Comparison of plant growth-promoting rhizobacteria in a pine forest soil and an agricultural soil.
Soil micro-organisms are involved in important processes for the functioning of ecosystems, and it is estimated that in the soil 80-90% of these processes are mediated by micro-organisms (Nannipieri et al. 2003). Micro-organisms actively participate in biogeochemical cycles and in the recycling of essential nutrients, and contribute to soil fertility and plant growth. In addition, they take part in the degradation of xenobiotic compounds and the maintenance of soil structure (Gkorezis et al. 2016). The diversity of micro-organisms present in the soil, as well as their activity and population dynamics, depends on several factors, such as chemical composition and soil texture, availability of water, sources of available energy, temperature, hydrostatic pressure, pH, oxidation-reduction potential and interactions with other micro-organisms (Delgado-Baquerizo et al. 2017). In addition, micro-organism diversity can be altered by anthropogenic factors, such as urbanisation, agriculture and pollution (Huot et al. 2017). Micro-organisms can be used as biomarkers to assess environmental disturbances and the biotic fertility of soils because they respond rapidly to changes, allowing them to adapt to certain environmental conditions (Lau and Lennon 2012). In this way, the qualitative and quantitative variations in the composition of the microbial communities of the soil serve as indicators of the changes occurring there. The evaluation of microbial communities for this purpose should include determinations of biomass and microbial diversity, as well as the distribution, function and nature of interactions between microbial species and the physicochemical properties of the soil (Zhang and Xu 2008).
Plant growth-promoting rhizobacteria (PGPR) arc a heterogeneous group of bacteria that inhabit the rhizosphere and exert a beneficial effect on plant growth through direct and indirect mechanisms that increase productivity and exert control over pathogenic micro-organisms. PGPR promote the growth of plants through the production of substances useful for their growth (e.g. nutrients, phytohormones) or promoting of the uptake of certain nutrients (e.g. iron and phosphorus, among others). Some examples of these mechanisms are nitrogen fixation, regulation of plant growth, phosphorus solubilisation and the production of siderophores (Ahemad and Khan 2011; Vejan et al. 2016).
Most studies on microbial diversity involve determination of the number of species and their relative abundance (Nannipieri et al. 2003). In addition, diversity has been defined in taxonomic, functional and genetic terms. However, the relationship between soil biodiversity and the ecological traits of the microbial community remains unknown (Barriuso et al. 2008). Few studies have evaluated functional microbial diversity between ecosystems subject to constant disturbances, such as agricultural activity. In view of the fact that PGPR and soil physicochcmical properties have been used as indicators of the quality of the soil, it is attractive to use these parameters to evaluate the possible changes in a soil in the same region, specifically a forest pine soil that became an agricultural soil. Years ago, Milpa Alta in Mexico City was considered a pine forest ecosystem; however, agricultural activity in this region has increased. The aim of the present study was to use PGPR diversity and the physicochemical properties of the soil to determine the effects of land use changes at Milpa Alta.
Materials and methods
Experimental site and soil sampling
Soil samples were collected at Milpa Alta, Mexico City (19[degrees]13'-19[degrees]04'N, 98[degrees]57'-98[degrees]10'W, altitude 2413 m). Rhizospheric soil from the pine forest (PF) and agricultural site (AG) was sampled during the rainy season. In the AG, maize and beans are cultivated intercropping with tillage. The sampling procedure was as follows. In the PF and AG, a 100-[m.sup.2] surface area in each was divided into three fields (PF1, PF2 and PF3; and AG1, AG2 and AG3 respectively) and eight soil subsamples were randomly collected to a depth of 15-20 cm per field. Subsequently, equivalent weights of the subsamples from each field were mixed in a vessel to obtain a composite sample. This procedure was repeated with subsamples from each of the six fields. The soil was transported in sealed bags and stored at 4[degrees]C until further processing.
Soil pH was measured in a 1 : 1 soil: distilled H20 suspension using a glass electrode. Determination of water holding capacity (WHC) was based essentially on the methods of Blazka and Fischer (2014) with the following modifications: a perforated base porcelain crucible containing Whatman filter paper at the bottom was weighed. Subsequently, a 10-g soil sample (previously sieved and dried at 105[degrees]C; oven dry weight of the soil) was placed in the crucible and the crucible was weighed again (unsaturated soil). The crucible was placed in a soaking tray that was filled with distilled water and the water was allowed to reach the surface of the sample, which was evident by the shining film of water on the soil surface. The timing varied between soil samples. The crucible was then removed from the soaking tray, the water was allowed to drain and the crucible was again weighed (water-saturated soil). The WHC was calculated using the following formulas:
Total water in wet soil
= water saturated soil - unsaturated soil
WHC = Total water in wet soil/Oven dry weight of the soil x 100
The soil particle size distribution was determined by the hydrometer method. Electrical conductivity (EC) was measured in a soil suspension as described by Rhoades et al. (1989). Soil cation exchange capacity (CEC) was measured by extracting soil with a solution of calcium chloride, followed by cation exchange of [Ba.sup.2+] for [Mg.sup.2+] in the extract. The [Mg.sup.2+] was then titrated with a solution of EDTA. Organic C was determined by oxidation with [K.sub.2][Cr.sub.2][O.sub.7] and titration of excess dichromate with [(N[H.sub.4]).sub.2]FeS[O.sub.4]. Total P was measured by aqua regia digestion with sodium carbonate fusion. Available P in the 0.5 M NaHC[O.sub.3] extract was determined by the antimony-potassium-tartrate method. Nitrogen in the form of the nitrate ion (N[O.sub.3.sup.+]-N) was extracted from the soil with water and measured colourimetrically after reaction with phenoldisulfonic acid. For determination of potassium, the soil was extracted with Bray's extracting reagent, and the available potassium was measured with an absorptiometer (Whittles and Little 1950).
Isolation and quantification of PGRP
Composite soil samples (10g each) of the six fields were weighed and put into flasks with 90 mL of 0.85% sterile saline and one drop of Tween 20. Later, decimal dilutions were made in flasks with 90 mL of 0.85% sterile saline. From the decimal dilutions, 0.2-mL aliquots were inoculated in triplicate onto: (1) nutrient agar plates for isolation of aerobic mesophilic bacteria; (2) chitin agar plates for the isolation of chitinolytic bacteria; (3) Pikovskaya agar plates for isolation of tricalcium phosphate-solubilising bacteria; and (4) Burk agar plates for isolation of asymbiotic nitrogen-fixing bacteria (Atlas 2010). For the isolation of aerobic sporulating bacteria, the dilutions were heated in a water bath at 92[degrees]C for 15min before being inoculated onto nutrient agar plates. Plates were incubated at 28[degrees]C for [greater than or equal to] 3 days. The number of colonies on each plate was counted, and the number of colony-forming units (CFU) per gram dry soil was calculated. The isolates with the same colony and microscopic morphology were discarded, as well as isolates that lost viability with consecutive transfers. The different morphotypes were selected and reinoculated onto nutritive plates until pure cultures were obtained for their bacterial identification by molecular methods.
