- Research article
- Open Access
Community terminal restriction fragment length polymorphisms reveal insights into the diversity and dynamics of leaf endophytic bacteria
© Ding et al.; licensee BioMed Central Ltd. 2013
Received: 18 August 2012
Accepted: 20 December 2012
Published: 3 January 2013
Plant endophytic bacteria play an important role benefiting plant growth or being pathogenic to plants or organisms that consume those plants. Multiple species of bacteria have been found co-inhabiting plants, both cultivated and wild, with viruses and fungi. For these reasons, a general understanding of plant endophytic microbial communities and their diversity is necessary. A key issue is how the distributions of these bacteria vary with location, with plant species, with individual plants and with plant growing season.
Five common plant species were collected monthly for four months in the summer of 2010, with replicates from four different sampling sites in the Tallgrass Prairie Preserve in Osage County, Oklahoma, USA. Metagenomic DNA was extracted from ground, washed plant leaf samples, and fragments of the bacterial 16S rDNA genes were amplified for analysis of terminal restriction fragment length polymorphism (T-RFLP). We performed mono-digestion T-RFLP with restriction endonuclease DdeI, to reveal the structures of leaf endophytic bacterial communities, to identify the differences between plant-associated bacterial communities in different plant species or environments, and to explore factors affecting the bacterial distribution. We tested the impacts of three major factors on the leaf endophytic bacterial communities, including host plant species, sampling dates and sampling locations.
Results indicated that all of the three factors were significantly related (α = 0.05) to the distribution of leaf endophytic bacteria, with host species being the most important, followed by sampling dates and sampling locations.
Bacteria are associated with plants in many ways. They include rhizosphere bacteria that are found in the soil surrounding roots, rhizoplane bacteria that reside on the root surfaces and phyllosphere bacteria that are associated with leaves. Within each of these spheres of plant influence, it is common to distinguish between those bacteria that are associated loosely with the outside of the roots or leaves, the epiphytes, from those that have colonized the internal parts of the organs, the endophytes. Rhizoplane bacteria have been extensively studied, as have root endophytic bacteria [1–3]. Numerous publications address leaf epiphytic bacteria [4–6]. Only few studies have examined specifically leaf endophytic bacteria as part of phyllosphere bacteria . The diversity of leaf endophytic bacteria in different plants is largely unexplored, and is the main subject of this study. We want to understand what factors shape the communities of leaf endophytic bacteria.
A universally accepted definition of plant endophytic bacteria has not been established. In this study, we follow Hallmann’s definition of endophytic bacteria  as those bacteria that “can be isolated from surface-disinfested plant tissue or extracted from within the plant and do not visibly harm the plant”. Endophytic bacteria have been found in most plants, colonize the internal tissues and construct diverse relationships with their host plants. Endophytic bacteria can be beneficial to the host plant, including by growth promotion , biological control against plant pathogens , and bioremediation of the contaminated environment . Although non-pathogenic to host plants, some endophytic bacteria may have the potential to become pathogens  to other plants, and may be harmful to animals or even humans. Assessing this potential requires gathering a general understanding of endophytic microbial communities, their diversity, and their distribution among plant species, plant individuals and plant organs.
Traditionally, most studies of endophytic bacterial communities [10–12] are based on bacterial culture methods. However, most environmental bacteria are not cultivable, as evidenced, for example, by the finding that culture-independent methods revealed a broader diversity of bacteria than did culture-dependent methods in a study of bacteria in the apple phyllosphere . In recent years, the study of endophytic bacteria often has employed culture-independent methods, most of which are based on the PCR amplification of bacterial 16S rDNA. Some notable studies of root endophytic bacteria [2, 14, 15] focused on single crop species, including maize and rice, because of their importance to food supply and safety. Several researchers have applied Terminal Restriction Fragment Length Polymorphism (T-RFLP) , a rapid fingerprint technique based on 16S rDNA PCR, to the evaluation of endophytic bacteria. T-RFLP can compare multiple microbial communities fast and accurately, especially when high-throughput bacterial community characterization is needed.
