Our goal was to investigate how root exudate flavonoids influence the soil bacterial community structure and to identify members of the community that change their relative abundance in response to flavonoid exudation. Using a model system that approximates flavonoid exudation of Medicago sativa roots, we treated a soil with 7,4′-dihydroxyflavone and naringenin in two separate experiments using three different rates: medium (equivalent to the exudation rate of 7,4′-dihydroxyflavone from M. sativa seedlings), high (10× the medium rate), and low (0.1× the medium rate). Controls received no flavonoid. Soil samples were subjected to ATP assays and 16S rRNA gene amplicon sequencing. The flavonoid treatments caused no significant change in the soil ATP content. With the high 7,4′-dihydroxyflavone treatment rate, operational taxonomic units (OTUs) classified as Acidobacteria subdivision 4 increased in relative abundance compared with the control samples, whereas OTUs classified as Gaiellales, Nocardioidaceae, and Thermomonosporaceae were more prevalent in the control. The naringenin treatments did not cause significant changes in the soil bacterial community structure. Our results suggest that the root exudate flavonoid 7,4′-dihydroxyflavone can interact with a diverse range of soil bacteria and may have other functions in the rhizosphere in addition to nod gene induction in legume—rhizobia symbiosis.
Citation: Szoboszlay M, White-Monsant A, Moe LA (2016) The Effect of Root Exudate 7,4′-Dihydroxyflavone and Naringenin on Soil Bacterial Community Structure. PLoS ONE 11(1): e0146555. https://doi.org/10.1371/journal.pone.0146555
Editor: Gabriele Berg, Graz University of Technology (TU Graz), AUSTRIA
Received: September 26, 2015; Accepted: December 19, 2015; Published: January 11, 2016
Copyright: © 2016 Szoboszlay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: 16S rRNA gene amplicon sequencing data are available at the National Center for Biotechnology Information Sequence Read Archive (www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA295777.
Funding: This work was supported by grant 2011-67020-30195 from the National Institute of Food and Agriculture of the United States Department of Agriculture to LAM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Flavonoids are plant secondary metabolites synthesized via the phenylpropanoid pathway. They are present in all tissues of higher plants and some are exuded from the roots into the rhizosphere . Their most studied functions in the soil are those associated with legume—rhizobia symbiosis, whereby they activate or repress bacterial nod gene expression  and trigger chemotaxis in nitrogen-fixing rhizobia . Aside from their association with rhizobia, flavonoids are present in root exudates of non-legume plants , and a growing body of data suggests that they influence the growth and activity of various soil bacteria. For example, some flavonoids, particularly isoflavonoids, are considered phytoalexins or phytoanticipins due to their antimicrobial effect . Furthermore, the experiments of Hartwig et al.  imply that root exudate flavonoids may control the proliferation of some rhizosphere bacteria. They found that the doubling time of Ensifer meliloti and Pseudomonas putida decreased when exposed to luteolin or quercetin in micromolar concentrations in laboratory cultures. Flavonoids can also be utilized as a source of carbon and have other direct and indirect effects on soil nutrient cycles . Root extracts with a high flavonoid content from Lupinus albus have been shown to decrease soil respiration without a significant change in microbial biomass based on soil ATP content, and to decrease phosphatase and increase urease activity . Flavonoids may also affect bacterial activity in the rhizosphere by influencing quorum sensing. Vandeputte et al. [8,9] found that catechin, apigenin, eridictyol, kaempferol, luteolin, myricetin, naringenin, naringin, quercetin, taxifolin, and chalcone had some effect on the production of quorum-sensing-dependent factors in Pseudomonas aeruginosa. Further, Pérez-Montaño et al.  showed that acyl-homoserine-lactone production by Ensifer fredii, Rhizobium etli, and R. sullae strains was enhanced in the presence of flavonoids that are known to induce expression of their cognate nod genes.
These findings indicate that root exudate flavonoids influence a diverse range of soil bacteria. To improve our understanding of how root exudate flavonoids influence the soil microbial community structure, we designed a model system that approximates flavonoid exudation of Medicago sativa roots. Our goal was to identify members of the soil bacterial community that change their relative abundance in response to flavonoid exudation. To achieve this, we explored the impact of simulated exudation of 7,4′-dihydroxyflavone, the most abundant nod gene inducing flavonoid among the root exudates of M. sativa seedlings , and naringenin, which is also a nod gene inducing flavonoid present in the root exudates of various legumes .
Materials and Methods
The soil chosen for the rhizosphere model system studies was from a pasture at the University of Kentucky Spindletop Farm (38°06'51.7"N, 84°29'41.7"W) that had not been fertilized or planted for the last 5 years; but prior to this, M. sativa had been grown for the study by Probst and Smith . The Maury silt loam (fine, mixed, semiactive, mesic Typic Paleudalf) was collected from the surface 10–15 cm, sieved (4 mm), dried at room temperature with regular mixing, and then stored at room temperature in a closed plastic container for over 3 months. This storage period was to decrease the effect of root exudate flavonoids in the soil from the vegetation at the collection site. Samples were sent to the University of Kentucky Regulatory Services Soil Testing Laboratory to determine basic soil properties (http://soils.rs.uky.edu/tests/methods.php).
The soil texture was 18.5% sand, 64.1% silt, and 17.4% clay. The pH was 5.95 (1:1 solution:soil using 1 M KCl) and the buffered pH was 7.03 (with Sikora buffer ). The soil contained 4.49% organic matter and 0.25% total nitrogen. The cation exchange capacity was 25.95 meq/100 g, the base saturation 87.77% and the exchangeable K, Ca, Mg, and Na were 0.36, 17.85, 4.55, and 0.02 meq/100 g. Mehlich III extractable P, K, Ca, Mg, and Zn were 243.5, 110.0, 2925.5, 486.5, and 1.2 mg/kg.
Before using the soil it was mixed with 30% m/m sand (previously bleached, washed, and oven dried) to facilitate drainage.
