A novel strategy to extract specific phylogenetic sequence information from community T-RFLP

Cultivation-independent analyses of soil microbial community structures are frequently used to describe microbiological soil characteristics. Semi-automated terminal restriction fragment length polymorphism (T-RFLP) analyses yield high-resolution genetic profiles of highly diverse soil microbial communities and hold great potential for use in routine soil quality monitoring. A serious limitation of T-RFLP analyses has been the inability to reliably affiliate observed terminal restriction fragments (T-RF) to phylogenetic groups. In the study presented here, we were able to overcome this limitation of T-RFLP. With a combination of adapter ligation, fragment size selection, and re-amplification with adapter site specific PCR, we were able to isolate a T-RF-fraction of a narrow size-range containing a T-RF that was significantly more abundant in heavy metal amended soils. Cloning the size-selected T-RF fraction allowed for the efficient isolation of clones containing this specific T-RF. Sequence determination and phylogenetic inference in RDP-II affiliated the sequence to unclassified cyanobacteria. Specific primer design and PCR amplification from bulk soil DNA allowed for independent confirmation of the results from bacterial T-RFLP and T-RF cloning. Our results show that specific T-RFs can be efficiently isolated and identified, and that the adapter ligation approach holds great potential for genetic profiling and for identification of community components of interest.     

Community structure analyses are more sensitive to differences in soil bacterial communities than anonymous diversity indices

Changes in the diversity and structure of soil microbial communities may offer a key to understanding the impact of environmental factors on soil quality in agriculturally managed systems. Twenty-five years of biodynamic, bio-organic, or conventional management in the DOK long-term experiment in Switzerland significantly altered soil bacterial community structures, as assessed by terminal restriction fragment length polymorphism (T-RFLP) analysis. To evaluate these results, the relation between bacterial diversity and bacterial community structures and their discrimination potential were investigated by sequence and T-RFLP analyses of 1,904 bacterial 16S rRNA gene clones derived from the DOK soils. Standard anonymous diversity indices such as Shannon, Chao1, and ACE or rarefaction analysis did not allow detection of management-dependent influences on the soil bacterial community. Bacterial community structures determined by sequence and T-RFLP analyses of the three gene libraries substantiated changes previously observed by soil bacterial community level T-RFLP profiling. This supported the value of high-throughput monitoring tools such as T-RFLP analysis for assessment of differences in soil microbial communities. The gene library approach also allowed identification of potential management-specific indicator taxa, which were derived from nine different bacterial phyla. These results clearly demonstrate the advantages of community structure analyses over those based on anonymous diversity indices when analyzing complex soil microbial communities.     

Community Structure Analyses Are More Sensitive to Differences in Soil Bacterial Communities than Anonymous Diversity Indices

Changes in the diversity and structure of soil microbial communities may offer a key to understanding the impact of environmental factors on soil quality in agriculturally managed systems. Twenty-five years of biodynamic, bio-organic, or conventional management in the DOK long-term experiment in Switzerland significantly altered soil bacterial community structures, as assessed by terminal restriction fragment length polymorphism (T-RFLP) analysis. To evaluate these results, the relation between bacterial diversity and bacterial community structures and their discrimination potential were investigated by sequence and T-RFLP analyses of 1,904 bacterial 16S rRNA gene clones derived from the DOK soils. Standard anonymous diversity indices such as Shannon, Chao1, and ACE or rarefaction analysis did not allow detection of management-dependent influences on the soil bacterial community. Bacterial community structures determined by sequence and T-RFLP analyses of the three gene libraries substantiated changes previously observed by soil bacterial community level T-RFLP profiling. This supported the value of high-throughput monitoring tools such as T-RFLP analysis for assessment of differences in soil microbial communities. The gene library approach also allowed identification of potential management-specific indicator taxa, which were derived from nine different bacterial phyla. These results clearly demonstrate the advantages of community structure analyses over those based on anonymous diversity indices when analyzing complex soil microbial communities.     