In this project, we studied leaf endophytic bacteria in diverse environments from the Tallgrass Prairie Preserve (TGPP), Osage County, Oklahoma, USA , managed by The Nature Conservancy, and which was the site of previous efforts by a Plant Virus Biodiversity and Ecology team to examine the diversity of viruses associated with plants growing in this setting . That study showed nucleotide sequence evidence of bacterial association with plants [17–19]. We extracted total DNAs from plant samples obtained in the TGPP and amplified bacterial 16S rDNA sequences using bacterial rDNA specific primers. Rather than using multi-digestion T-RFLP with three or more restriction endonucleases, we performed mono-digestion T-RFLP with restriction endonuclease DdeI, to reveal the structures of leaf endophytic bacterial communities, to identify the differences between plant-associated bacterial communities in different plant species or environments, and to explore the factors affecting the bacterial distribution.
Healthy, above-ground parts of plant samples were collected monthly from May to August, 2010, in the TGPP). Four sites were randomly chosen (Additional file 1: Table S1). At each site, samples of 5 species of plants (Asclepias viridis, Ambrosia psilostachya, Sorghastrum nutans, Panicum virgatum, and Ruellia humilis) that are among the most frequent in the TGPP were collected. At each site, three multi-branched individuals of A. viridis were identified and labeled with tags on May 14th 2010, and one branch was harvested. On June 16th and July 14th (in August A.viridis samples were not found in the TGPP due to senescence), additional branches were removed for processing. One individual of each of the other four species was collected at each site in four consecutive months from May to August. Healthy leaves were collected and processed for DNA extraction.
Extraction of total DNA from plants
All leaves were recovered from each plant sample and then washed with running tap water for at least 5 min to remove soil, dust and epiphytic organisms, followed by shaking in 75% ethanol twice each for 3 min, and then rinsed with running distilled water for 3 min. To validate the effect of the protocol, treated leaves were rinsed with 10 ml double distilled water for 3 min. The rinse water was collected and incubated on Lysogeny Broth (LB) plates at 37% overnight. No colonies were observed. Treated leaf samples were ground into a fine powder with liquid nitrogen. Then, 0.1 g of the grindate was resuspended in a 1.5 ml microcentrifuge tube containing 1 ml CTAB extraction buffer [2% (w/v) cetyltrimethylammonium bromide, CTAB; 100 mM Tris–HCl (pH 8.0), 1.4 M NaCl, 20 mM EDTA, 1.5% polyvinyl-pyrolidone, PVP; 0.5% 2-mercaptoethanol] preheated to 65%. Contents were mixed by inverting the tube several times, followed by incubating the tubes in a 60% water bath for 60 min. The tube was centrifuged at 12,000 rpm for 5 min at 4°C and the supernatant was transferred to a new tube. DNA was then extracted twice with chloroform-isoamylalcohol (24:1 v/v) until the aqueous phase was clear. DNA was precipitated using 2 to 2.5 volumes of absolute ethanol, and 0.1 volume 3 M sodium acetate for 2 h at −20°C, followed by centrifugation at 12,000 g for 10 min at 4°C, washed with 1 ml DNA wash solution (0.1 M trisodium citrate in 10% ethanol) twice (30 min incubation and 5 min centrifugation) and 1.5 ml 75% ethanol once (15 min incubation and 5 min centrifugation), then air dried. Finally, DNA was resuspended in 50 μl DNase-free water.
Because the bacterial 16S rDNA sequences are highly similar to plant mitochondrial and chloroplast rDNA sequences, popular universal bacterial 16S rDNA primers are not appropriate for specific amplification of bacterial rDNA from plant DNA extracts . Primers 799F and 1492R  designed to exclude amplification of plastid 16S rDNA, were used in PCR. Each 50 μl PCR contained PCR buffer (Promega, MadisonWI), 2.5 mM MgCl2, 200 μM each dNTP, 0.5 mg/ml BSA, 15 pmol of each primer, and 2.5 U Taq polymerase. Thermal cycling conditions were: an initial denaturation at 95°C for 3 min followed by 30 cycles of 94°C for 20 sec, 53°C for 40 sec, 72°C for 40 sec, and a final extension at 72°C for 7 min. The PCR yielded a 1.1 kbp mitochondrial product and a 0.74 kbp bacterial product. These were electrophoretically separated in an agarose gel and recovered from the gel using Qiaquick gel extraction kit (Qiagen). Bacterial rDNA amplicons from multiple PCRs from the same template were pooled for restriction.