The rhizosphere model system
The soil—sand mixture was moistened with distilled water (60.0 g water to 400.0 g soil—sand mixture) and thoroughly mixed in plastic bags before filling 60.0 g into 50-ml polypropylene conical centrifuge tubes (Fisher Scientific, USA). The conical bottom portions of the tubes were previously cut off and replaced with plastic mesh to allow drainage. Each tube was wrapped in aluminum foil to protect the soil from light. We used rhizon soil moisture samplers (Rhizon MOM, Rhizosphere Research Products, Netherlands) to model the root exudation process. The rhizons had 5-cm long porous parts, 2.5 mm diameter, pore size 0.12–0.18 μm, a glass fiber strengthener, and polyethylene/polyvinylchloride tubing. To the soil in each tube, 6.00 ml distilled water was added before inserting three rhizons vertically and equidistant to each other and the wall of the tube. The rhizons were previously bleached and then washed in sterile distilled water.
Initial experiments studying the effect of root exudates on the soil microbial community delivered exudates to the soil via artificial roots such as membrane filters [14,15] or cylindrical tubes or wicks prepared from such filters [16,17]. In later experiments, rhizon soil moisture samplers were employed as root exudation models [18–20] by using them in the reverse direction: to pump a solution into the soil instead of sampling the soil solution. An alternative approach developed by Ziegler et al.  used glass slides coated with agarose containing the exudate compounds that were inserted into the soil. Our goal was to study the microbial community in a volume of soil homogenously exposed to the exudates; therefore, we adopted the use of rhizon soil moisture samplers in our rhizosphere model systems.
Treatment solutions and applications
The rhizosphere model systems received 7,4′-dihydroxyflavone or naringenin at high (24.00 nmol/day), medium (2.40 nmol/day), low (0.24 nmol/day), or no flavonoid (control) rates. The medium treatment rate was determined from the calculations of Cesco et al.  which were based on the results of Maxwell and Phillips  to match the exudation rate of 7,4′-dihydroxyflavone from M. sativa seedlings assuming that 0.4 g (fresh weight) root biomass occupies the soil in a rhizosphere model system. We determined the root biomass value experimentally by growing M. truncatula using the same soil and tube setup as the present experiment (unpublished). The treatment solutions were applied in 1.2 ml aqueous volume slowly pumped through the rhizons (400 μl per rhizon) into the soil using 1-ml syringes, once every 24 hours. After each application, approximately 0.2 ml of air was injected into the rhizon to ensure that the entire volume of the treatment solution had reached the soil and that no liquid remained in the tubing.
Stock solutions of 7,4′-dihydroxyflavone (Indofine, USA) and naringenin (MP Biomedicals, USA) were prepared in dimethyl sulfoxide and stored at −20°C until the start of the treatments. The final treatment solutions each contained 0.0625% v/v dimethyl sulfoxide. The treatment solutions for each flavonoid treatment rate and the control were supplemented with the major carbohydrates, amino acids, and other organic acids present in the root exudates of M. sativa [22–24] assuming 0.4 g (fresh weight) root biomass in the rhizosphere model system: 1396 μM glucose, 223 μM arabinose, 115 μM maltose, 108 μM mannose, 690 μM serine, 250 μM glycine, 26 μM malate, 24 μM citrate, and 4 μM succinate. Stock solutions were prepared in water, filter sterilized (0.22 μm), and stored at 4°C. The control treatments received the carbohydrates, amino acids, and other organic acids, and dimethyl sulfoxide, but no flavonoid. The treatment solutions were adjusted to pH 7 with KOH.
Six rhizosphere model systems were set up for each of the high, medium, low, and control treatment rates, and then placed on plastic trays and incubated in a dark cabinet at room temperature (21–23°C). A 400-μl aliquot of distilled water was pumped through each rhizon once a day for 5 days before the treatments started. In the first days of incubation a few seedlings germinated in the soil. This affected 3 rhizosphere model systems receiving the high 7,4′-dihydroxyflavone rate, 1 receiving the medium rate, 3 receiving the low rate and 2 receiving the control treatment. From the naringenin treatments 2 rhizosphere model systems receiving the high rate, 1 receiving the medium rate, 1 receiving the low rate, and 2 receiving the control treatment were affected. Seedlings were removed as soon as they emerged from the soil. The last seedling appeared 6 days before sampling. The treatments were applied for 10 days. Every 2 days the rhizosphere model systems were weighed before administering the treatments and then sufficient distilled water was applied to the soil to maintain a constant water content, and they were randomly rearranged on the trays.
The experiment was first conducted with 7,4′-dihydroxyflavone and then repeated with naringenin using the same procedures.
Soil samples were harvested approximately 3 hours after the last treatment application. The rhizons were removed and the soil column was pushed out of the tube. The top 1 cm of soil was discarded and the next 4 cm was collected in a sterile plastic bag using a sterile spatula, mixed, and then frozen in liquid nitrogen. Samples were stored at −80°C prior to analysing all samples for microbial biomass and microbial community structure.
Some rhizosphere model systems contained germinating seeds that did not reach the surface, and thus were only detected during sampling. These systems were removed from the study leaving the number of replicates to 5 high, 4 medium, 5 low, and 4 control in the 7,4′-dihydroxyflavone experiment, and 5 high, 5 medium, 4 low, and 5 control in the naringenin experiment.
To assess total microbial biomass ATP was extracted from 1.0 g of the soil samples with dimethyl sulfoxide and trisodium phosphate as described by Bai et al. , but with 1:10 instead of 1:100 dilution in glycine-EDTA buffer. Diluted extracts were treated with benzalkonium chloride following the protocol of Martens  by mixing a 100-μl aliquot with 100 μl 0.05% m/m benzalkonium chloride in Tris-Mg2+ buffer (50 mM Tris, 10 mM MgSO4, pH 7.8) in a 12 mm × 50 mm autoclaved glass tube. The tube was sonicated for 5 s and 190 μl Tris-Mg2+ buffer was added, followed by 10 μl StayBrite Highly Stable Luciferase/Luciferin Reagent (BioVision, USA). The sample was quickly mixed with a pipet and the luminescence was measured five consecutive times using a Turner Design 20/20 luminometer with 10-s integration periods and 54.8% sensitivity. Out of the five measurements, generally the first two were higher than the last three, which showed less variation. Therefore, the average of the third, fourth, and fifth readings was used in the calculation. Blanks were used to measure the background luminescence using glycine-EDTA buffer instead of a soil extract. A standard curve using log10 (ATP concentration in nM) vs. log10 (luminescence) was obtained by measuring ATP standards comprising blank samples supplemented with 0.1, 0.5, 1, 5, and 10 nM ATP. The ATP stock solution was prepared from ATP-Na2 salt (SERVA Electrophoresis GmbH, Germany) in Tris-Mg2+ buffer and then filter sterilized (0.22 μm) and stored at −80°C. To estimate the extraction efficiency, soil samples from the 7,4′-dihydroxyflavone (n = 3) and the naringenin (n = 3) experiments were randomly chosen, and ATP extracts were prepared from them by adding 20 μl 0.1 mM ATP solution when adding the dimethyl sulfoxide.