Genotyping of bacteria by random amplified polymorphic DNA fingerprints
Random amplified polymorphic DNA (RAPD) fingerprints were obtained to distinguish morphologically similar strains. Genomic DNA from the PGPR isolates was obtained using the cetyltrimethylammonium bromide (CTAB) method (Wilson 2001). DNA quality was assessed by electrophoresis on a 1% agarose gel in 1 x Tris-Acetate-EDTA (TAE) buffer and stained with ethidium bromide (1 [micro]L per 100mL). The polymerase chain reaction (PCR) reaction mixture contained 2.5 [micro]L of 10x buffer (5mM KC1, ImM Tris-HCl, pH 9), 3 [micro]L Mg[Cl.sub.2] (25 mM), 1 [micro]L dNTPs (10 [micro]M each), 5 [micro]L initiator (10 [micro]M) and 1 [micro]L template DNA and the volume made up to 25 [micro]L with injectable water. The primers used were RAPD1 (5'-GGT GCGGGAA-3') and RAPD2 (5'-GTTTCGCTCC-3'); RAPD1 was used only in those strains whose profile was not resolved by the RAPD2 primer. The reaction conditions were one cycle 95[degrees]C for 5 min, followed by 40 cycles of 95[degrees]C for 1 min, 28.3[degrees]C (RAPD1) or 35.3[degrees]C (RAPD2) for 1 min and 72[degrees]C for 1 min, with a final step at 72[degrees]C for 5 min. The RAPD products were electrophoresed on 1.5% agarose gels in lx TAE and stained as described above. At least two isolates from each RAPD group were selected for 16S rRNA sequencing.
Molecular identification of bacteria
The 16S rRNA gene was amplified by PGR using Primer 8 (5'-CCG CGG GCG CCG CTG GAT AGT TTG CAG ATC CTG GCT CAG-3) and Primer 1492 (5'-GGC TCG AGC GGC CGC CCG GGT TAC CTT GIT ACG ACT T-3; Relman 1993). The reaction mixture and PCR conditions were similar to those described above, except that the number of amplification cycles was 35, the alignment temperature was 53[degrees]C and the amplification stage was 2 min. The amplified fragments were purified using the Zymoclean Gel DNA recovery kit (Zymo Research, Irvine, CA, USA) following the manufacturer's instructions, and the integrity was confirmed by electrophoresis in a 1% (w/v) agarose gel in 1 x TAE buffer. The amplified DNA fragments were sequenced on an ABI PRISM 310 Genetic Analyzer PE (Applied Biosystems, Foster City, CA, USA). A collection of taxonomically related sequences obtained from the EzBioCloud database (Yoon et al. 2017) using Blast (Camacho et al. 2009) was included in the multiple alignment analyses with CLUSTAL X (Thompson et al. 1997) and was manually edited using SEA VIEW software (Galtier et al. 1996). Each sequence was compared with that of the type strain of the closest bacterial species, and tentative species identification given at >99.8% similarity. Otherwise, identification was accomplished at the genus level. Similitude analysis was estimated using nucleotide sequences with MEGA (Tamura et al. 2011). The sequences of the 16S rRNA genes reported in the present study have been deposited in the GenBank database under accession numbers MF114382-MF114424.
The bacteria identified were selected and inoculated onto chitin agar plates (for chitinolytics), Pikovskaya agar (for tricalcium phosphate solubilisers) and CAS agar (for siderophore producers). Subsequently, the plates were incubated at 28[degrees]C for 3-7 days. This was considered as a positive test if a hydrolysis halo was present for the chitinolytic bacteria, a solubilisation halo was present for the tricalcium phosphate solubilisers and yellow or orange halos were present for siderophore-producing bacteria. The strain of Serratia marcescens Nima was used as a positive control of chitinolysis. The hydrogen cyanide (HCN)-producing bacteria were inoculated by cross-streak onto King B agar plates to which glycine (4.4 g [L.sup.-1]) and a filter paper saturated with a solution of 0.5% picric acid in carbonate sodium 2% had been added. The plates were sealed with Parafilm and incubated at 28[degrees]C for 7 days. The development of a reddish-brown colour on the filter paper indicated the production of HCN (Kumar et al. 2012). For the indole acetic acid (IAA)-producing bacteria, the selected isolates were adjusted to 0.5 optical density at 600 nm, and 0.1 mL of each suspension was inoculated into tubes containing 4.9 mL nutrient broth in triplicate. Cultures were incubated at 28[degrees]C for 5 days under constant stirring. At the end of incubation, the supernatant was obtained by centrifugation of cultures at 6000g for 5 min at 4[degrees]C. Then, 1 mL of each supernatant was added to 2 mL Salkowski reagent. The samples were incubated at room temperature in the dark for 25 min, and absorbance was determined at 530 nm. The amount of IAA produced was determined using a standard curve for different concentrations of IAA. The isolates were named based on their molecular identification: the first letter corresponding to the bacterial genus, the second letter corresponding to the bacterial species or sp. (species not determined), followed by the type of soil from which they were isolated (P = pine forest soil; A = agricultural soil), the corresponding sample number (1,2 or 3) and finally the plant growth-promoting (PGP) traits (I-IAA; P=phosphate; S = siderophore; C = chitin).
Canonical correspondence analysis
To determine the relationship between the physicochemical parameters of the soil and the presence or absence of PGPR species, a canonical correspondence analysis (CCA) was performed using the VEGAN (Oksanen et al. 2016) package in R software (R Core Team 2014) with default parameters.
Physicochemical properties of PF and AG
The physicochemical properties of the soils were determined. When the three composite soil samples of each ecosystem were compared, significant differences were observed in the CEC, EC, WHC, organic carbon, silt, clay and sand. In general, the three composite PF samples presented higher values than AG samples in most of the parameters studied. The pH in two soil groups was slightly acidic (between 5.64 and 6.61). Regarding soil texture, the three composite PF samples were silt loam (PF1), sandy loam (PF2) and loam (PF3), whereas the three agricultural soil samples were silt loam (AG1 and AG3) and loam (AG2; Table 1). These results indicated significant variations among the samples of the same soil, suggesting a spatial heterogeneity; thus, the samples were studied independently in the following experiments.
Quantification of bacterial functional groups
The microbial load of aerobic mesophilic bacteria (AMB) was determined in the three composite samples of the two soils, indicating that PF1 and PF2 had a lower AMB load than PF3 (P<0.05; Fig. la). The three AG samples had values similar to the three PF samples. The load of sporulated aerobic bacteria was similar in the three PF samples, but loads were significantly lower in the three AG samples compared with the three PF samples (P<0.0001; Fig. 16). The lowest load of chitinolytic bacteria was in PF1 (P<0.05; Fig. lc). The nitrogen-fixing bacteria load was lower in PF1 and PF2 than in PF3 (P<0.05), and lower in AG3 than in AG1 (P< 0.05; Fig. 1 d). Finally, the load of tricalcium phosphate solubilisers was lower in PF1 than in PF2 and PF3 (P<0.05), and higher in AG3 than in AG1 and AG2 (P<0.05). In addition, there was a significant difference between both soils (P<0.0001; Fig. 1e).