The selection of restriction endonuclease and T-RFLP
Engebretson et al.  suggested that four restriction endonucleases including BstUI, DdeI, Sau96I, and MspI had the highest frequency of resolving single populations from bacterial communities. To select the endonuclease with the highest power to resolve leaf endophytic bacterial communities, we cloned 16 s rDNA PCR products and randomly selected and sequenced inserts from 50 colonies. Computer-simulated virtual digestions indicated that DdeI generated the most distinct T-RFs and thus had the highest resolution. Therefore, we chose DdeI (Promega) to perform the mono-digestion T-RFLP to generate T-RFLP profiles from five species of plants.
Restriction digestion reactions were incubated at 37°C for 4 h, followed by 20 min at 65°C to denature the enzyme. Two microliters of the restricted PCR product were mixed with 0.75 μl of size standard LIZ1200 (ABI, Foster City, CA) and 7.25 μl of Hi-Di formamide (ABI). DNA fragments were scanned on an ABI 3730 automated DNA sequencer at Oklahoma State University’s Recombinant DNA/Protein Core Facility. The T-RFLP data profiles were obtained and analyzed by using GeneMapper Software version 4.0 (ABI).
Data processing and statistical analysis
In 16S-rDNA-T-RFLP profiles, a baseline threshold of 50 relative fluorescence units was used to distinguish ‘true peaks’ from background noise. Considering T-RF drift (improperly sized T-RFs due to differences in fragment migration and purine content), peaks were manually aligned using the method described by Culman et al. . After background removal, raw peak height was normalized to balance the uncontrolled differences in the amount of DNA between samples by dividing the peak height by the sum of all peak heights of each sample. Culman et al.  determined that relative peak heights are better than peak areas for comparisons in T-RFLP profile analysis, yielding greater signal to noise ratios.
All the T-RFLP data were arranged into a matrix with each row as a community sample and each column as the relative abundance of each T-RF. The matrix was analyzed by partial Canonical Correspondence Analyses (pCCA) using Canoco for Windows 4.5 (Plant Research International) (32). We performed three kinds of pCCAs: using, as explanatory variables: sites, months, and host species. For each of these analyses, the other variables (e.g. for the third analysis, months and sites) were used as covariables. This approach allowed us to isolate the independent effects of each factor. For each analysis, we performed a permutation test of significance with 9,999 permutations, conditioned on the covariables.
Based on the complete T-RFLP data matrix, we calculated also the percentage of empty cells in the data matrix  as 100% x (total number of cells in the data matrix of T-RFs vs. samples - count of all cells with non-zero values)/(total number of cells in data matrix). Multivariate Analysis of Variance (MANOVA) was conducted using SAS v9.2 (SAS Institute Inc.) and Hierarchical Clustering Analysis was carried out with R (R development core team, 2003).
where Pi is the relative proportion of the T-RF in ith sample, m is the total number of samples, and n is the number of these which have the T-RF.
In this study, we used T-RFLP profiles to study the features of the distribution of leaf endophytic bacterial communities. Rather than using multiple restriction digestions and then comparing the combined T-RFLP profiles to entries in a pre-computed database, here we chose to use only one restriction endonuclease and the T-RFs with a certain length were treated as a special kind of OTU (Operational Taxonomic Unit) - Operational T-RFLP Unit, a unit that can be directly used to describe a community. In this manner we avoided the problems caused by T-RFs not referring to a known bacterial species in the database. This approach allows direct study of the complexity of, and changes in, distribution of leaf endophytic bacteria without requiring taxonomic identification.
Osborn et al.  have demonstrated that T-RFLP is highly reproducible and robust in studying microbial communities and yields high-quality fingerprints consisting of fragments of precise sizes. In this research we also confirmed the reproducibility of T-RFLP to validate the application of T-RFLP to study endophytic bacterial communities. We repeated the complete procedure from DNA extraction to final T-RFLP scanning, and the results indicated that the T-RFLP profiles from the same sample were indistinguishable (Additional file 2: Figure S1).
General analysis of T-RFLP profiles of endophytic bacterial communities in A. viridis
Summary statistics for T-RFs of Asclepias viridis samples from different months and sites
Percent empty cells in matrix
Data summarized by months
Data summarized by sites
A. viridisT-RFLP pattern variation contributed by sampling sites and dates
Unlike the samples from different months, the samples from different sites did not show significant variation when the data were analyzed for the presence or absence of individual T-RFs (Table 1) even though samples from site 4 appeared to have a lower diversity of leaf endophytic bacteria than others. Although the general level of diversity of leaf endophytic bacteria did not show variation among sites when presence/absence data were considered, the T-RFLP profiles of samples from different sites suggested that the compositions and the relative abundances of individual T-RFs varied with the site/location of host plants, revealing a possible connection of leaf endophytic bacterial species with host locations. Figure 1(b) shows the T-RFLP patterns of two A. viridis plants both collected on July 14, 2010, but from different sites. In the sample from site 2, the T-RF 75 bp was more prominent than the T-RF 85 bp; while in the sample from site 3, the T-RF 85 bp was more prominent. Other dominant T-RFs, including the T-RF 364 bp and the T-RF 529 bp, also show differences in relative abundance. The influence of host locations may contribute to differences in endophytic bacterial community compositions. Alternatively, the differences could reflect sample to sample variation.
