these compounds and reduce their toxic effects.
Microarray technology is becoming popular because up to 10 000 probes can be immobilized on a glass support, making high-throughput processing possible when a large number of targets or samples are being evaluated (Churchill, 2002; Wilson et al., 2002
). Arrays have principally been used to determine microbial species identity (Cho & Tiedje, 2002
), and to profile gene expression by micro-organisms (Cho et al., 2002
; Firoved & Deretic, 2003
).
In this paper, we describe the development and application of a community genome array (CGA) approach based on the immobilization of whole rumen bacterial chromosomes (the probes) on a nylon support. This approach is similar to the reverse genome probing methodology previously developed (Voordouw et al., 1991, 1993
) but modifications to experimental approach, quantification methodology and statistical analysis have been made (Fig. 1
). CGAs were applied to rumen samples obtained from sheep that had been consuming tannin-containing diets consisting of Acacia angustissima to assess the effects of this plant on rumen microbial populations. These studies were also validated with Northern analysis of key microbial species. We have also incorporated extensive mixed-model statistical analyses to account for multiple random and fixed effects. In this paper, we will refer to DNA bound to the membrane as the probe, and to the rumen sample as the target, or sample.
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METHODS |
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Feeding trials were conducted longitudinally, with three sheep fed four diets consecutively. Diets were as follows: (1) establishment of baseline, 50 % Rhodes grass plus 50 % lucerne for a period of 30 days (RL1); (2) step-wise adaptation (13 days) to A. angustissima by substituting 50 g per day of lucerne for Acacia up to 37 % (RLA); (3) RLA plus 60 g per day polyethylene glycol (PEG; mol. mass 4000) (RLAP) for 10 days; and (4) back onto basal diet of 50 % Rhodes grass plus 50 % lucerne for 10 days (RL2). PEG was added because it tends to reverse the effects of tannins. Rumen samples were taken on three consecutive days at the end of each feeding period (total of 12 samplings, from each of four periods). Samples were taken 2 h after each morning feed and stored at 70 °C. Stepwise adaptation to the Acacia was followed because of reports of its toxicity in the literature (Odenyo et al., 1997). The Acacia plants used are not native to Australia, had to be grown under quarantine conditions, were not allowed to flower, and all residues had to be incinerated after the experiment. There was therefore a lack of material for follow-up experiments and there was not sufficient material to feed sheep for more than 10 days in each period, except for the adaptation period when animals were fed for 13 days.
Microbial strains.
All microbial strains were from our own laboratory collection.
Medium.
Bacterial growth medium composition was (per litre): 5 g cellobiose, 5 g casitone, 150 ml mineral solution A (contents per 100 ml: 3 g K2HPO4.3H2O), 150 ml minerals solution B (contents per 100 ml: 3 g KH2PO4, 6 g (NH4)2SO4, 6 g NaCl, 1·23 g MgSO4.7H2O, 1·58 g CaCl2.H2O), 2 ml trace mineral salts (contents per 100 ml: 0·5 mg ZnSO4.7H2O, 0·15 mg MnCl2.4H2O, 1·5 mg H3BO3, 1·0 mg CoCl2.6H2O, 0·1 mg NiCl2.H20, 0·15 mg Na2MO4.2H2O, 7·5 mg FeCl2.4H2O), 3·1 ml volatile fatty acid (VFA) solution (contents per 100 ml: 0·68 ml acetic acid, 0·3 ml propionic acid, 0·18 ml butyric acid, 0·05 ml isobutyric acid, 0·06 ml methylbutyric acid, 0·06 ml valeric acid, 0·06 ml isovaleric acid, 0·1 g phenylacetic acid), 2 g yeast extract, 1 g L-cysteine.HCl, and 0·01 % resazurin. Media were prepared anaerobically according to published methods (Bryant, 1972). The anaerobic gas was a 95 % CO2/5 % H2 mix, and 4 g Na2CO3 l1 was included to buffer the medium at pH 6·7. Aliquots (9 ml) of anaerobically prepared medium were dispensed into 25 ml Balch tubes (18 mmx250 mm) inside an anaerobic cabinet (Coy Laboratories), stoppered and autoclaved for 15 min at 100 kPa.