Molecular identification of isolates with PGP traits
To ensure bacterial diversity in the different isolates, the molecular marker RAPD was used in the 74 isolates obtained from the different functional bacterial groups. As a selection criterion, it was considered that if the isolates produced different RAPD profiles, these were unique from each soil. Thus, 43 different RAPD profiles were obtained, of which 16 were exclusive for PF and 21 were exclusive for AG; the remainder were common in both soils (see Fig. SI, available as Supplementary Material to this paper). An isolate from each RAPD profile was selected for identification by sequencing of its 16S rDNA gene and subsequent phylogenetic analysis (Figs. S2-S5). The 53 isolates belonged to four phyla: Firmicutes (39.62%), Proteobacteria (24.53%), Actinobacteria (26.42%) and Bacteroidetes (9.43%). All firmicutes identified belonged to the order Bacillalcs. The most abundant genus was Bacillus, followed by Paenihacillus, whereas the genus Viridibacillus was only recovered from PF. With regard to Proteobacteria, the genera Pseudomonas ([gamma]-Proteobacteria) and Burkholderia ([beta]-proteobacteria) were isolated in both ecosystems, and Pseudomonas mohnii was the most frequent bacterial species in the three samples of both soils. Unlike in the pine forest, the genera Enterobacteria ([gamma]-proteobacteria) and Phylobacterium ([alpha]-proteobacteria) were isolated in agricultural soil. Most of the actinobacteria isolated were of the genus Streptomyces, and only two isolates were identified as Rhodococcus sp. Within the Bacteroidetes phylum, only the Chitinophaga genus was found in both ecosystems (Tables 2, 3).
Determination of PGP traits in the identified bacteria
After the bacteria were identified, their PGP traits were determined to find the greatest microbial diversity and exclusivity in both soils. Seventeen PGPR were exclusive to the PF (Table 2), 22 were exclusive to the AG (Table 3) and 14 were shared in both soils (Table 4). In the pine forest, PGPR were distributed in a similar proportion in the phyla Firmicutes (35.29%; n = 17), Proteobacteria (29.41%; n = 17) and Actinobacteria (29.41%; n= 17). Conversely, in the agricultural soil, most of the PGPR belonged to the Phylum Firmicutes (50%; n = 22), and Bacillus was the largest genus isolated, followed by Streptomyces. Regarding the origin of the isolates, the three PF samples contributed similarly to the isolation; however, in the AG samples, most of the isolates were obtained from the AG1, and AG3 only contributed to two isolates.
Regarding PGP traits, IAA production (90.91%; n = 22) and siderophore production (40.91%; n = 22) were higher in AG than in PF isolates (76.47% IAA and 29.41% siderophore; n = 17). In contrast, tricalcium phosphate solubilisers (70.59%; n = 17) and chitinase activity (41.18%; n = 17) were higher in PF isolates than in AG isolates (63.64 and 27.27%, respectively; n = 22). In addition, most of the isolates produced low concentrations of IAA (<10[micro]g [m.sup.L-1]; data not shown). When comparing bacteria with three PGP traits (29.41 % (n = 17) and 31.82% (n = 22) of PF and AG isolates respectively) or one trait (11.76% (n = 17) and 18.18% (n = 22) of PF and AG isolates respectively), the proportion was similar in both soils. However, the percentage of isolates with two PGP traits was higher in PF than AG (58.82% (n = 17) vs 45.45% (n = 22) respectively). In addition, the isolate SspA1IPSC member of the Streptomyces genus, with four PGP traits (IAA, siderophore production, phosphate solubilisation and chitinase activity) was only isolated from the agricultural soil. A single bacterium, Bacillus sp. BspA2I, which produces HCN, was isolated from the AG2 sample.
Relationship between physicochemical properties of soils and PGPR
CCA was used to correlate the physicochemical properties of samples from both soils with the location of the PGPR. This analysis showed that silt, EC, CEC, sand and WHC were the parameters that best explain the variation in the data (60% of the variance), with silt more separated from the other parameters. Samples AG2 and PF1 were correlated with silt, whereas samples AG3, PF2 and PF3 were correlated with EC and CEC, and sample AG 1 was in another quadrant and is linked with sand-WHC and, to a lesser extent, with silt. Most PGPR were correlated with CEC, EC and silt. In addition, most agricultural soil PGPR were most correlated with sand-WHC, adjacent to sample AG1. In general, the PGPR from the pine forest were in a different location (left side) of the PGPR from agricultural soil (right side; Fig. 2). Fig. 2 shows that there were substantial differences between samples of the same soil, as well as samples of the two ecosystems and locations of the PGPR, and that these differences were correlated with the physicochemical properties of the soil.
Most of the PGPRs in both soils coincide in their distribution. For this reason, they are shown as bacterial complexes in Fig. 2. Pine forest bacterial Complexes 1 and 2 had a large proportion of phosphate-solubilising bacteria and were correlated with EC and CEC, whereas Complex 3 had a lower proportion of phosphate-solubilising bacteria and was more distant from EC and CEC. The bacterial Complex 4 consisted of 15 PGPR from agricultural soil, which was further from the other PGPR and was related to sand and WHC. In contrast, bacterial Complex 5, consisting of four PGPR bacteria from the agricultural soil, was correlated with silt.
Caption: Fig. 2. Results of canonical correspondence analysis (CCA) showing the differential physicochemical properties of the soil and the distribution of exclusive plant growth-promoting rhizobacteria (PGPR). The three samples from each soil type are shown according to their physicochemical properties. PF, pine forest; AG, agricultural soil; PmA3I, Pseudomonas mohnii PmA3I; BpP 13IP, Burkholderia phytofirmans BpP13IP; BdA23IS, Bacillus drentensis BdA23IS. Bacterial complexes are as follows: Complex 1, Paenibacillus sp. PspP3IP, Bacillus megaterium BmP3IP, Pseudomonas corrugate PcP3IPS, Pseudomonas mohnii PmP3IP and Streptomyces prunicolor SpP3IPC; Complex 2, Bacillus sp. BspP2P, Paenibacillus castaneae PcP2C, Streptomyces sp. SspP2IPC, Streptomyces sp. SspP2PSC and Streptomyces sp. SspP2SC; Complex 3, Bacillus subtilis BsP1IS, Viridibacillus sp. VspP1IS, Pseudomonas sp. PspP1IP, Streptomyces sp. SspP1IC and Chitinophaga sancti CsP1IPC; Complex 4, Bacillus atrophaeus BaA1IPC, Bacillus muralis BmA1IS, Bacillus sp. BspA1IP, Bacillus subtilis BsA1l, Paenibacillus sp. PspA1PC, Bacillus drentensis BdA1I, Burkholderia graminis BgA1IPS, Enterobacter hormaechei EhA1IP, Phyllobacterium sp. PspA1lP, Streptomyces sp. SspA1IPSC, Streptomyces sp. SspA1IPS, Streptomyces sp. SspA1ISC, Streptomyces sp. SspA1PC and Chitinophaga japonensis CjA1IPC; Complex 5, Bacillus atrophaeus BA2IPS, Bacillus sp. BspA2I, Burkholderia graminis BgA2IP and Chitinophaga sp. CspA2IP. CEC, cation exchange capacity; EC, electrical conductivity; WHC, water-holding capacity.