Partial canonical correspondence analysis (pCCA) of T-RFLP profiles
The pCCA result of T-RFLP profiles of A. viridis treating location of host plants as environmental factor with sampling dates as covariable (Figure 2 (b)) indicated that the differences between samples from site 1 and other sites were stronger than the differences between sites 2 and 3. Permutation tests revealed location of host plants was a significant factor (p-value = 0.0005).
Extension of the analysis to multiple host species
Having established month to month variation and sites as significant factors shaping endophytic bacterial communities in A. viridis, we asked whether the A. viridis communities were shared in other species growing at the same times in the same locations and whether those species had similar time and location influences on their community compositions. Host plant species may influence leaf endophytic bacterial communities because of their different physiological and biochemical features. Indeed, the T-RFLP patterns of A. viridis, A. psilostachya, and P. virgatum individuals were distinct (Figure 1(c)). The total number of T-RFs detected varied from 16 for R. humilis to 72 for A. viridis (Additional file 1: Table S3). The beta-diversity calculated for each host species was significantly lower than the diversity when samples were grouped by sample date or site (Additional file 1: Table S3). The dominant T-RFs (the group of the T-RFs which have an average proportion more than 3% of the total) for these three species (Additional file 1: Table S2) reveal that each host species had its own characteristic group of dominant T-RFs. Especially the most dominant T-RFs differed among these three species. These observations indicate that the host species has properties determining the compositions of their leaf endophytic bacterial populations.
The pCCA result of treating host species as the environmental factor with sampling dates and locations as covariables in analyzing T-RFLP profiles using data from five host plant species supports that T-RF patterns are influenced by the host species identity (Figure 2 (c)). In the pCCA biplots, S. nutans and P. virgatum were close to each other, indicating that the leaf endophytic bacterial communities from these two species were similar to each other. Those of the other three host species were distinct from each other with A. viridis the most distinct, since the data point of A. viridis lay on the other end of the first axis. The analysis was performed also using only May, June and July data to guard against bias introduced by the absence of A. viridis August data. The results were essentially the same. These results are consistent with the features of the host plant species: both S. nutans and P. virgatum are grass species; A. viridis is different from the other four species because it contains latex, giving it the common name “milkweed”. Permutation tests revealed host species as a significant factor (p-value = 0.0001).
We also studied the impacts of the sampling dates and host plant locations based on the 5-species dataset using pCCA. Results (data not shown) indicate that both of these factors were also significant with p-values < 0.01. The 5-species pCCA biplots confirm the inference we obtained from the A. viridis pCCA biplots, that samples from May were more distinct from other samples considering sampling date as an environmental factor, and samples from Site 1 were more distinct from other samples considering sampling site as an environmental factor. After an analysis using all three factors as environmental factors, we were able also to partition the overall variation to reveal how much variation was contributed by each factor. Results calculated from pCCA eigenvalues indicated that host plant species contributed 49.8% of the overall variation, sampling date contributed 28.5%, and host plant locations contributed 14.2%. Thus although these three factors all significantly determined the structure of endophytic bacteria, host plant species was the most important factor, followed by sampling date and host locations.
Statistical analyses of the diversity of leaf endophytic bacteria
Average numbers of T-RFs of endophytic bacterial communities from each host plant species, sampling date and location
Average number of T-RFs
Data collated by host species
17.38 +/− 4.98
15.00 +/− 10.46
14.89 +/− 7.04
12.92 +/− 5.09
5.50 +/− 2.72
Data collated by site
Site 1 Samples
14.71 +/− 7.46
Site 2 Samples
13.86 +/− 6.94
Site 3 Samples
12.45 +/− 7.84
Site 4 Samples
14.60 +/− 8.24
Data collated by sampling date
9.29 +/− 7.95
14.72 +/− 6.16
18.04 +/− 5.91
12.73 +/− 7.47
We also calculated the average frequencies of the T-RFs over all the five host species based on the frequencies of the T-RFs in each species. The average frequency reflects the general distribution of endophytic bacteria among multiple species of host plants. In Additional file 1: Table S5, the average frequencies of all recognized T-RFs were also compared: for example, the T-RF 529 bp had an average frequency more than 80% in these five selected host species and was the most frequent T-RF.
