Analyses of microbial properties in soil and manure had always included the problem that there was no available standard method to evaluate microbial property. The one of the major problems was the vast diversity and the enormous population of soil microorganisms [1], the other was an existence of numerically dominant unculturable microorganisms which comprise 99% of soil habitat [2]. We evaluated whether our newly developed method, by which taxonomies and their number of each bacterial groups were estimated, could be used as evaluation method of microbial properties of soils and manures. In the forest soil, β -Proteobacteria, which included Burkholderi a sp., Ralstonia sp., and Alcaligenes sp., was numerically dominant bacteria (3.64 × 10 6 MPN g -1 dry soil), followed by γ -Proteobacteria (1.32 × 10 6 MPN), δ -Proteobacteria (0.006 × 10 6 MPN), and the other gram negative bacteria (0.006 × 10 6 MPN). In the commercial manure, Actinobacteria, which included Streptoverticillium salmonis , Mycrococcus sp., Streptomyces bikiniensis , and Microbacterium ulmi , was numerically dominant bacterial group (30.8 × 10 6 MPN), followed by α -Proteobacteria (26.0 × 10 6 MPN), β -Proteobacteria (17.1 × 10 6 MPN), δ -Proteobacteria (11.2 × 10 6 MPN), the other Firmicutes (1.71 × 10 6 MPN), γ -Proteobacteria (0.5 × 10 6 MPN), and the other gram negative bacteria (0.05 × 10 6 MPN). In the upland field, the other Firmicutes, which included Paenibacillus sp., was numerically dominant bacteria (4.41 × 10 6 MPN), followed by Actinobacteria (2.14 × 10 6 MPN), Bacillus sp. (2.14 × 10 6 MPN), and γ -Proteobacteria (0.35 × 10 6 MPN). Although the precision of the affiliations became lower because of higher diversity of samples and the number of some Antinobacteria and Firmicutes might be underestimated by the used PCR condition, the method was found suitable as a candidate of a new evaluation system of soil and manure.
To establish sustainable agricultural system, by which crops and vegetables had been produced stably, maintaining soil fertility was primary important. In order to utilize the soil microorganisms effectively for soil management, property of soil microorganisms had to be evaluated as similarly as those of physical and chemical properties; while analyses of microbial properties in soil and manure had always included the problem that there was no available standard method to evaluate microbial property. The one of the major problems was the vast diversity and the enormous population of soil microorganisms [
Although denaturing gradient gel electrophoresis (DGGE), by which microbial flora could be analyzed without any culture steps [
Until now, we had found a new affiliation method of microorganisms based on restriction fragment polymorphism analysis, and developed a system and method by which bacterial affiliations could be completed systematically [
As the method also seemed suitable as simple evaluation method of microbial properties in soils and manures, we presented our evaluation results whether the method could be used as systematic analyses method of soils and manure in this manuscript.
Soil samples were obtained from surface of upland field (U; Gleysol) at Itoshima, Fukuoka, Japan, where vegetable had been cultivated under conventional field management, and from the surface horizon of forest soil (F; brown forest soil) at Wajiro-Hgashi, Fukuoka, Japan. Commercial manure (M) made from rice straw and cattle feces was used in this study. To test vials (5 replicates) including Biolog Universal Growth Medium (BUGM; BIOLOG Hayworth, CA, USA) broth [
Using the V2 forward primer (41f), and the V6 reverse primer (1066r) [
The newly constructed database was used for this research, which was edited using the method of Watanabe and Okuda [
As the reference MERFL database was edited from the homogeneous 16S rDNA sequences, the measured MERFL digested from the homogeneous 16S rDNA had to be used for phylogenetic estimation.