Samples from the 7,4′-dihydroxyflavone and naringenin experiments were processed separately. Three blanks and a set of ATP standards were measured for both experiments. The average of the blanks was subtracted from the readings and the ATP content per gram of soil (dry weight) was calculated according to the standard curves in Microsoft Excel. The results were analyzed in JMP 10.0.0 (SAS Institute, USA) using ANOVA and Tukey’s HSD.
16S rRNA gene amplicon sequencing and data processing
DNA was extracted from 250 mg of soil from each sample with Power Soil DNA Isolation Kit (MO BIO Laboratories, USA). The V4 region of the 16S gene was amplified with PCR using the primers of Kozich et al. . The reactions contained 22.5 μl AccuPrime Pfx SuperMix (Invitrogen, USA), 7.5 ng DNA, and 7.5 pmol forward and reverse primers (Integrated DNA Technologies, USA) in 25 μl final volume. Amplification was carried out in a Bio-Rad My Cycler version 1.065 thermocycler (Bio-Rad, USA) with 4 min initial denaturation at 95°C followed by 30 cycles of 20 s at 95°C, 15 s at 55°C, and 2 min at 68°C, and a final extension for 10 min at 68°C. The size and quality of the PCR products were checked using agarose gel electrophoresis. The PCR products were cleaned, quantified, pooled, and sequenced on an Illumina MiSeq instrument with a 500 cycle v2 kit (Illumina, USA) at the University of Kentucky Advanced Genetics Technologies Center according to the protocol of Kozich et al. . Samples from the 7,4′-dihydroxyflavone and the naringenin experiments were sequenced separately. The sequence data is accessible at the National Center for Biotechnology Information Sequence Read Archive (www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA295777.
Forward and reverse sequence reads were joined, and then sequences that were low quality, chimeric, mitochondrial, chloroplast, archaeal, eukaryotic, and unclassifiable were removed in mothur v1.34  as described in the MiSeq SOP (http://www.mothur.org/wiki/MiSeq_SOP, accessed December 2014) using the SILVA alignment  release 119, and the Ribosomal Database Project  release 10. Sequences were binned to operational taxonomic units (OTUs) using minimum entropy decomposition (MED)  with the following settings: m = 0.0965, c = 4, M = (number of sequences in the dataset/10 000), and V = 3. A representative sequence from each OTU was classified according to the Ribosomal Database Project release 10 using mothur with 70% bootstrap cutoff.
Instead of rarefying , centered log-ratio (CLR) transformation [33,34] was applied to the data matrices using the compositions package  in R version 3.2.1 (www.R-project.org). The data matrices from the 7,4′-dihydroxyflavone and naringenin experiments contained 13 and 43 zeroes, respectively. These were replaced with ones to allow this transformation. Ordination plots were made with non-metric multidimensional scaling (NMS) in PC-ORD 6.0 (MJM Software Design, USA) with Euclidean distances: 250 runs were performed with random starting configurations in one to six dimensions with a 10−7 instability criterion and 500 maximum iterations with 0.2 initial step length to find the best starting configurations in each dimensionality. Statistics for the final stress for each dimensionality were obtained from 250 runs with randomized data. Dimensions were only accepted if they decreased the stress to a lower value than that from 95% of the randomized runs. Based on the results, two-dimensional solutions were selected. The final run was conducted using the determined best starting configuration. Multi-response permutation procedure (MRPP)  was used on the CLR transformed datasets with Euclidean distances to test the significance of the differences between the high, medium, low, and control treatments. To find differentially abundant OTUs in the treatments, we used DESeq2 (without CLR transformation)  as recommended by McMurdie et al.  with the DESeq2 package version 1.8.1  in R. To account for the high number of simultaneous tests, q-values were calculated according to the positive false discovery rate method  with the smoother option in QVALUE (http://genomine.org/qvalue). The q-value of a test estimates the proportion of false positive findings in the dataset if tests with equal or lower p-values are accepted as significant.
16S sequencing results from the 7,4′-dihydroxyflavone experiment
The dataset contained 117,276–359,427 sequences per sample with 253 bp average read length, which were binned to 1580 OTUs. On the NMS plot (Fig 1A) the high, medium, and low treatment samples overlapped each other, but were separate from the control samples, indicating a difference in bacterial community structure between the control and 7,4′-dihydroxyflavone treatments. This difference however, was not significant in the MRPP after correcting for multiple comparisons (Table 2).
(A) 7,4′-Dihydroxyflavone experiment, (B) naringenin experiment. Points represent samples, crosses are group centroids. Samples of the same treatment are enclosed in convex hulls. Stress is calculated on a scale of 0 to 100. (Treatments: ● high, ♦ medium, ■ low, and ▲ control).
We used DESeq2 to find differentially abundant OTUs in the control compared with the medium and high treatments. These comparisons gave the highest A-values in the MRPP (Table 2). The DESeq2 results including the normalized mean abundance, abundance fold change, p- and q-values, and the taxonomic classification for all OTUs are listed in S1 Table (comparing the control and high treatments) and S2 Table (comparing the control and medium treatments). There were 37 differentially abundant OTUs between the control and high treatments with q-values below 0.1. This q-value indicates the proportion of false positives if the abundances of all these OTUs are accepted as significantly different. The normalized mean abundance, abundance fold change, p-values, and the taxonomic classification of these 37 OTUs are shown in Table 3.
Differentially abundant OTUs between the control and high treatments for the 7,4′-dihydroxyflavone experiment (q-values < 0.1).