DNA and RNA extraction.
DNA and RNA were extracted using a mechanical lysis method adapted to rumen samples which lyses more than 95 % of cells (Krause et al., 2001a). The main difference between the extraction of DNA and RNA was the composition and pH of the buffers used. In brief, DNA/RNA extraction was as follows: (a) a rumen sample was extracted three separate times to minimize extraction bias; (b) quality and quantity of nucleic acids were determined by electrophoresis (RNA with a formamide gel, DNA an agarose gel), and pooled if of sufficient quality and quantity; (c) each pooled sample was arrayed in triplicate on three separate membranes (see below). If the RNA, or DNA, was not of sufficient quality another aliquot was extracted. Cells from bacterial cultures were lysed using an enzymic method, instead of mechanical lysis (Krause et al., 2001a
).
Fabrication of microarrays.
Chromosomal DNA from each bacterial strain, or rumen sample, was diluted 50 : 50 with sterile distilled water containing 0·0002 % bromophenol blue, so that once arrayed, the DNA could easily be located on the membrane. The concentration of DNA was 1 µg µl1 so that once diluted the final concentration was 500 ng µl1. Using this concentration, it was possible to array 1 µl droplets of DNA in a 96 (16x6) gridded format on a positively charged nylon membrane (Roche Diagnostics), the size of a standard microscope slide (7·5 cmx2·5 cm). To speed up manufacture of membranes, DNA was aliquoted into 384-well plates and arrayed onto the nylon using a 384-solid-pin multi-block replicator (V&P Scientific). The small size of the membrane allowed up to 44 individual membranes to be processed at once in a rotary hybridization oven (Thermo Hybaid).
Hybridization conditions for arrays.
The DNA-containing membranes were baked at 120 °C for 30 min to cross-link the DNA to the nylon. A prehybridization solution [25 % formamide, 5x SSC, 2 % blocking reagent (Roche Diagnostics), 2 % SDS, 0·1 % N-lauroylsarcosine] was incubated with the membranes for at least 2 h before the addition of the digoxigenin-labelled probe (10 ng ml1). Probes were allowed to hybridize overnight and membranes were then washed twice, for 1 h, at 68 °C with 5x SSC. Membranes were subsequently processed according to the manufacturer's instructions (Roche Diagnostics). The amount of DNA on the membrane was determined by quantifying the 16S rRNA genes using a universal oligonucleotide probe and a standard curve of Escherichia coli DH5 DNA.
Hybridization conditions for Northern blots.
Prehybridization for 16S rRNA was done under the same conditions as described above for DNA arrays. Probes used, and the experimental conditions, have previously been described (Krause et al., 1999). Each sample was blotted in triplicate on each of three membranes. A dilution series of the reference organism was included on each membrane with the samples, or alternatively, the dilution series was carried out on a membrane with only selected samples and then cross-referenced to the master membrane.
Cross-hybridization assays.
The bacteria included in the array are underlined in Fig. 2. With this hybridization approach it is important to determine the degree of cross-hybridization between the genomes of bacteria that are to make up the master array. Each candidate organism to be included on the array was cross-hybridized to all other candidates. The degree of cross-hybridization was determined by measurement of the hybridization signal obtained with non-target organisms and expressed as a percentage of self-hybridization (100 %) (Table 1
). The diagonal gives the minimum amount of DNA detected under assay conditions.
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Measurement of pH and VFAs.
Rumen fluid was obtained by filtering rumen contents through a 50 µm polyester sieve. A 15 ml portion of filtered rumen fluid was mixed with 300 µl 4 % mercuric chloride, and centrifuged at 20 000 g for 20 min. The supernatant was frozen at 20 °C until ready for analysis of pH and VFA (Bush et al., 1979).