The major RFs, which had the highest relative mole concentration (ratio of fluorescent intensity to fragment size) and represented as H in
The pairwise distance (DAB) between the measured RFLP (A) and the theoretical RFLP (B) was calculated according to Nei and Li [
In phylogenetic estimation, identical theoretical MERFL (100%) was searched preferentially by using all the 4 measured MERFL data at first. When the completely identical theoretical MERFL was not found, combinations of 3 restriction enzymes were used for the next searches (
After differentiation of the measured MERFLs into 8 groups (A~J) based on the phylogenetic estimation. Numbers of each group were estimated by MPN for five-tube, three-decimal-dilution experiment (
Affiliations of fifty MERFLs were summarized in
Vial No.b | Restriction enzymesc | Similarity (%) | Name (Accession number)d | |
---|---|---|---|---|
A | M10−71M | R, Sc | 100 | Streptoverticillium salmonis (X53169) |
M10−74H | Ha, R, Hh | 90.5 | Micrococcus lylae (X80750), Agrococcus jenensis (AJ717350) | |
M10−75H | Ha, R, Hh | 95.2 | Streptomyces bikiniensis (AB208713) | |
M10−81H | Ha, Hh | 100 | Microbacterium ulmi (AY06021) | |
U10−51M | Ha, Hh | 90 | rubrobacteridae bacterium (AB245333) | |
U10−52M | R, Hh | 87.5 | Mycobacterium sp.S19 (AB355701), M. mucogenicum (AY457073) | |
U10−53L | R, Hh | 92.5 | uncultured Actinobactereria (AY921946) | |
U10−62M | R, Sc, Hh | 89 | Actinomadura pelletieri (AF163119), Microtetraspora pusilla (D85491), Excellospora viridulutea (D86943) | |
U10−64M | R, Hh | 93 | Corynebacterium genitalium (U87820) | |
U10−75H | Ha, R, Sc | 95 | Arthrobacter citeus (Arb.citrus) | |
B | U10−51H | Ha, R, Hh | 100 | B.cereus (AY907828) |
U10−54H | Ha, R, Hh | 100 | B. firmus (DQ173158), B. smithii (X60643), B. azotoformans (B.axzotofos) | |
U10−64H | R, Sc, Hh | 100 | B. fusiformis (L14013), B. spaericus (L15015) | |
U10−65H | R, Sc, Hh | 100 | ||
U10−75H | R, Sc, Hh | 89 | ||
C | M10−64H | Ha, R, Sc | 91.7 | Paenibacillus gluconolyticus (D78470) |
M10−72M | R, Sc | 92.7 | Eubacterium cylindoides (Eub.cylin2) | |
M10−73M | Sc, Hh | 82.9 | Staphylococcus arlettae (AB009933), S. cohnii (AB009936), S. delphini (AB009938), Macrococcus carouselicus (X15713) | |
U10−53M | Ha, Hh | 87.5 | Paenibacillus sp. (DQ112248), P. azotofixans (Pae.azofix), P. glucanolyticus (Pae.glulyt) | |
U10−55M | Ha, R, Hh | 84.1 | uncultured Clostridiaceae (AY684073, AY684096, AY684098) | |
U10−61H | Ha, R, Hh | 100 | Paenibacillus azoreducens (AJ27229), P. rhizoshaerae (AY751754), Paenibacillus sp. (B518; AY839866, 2S3; DQ243814) | |
U10−62H | Ha, R, Hh | 100 | Paenibacillus turicensis (AF378699), P. marquariensis (Pae.macqr), Paenibacillus sp. (CWBI-B; DQ112248, Tibet-IB15; DQ177465) | |
U10−65M | Ha, Hh | 100 | Weissella paramesenteroides (AB362621) | |
U10−71H | Ha, R, Hh | 100 | Paenibacillus pocheonensis (AB245386), P. ginsengarvi (AB271057) | |