From the 37 OTUs, 14 had a higher relative abundance in the high treatment than in the control. Half of these OTUs were classified as Acidobacteria subdivision 4 (Table 3). These seven OTUs cover 18.9–22.8% of the Acidobacteria subdivision 4 sequences in the high treatment and 14.1–17.2% in the controls. Most of the OTUs in Table 3 that had a lower relative abundance in the high treatment compared with the control were classified as Actinobacteria. Six of these Actinobacteria OTUs belong to genus Gaiella, together covering 13.0–14.5% and 15.0–18.7% of the sequences from this genus in the high and control treatments, respectively. Three OTUs were classified as Nocardioidaceae, encompassing 37.8–47.9% and 46.3–59.5% of the sequences from OTUs classified into this family in the high and control treatments, respectively. Thermomonosporaceae was also represented by three OTUs, which contain 69.3–77.6% and 72.4–75.9% of the sequences from this family in the high and control treatments, respectively.
In a comparison of the control with the medium treatment, only six differentially abundant OTUs had q-values below 0.1 (S2 Table). Three of these OTUs (OTU# 886, 10591, and 12806) were also differentially abundant between the control and the high treatment (Table 3). Two of the other three OTUs were classified as Micromonosporaceae and Acidobacteria subdivision 16, and one could not be classified at phylum level with higher than 70% bootstrap support. Several OTUs listed in Table 3 that had significantly different abundances between the control and high treatment showed a similar response in the medium treatment compared with the control. From the Acidobacteria subdivision 4 OTUs listed in Table 3 that were significantly more abundant in the high treatment than in the control, OTU# 10591, 13198, and 11314 were also more abundant in the medium treatment compared with the control and had low q-values (0.049, 0.115, and 0.115, respectively) (S2 Table). OTU# 11842 and 14169 were classified as Gaiella, OTU# 4243 and 9952 were classified as Nocardioidaceae, and OTUs 11413 and 9555 were classified as Thermomonosporaceae (Table 3). Similar to the control versus high treatment comparison, these OTUs had higher relative abundance in the control than in the medium treatment and had q-values of 0.176, 0.209, 0.115, 0.163, 0.171, and 0.202, respectively (S2 Table).
16S sequencing results from the naringenin experiment
The dataset contained 149,471–339,727 sequences per sample with 253 bp average read length. The MED resulted in 1589 OTUs. Interestingly, the control samples showed no separation from the high or medium treatments in the NMS plot, but the low treatment appeared to separate (Fig 1B). This difference however, was not significant in the MRPP (Table 2). The comparisons between the low and high treatments and between the control and low treatments resulted in the largest effect sizes in the MRPP (Table 2); thus they were investigated with DESeq2 to find differentially abundant OTUs. The normalized mean abundance, abundance fold change, p- and q-values, and the taxonomic classification for all OTUs are listed in S3 Table (comparing the control and low treatments) and S4 Table (comparing the low and high treatments). Only three OTUs had significantly different abundances between the control and the low treatment and had q-values below 0.1 (S3 Table). One OTU was classified as Bacillus, one as Gaiella, and one was not possible to classify at the phylum level with higher than 70% bootstrap support. Comparison of the low and the high treatments resulted in four OTUs with significantly different abundances and with q-values below 0.1 (S4 Table). These were classified into Oxalobacteraceae, Intrasporangiaceae, Acidobacteria subdivision 6, and Chitinophagaceae.
Soil ATP content is a measure of the total living microbial biomass including active and dormant organisms . The 7,4′-dihydroxyflavone and naringenin treatments had no significant effect on the ATP content of the soil. This suggests that the change in relative abundance of OTUs found in DESeq2 is due to a growth response of those particular bacterial groups and is not due to a general antimicrobial or growth promoting effect on the other members of the bacterial community. However we note that soil ATP content reflects the biomass of all soil organisms, including fungi, other eukaryotic microbes, and Archaea, which are not represented in our 16S sequencing results.
Naringenin has been shown to inhibit the growth of a variety of Gram positive and negative bacteria as well as Saccharomyces cerevisiae with minimum inhibitory concentrations ranging from 250 to 1000 μg/ml [40–42]. A broad-spectrum inhibition of growth would likely cause a decrease in soil ATP content, but this did not occur. The high flavonoid treatment solution in our experiment had a concentration of 5.45 μg/ml, which is about two magnitudes lower than the reported minimum inhibitory concentrations for naringenin. It seems likely that some root exudate flavonoids that have been considered antimicrobial based on laboratory culture studies do not function as inhibitors of bacterial growth in the soil. This is simply because the determined minimum inhibitory concentrations are higher than those expected in the rhizosphere, or they only have an antimicrobial effect adjacent to the site of exudation, such as in the rhizoplane where their local concentrations may be high.
For soils with a similar texture and pH to the Maury silt loam used in the present study, Bai et al.  reported a soil ATP content of 0.76–2.20 μg/g. Our results using the same extraction method also occurred within this range (Table 1). However, Bai et al. did not incubate their soils with carbon sources before extraction, whereas our samples were treated daily for 10 days with a mixture of carbohydrates and organic acids, which should promote the growth of heterotrophic microorganisms, and thus increase the microbial biomass and ATP content. In their review, Blagodatskaya and Kuzyakov  concluded that an ATP content below 1–2 μg/g soil is common when the soil microbial community is not activated, and that the addition of readily available substrates, such as glucose, causes several-fold increases, which implies that our results are lower than expected. A likely explanation for the relatively low ATP content of our samples is that the treatment solution added only 0.0040 mg carbon per g soil daily as glucose and other substrates, which is about three magnitudes less than the amount used in other studies to activate the soil [43,44]. The concentrations of carbon sources and the C:N ratios in our treatment solutions were closer to those used by Drake et al.  in their C + N treatment (carbon at 500 mg/l versus 172.3 mg/l in the present study, C:N of 10 versus 13.1 in the present study), which did increase soil microbial biomass significantly, but not several-fold as reported by Blagodatskaya and Kuzyakov .