Linear statistical modelling.
Although similar hybridization profiles within a probe/sample combination were observed, the signal intensities between replicate arrays and Northern blots varied considerably. Thus, an analysis-of-variance (ANOVA) model was developed to account for the observed variability between replicate arrays, so that we could statistically evaluate the effects of probesample hybridization that would also accommodate variation within arrays and within Northern blots, and between replicates and treatments. For this application, the linear (ANOVA) statistical model included fixed and random terms (Tables 2 and 3). The model is a direct reflection of the experimental design, which featured random effects such as membrane, and fixed effects such as bacterium. Table 2
lists the factors with levels and types that were considered in this study. The full mixed-effects linear model is listed in Table 3
. The model was analysed by using the ANOVA capabilities in the Statistica statistical analysis software package (StatSoft).
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RESULTS |
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Cross-hybridization experiments
Each bacterial chromosome was labelled and cross-hybridized to all other bacterial genomes potentially to be included on the array (Table 1). The extent of hybridization was quantified with a reference series of labelled DNA and cross-hybridization was expressed as a percentage of self-hybridization (100 %). The minimum amount of DNA detectable is also given in Table 1
; it was within an order of magnitude for most bacterial strains, and indicates that low levels of cross-hybridization due to low levels of DNA detectability were not a confounding factor. It should be noted that the amount of detectable DNA reported was that under the experimental conditions used; smaller quantities of DNA could be detected under less stringent conditions, or longer exposure to X-ray, but this led to lower specificity, or more background signal. As expected, there was no cross-hybridization between genomes of separate species, but there was cross-hybridization between strains of the same species such as Butyrivibrio fibrisolvens and Selenomonas ruminantium. The cross-hybridization matrix could essentially be predicted from the degree of sequence similarity between 16S rRNA genes (Fig. 2
).
We concluded that the sensitivity of the cross-hybridization methodology was robust because initial experiments revealed cross-hybridization between phylogenetically distantly related strains (e.g. Streptococcus bovis JB1 and Prevotella bryantii B14) which were the result of contamination. P. bryantii B14 was restreaked and picking of discrete colonies revealed that this culture had been contaminated with Strep. bovis JB1. This occurred with several other cultures and purification proved to be extremely time-consuming. However, once the chromosomal DNA had been prepared it could be stored for long periods (up to 2 years) without deterioration. We assessed deterioration of the DNA by evaluating the extent of fragmentation (smearing) on an agarose gel. Additionally, we were able to prepare up to 100 membranes at a time using the small-membrane format and these could be stored at 20 °C for up to 2 years.
Quantification of array experiments
Each membrane included the chromosomal DNA of the sample to which it was hybridized to provide a self-hybridization control; using our definition, rumen-extracted DNA was both a sample and a probe. Self-hybridization was the benchmark to which individual strains were compared, and quantitative results were expressed as a proportion thereof. The variation for each probe/sample combination was compared across the membrane triplicate as well as triplicate membranes (nine spots per probe). If the variation was greater than 5 %, the experiment was repeated. We found this to be an essential element in controlling experimental variation and ensuring data quality. Our approach to controlling experimental variation is borne out by the results of the mixed-model ANOVA analysis because membrane and extraction random effects were not significant (Table 3).
The response of individual strains of bacteria varied widely depending on diet (Fig. 3). In some cases the bacterial strains were sensitive to the presence of Acacia in the diet (e.g. B. fibrisolvens strains), while others appeared to be quite resistant (e.g. S. ruminantium strains). Individual strains within the same species (e.g. B. fibrisolvens and S. ruminantium) reacted differently to the change in diet, indicating within-species variation in phenotype with respect to the diets. The arrays also included Ruminococcus albus AR67, Ruminococcus flavefaciens Y1 and Fibrobacter succinogenes S85 (Fig. 3
). These organisms increased in abundance as Acacia increased in the diet, which we found surprising, because our previous work with Calliandra callothyrsus, another tannin-containing plant, resulted in a decline in these species (McSweeney et al., 2001
).