U10−73H | Ha, R, Hh | 100 | Paenibacillus sp. (GT05-08; AM162296, YT0011; AB362822), P. agaridevorans (AJ345023, D84023) | |
U10−61M | Ha, Hh | 89 | uncultured gram positive bacteria (AY177762) | |
D | M10−71He | Ha, R | 92.9 | Agrobacterim sp. (AB006037) |
M10−74M | R, Hh | 90 | Sphingomonas sp. (BHC-A; AY973169, HI-D4; DQ205302), S. yanoikuyae (Spg.yano10), Blastomonas natatoria (X73043) | |
M10−83Hf | Ha, R, Sc | 90.5 | ||
M10−83Hf | R, Sc, Hh | 90.5 | Erythrobacter citreus (AF118020), Sphingomonas terrae (Spg.terrae) | |
M10−83M | Ha, R | 100 | Erlichia caffeensis (CP000236,U60476), E. ruminantium (CR925677, CR925678), E. ewingii (M73227) |
E | F10−55M | Ha, Hh | 82.9 | Burkholderia sp. (SFA1; AB232333, AK-5; AB103080) |
---|---|---|---|---|
F10−64H | Ha, R, Sc | 90.5 | Hydeogenophaga pseudoflave (AF078770), Streptoverticillium abikoense (X53168) | |
F10−64L | Ha, Hh | 80 | Burkholderia koreensis (AB201286), Halomonas venusta (L42618) | |
F10−65Hf | Sc, Hh | 92.9 | Alcaligenes latus (D88007), Dactylosporangium roseum (Dct.roseu2) | |
F10−65Hf | R, Hh | 92.9 | beta proteobacteria (AB076863) | |
F10−65Hf | Ha, Hh | 92.9 | Pandoraea sp. (AF247691, AF247696) | |
F10−71H | R, Sc, Hh | 95.2 | Ralstonia eutropha (AF027407), Burkholderia cepacia (Bur.cepaci), Streptomyces sp. (U93336, U93338), Streptverticillium baldaccii (X53164) | |
M10−72H | Ha, Rs, Hh | 91.7 | Alcaligenes ap. H (AJ412685) | |
M10−82H | Rs, Hh | 89 | ||
M10−84H | Ha, Rs, Hh | 91.7 | ||
F | F10−64M | Ha, Sc | 87.5 | Haemophilus haemolyticus (H.haemolyt), H. paraomfluenzae (H.parainfl), Pasteurella mairii (Pas.mair89, Pas.mairii) |
F10−72H | Ha, R, Hh | 100 | gamma proteobacterium FI1 (AY139001) | |
M10−73H | Ha, R, Hh | 100 | uncultured gamma proteobacteria (AJ318204) | |
U10−52Hf | R, Sc, Hh | 93.7 | Pseudomonas alcaligenes (D84006), P. fulva (D84015) | |
U10−52Hf | Ha, R, Hh | 93.7 | P. alcaligenes (D84006), P. putida (DQ229317), P. straminea (D84023) | |
U10−53H | Ha, R, Hh | 93.7 | P. aeruginosa (AY771716), Pseudomonas sp. (DY-A; AJ544239, SF1; AJ135269) | |
U10−55Hf | R, Sc, Hh | 93.3 | P. fulva (D84015) | |
U10−55Hf | Ha, R, Hh | 93.3 | Pseudomonas sp. (FP1-3; DQ118952, F25; DQ1275322, BWDY-5; DQ2008562, H; DQ205301) | |
U10−63H | Ha, R, Hh | 100 | Pseudomonas graminis (DQ59301), Pseudomonas sp. BWDY-29 (DQ200851) | |
G | F10−55H | R, Hh | 90 | Desulforegula conservatrix (AF243334), Emiliania huxleyi (AY741371) |
M10−71He | R, Sc | 92.9 | Desulfobacterrium cetonicum (AJ237603), Desulfosarcina variabilis (M34407), Desulfonega mgnum (U45989), Syntrophus buswellii (Syt.buswel) | |
M10−72M | R, Sc | 85.7 | Desulfovibrio fructosavorans (AF050101), Micrococcus luteus (AF057289), Pedomicrobium manganicum (X97691) | |
M10−84M | Sc, Hh | 83.7 | Chondromyces robustus (AJ233941) | |
H | F10−52H | Sc, R | 100 | Leptospira interrogans (Lps.interK) |