7,4′-Dihydroxyflavone is known for its ability to induce the expression of nod genes in species like Rhizobium leguminosarum bv. trifolii , Ensifer meliloti , and Bradyrhizobium japonicum . There are numerous OTUs in our dataset classified as Rhizobium and Bradyrhizobium (S1 Table), and the soil originated from a site where M. sativa had been grown for years. Therefore, it is likely that rhizobial species able to react to 7,4′-dihydroxyflavone as a nod gene inducer were present in the soil microbial community. However, we only found a single OTU classified as Rhizobiales that significantly changed (q-value < 0.1) its relative abundance in the high 7,4′-dihydroxyflavone treatment compared with the control (Table 3), and the abundance of this OTU decreased in the flavonoid treatment. This implies that nod gene induction in bacteria by 7,4′-dihydroxyflavone is not necessarily coupled with significant growth promotion. Our results concur with Hartwig et al. , who found that 10 μM 7,4′-dihydroxyflavone did not affect the doubling time in the cultures of R. leguminosarum bv. trifolii and E. meliloti.
Our results show that a large portion of the Acidobacteria subdivision 4 community in the soil increased its relative abundance in the high 7,4′-dihydroxyflavone flavonoid treatment. Based on culture-independent studies, Acidobacteria is one of the most abundant bacterial phyla in the rhizosphere , and subdivision 4 is among the most dominant of the 26 subdivisions in a wide diversity of soils, especially in soils with low acidity . However, there are only three validly described species in this taxon [50–52], which impedes our understanding of their metabolism and activity. In clone libraries constructed from the rhizosphere of Noccaea caerulescens and from unplanted bulk soil that was contaminated with heavy metals, fewer clones from subdivision 4 were found in the rhizosphere compared with the bulk soil . The abundance of various acidobacterial subdivisions in unplanted soil and in the rhizospheres of potato (Solanum tuberosum ‘Agria’), leek (Allium porrum ‘Kenton’), and mixed grass species dominated by Lolium perenne were studied in a field experiment using real-time quantitative PCR by da Rocha et al. . During their four-month study, the abundances of subdivision 4 declined, but relative to the bulk soil, their abundances increased in the rhizospheres of leek and grass. The flavonoid composition of the root exudates of these plants have not been reported yet, and studies investigating the flavonoid composition of N. caerulescens shoots, potato tubers, leek bulbs, and L. perenne shoots did not find 7,4′-dihydroxyflavone [55–60]. These culture-independent studies on the abundance of Acidobacteria subdivision 4 point out that its growth can be influenced by plants, implying plant—microbe interactions. Our results indicate that root exudate flavonoids like 7,4′-dihydroxyflavone may be involved in mediating these interactions.
It is noteworthy that our experimental setup was not capable of distinguishing direct and indirect effects of the flavonoid treatment. Root exudate flavonoids can be modified and degraded by soil microorganisms or abiotic processes [61–63], thus some of the OTUs with significantly different abundances may have reacted to a degradation product instead of 7,4′-dihydroxyflavone itself, or to factors produced by other organisms in response to the flavonoid.
In the high 7,4′-dihydroxyflavone treatment, six OTUs classified as Gaiella had significantly (q-value < 0.1) lower relative abundances compared with the control (Table 3). This genus and the order Gaiellales currently have only a single described species, which was isolated from water from a deep aquifer . Gaiella 16S rRNA sequences have been found in sediments from a thermal spring , in high abundance in soil , and in rhizosphere and root samples of rice . Their presence in the rhizosphere soil and root tissues shows that some members of this genus interact with plants. According to our results, 7,4′-dihydroxyflavone from root exudates may have a role in these interactions.
In the 7,4′-dihydroxyflavone experiment, several OTUs that decreased their relative abundance in the flavonoid treatment were classified as Nocardioidaceae and Thermomonosporaceae (Table 3). These OTUs comprise a large portion of the sequences classified into these families in the dataset. Many members of these families have been isolated from soil where they can play a significant role in degradation processes and nutrient cycling [68,69]. They were found in the rhizospheres of L. perenne , Solanum melongena , cucumber , and various medicinal plants . Endophytic members of Nocardioidaceae were isolated from the roots of wheat  and rice , whereas Thermomonosporaceae strains were isolated from Aquilaria crassna  and mandarin . Furthermore, some of these isolates were shown to produce siderophores, which may help plant nutrient uptake, and to produce indole-3-acetic acid [73, 76, 77], and therefore potentially promote plant growth. Isolates related to Thermomonosporaceae were also obtained from the outer cells of the nitrogen-fixing nodules of Casuarina equisetifolia and were demonstrated to be able to fix atmospheric nitrogen , whereas other Thermomonosporaceae strains were shown to increase nodule biomass, nitrogen fixation rate, and to promote the growth of soybean when co-inoculated with Bradyrhizobium japonicum . Nocardioides endophytes from wheat roots were also proven to be beneficial to plants as they express antifungal activity against different plant pathogens and reduce disease symptoms . Therefore, it appears that some soil-dwelling members of Nocardioidaceae and Thermomonosporaceae are part of the rhizosphere microbial community of certain plants and that some colonize root tissues as neutral or plant growth promoting endophytes. However, it is currently unknown what chemical signals are involved in host recognition or induction of the colonization of plant tissues. Our results suggest that root exudate flavonoids like the 7,4′-dihydroxyflavone are potential signaling candidates influencing these plant—microbe interactions.
In the naringenin experiment, we found no significant difference in the soil bacterial community structure between the control and flavonoid treatments. Nor was there a gradually increasing response with the increasing flavonoid concentration in the treatment solution. Instead, we found the largest difference between the low and the high treatments, but there were very few differentially abundant OTUs. Longer application times or higher treatment rates may be required to study the effect of naringenin on soil bacterial community structure in a rhizosphere model system.
We investigated the effect of 7,4′-dihydroxyflavone and naringenin on the total soil microbial biomass and the soil bacterial community structure in rhizosphere model systems that approximate the root exudation of M. sativa seedlings. Neither 7,4′-dihydroxyflavone, nor naringenin had an effect on the soil microbial biomass. Despite its antibacterial properties in laboratory culture studies, naringenin did not appear to inhibit microbial growth in the soil. In the rhizosphere model systems, members of Acidobacteria subdivision 4, Gaiella, Nocardioidaceae, and Thermomonosporaceae changed their relative abundance due to 7,4′-dihydroxyflavone exudation. Members of these taxa are known to interact with plants, but the signaling compounds regulating these interactions are currently unknown. Our results indicate that 7,4′-dihydroxyflavone could be among these compounds, and future studies should investigate the effect of 7,4′-dihydroxyflavone and other root exudate flavonoids on the activity of these bacteria in the rhizosphere and root tissues.