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DISCUSSION |
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Greene & Voordouw (2003) discussed the relationship between hybridization intensity (Ix), the fraction of community DNA (fx) containing genome x, and Cx, the concentration of x on the membrane, so that Ix=k·fx·Cx. They also used an efficiency constant (k). This means that the intensity (Ix), or hybridization signal, increases as the abundance of a species in the sample (fx) increases, or the concentration of the probe on the membrane (Cx) increases. To increase sensitivity the concentration of the probe (Cx) can be increased, but at higher concentrations, the hybridization intensity (Ix) is non-linear. A decrease in sensitivity also occurs because cross-hybridization increases as Cx increases, and if cross-hybridization is greater than fx, then only populations at high abundance can be detected with confidence. As discussed below we formed a compromise, by increasing Cx (from 50 ng to 500 ng; Voordouw et al., 1991
), and increasing the stringency of the post-hybridization washes (from 6x SSC to 5x SSC; Voordouw et al., 1991
), so that cross-hybridization error was kept to a minimum. Lastly, by increasing Cx, we typically worked in the non-linear range for Ix and we thus used CGA primarily as a tool to survey rumen populations for candidates. Candidate populations could then be carefully quantified against a standard curve using a robust method such as rRNA Northern-blot analysis. Our logic was that since CGA is not robust enough to give accurate quantitative data, it was better to try increasing sensitivity, decreasing cross-hybridization, and dealing with quantification separately (Fig. 1
).
In general, we found little cross-hybridization between bacterial strains except for strains within a species (Table 1). For example, we observed cross-hybridization among stains of B. fibrisolvens, Strep. bovis and S. ruminantium species (Table 1
). This was expected because the 16S rRNA sequence similarity among these organisms is greater than 98 % (Maidak et al., 2001
). At 65 °C wash temperature there was also some cross-hybridization among different species (e.g. B. fibrisolvens spp. and Eubacterium cellulosolvens) but this could be eliminated by increasing the hybridization temperature to 68 °C. Cross-hybridization among strains of the same species (e.g. B. fibrisolvens and Streptococcus spp.) was almost entirely abolished by increasing the wash temperature to 72 °C but at this temperature almost no hybridization signal was obtained from rumen samples. Two reasons could account for this result: (a) the hybridization conditions are so stringent that only homologous chromosomes will produce a signal, and (b) the abundance of the specific homologous DNA is at low levels in the rumen. For these reasons we decided to use a wash temperature of 68 °C. We did not attempt to correct data for cross-hybridization because this approach is questionable if the rumen metagenome has not been characterized. In contrast, eliminating cross-hybridization between strains of closely related species with 16S rRNA-based approaches is far more difficult because the lack of sequence variation limits experimental options (Koizumi et al., 2002
; Peplies et al., 2003
).
Voordouw et al. (1991) included lambda DNA as an external marker for quantification. The assumption is that lambda DNA and the environmental DNA will be labelled with the same efficiency. We found this not to be the case: the lambda DNA was more efficiently labelled than the sample DNA. This was particularly problematic with the tannin-containing Acacia samples, which are rich in phenolics that are known to inhibit enzymic reactions (Haslam, 1989
). Greene & Voordouw (2003)
indicated that differential labelling could result in an fx of greater than one. We obtained good results by measuring the abundance of rDNA with a universal oligonucleotide probe and quantified the signal by comparison to a standard curve.
Strains within a species tended to respond to dietary changes in a similar manner. For example, B. fibrisolvens strains were sensitive to tannins, while S. ruminantium was tolerant (Fig. 3). Closely related species such as Strep. bovis and Strep. gallolyticus responded differently: Strep. bovis was sensitive while Strep. gallolyticus was tolerant, results which have been confirmed in vitro (Brooker & O'Donovan, 2001
) and in vivo (Brooker et al., 2002
). Actinomyces spp. LP1283 and Clostridium botulinum LP1284 were tolerant to tannins and both these strains were previously isolated in association with tannins (McSweeney et al., 1999
). Ruminococcus spp. and F. succinogenes increased in the presence of Acacia (Fig. 4
). This was a surprising result because it had previously been shown that Ruminococcus and Fibrobacter were sensitive to tannins both in vivo (McSweeney et al., 2001
) and in vitro (Odenyo et al., 1997
).