The physicochemical properties of soils affect the richness and abundance of microbial species. The samples of each soil studied herein had different physicochemical properties and there was bacterial diversity in each one. With regard to the different physicochemical properties, it is possible that this occurred because some studies have shown that Mexican soils may have different physicochemical properties within the same geographic entity (Barajas-Aceves and Dendooven 2001; Ruiz-Valdiviezo et al. 2010) and even in the same region (Vasquez-Murrieta et al. 2006). The abiotic factors that affect the functional microbial groups are the organic matter and mineral particles, which are two of the four components that form the soil aggregates. The organic matter content was lower in the agricultural soil, and this could be correlated with the change in soil use, because organic matter content declines with excessive tillage, monoculture and the rotation of crops in very short periods, among other factors (Liu et al. 2006). Regarding mineral particles, EC and CEC also have an effect on pine forest soil, but these characteristics are closely related to the organic matter content because it has the capacity to retain cations (e.g. [Ca.sup.2+], [Mg.sup.2+], [K.sup.+], [Na.sup.+], N[H.sub.4.sup.+], [H.sup.+]) and the presence of these cations in soils increases EC (Liu et al. 2006). The content of particles such as silt, clay and sand also affects the microbial load in soils (Sessitsch et al. 2001). In the present study, it was observed that these three parameters were higher in the pine forest composite samples. Comparative studies of soils with a high silt content and those with a high sand content have shown that the former support a higher load of micro-organisms (Neumann et al. 2013). However, there are few studies that have correlated PGPR with soil type. Sessitsch et al. (2001) studied the microbial population correlated with soil particle size fractions and found bacterial communities in all the fractions, but the bacteria associated with finer fractions (silt and clay) were the consequence of aggregate formation along with the decomposition of organic matter, whereas in the sand fraction there were bacterial species better adapted to limited nutrient conditions or with the capacity to use a wider range of substrates. According to this, Da Costa et al. (2014) correlated soil quality with the isolation of PGPR, proposing that PGPR are frequently isolated from poor-nutrient soils with low C organic content. The agricultural soils used in the present study have this characteristic. Thus, most of the PGPR isolated from the agricultural soil were from the AG1 sample, which had the lowest C organic content and a high percentage of sand (Table 1). Therefore, the AG 1 sample could be considered as a poor-nutrient soil.
The identification of bacteria in the soils showed that there were differences between them and that there were bacteria exclusive to each soil. The pine forest soil had a greater richness of bacterial genera than the agricultural soil. In the agricultural soil, the dominant genera were Bacillus and Streptomyces. These are representative cultivable bacteria of agricultural soils and are genera recognised as PGPR (Adesemoye et al. 2008). In the pine forest, B. megaterium and the genus Viridibacillus were found. The former has been reported as one of the most abundant species in the soil and has been used as a commercial inoculant (McSpadden Gardener 2004; Kumar et al. 2011). Viridibacillus sp. has been isolated from weed roots and characterised as a PGPR in cucumber seedlings (Kim et al. 2011). The genus Paenihacillus is easily isolated from the soil and rhizosphere (McSpadden Gardener 2004). It is also known as a PGP via various mechanisms and its antagonistic activity against pathogens (Kumar et al. 2011). Some members of the genus Burkholderia are capable of fixing nitrogen asymbiotically; in the present study, B. phytofirmas was found in soil from the pine forest, whereas B. graminis was found in the agricultural soil. B. phytofirmas is an endophyte bacterium of several plants, but it can also be established in the rhizosphere (Sessitsch et al. 2005) and can produce 1-aminocyclopropane-1-carboxylate (ACC) deaminase. In relation to B. graminis, members of this genus have been isolated from grasses; in particular, the G2Bd5 strain was isolated from annual ryegrass in Portugal and it produces IAA (10[micro]g [mg.sup.-1] protein) and solubilises phosphate, but it was unable to produce sidcrophores (Castanheira et al. 2016). The two first traits are similar to B. graminis BgA1IPS, which was isolated in the present study; however, BgA1IPS also produces siderophores. Phylobacterium sp. PspA1IP, which is an IAA producer and tricalcium phosphate solubiliser, was exclusive to the agricultural soil. Bacteria of this genus have been isolated from the phylosphere and rhizosphere of higher plants (Mergaert and Swings 2005), and their antifungal and antibacterial activity has been reported (Lambert et al. 1990). Therefore, the PspA1IP strain is a good candidate for assays of PGP and biocontrol. In general, the Proteobacteria identified in both soils are IAA producers and tricalcium phosphate solubilisers, and some also produce siderophores. Kim et al. (2011) reported that these characteristics are well distributed among Proteobacteria. The genus Streptomyces is the most frequently isolated from soils (Xu et al. 1996). In the present study, an isolate of this family obtained from the agricultural soil had four of the five PGP traits evaluated herein, noting that most Streptomyces have the ability to hydrolyse chitin and this ability is common among Streptomyces (Beaulieu 2001). Franco-Correa et al. (2010) evaluated the production of siderophores and tricalcium phosphate solubilisation in actinomycetes, and more than 30% and 50% of their isolates respectively had these characteristics. In the present study Bacillus atrophaeus and Bacillus drentensis were isolated from the agricultural soil; the former has not been reported as a PGPR, whereas the latter was isolated by Kim et al. (2011) without PGP activity.
The PGP traits evaluated in the present study were distributed among the isolates. That is, there was a redundancy of these metabolic characteristics in both soils, but they were produced by different bacterial genera in each soil. Torsvik and 0vreas (2007) suggested that the redundancy of PGP traits in a microbial community is important to maintain the stability and function of ecosystems subject to change. The production of IAA was the most distributed PGR trait; 90.91% (n = 22) of the agricultural soil isolates had this trait, compared with 76.47% (n = 17) of the pine forest isolates. However, most of the isolates produced low concentrations of IAA (< 10 [micro]g m[L.sup.-1]). Similarly, Khalid et al. (2004) and Nimnoi and Pongsilp (2009) reported that 77% of the bacteria isolated produced 1.1-12.1 [micro]g m[L.sup.-1] IAA. The proportion of siderophore-producing bacteria was higher in the agricultural soil (40.91%; n = 22) than in the pine forest soil (29.41%; n = 17). This is consistent with the findings of Da Costa et al. (2014), who analysed 2,211 bacterial isolates obtained from rhizospheric soil from various crops and determined that 64% of their isolates were able to produce siderophores. The distribution of bacteria with two or three PGP traits was very similar, and the differences were not statistically significant in both ecosystems. The most abundant bacteria were the IAA producers and solubilisers of tricalcium phosphate, followed by bacteria with the additional trait of siderophore production (those with three PGP traits), indicating the isolates were potential PGPR. Ahemad and Khan (2011) reviewed the functional aspects of PGP and observed that most of the genera studied exhibited the three PGP traits found in the present study. Similarly, Da Costa et al. (2014) found the same three PGP traits widely distributed in the strains analysed in their study, and the genera they isolated are consistent with the genera isolated in the present study. In addition, Da Costa et al. (2014) determined that phosphate-solubilising and siderophore-producing bacteria increase as soil richness decreases and that concentrations of bacterial indole compounds increase as the soil richness increases.