S1 Table. DESeq2 results comparing the control and high treatments from the 7,4′-dihydroxyflavone experiment.
S2 Table. DESeq2 results comparing the control and medium treatments from the 7,4′-dihydroxyflavone experiment.
S3 Table. DESeq2 results comparing the control and low treatments from the naringenin experiment.
We are grateful to Atanas Radkov and Muhammad Saleem for their comments on the manuscript and to Jennifer Webb and Jolanta Jaromcyzk for establishing the 16S amplicon sequencing protocol on the Illumina MiSeq instrument at the University of Kentucky Advanced Genetics Technologies Center.
Conceived and designed the experiments: MS AWM LAM. Performed the experiments: MS AWM. Analyzed the data: MS AWM LAM. Wrote the paper: MS AWM LAM.
- 1. Cesco S, Neumann G, Tomasi N, Pinton R, Weisskopf L. Release of plant-borne flavonoids into the rhizosphere and their role in plant nutrition. Plant Soil. 2010; 329: 1–25.
- 2. Dharmatilake AJ, Bauer WD. Chemotaxis of Rhizobium meliloti towards nodulation gene-inducing compounds from alfalfa roots. Appl EnvironMicrobiol 1992; 58: 1156–1158.
- 3. Hassan S, Mathesius U. The role of flavonoids in root-rhizosphere signaling: opportunities and challenges for improving plant-microbe interactions. J Exp Bot. 2012; 63: 3429–3444. pmid:22213816
- 4. Dakora FD, Phillips DA. Diverse functions of isoflavonoids in legumes transcend anti-microbial definitions of phytoalexins. Physiol Mol Plant Pathol. 1996; 49: 1–20.
- 5. Hartwig UA, Joseph CM, Phillips DA. Flavonoids released naturally from alfalfa seeds enhance growth rate of Rhizobium meliloti. Plant Physiol. 1991; 95: 797–803. pmid:16668056
- 6. Cesco S, Mimmo T, Tonon G, Tomasi N, Pinton R, Terzano R, et al. Plant-borne flavonoids released into the rhizosphere: impact on soil bio-activities related to plant nutrition. A review. Biol Fertil Soils. 2012; 48: 123–149.
- 7. Tomasi N, Weisskopf L, Renella G, Landi L, Pinton R, Varanini Z, et al. Flavonoids of white lupin roots participate in phosphorus mobilization from soil. Soil Biol Biochem. 2008; 40: 971–1974.
- 8. Vandeputte OM, Kiendrebeogo M, Rajaonson S, Diallo B, Mol A, El Jaziri M, et al. Identification of catechin as one of the flavonoids from Combretum albiflorum bark extract that reduces the production of quorum-sensing-controlled virulence factors in Pseudomonas aeruginosa PAO1. Appl Environ Microbiol. 2009; 76: 243–253. pmid:19854927
- 9. Vandeputte OM, Kiendrebeogo M, Rasamiravaka T, Stevigny C, Duez P, Rajaonson S, et al. The flavanone naringenin reduces the production of quorum sensing-controlled virulence factors in Pseudomonas aeruginosa PAO1. Microbiology. 2011; 157: 2120–2132. pmid:21546585
- 10. Pérez-Montaño F, Guasch-Vidal B, González-Barroso S, López-Baena FJ, Cubo T, Ollero FJ, et al. Nodulation-gene-inducing flavonoids increase overall production of autoinducers and expression of N-acyl homoserine lactone synthesis genes in rhizobia. Res Microbiol. 2011; 162: 715–723. pmid:21600980
- 11. Maxwell CA, Phillips DA. Concurrent synthesis and release of nod-gene-inducing flavonoids from alfalfa roots. Plant Physiol. 1990; 93: 1552–1558. pmid:16667655
- 12. Probst TA, Smith SR. Harvest frequency effects on yield, persistence, and regrowth rate among new alfalfa cultivars. Forage and Grazinglands. 2011; 9.
- 13. Sikora FJ. A buffer that mimics the SMP buffer for determining lime requirement of soil. Soil Science Society of America Journal. 2006; 70: 474–486.
- 14. Odham G, Tunlid A, Valeur A, Sundin P, White DC. Model system for studies of microbial dynamics at exuding surfaces such as the rhizosphere. Appl Environ Microbiol. 1986; 52: 191–196. pmid:11536565
- 15. Landi L, Valori F, Ascher J, Renella G, Falchini L, Nannipieri P. Root exudate effects on the bacterial communities, CO2 evolution, nitrogen transformations and ATP content of rhizosphere and bulk soils. Soil Biol Biochem. 2006; 38: 509–516.
- 16. Martens R. Apparatus to study the quantitative relationships between root exudates and microbial populations in the rhizosphere. Soil Biol Biochem. 1982; 14: 315–317.
- 17. Griffiths BS, Ritz K, Ebblewhite N, Dobson G. Soil microbial community structure: Effects of substrate loading rates. Soil Biol Biochem. 1998; 31: 145–153.
- 18. Kuzyakov Y, Hill PW, Jones DL. Root exudate components change litter decomposition in a simulated rhizosphere depending on temperature. Plant Soil. 2006; 290: 293–305.
- 19. Paterson E, Gebbing T, Abel C, Sim A, Telfer G. Rhizodeposition shapes rhizosphere microbial community structure in organic soil. New Phytol. 2006; 173: 600–610.
- 20. Drake JE, Darby BA, Giasson M, Kramer MA, Phillips RP, Finzi AC. Stoichiometry constrains microbial response to root exudation- insights from a model and a field experiment in a temperate forest. Biogeosciences. 2013; 10: 821–838.
- 21. Ziegler M, Engel M, Welzl G, Schloter M. Development of a simple root model to study the effects of single exudates on the development of bacterial community structure. J Microbiol Methods. 2013; 94: 30–36. pmid:23611840
- 22. Hamlen RA, Lukezic FL, Bloom JR. Influence of age and stage of development on the neutral carbohydrate components in root exudates from alfalfa plants grown in a gnotobiotic environment. Can J Plant Sci, 1972; 52: 633–642.
- 23. Lesuffleur F, Paynel F, Bataillé M, Le Deunff E, Cliquet J. Root amino acid exudation: measurement of high efflux rates of glycine and serine from six different plant species. Plant Soil. 2007; 294: 235–246.