M10−64M | R, Hh | 87.5 | Kouleothrix aurantiace (AB079638, AB079639), Polyangium cellulosum (AF387627) |
aGrouping was based on affiliation by MERFL; Actinobacteria (Group A), Bacillus spp. (Group B), the other Firmicutes (Group C), α-Proteobacteria (Group D), β-Proteobacteria (Group E), γ-Proteobacteria (Group F), δ-Proteobacteria (Group G), and the other gram negative bacterial group (Group H); bThe 1st letter in vial indicates samples; “F” stands for the sample from forest soil, “M” stands for the sample from commercial manure, and “U” stands for the sample from upland field soil . Exponential of vial number represents the decimal dilution of the vial. The 2nd number of vial number (1 - 5) represents number in 5 replicates for the each decimal dilution. H of last letter represents MERFL originating from the major 16S rDNA, M represents from the 2nd major 16S rDNA, and L represents from the 3rd major 16S rDNA; cRestriction enzymes used for similarity search; “Ha”, “R”, “Sc”, and “Hh” stand for Hae III, Rsa I, Scr F1, and Hha I. For the measured MERFLP which had no completely identical theoretical MERFLP, the theoretical MERFLP having the highest similarity using all the RFLPs was presented with the similarity as described in the materials and method; dSpecies name (accession number) of the theoretical MERFL having the highest similarity with the measured MERFL; eAdditional name (accession number) of the theoretical MERFL using the different restriction enzymes; fDifferent accession number of the theoretical MERFL in the same group using the different restriction enzymes.
a | Forest soil | Manure | Upland field | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Three dilutions | Score | ×106 MPN g−1 dry soil | 5% limits | Three dilutions | Score | ×106 MPN g−1 dry soil | 5% limits | Three dilutions | Score | ×106 MPN g−1 dry soil | 5% limits | |
Low/High | Low/High | Low/High | ||||||||||
A | 10−710−810−9 | 3-1-0 | 30.8 | 9.79/72.7 | 10−610−710−8 | 2-1-0 | 2.14 | 0.57/5.35 | ||||
B | 10−610−710−8 | 2-1-0 | 2.14 | 0.57/5.35 | ||||||||
C | 10−610−710−8 | 1-2-0 | 1.71 | 0.5/4.2 | 10−610−710−8 | 3-2-0 | 4.41 | 1.79/11.3 | ||||
D | 10−710−810−9 | 2-2-0 | 26.0 | 9.51/61.5 | ||||||||
E | 10−610−710−8 | 3-1-0 | 3.64 | 1.16/6.94 | 10−710−810−9 | 1-2-0 | 17.1 | 5.03/42 | ||||
F | 10−610−710−8 | 1-1-0 | 1.32 | 0.23/3.97 | 10−610−710−8 | 0-1-0 | 0.5 | 0.03/1.93 | 10−510−610−7 | 3-1-0 | 0.35 | 0.11/0.66 |
G | 10−410−510−6 | 0-1-0 | 0.006 | 0.0003/0.023 | 10−710−810−9 | 1-1-0 | 11.2 | 1.96/33.6 | ||||
H | 10−410−510−6 | 0-1-0 | 0.006 | 0.0003/0.023 | 10−510−610−7 | 0-1-0 | 0.05 | 0.003/0.19 | ||||
b | 10−610−710−8 | 2-2-0 | 3.07 | 1.12/7.27 | 10−710−810−9 | 5-4-0 | 364 | 101/1119 | 10−810−910−10 | 5-4-0 | 4048 | 1132/12579 |
aGroups: A: B. cereus, B: Bacillus spp., C: Clostridium, D: The other Fumicutes, E: Actinobacteria, F: Proteobacteria, G: Prevotella, H: Cytophagales, I: Gram negative bacteria; bTotal number of bacteria.