In the present study, the CCA revealed differences between the two soils studied; in addition, the diversity of PGPR was found to be related to the locality and physicochemical property of the soil. This demonstrates that the physicochemical properties of soils affect the isolated bacterial species and their distribution. In a similar study, the edaphic bacterial communities of three contrasting desert terrain types (gravel plains, sand dunes, and ephemeral rivers) with different physicochemical properties in the Central Namib Desert were investigated, and the CCA indicated that the variables of fine silt, medium and fine sand content, pH, S, Na, Zn, Al, V and Fe concentrations contributed significantly to the variance in the bacterial communities (determined by terminal restriction fragment length polymorphism). These results suggest that local physicochemical conditions play a significant role in shaping the bacterial structures in the Central Namib Desert (Gombeer et al. 2015). In another study, CCA was used to determine the effect of the wheat growth stage and the type of field on the bacterial community and revealed that the wheat rhizobacterial community structure is highly dynamic and substantially influenced by plant age, as the growth stage for the rhizosphere soil (Roesti et al. 2006). Regarding the distribution of PGPR, in the present study the CCA showed that the PGPR of the pine forest are constrained (or given) by a gradient of silt, EC and CEC, whereas the PGPR of agricultural soil are constrained (or given) by a gradient of sand and WHC. With regard to EC and CEC, these parameters are closely related to the concentration of ions, such as iron and phosphates. The PGPR correlated with these parameters were the phosphate-solubilising bacteria and siderophore producers, which is in agreement with the findings described above. It is important to emphasise that the present study was focused on the cultivable fraction of PGPR; it is possible that taking the non-cultivable fraction of PGPR in consideration in the CCA would reveal a greater detail of differences between the two ecosystems.
In summary, the present study showed that by analysing PGPR diversity and the physicochemical properties of two adjacent ecosystems of the same region it is possible to determine the changes of these soils caused by their use. However, even though the soils are from the same region, the soil composite samples from the same ecosystem had different physicochemical properties and PGPR, indicating the presence of spatial heterogeneity. With regard to PGP traits, there was a greater proportion of IAA-producing bacteria and siderophore-producing bacteria in the agricultural soil than in the pine forest soil. In addition, the CCA was able to estimate the differences in PGPR diversity and correlate them with the physicochemical properties of the soils and their location in both ecosystems. Using this type of analysis, it was possible to determine the changes brought about by the agricultural manipulation of the soils studied.
Conflicts of interest
The authors declare no conflicts of interest.
This work was supported by Grants no. SIP20170243 and SIP20180359 from the Instituto Politecnico Nacional. Janet Jan-Roblero Angelica Rodriguez-Dorantes, and Juan A. Cruz-Maya acknowledge the Comision de Operation y Fomento de Actividades Academicas and Estimulo al Desempeno de los Investigadores, Instituto Politecnico Nacional fellowships and the support of the Sistema Nacinal de Investigadores, Consejo Nacional de Ciencia y Tecnologia.
Adesemoye AO, Obini M, Ugoji EO (2008) Comparison of plant growth-promotion with Pseudomonas aeruginosa and Bacillus subtilis in three vegetables. Brazilian Journal of Microbiology 39, 423-126. doi: 10.15 90/S1517-83822008000300003
Ahemad M, Khan MS (2011) Functional aspects of plant growth promoting rhizobacteria: recent advancements. Microbiology Insights 1. 39-54. doi: 10.5567/1MICRO-IK.2011.39.54
Atlas RM (2010) 'Handbook of microbiological media.' 4th edn. (CRC and ASM Press: Washington DC, USA)
Barajas-Aceves M, Dendooven L (2001) Nitrogen, carbon and phosphorus mineralization in soils from semi-arid highlands of central Mexico amended with tannery sludge. Bioresource Technology 77, 121-130. doi: 10.1016/S0960-8524(00)00157-7
Barriuso J, Ramos SB, Lucas JA, Probanza LA, Garcia-Villaraco A, Gutierrez MFJ (2008) Ecology, genetic diversity and screening strategies of plant growth promoting rhizobacteria (PGPR). In 'Plant-bacteria interactions. Strategies and techniques to promote plant growth'. (Ed. I. Ahmad.) pp. 1-18. (Wiley: Weinheim, Germany)
Beaulieu C (2001) Actinomycetes, promising tools to control plant diseases and to promote plant growth. Phytoprotection 82, 85-102. doi: 10.7202/ 706219ar
Blazka P, Fischer Z (2014) Moisture, water holding, drying and wetting in forest soils. Open Journal of Soil Science 4, 174-184. doi: 10.4236/ojss. 2014.45021
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden T (2009) BLAST+: architecture and applications. BMC Bioinformatics 10, (Article no. 421). doi:10.1186/1471-2105-10-421
Castanheira N, Dourado AC, Kruz S, Alves PIL, Delgado-Rodriguez AI, Pais I, Semedo J, Scotti-Campos P, Sanchez C, Borges N, Carvalho G, Barreto-Crespo MT, Fareleira P (2016) Plant growth-promoting Burkholderia species isolated from annual ryegrass in Portuguese soils. Journal of Applied Microbiology 120, 724-739. doi: 10.1111/jam. 13025
Da Costa PB, Granada CE, Ambrosini A, Moreira F, de Souza R, dos Passos JF, Arruda L, Passaglia LM (2014) A model to explain plant growth promotion traits: a multivariate analysis of 2,211 bacterial isolates. PLoS One 9, el 16020. doi:10.1371/journal.pone.0116020
Delgado-Baquerizo M, Reich PB, Khachane AN, Campbell CD, Thomas N. Freitag TE, Abu Al-Soud W, S0rensen S, Bardgett RD, Singh BK (2017) It is elemental: soil nutrient stoichiometry drives bacterial diversity. Environmental Microbiology 19, 1176-1188. doi: 10.1111/1462-2920 13642
Franco-Correa M, Quintana A, Duque C, Suarez C, Rodriguez MX, Barea JM (2010) Evaluation of actinomycete strains for key traits related with plant growth promotion and mycorrhiza helping activities. Applied Soil Ecology 45. 209-217. doi: 10.1016/j.apsoil 2010.04.007
Galtier N, Gouy M, Gautier C (1996) SEAVIEW and PHYLO_WIN: Two graphic tools for sequence alignment and molecular phylogeny. Computer Applications in the Biosciences 12, 543-548. doi: 10.1093/bio informatics/12.6.543
Gkorezis P, Daghio M, Franzetti A, Van Hamme JD, Sillen W, Vangronsveld J (2016) The interaction between plants and bacteria in the remediation of petroleum hydrocarbons: an environmental perspective. Frontiers in Microbiology 7, (Article no. 1836). doi:10.3389/ftnicb.2016.01836