- 24. Lipton DS, Blanchar RW, Blevins DG. Citrate, malate, and succinate concentration in exudates from P-sufficient and P-stressed Medicago sativa L. seedlings. Plant Physiol. 1987; 85: 315–317. pmid:16665693
- 25. Bai QY, Zelles L, Scheunert I, Korte F. A simple effective procedure for the determination of adenosine triphosphate in soils. Chemosphere. 1988; 17: 2461–2470.
- 26. Martens R. Estimation of ATP in soil: extraction methods and calculation of extraction efficiency. Soil Biol Biochem. 2001; 33: 973–982.
- 27. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013; 79: 5112–5120. pmid:23793624
- 28. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009; 75: 7537–7541. pmid:19801464
- 29. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007; 35: 7188–7196. pmid:17947321
- 30. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014; 42: D633–D642. pmid:24288368
- 31. Eren AM, Morrison HG, Lescault PJ, Reveillaud J, Vineis JH, Sogin ML. Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 2014; 9: 968–979.
- 32. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014; 10: e1003531. pmid:24699258
- 33. Aitchison J. The statistical analysis of compositional data. London: Chapman and Hall; 1986.
- 34. Fernandes AD, Redi JNS, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014; 2: 15
- 35. Van Der Boogaart KG, Tolosana-Delgado R. Compositional data analysis with ‘R’and the package ‘compositions’. Geological Society, London, Special Publications. 2006; pp 264: 119–127.
- 36. McCune B, Grace JB, Urban DL. Analysis of ecological communities. Gleneden Beach, OR: MjM Software Design; 2002.
- 37. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15: 550. pmid:25516281
- 38. Storey JD, Tibshirani R. Statistical significance for genomewide studies. PNAS. 2003; 100: 9440–9445. pmid:12883005
- 39. Blagodatskaya E, Kuzyakov Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol Biochem 2013; 67: 192–211.
- 40. Mandalari G, Bennett RN, Bisignano G, Trombetta D, Saija A, Faulds CB, et al. Antimicrobial activity of flavonoids extracted from bergamot (Citrus bergamia Risso) peel, a byproduct of the essential oil industry. J Appl Microbiol. 2007; 103: 2056–2064. pmid:18045389
- 41. Mandalari G, Bisignano C, D’Arrigo M, Ginestra G, Arena A, Tomaino A, et al. Antimicrobial potential of polyphenols extracted from almond skins. Lett Appl Microbiol. 2010; 51: 83–89. pmid:20497495
- 42. Rauha J, Remes S, Heinonen M, Hopia A, Kähkönen M, Kujala T, et al. Antimicrobial effects of Finnish plant extracts containing flavonoids and other phenolic compounds. Int J Food Microbiol. 2000; 56: 3–12. pmid:10857921
- 43. Nannipieri P, Johnson RL, Paul EA. Criteria for measurement of microbial growth and activity in soil. Soil Biol Biochem. 1978; 10: 223–229.
- 44. De Nobili M, Diaz-Raviña M, Brookes P, Jenkinson D. Adenosine 5’-triphosphate measurements in soils containing recently added glucose. Soil Biol Biochem. 1996; 28: 1099–1104.
- 45. Djordjevic MA, Redmond JW, Batley M, Rolfe BG. Clovers secrete specific phenolic compounds which either stimulate or repress nod gene expression in Rhizobium trifolii. EMBO J. 1987; 6: 1173–1179. pmid:16453763
- 46. Maxwell CA, Hartwig UA, Joseph CM, Phillips DA. A chalcone and two related flavonoids released from alfalfa roots induce nod genes of Rhizobium meliloti. Plant Physiol. 1989; 91: 842–847. pmid:16667146
- 47. Ryu J, Hur H. Comparative analyses of flavonoids for nod gene induction in Bradyrhizobium japonicum USDA110. J Microbiol biotechnol. 2005; 15: 1280–1285.
- 48. Da Rocha UN, Van Overbeek L, Van Elsas JD. Exploration of hitherto-uncultured bacteria from the rhizosphere. FEMS Microbiol Ecol. 2009; 69: 31–328.
- 49. Jones RT, Robeson MS, Lauber CL, Hamady M, Knight R, Fierer N. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 2009; 3: 442–453. pmid:19129864
- 50. Foesel BU, Rohde M, Overmann J. Blastocatella fastidiosa gen. nov., sp. nov., isolated from semiarid savanna soil—The first described species of Acidobacteria subdivision 4. Syst Appl Microbiol. 2013; 36: 82–89.
- 51. Crowe MA, Power JF, Morgan XC, Dunfield PF, Lagutin K, Rijpstra WIC, et al. Pyrinomonas methylaliphatogenes gen. nov., sp. nov., a novel group 4 thermophilic member of the phylum Acidobacteria from geothermal soils. Int J Syst Evol Microbiol. 2014; 64: 220–227. pmid:24048862
- 52. Huber KJ, Wüst PK, Rohde M, Overmann J, Foesel BU. Aridibacter famidurans gen. nov., sp. nov. and Aridibacter kavangonensis sp. nov., two novel members of subdivision 4 of the Acidobacteria isolated from semiarid savannah soil. Int J Syst Evol Microbiol. 2014; 64: 1866–1875. pmid:24573163
- 53. Gremion F, Chatzinotas A, Harms H. Comparative 16S rDNA and 16S rRNA sequence analysis indicates that Actinobacteria might be a dominant part of the metabolically active bacteria in heavy metal‐contaminated bulk and rhizosphere soil. Environ Microbiol. 2003; 5: 896–907.
- 54. Da Rocha UN, Plugge CM, George I, Van Elsas JD, Van Overbeek LS. The rhizosphere selects for particular groups of Acidobacteria and Verrucomicrobia. PLoS One. 2013; 8: e82443. pmid:24349285
- 55. Llugany M, Tolrà R, Martín SR, Poschenrieder C, Barceló J. Cadmium‐induced changes in glutathione and phenolics of Thlaspi and Noccaea species differing in Cd accumulation. J Plant Nutr Soil Sci. 2013; 176: 851–858.
- 56. Lewis CE, Walker JRL, Lancaster JE, Sutton KH. Determination of anthocyanins, flavonoids and phenolic acids in potatoes. I: Coloured cultivars of Solanum tuberosum L. J Sci Food Agri. 1998; 77: 45–57.