More accurate assessment of the changes in populations of Ruminococcus spp. and F. succinogenes was performed by Northern blot analysis. In the case of both Ruminococcus and Fibrobacter, the increase in abundance when Acacia was added to the diet was significant (P<0·05) when expressed as a proportion of the bacterial 16S rRNA population (4·3 to 9·5 % for Ruminococcus and 1·7 to 5·6 % for Fibrobacter) (Fig. 4) and there was little change (P>0·05) in these two populations when PEG was added to the diet. In contrast the array data (Fig. 3
) demonstrated a significant (P<0·05) decline in Ruminococcus, but not in F. succinogenes, when PEG was added to the diet.
We hypothesized that Ruminococcus and F. succinogenes were indeed sensitive, but were less so than most other bacterial species, leading to an increase in their apparent relative abundance. When Ruminococcus and F. succinogenes are expressed as a proportion of the total bacterial, or microbial population, there was a significant increase in their abundance (Fig. 4) (P<0·05). There was also a decline in bacteria as a proportion of the total microbial population, but this was not significant (P>0·05) (Fig. 5
). However, Ruminococcus declined as a percentage of the total microbial population (P<0·05), but not as a proportion of the total bacterial population, when PEG was added to the diet (Fig. 4
). Expressing the Ruminococcus and F. succinogenes populations as a proportion of total microbial population more accurately reflects the array data because both are proportions of the total microbial population and not only the bacterial population.
The decline observed with Ruminococcus in the presence of PEG would suggest that a subset of the bacterial population adapt to the tannins. The differences in the Acacia and Calliandra results observed previously (McSweeney et al., 2001) can also be explained by the differences in the neutral detergent fibre (NDF)-bound tannins and the NDF digestibility (Hove et al., 2001
). The NDF-bound tannins are 50 % less than in Calliandra and the NDF digestibility of Calliandra is extremely low. Ruminococcus and F. succinogenes typically adhere to the fibre surface (Krause et al., 2003a
) and if the local concentration of tannins is lower in Acacia then high numbers of Ruminococcus and F. succinogenes might be expected. It was also not possible that the discrepancies in bacterial populations were the result of differential extraction of DNA or rRNA, because we used mechanical shearing which lyses at least 95 % of the microbial population.
From the above experiments, we conclude that there were no inherent deficiencies in our experimental method and thus decided to do a survey of the literature to ascertain to what extent variation in microbial population responses occurred. Close examination of the literature demonstrates that the responses of a bacterium can vary significantly depending on the type of plant and type of assay (in vitro or in vivo). Table 4 summarizes a range of studies that illustrate this point. Studies with Prevotella bryantii B14 have indicated that this strain is tolerant in vitro to Onobryachis viciifolia (Jones et al., 1994
) and Yucca shidigera (Wallace et al., 1994
) tannins, but sensitive in vitro to Lotus corniculatus and Lotus pedunculatus (Molan et al., 2001
). Strep. bovis and Strep. gallolyticus are very closely related at the 16S rDNA sequence level, but Strep. bovis tends to be sensitive and Strep. gallolyticus tolerant (Brooker & O'Donovan, 2001
). It is also clear that the response in vivo is far smaller than it is in vitro (McSweeney et al., 2001
; Min et al., 2002
; Molan et al., 2001
) but small changes in the 16S rRNA-described population can often be far smaller than the functional changes in the system (Hashsham et al., 2000
).
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ACKNOWLEDGEMENTS |
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Received 1 December 2003;
revised 28 April 2004;
accepted 11 May 2004.
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