Multivariate Analysis of Variance (MANOVA) of the T-RFLP profile also indicated that the three major factors are significant, consistent with the pCCA result. The T-RFLP profiles of all samples that include only those T-RFs present in highest proportions shown in Figure 3 (b) were also used to test the three major factors by MANOVA. Generally, for the data including all samples, Wilk’s Lambda Analysis and Hotelling-Lawley Trace Analysis both indicated that the three major factors (host species, dates and sampling sites) were significant factors at alpha = 0.05. For these nine T-RFs, at alpha = 0.05, the host species factor was significant for seven T-RFs; the sampling dates factor was significant for seven T-RFs; the sampling sites factor was significant for six T-RFs. In aggregate, these three major factors were all significant at alpha = 0.05 for four T-RFs: 75 bp, 79 bp, 236 bp and 355 bp. The three factor models for these four T-RFs gave R-square coefficients greater than 0.9. Thus, the results of MANOVA were consistent with pCCA, again confirming the importance of the three major factors.
Average proportion per existence a in five different host species of selected b significant T-RFs (Average frequencies > 0.3)
The Hallman et al.  definition of endophytic bacteria requires “surface-disinfested plant tissue” or extraction from the plant. “Disinfestation” by killing all the epiphytic bacteria may be effective when culture-dependent protocols are used, but is not appropriate in culture-independent protocols, such as the present one, since the DNA or RNA of dead epiphytes, if not removed, would still be amplified by bacteria-specific PCR. For those organs, like tubers, whose outer layers can be easily peeled off, endophytic bacteria can be isolated from inside of the plants unambiguously. However, peeling the epidermis off leaves, while possible, is not practical for a study like the present one. Therefore, to study leaf endophytic bacterial communities, it is critical to dislodge epiphytic bacteria from the leaf surfaces as far as possible. We have dislodged epiphytes using methods similar to those reported by others [13, 26–28]. Since we did not test the rinse water for rDNA amplicons, we cannot be sure that we have removed all epiphytic bacteria. However, the observation that the complexities of the populations (Additional file 1: Table S5) were substantially lower than those reported for leaf epiphytic bacteria [29, 30] suggests that most epiphytes have been removed.
Past studies have applied multiple enzyme digestion T-RFLP to environmental bacterial community research [31–33]. Some studies have focused on the rhizosphere, rhizoplane and the epiphytic phyllosphere bacterial communities using fingerprint techniques of 16S rRNA genes, especially the rhizosphere of single cultivated plant species including potato and rice [34–36] and the phyllosphere of soybean, rice and maize [6, 37]. The present research is the first to apply single digestion T-RFLP to leaf endophytic bacteria in multiple host species. Multi-enzyme studies depend on a reliable T-RFLP database to deduce species information; however most T-RFLP databases are still developing, so that a large proportion of novel bacteria, which are highly abundant in the environment, may not be matched using current databases . Although closely related bacterial species will usually produce the same T-RF, one or more other distinct taxonomic groups may also produce the same T-RF. Therefore variation in abundance of a T-RF may be due to changes in one of the represented taxonomic groups, while a second is unchanged. Multi-enzymes are used in an effort to make taxonomic assignments; however taxonomic assignments are not necessary for identification of the factorial influences on the leaf endophytic bacterial communities, as studied in this work. Single digestion T-RFLP peaks represent OTUs (Operational T-RFLP Unit) that provide information on the diversity of leaf endophytic bacteria in different environments.
In order to assess the abilities of T-RF OTUs present in individual plants to compete with other bacteria, we focused on the relative amounts of T-RF OTUs in different plants only in those plants in which they were found. The APE of a T-RF in one host species was defined as the average proportion of a T-RF in all the samples of one plant species which have this T-RF. Calculating APE rather than regular average proportion can avoid the problem of underestimation of the abundance of a T-RF in one host species due to non-infection of the bacterial species represented in some samples. The APE of a T-RF can more accurately reflect the overall compositions of leaf endophytic bacterial communities in a plant species than can methods that include absence in the analysis.