- 57. Lewis CE, Walker JRL, Lancaster JE, Sutton KH. Determination of anthocyanins, flavonoids and phenolic acids in potatoes. II: Wild, tuberous Solanum species. J Sci Food Agri. 1998; 77: 58–63.
- 58. Fattorusso E, Lanzotti V, Taglialatela-Scafati O, Cicala C. The flavonoids of leek, Allium porrum. Phytochemistry. 2001; 57: 565–569. pmid:11394858
- 59. Soininen TH, Jukarainen N, Soininen P, Auriola SOK, Julkunen‐Tiitto R, Oleszek W, et al. Metabolite profiling of leek (Allium porrum L) cultivars by 1H NMR and HPLC—MS. Phytochem Anal. 2014; 25: 220–228. pmid:24408868
- 60. Qawasmeh A, Obied HK, Raman A, Wheatley W. Influence of fungal endophyte infection on phenolic content and antioxidant activity in grasses: interaction between Lolium perenne and different strains of Neotyphodium lolii. J Agric Food Chem. 2012; 60: 3381–3388. pmid:22435921
- 61. Cooper JE. Multiple responses of rhizobia to flavonoids during legume root infection. Adv Bot Res. 2014; 41: 1–62.
- 62. Shaw LJ, Morris P, Hooker JE. Perception and modification of plant flavonoid signals by rhizosphere microorganisms. Environ Microbiol. 2006; 8: 1867–1880. pmid:17014487
- 63. Shaw LJ, Hooker JE. The fate and toxicity of the flavonoids naringenin and formononetin in soil. Soil Biol Biochem. 2008; 40: 528–536.
- 64. Albuquerque L, França L, Rainey FA, Schumann P, Nobre MF, Da Costa MS. Gaiella occulta gen. nov., sp. nov., a novel representative of a deep branching phylogenetic lineage within the class Actinobacteria and proposal of Gaiellaceae fam. nov. and Gaiellales ord. nov. Syst Appl Microbiol. 2011; 34: 595–599. pmid:21899973
- 65. Rozanov AS, Bryanskaya AV, Malup TK, Meshcheryakova IA, Lazareva EV, Taran OP, et al. Molecular analysis of the benthos microbial community in Zavarzin thermal spring (Uzon Caldera, Kamchatka, Russia). BMC Genomics. 2014; 15: S12.
- 66. Kim Y, Yoon H, Kim M, Nam Y, Kim H, Seo Y, et al. Metagenomic analysis of bacterial communities on Dokdo Island. J Gen Appl Microbiol. 2014; 60: 65–74. pmid:24859864
- 67. Hernández M, Dumont MG, Yuan Q, Conrad R. Different bacterial populations associated with the roots and rhizosphere of rice incorporate plant-derived carbon. Appl Environ Microbiol. 2015; 81: 2244–2253. pmid:25616793
- 68. Tóth EM, Borsodi AK. 33 The Family Nocardioidaceae. In: The prokaryotes: Actinobacteria. Berlin, Heidelberg: Springer-Verlag; 2014: pp 651–694.
- 69. Kroppenstedt RM, Goodfellow M. The family Thermomonosporaceae: Actinocorallia, Actinomadura, Spirillospora and Thermomonospora. The prokaryotes. Springer New York; 2006: pp 682–724.
- 70. Anderson CR, Condron LM, Clough TJ, Fiers M, Stewart A, Hill RA, et al. Biochar induced soil microbial community change: implications for biogeochemical cycling of carbon, nitrogen and phosphorus. Pedobiologia. 2011; 54: 309–320.
- 71. Singh AK, Singh M, Dubey SK. Changes in actinomycetes community structure under the influence of Bt transgenic brinjal crop in a tropical agroecosystem. BMC Microbiol. 2013; 13: 122. pmid:23718216
- 72. Chen F, Wang M, Zheng Y, Li S, Wang H, Han D, et al. The effect of biocontrol bacteria on rhizosphere bacterial communities analyzed by plating and PCR-DGGE. Curr Microbiol. 2013; 67: 177–182. pmid:23483308
- 73. Khamna S, Yokota A, Lumyong S. Actinomycetes isolated from medicinal plant rhizosphere soils: diversity and screening of antifungal compounds, indole-3-acetic acid and siderophore production. World J Microbiol Biotechnol. 2009; 25: 649–655.
- 74. Coombs JT, Franco CMM. Isolation and identification of Actinobacteria from surface-sterilized wheat roots. Appl Environ Microbiol. 2003; 69: 5603–5608. pmid:12957950
- 75. Tian X, Cao L, Tan H, Han W, Chen M, Liu Y, et al. Diversity of cultivated and uncultivated actinobacterial endophytes in the stems and roots of rice. Microb Ecol. 2007; 53: 700–707. pmid:17334856
- 76. Nimnoi P, Pongslip N, Lumyong S. Endophytic actinomycetes isolated from Aquilaria crassna Pierre ex Lec and screening of plant growth promoters production. World J Microbiol Biotechnol. 2010; 26: 193–203.
- 77. Shutsrirung A, Chromkaew Y, Pathom-Aree W, Choonluchanon S, Boonkerd N. Diversity of endophytic actinomycetes in mandarin grown in northern Thailand, their phytohormone production potential and plant growth promoting activity. Soil Sci Plant Nutr. 2013; 59: 322–330.
- 78. Valdés M, Pérez NO, Estrada-de Los Santos P, Caballero-Mellado J, Peña-Cabriales JJ, Normand P, et al. Non-Frankia actinomycetes isolated from surface-sterilized roots of Casuarina equisetifolia fix nitrogen. Appl Environ Microbiol. 2005; 71: 460–466. pmid:15640222
- 79. Nimnoi P, Pongslip N, Lumyong S. Co-inoculation of soybean (Glycine max) with actinomycetes and Bradyrhizobium japonicum enhances plant growth, nitrogenase activity and plant nutrition. J Plant Nutr. 2014; 37: 432–446.
- 80. Coombs JT, Michelsen PP, Franco CMM. Evaluation of endophytic actinobacteria as antagonists of Gaeumannomyces graminis var. tritici in wheat. Biological Control. 2004; 29: 359–366.