The precision of the affiliations of each MERFLs was lower than that of the former studies. With respect to the major MERFL, ratio of the MERFLs with 100% similarity to the corresponding theoretical MERFLs (43.3%) was lower than that of field soils using selective medium (90.5%) [
There was a large difference in microbial properties among the three samples as the followings. In the forest soil (F), Group E, which included Burkholderia sp., Ralstonia sp., and Alcaligenes sp., was numerically dominant bacterial group (3.64 × 106 MPN g−1 dry soil), followed by Group F (1.32 × 106 MPN g−1), Group G (0.006 × 106 MPN g−1), and Group H (0.006 × 106 MPN g−1) (
In the commercial manure (M), Group A, which included Streptoverticillium salmonis, Mycrococcus sp., Streptomyces bikiniensis, and Microbacterium ulmi, was numerically dominant bacterial group (30.8 × 106 MPN g−1), followed by Group D (26.0 × 106 MPN g−1), which included Agrobacterim sp., Sphingomonas sp., Erythrobacter citreus and Erlichia sp., Group E (17.1 × 106 MPN g−1), which included Alcaligenes sp., and Ralstonia sp., Group G (11.2 × 106 MPN g−1), which included various sulfate reducing bacteria and Chondromyces robustus, Group C (1.71 × 106 MPN g−1), which included Paenibacillus gluconolyticus, Eubacterium cylindoides, and Staphylococcus sp., Group F (0.5 × 106 MPN g−1), and the Group H (0.05 × 106 MPN g−1) (
The microbial property of M was different from those of the manures during composting in the former paper as the followings [
In the upland field (U), Group C, which included Paenibacillus sp., and Weissella paramesenteroides, was numerically dominant bacterial group (4.41 × 106 MPN g−1), followed by Group A (2.14 × 106 MPN g−1), which
included Mycobacterium sp., Corynebacterium genitalium, and Arthrobacter citeus, Group B (2.14 × 106 MPN g−1), which included B.cereus, B.fusiformis/B.spaericus and B.firmus/B.smithii/B.azotoformans, and Group F (0.35 × 106 MPN g−1), which included Pseudomonas sp. (
There was a difference between the total bacterial number estimated by MPN using all the amplified vials and those of the sum of the each bacterial MPN (
In this method PCR inhibiting substances included in manure and soils had no serious effect on the results in spite of the used DNA extraction method, which included no extra purification step, because the effect of humic substances was decreased by using DNA extracted after proliferation in the growth medium, especially in higher decimal dilution vials of MPN, where the numerically dominant microorganisms were detected, concentration of the inhibiting substance was minimized. Only in forest soil, the inhibiting substance might cause the under estimation of some microbial group, because amplification band was observed until under 10−7 dilution vials and PCR inhibition was observed until 10−5 ~ 10−6 dilution vails, which afforded 6 positive vials. The under estimation might be avoided by using the conventional extraction method for environmental DNA, which included purification step.
Although the present method was culture based method, which eliminated unculturable microorganisms, we thought that the method was suitable as evaluation system of soil and manure in aim to maintain soil fertility. Because one of the unculturable microorganism, which couldn’t proliferate without the other microorganisms, was detected by this method [
Classification and affiliation in species or genus level was possible by this method [
As the system required lower cost for instrument and running and RFLP data was automatically obtained by MultiNA, the method was suitable as evaluation system of soil and manure. Although some data processing was manually processed at this moment, the method was the versatile system used not only as evaluation system of environmental microorganisms, but also inspection method of food microorganisms (unpublished results). Compared to the next-generation method such as pyro-sequencing, reliable affiliations of all the bacteria might be difficult by our method, our method might not be suitable for pure research purpose, but suitable as inspection method due to its lower running cost and simplicity. A difference of the results obtained by this culture- based technique and by the unculture-based technique, such as DGGE, will be presented in the next manuscripts.
We thank Mr. Y. Sogabe, Global Application Center, Shimadzu Co., for variable suggestion and support for MultiNA. We thank Dr. H. Yosikawa, the former Prof. of Fukuoka Institute of Technology, Dr. A. Hosoda, and Prof. H. Tamura, Meijyo University, for their encouragements during this work.
NaotoHorinishi,KunimasaMatsumoto,KatsujiWatanabe, (2016) A Simple Evaluation System for Microbial Property in Soil and Manure. Advances in Microbiology,06,88-97. doi: 10.4236/aim.2016.62009