In this research, we explored the diversity of leaf endophytic bacteria in selected plant species over time and the physical environment, in order to propose a model describing how multiple factors influence endophytic bacterial communities. Past studies have found the plant genotype and growth conditions have significant impacts on the rhizosphere bacterial communities [34–36] and on the phyllosphere bacterial communities [6, 38]. Here we considered three major influencing factors: host plant species, time and sampling sites. The distributions of leaf endophytic bacteria must be influenced by many factors; however, we hypothesized that these three major factors include most variables affecting community composition. We analyzed leaf endophytic bacterial communities from samples differing in these factors by pCCA and MANOVA of T-RFs and comparisons of the average amounts of T-RFs present in samples.
The factor of host plant species includes the effect of inner biochemical environment and physiological features of the host plant. The results show that the communities in the two grass species, P. virgatum and S. nutans, are similar to one another and distinct from those in the non-grass species. This may be due to similar environments inside grass plants, different from those inside the other plants. The coevolution and codivergence of host plants and leaf endophytic bacterial communities may also contribute to the similarities and differences in the leaf endophytic bacterial communities from different host species. The expectation of a major influence of host plant species on the communities was supported by distinct T-RF patterns from each host species (Figure 1 and Additional file 1: Table S5), by the results of pCCA which assigned half of the total variation to plant species, and the APE analysis (Table 3).
The time factor includes changes in the physical environment, such as temperature, humidity, irradiance and wind speed, and the dynamics of host plant growth. Jackson and Denney  studied the annual and seasonal variation of phyllosphere bacteria and found that compared to significant seasonal variation, the annual variation was not significant. Yadav et al.  also found that the mature leaves have higher populations of phyllosphere bacteria than young leaves. These studies motivated us to consider the seasonal variation of plant-associated bacteria. The pCCA examination of T-RFs treating sampling date as the environmental factor implicated it as a significant factor (Figure 2). The impacts of sampling date on the distribution of plant-associated bacteria were also seen in the average numbers of T-RFs at different sampling dates (Table 2). The temporal variations in relative abundance of different T-RFs suggest that during host plant growth, the structures of plant leaf-associated bacterial communities are also developing to respond to the changes of the inner biochemical environments of host plants and the variations of the weather and overall environment. The host species selected for study begin growth in late April or May. The ratios of the standard deviations of the average number of T-RFs to the average number are smaller in June and July than those in May and August, indicating that the plant-associated bacterial communities are more stable and complex when the host plants are growing in the peak of summer.
The factor of physical environment includes the soil and geobiochemical conditions, the effect of surrounding plants and animals, and the burning and grazing history of the sampling field, records of the latter of which are available. Again, pCCA attributed a significant contribution of sampling site to the total variation (Figure 2b) consistent with T-RF profile differences for the same plant species on the same date (Figure 1).
We recognize that the three targeted factors may not account for all the variation in the communities and that we did encounter a residual variation. Sources of this variation could include: occasional animal disturbance, insect-induced damages and other factors that cannot be measured accurately and parameterized in a mathematical model. Nevertheless, we suggest that the three-factor model describes an important part of the variation of plant-associated bacteria. The plant-associated bacterial communities are not static, but dynamic and evolve with host plants and environments.
In this research of leaf endophytic bacteria, we used the method of mono-digestion T-RFLP and observed the variations of T-RFLP patterns that were contributed by three environmental factors: sampling sites, dates and host plant species. T-RFLP profiles were also analyzed by pCCA and indicated that all the three factors are statistically significant; considering the contributions to the overall variations of T-RFLP, the host plant species is the most important factor that determine the leaf endophytic bacterial communities. This discovery was also confirmed by other statistical analyses including Tukey test of the number of T-RFs, hierarchical clustering of the frequencies of T-RFs and MANOVA. These three environmental factors summarized most influencing factors and defined a well-characterized model to describe how the endophytic bacterial communities were shaped. APE was introduced to estimate the abundance of each T-RF, and dominant T-RFs have been found which represent major bacterial groups in leaf endophytic communities.
Authors acknowledge the support of the Oklahoma Agricultural Experiment Station, whose Director has approved this publication, the R. J. Sirny Professorship at Oklahoma State University and the National Science Foundation through EPS-0447262. They thank Michael Anderson, Mostafa Elshahed for critical readings of the manuscript and Joshua Habiger for suggesting additional statistical analyses.
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