Gombeer S, Ramond JB, Eckardt FD, Seely M, Cowan DA (2015) The influence of surface soil physicochemistry on the edaphic bacterial communities in contrasting terrain types of the Central Namib Desert. Geobiology 13, 494-505. doi: 10.1111/gbi.12144
Huot H, Joyner J, Cordoba A, Shaw RK, Wilson MA, Walter R, Muth TR, Cheng Z (2017) Characterizing urban soils in New York City: profile properties and bacterial communities. Journal of Soils and Sediments 17, 393-407. doi: 10.1007/s11368-016-1552-9
Khalid A, Arshad M, Zahir ZA (2004) Screening plant growth-promoting rhizobacteria for improving growth and yield of wheat. Journal of Applied Microbiology 96, 473-480. doi: 10.1046/j.1365-2672.2003. 02161.x
Kim WI, Cho WK, Kim SN, Chu H, Ryu KY, Yun JC, Park CS (2011) Genetic diversity of cultivable plant growth-promoting rhizobacteria in Korea. Journal of Microbiology and Biotechnology 21, 777-790. doi: 10.4014/jmb.1101.01031
Kumar A, Prakash A, Johri BN (2011) Bacillus as PGPR in crop ecosystem. In 'Bacteria in agrobiology: crop ecosystems'. (Ed. D. K. Maheshwari.) pp. 37-59. (Springer: Berlin, Germany)
Kumar A, Kumar A, Devi S, Patil S, Payal C, Negi S (2012) Isolation, screening and characterization of bacteria from rhizospheric soils for different plant growth promotion (PGP) activities: an in vitro study. Recent Research in Science and Technology 4, 1-5.
Lambert B, Joos H, Dierickx S, Vantomme R, Swings J, Kersters K, Montagu VM (1990) Identification and plant interaction of a Phyllobacterium sp., a predominant rhizobacterium of young sugar beet plants. Applied and Environmental Microbiology 56, 1093-1102.
Lau JA, Lennon JT (2012) Rapid responses of soil microorganisms improve plant fitness in novel environments. Proceedings of the National Academy of Sciences of the United States of America 109, 14058-14062. doi: 10.1073/pnas.1202319109
Liu X, Herbert SJ, Hashemi AM, Zhang X, Ding G (2006) Effects of agricultural management on soil organic matter and carbon transformation--a review. Plant, Soil and Environment 52, 531-543.
McSpadden Gardener BB (2004) Ecology of Bacillus and Paenihacillus spp. in agricultural ecosystems. Phytopathology 94, 1252-1258. doi: 10.1094/PHYT0.2004.94.11.1252
Mergaert J, Swings J (2005) Phyllobacteriaceae fam. nov. In 'Bergey's manual of systematics of Archaea and Bacteria'. (Ed. W. B. Whitman.) pp. 1-3. (John Wiley & Sons in association with Bergey's Manual Trust: New York, NY, USA)
Nannipieri P, Ascher J, Ceccherini MT, Landi L, Pietramellara G, Renella G (2003) Microbial diversity and soil functions. European Journal of Soil Science 54, 655-670. doi:10.1046/j.l351-0754.2003.0556.x
Neumann D, Heuer A, Hemkemeyer M, Martens R, Tebbe CC (2013) Response of microbial communities to long-term fertilization depends on their microhabitat. FEMS Microbiology Ecology 86. 71-84. doi: 10. 1111/1574-6941.12092
Nimnoi P, Pongsilp N (2009) Genetic diversity and plant-growth promoting ability of the indole-3-acetic acid (IAA) synthetic bacteria isolated from agricultural soil as well as rhizosphere, rhizoplane and root tissue of Ficus religiosa L., Leucaena leucocephala and Piper sarmentosum Roxb. Research Journal of Agriculture and Biological Sciences 5, 29-41.
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'Hara RB, Simpson GL, Solymos M, Stevens HH, Szoecs E, Wagner H (2016) Vegan: community ecology package. R package version 2.4-0. Available at https://cran.r-project.org/web/packages/ vegan/[verified June 2016].
R Core Team (2014) R: A language and environment for statistical computing. (R Foundation for Statistical Computing: Vienna, Austria.) Available at http://www.R-project.org/[verified October 2015].
Relman DA (1993) Universal bacterial I6S rDNA amplification and sequencing. In 'Diagnostic molecular microbiology: principles and applications'. (Eds D. H. Persing, T. F. Smith, F. C. Tenover, T. T. White) pp. 489-495 (American Society for Microbiology: Washington DC, USA)
Rhoades JD, Mantghi NA, Shause PJ, Alves W (1989) Estimating soil salinity from saturated soil-paste electrical conductivity. Soil Science Society of America Journal 53, 428-433. doi: 10.2136/sssaj1989.03615 995005300020019x
Roesti D, Gaur R, Johri BN, Imfeld G, Sharma S, Kawaljeet K., Aragno M (2006) Plant growth stage, fertiliser management and bio-inoculation of arbuscular mycorrhizal fungi and plant growth promoting rhizobacteria affect the rhizobacterial community structure in rain-fed wheat fields. Soil Biology & Biochemistry 38, 1111-1120. doi: 10.1016/j.soilbio. 2005.09.010
Ruiz-Valdiviezo VM, Luna-Guido M, Galzy A, Gutierrez-Miceli FA, Dendooven L (2010) Greenhouse gas emissions and C and N mineralization in soils of Chiapas (Mexico) amended with leaves of Jatropha curcas L. Applied Soil Ecology 46, 17-25. doi: 10.1016/j.ap soil.2010.06.002
Sessitsch Coenye Sturz Vandamme Barka Salles Van Elsas Faure Reiter Glick Wang-Pruski Nowak (2005) Burkholderia phytofirmans sp. nov., a novel plant-associated bacterium with plant-beneficial properties. International Journal of Systematic and Evolutionary Microbiology 55, 1187-1192. doi: 10.1099/ijs.0.63149-0
Sessitsch A, Weilharter A, Gerzabek MH, Kirchmann H, Kandeler E (2001) Microbial population structures in soil particle size fractions of a long-term fertilizer field experiment. Applied and Environmental Microbiology 67, 4215-1224. doi: 10.1128/AEM.67.9.4215-t224.2001
Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Molecular Biology and Evolution 28, 2731-2739. doi:10.1093/molbev/ msrl21
Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG (1997) The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Research 25, 4876-4882. doi:10.1093/nar/25.24.4876
Torsvik V, Ovreas L (2007) Microbial phylogeny and diversity in soil. In 'Modern soil microbiology'. (Eds J. D. Elsas, J. K. Jansson, J. T. Trevors.) pp. 41-47. (CRC Press: Boca Raton, FL, USA)
Vasquez-Murrieta MS, Migueles-Garduno I, Franco-Hernandez O, Govaerts B, Dendooven L (2006) C and N mineralization and microbial biomass in heavy-metal contaminated soil. European Journal of Soil Biology 42, 89-98. doi: 10.1016/j.ejsobi.2005.10.002
Vejan P, Abdullah R, Khadiran T, Ismail S, Nasrulhaq-Boyce A (2016) Role of plant growth promoting rhizobacteria in agricultural sustainability--a review. Molecules (Basel, Switzerland) 21, (Article no. 573) doi:I0.3390/molecules21050573
Whittles CL, Little RC (1950) A colorimetric method for the determination of potassium and its application to the analysis of soil extracts. Journal of the Science of Food and Agriculture 1, 323-326. doi:10.1002/jsfa. 2740011103
Wilson K (2001) Unit 2.4: preparation of genomic DNA from bacteria. In 'Current protocols in molecular biology'. Chapter 2: Unit 2.4: 2.4.1-2.4.5. (John Wiley & Sons: New York, NY, USA)
Xu LH, Li QR, Jiang CL (1996) Diversity of soil actinomycetes in Yunnan, China. Applied and Environmental Microbiology 62, 244-248.
Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, Chun J (2017) Introducing EzBioCloud: a taxonomically united database of 16S rRNA and whole genome assemblies. International Journal of Systematic and Evolutionary Microbiology 67, 1613-1617. doi: 10.1099/ijsem.0.001755
Zhang L, Xu Z (2008) Assessing bacterial diversity in soil: A brief review. Journal of Soils and Sediments 8, 379-388. doi: 10.1007/s11368-0080043-z
Victor M. Flores-Nunez (A), Enriqueta Amora-Lazcano (A), Angelica Rodriguez-Dorantes (B), Juan A. Cruz-Maya (C), Janet Jan-Roblero (A,D)
(A) Departamento de Microbiologia, Escuela Nacional de Ciencias Biologicas, Instituto Politecnico Nacional, Prolongacion de Carpio y Plan de Ayala s/n. Col. Sto. Tomas, Ciudad de Mexico, 11340, Mexico.
(B) Departamento de Botanica, Escuela Nacional de Ciencias Biologicas, Instituto Politecnico Nacional, Prolongacion de Carpio y Plan de Ayala s/n. Col. Sto. Tomas, Ciudad de Mexico, 11340, Mexico.
(C) Unidad Profesional Interdisciplinaria en Ingenieria y Tecnologias Avanzadas, Instituto Politecnico Nacional, Av. IPN 2580, Col. La Laguna Ticoman, Ciudad de Mexico, 07340, Mexico.
(D) Corresponding author. Email: firstname.lastname@example.org
Received 26 August 2017, accepted 26 November 2017, published online 10 April 2018
Caption: Fig. 1. Bacterial load of different functional groups of samples of pine forest and agricultural soil, (a) Aerobic mesophilic bacteria, (b) sporulated aerobic bacteria, (c) chitinolytic bacteria, (d) nitrogen-fixing bacteria and (e) tricalcium phosphate-solubilising bacteria. Data are the mean [+ or -] s.d. of three replicates. Different letters indicate significant differences between composite samples of the same soil (P < 0.05; two-way ANOVA with Sidak's multiple comparisons test). There were significant variations in sporulated aerobic bacteria (b) and tricalcium phosphate-solubilising bacteria (e) between ecosystems (P< 0.0001).
Table 1. Physicochemical properties of pine forest and agricultural soils P[O.sub.4.sup.3-] , N[O.sub.3] and [K.sup.+] were similar in all samples, with mean values of 10, 20 and 130 mg [kg.sup.-1] soil respectively (data not shown). The significance of differences between mean values was tested using one-way ANOVA followed by Tukey's test. Mean values (n = 3) with different letters differ significantly (P<0.05). CEC, cation exchange capacity; EC, electrical conductivity; WHC, water-holding capacity Site Sample pH CEC EC (mS (mEq [Ca.sup.2+] [cm.sup.-1]) lOO[g.sup.-1]) Pine forest PF1 6.43 16.9a 0.09b PF2 6.61 17.6a 1.13a PF3 6.59 14.1b 0.13b Agricultural AG1 6.53 12.5b 0.01b AG2 5.64 10.8b 0.71a AG3 6.55 13.7b 0.16b Site Sample WHC Organic C Silt (%) (%) (%) Pine forest PF1 102.1a 6.88a 58a PF2 101.4a 8.28a 48b PF3 92.8b 6.97a 38b Agricultural AG1 119.7a 2.25b 52a AG2 72.9b 3.57b 48b AG3 81.4b 5.06a 62a Site Sample Clay Sand Textural soil (%) (%) classification Pine forest PF1 2.7b 39.3b Silt loam PF2 6.7b 45.3a Sandy loam PF3 16.7a 45.3a Loam Agricultural AG1 0.7b 47.3a Silt loam AG2 6.7b 45.3a Loam AG3 2.7b 35.3b Silt loam Table 2. Plant growth-promoting rhizobacteria in pine forest soil The nomenclature of the strains is as follows: the first letter represents the bacterial genus, the second letter represents the bacterial species, or sp. (species not determined), 'P' indicating that it was obtained from pine forest soil, a number (1, 2 or 3) corresponding to the number of the sample and finally the plant growth-promoting traits (I, indole acetic acid; P, phosphate; S, siderophore; C, chitin) Phylum Microbial group Similarity Strain Sample affiliation (%) Firmicutes Paenihacillus sp. 97.27 PspP3IP 3 (35.29%) Bacillus sp. 96.12 BspP2P 2 Bacillus subtilis 99.9 BsP1IS 1 Bacillus megaterium 99.02 BmP3IP 3 Viridibacillus sp. 99.35 VspP1IS 1 Paenihacillus 99.77 PcP2C 2 castaneae Proteobacteria Pseudomonas corrugata 99.63 PcP3IPS 3 (29.41%) Pseudomonas mohnii 98.75 PmP3IP 3 Pseudomonas sp. 91.5 PspP1IP 1 Burkholderia 99.32 BpP13IP 1,3 phytofirmans Actinobacteria Streptomyces sp. 99.28 SspP2IPC 2 (29.41%) Streptomyces 99.87 SpP3IPC 3 prunicolor Streptomyces sp. 99.51 SspP2PSC 2 Streptomyces sp. SspP2SC 2 Streptomyces sp. 99.87 SspP1IC 1 Bacteroidetes Chitinophaga sancti 100 CsP1IPC 1 (5.88%) Phylum Microbial group IAA Phosphate affiliation (A) Firmicutes Paenihacillus sp. + + (35.29%) Bacillus sp. + Bacillus subtilis + - Bacillus megaterium + + Viridibacillus sp. + - Paenihacillus - - castaneae Proteobacteria Pseudomonas corrugata + + (29.41%) Pseudomonas mohnii + + Pseudomonas sp. + + Burkholderia + + phytofirmans Actinobacteria Streptomyces sp. + + (29.41%) Streptomyces + + prunicolor Streptomyces sp. - + Streptomyces sp. - - Streptomyces sp. + - Bacteroidetes Chitinophaga sancti + + (5.88%) Phylum Microbial group Siderophore Chitin affiliation Firmicutes Paenihacillus sp. - - (35.29%) Bacillus sp. - - Bacillus subtilis + - Bacillus megaterium - - Viridibacillus sp. + - Paenihacillus - + castaneae Proteobacteria Pseudomonas corrugata + - (29.41%) Pseudomonas mohnii - - Pseudomonas sp. - - Burkholderia - - phytofirmans Actinobacteria Streptomyces sp. - + (29.41%) Streptomyces - + prunicolor Streptomyces sp. + + Streptomyces sp. + + Streptomyces sp. - + Bacteroidetes Chitinophaga sancti - + (5.88%) (A) Tricalcium phosphate-solubilising bacteria. Table 3. Plant growth-promoting rhizobacteria in agricultural soil The nomenclature of the strains is as follows: the first letter represents the bacterial genus, the second letter represents the bacterial species, or sp. (species not determined), 'A' indicating that it was obtained from agricultural soil, a number (1, 2 or 3) corresponding to the number of the sample and finally the plant growth-promoting traits (I, indole acetic acid; P, phosphate; S, siderophore; C, chitin) Phylum Microbial group Similarity Strain affiliation (%) Firmicutes Bacillus atrophaeus 100 BaA2IPS (50%) Bacillus drentensis 100 BdA2IPS Bacillus atrophaeus 100 BaA1IPC Bacillus muralis 100 BmA1IS Bacillus sp. 99.61 BspA1IP Bacillus subtilis 99.93 BsA1I Bacillus drentensis 100 BdA231S Paenibacillus sp. 99.70 PspA1PC Bacillus sp. 98.60 BspA2I (B) Bacillus drentensis 100 BdA1I Proteobacteria Burkholderia graminis 99.91 BgA1IPS (22.73%) Enterobacter hormaechei 99.85 EhAI1P Burkholderia graminis 99.90 BgA2IP Phyllobacterium sp. 98.45 PspA1IP Pseudomonas mohnii 99.83 PmA3I Actinobacteria Streptomyces sp. 99.32 SspA1IPSC (18.18%) Streptomyces sp. 99.54 SspA1IPS Streptomyces sp. 98.48 SspA1ISC Streptomyces sp. 97.34 SspA1PC Bacteroidetes Chitinophaga 99.99 CjA1IPC (9.09%) japonensis Chitinophaga sp. 98.95 CspA2IP Phylum Microbial group Sample IAA Phosphate affiliation (A) Firmicutes Bacillus atrophaeus 2 + + (50%) Bacillus drentensis 2 + + Bacillus atrophaeus 1 + + Bacillus muralis 1 + - Bacillus sp. 1 + + Bacillus subtilis 1 + - Bacillus drentensis 2, 3 + - Paenibacillus sp. 1 - + Bacillus sp. 2 + - Bacillus drentensis 1 + - Proteobacteria Burkholderia graminis 1 + + (22.73%) Enterobacter hormaechei 1 + + Burkholderia graminis 2 + + Phyllobacterium sp. 1 + + Pseudomonas mohnii 3 + - Actinobacteria Streptomyces sp. 1 + + (18.18%) Streptomyces sp. 1 + + Streptomyces sp. 1 + - Streptomyces sp. 1 - + Bacteroidetes Chitinophaga 1 + + (9.09%) japonensis Chitinophaga sp. 2 + + Phylum Microbial group Siderophore Chitin affiliation Firmicutes Bacillus atrophaeus + - (50%) Bacillus drentensis + - Bacillus atrophaeus - + Bacillus muralis + - Bacillus sp. - - Bacillus subtilis - - Bacillus drentensis + - Paenibacillus sp. - + Bacillus sp. - - Bacillus drentensis - - Proteobacteria Burkholderia graminis + - (22.73%) Enterobacter hormaechei - - Burkholderia graminis - - Phyllobacterium sp. - - Pseudomonas mohnii - - Actinobacteria Streptomyces sp. + + (18.18%) Streptomyces sp. + - Streptomyces sp. + + Streptomyces sp. - + Bacteroidetes Chitinophaga - + (9.09%) japonensis Chitinophaga sp. - - (A) Tricalcium phosphate-solubilising bacteria. (B) Hydrogen cyanide producing bacteria. Table 4. Plant growth-promoting rhizobacteria common in both pine forest and agricultural soil The nomenclature of the strains is as follows: the first letter represents the bacterial genus, the second letter represents the bacterial species, or sp. (species not determined), 'A' or 'P' indicating that it was obtained from agricultural or pine forest soil respectively, a number (1,2 or 3) corresponding to the number of the sample and finally the plant growth-promoting traits (I, indole acetic acid; P, phosphate; S, siderophore; C, chitin) Phylum Microbial group Similarity Strain affiliation (%) Firmicutes Bacillus simplex 99.91 BsA2P2IS Bacillus sp. 98.95 BspA2PI1P Proteobacteria Pseudomonas mohnii 99.85 PmA1P1P2ISP Actinobacteria Rhodococcus sp. 99.12 RspA3P3IP Streptomyces sp. 99.00 SspA2A3PI1S Bacteroidetes Chitinophaga 100 CaA3P2IP arvensicola Phylum Microbial group Sample IAA affiliation Firmicutes Bacillus simplex A2, P2 + Bacillus sp. A2, P1 + Proteobacteria Pseudomonas mohnii Al, P1, P2 + Actinobacteria Rhodococcus sp. A3, P3 + Streptomyces sp. A2, A3, P1 + Bacteroidetes Chitinophaga A3, P2 + arvensicola Phylum Microbial group Phosphate Siderophore affiliation (A) Firmicutes Bacillus simplex - + Bacillus sp. + - Proteobacteria Pseudomonas mohnii + + Actinobacteria Rhodococcus sp. + - Streptomyces sp. - + Bacteroidetes Chitinophaga + - arvensicola (A) Tricalcium phosphate-solubilising bacteria.
|Printer friendly Cite/link Email Feedback|
|Author:||Flores-Nunez, Victor M.; Amora-Lazcano, Enriqueta; Rodriguez-Dorantes, Angelica; Cruz-Maya, Juan A.;|
|Date:||Jul 1, 2018|
|Previous Article:||Revisiting the wet and dry ends of soil integral water capacity using soil and plant properties.|
|Next Article:||Soil erosion analysis by RUSLE and sediment yield models using remote sensing and GIS in Kelantan state, Peninsular Malaysia.|