Comparison of molecular fingerprinting methods for analysis of soil microbial community structure

Denaturing gradient gel electrophoresis (DGGE), terminal-restriction fragment length polymorphism (T-RFLP) analysis, and automated ribosomal intergenic spacer analysis (ARISA) have been widely used as molecular fingerprinting methods for analysis of microbial communities. To find suitable methods, we compared the three fingerprinting methods by analyzing soil fungal communities in four differing land-use types: bare ground, crop fields, grasslands, and forests. We also examined optimal primer pairs for DGGE analysis by comparing single and mixed DNA samples of cultured fungal populations. Principal coordinate analysis (PCO), nonmetric multidimensional scaling method (NMDS), and analysis of similarities (ANOSIM), which are major multivariate statistical analyses for quantifying fingerprint patterns, were compared. All three fingerprinting methods yielded clear discrimination of soil fungal communities among the four land-use types, irrespective of statistical methods. The advantages and disadvantages of the three fingerprinting methods were discussed.     

Use of the T-RFLP technique to assess spatial and temporal changes in the bacterial community structure within an agricultural soil planted with transgenic and non-transgenic potato plants

The aim of this study was to examine whether the terminal restriction fragment length polymorphism (T-RFLP) analysis represents an appropriate technique for monitoring highly diverse soil bacterial communities, i.e. to assess spatial and/or temporal effects on bacterial community structure. The T-RFLP method, a recently described fingerprinting technique, is based on terminal restriction fragment length polymorphisms between distinct small-subunit rRNA gene sequence types. This technique permits an automated quantification of the fluorescence signal intensities of the individual terminal restriction fragments (T-RFs) in a given community fingerprint pattern. The indigenous bacterial communities of three soil plots located within an agricultural field of 110 m(2) were compared. The first site was planted with non-transgenic potato plants, while the other two were planted with transgenic GUS and Barnase/Barstar potato plants, respectively. Once prior to planting and three times after planting, seven parallel samples were taken from each of the three soil plots. The T-RFLP analysis resulted in very complex but highly reproducible community fingerprint patterns. The percentage abundance values of defined T-RFs were calculated for the seven parallel samples of the respective soil plot. A multivariate analysis of variance was used to test T-RFLP data sets for significant differences. The statistical treatments clearly revealed spatial and temporal effects, as well as spacextime interaction effects, on the structural composition of the bacterial communities. T-RFs which showed the highest correlations to the discriminant factors were not those T-RFs which showed the largest single variations between the seven-sample means of individual plots. In summary, the T-RFLP technique, although a polymerase chain reaction-based method, proved to be a suitable technique for monitoring highly diverse soil microbial communities for changes over space and/or time.     

Changes in archaeal,bacterial and eukaryal assemblages along a salinity gradient by comparison of genetic fingerprinting methods in a multipond solar saltern

Microbial communities inhabiting a multipond solar saltern were analysed and compared using SSU rRNA polymerase chain reaction (PCR)-based fingerprintings carried out in parallel by four laboratories. A salinity gradient from seawater (3.7%) to NaCl precipitation (37%) was studied for Bacteria, Archaea and Eukarya, and laboratories applied their own techniques and protocols on the same set of samples. Members of all three domains were retrieved from all salt concentrations. Three fingerprinting techniques were used: denaturing gradient gel electrophoresis (DGGE), ribosomal internal spacer analysis (RISA), and terminal-restriction fragments length polymorphism (T-RFLP). In addition, each laboratory used its own biomass collection method and DNA extraction protocols. Prokaryotes were addressed using DGGE and RISA with different 'domain-specific' primers sets. Eukaryotes were analysed by one laboratory using DGGE and T-RFLP, but targeting the same 18S rDNA site. Fingerprints were compared through cluster analysis and non-metric multidimensional scaling plots. This exercise allowed fast comparison of microbial assemblages and determined to what extent the picture provided by each laboratory was similar to those of others. Formation of two main, salinity-based groups of samples in prokaryotes (4-15% and 22-37% salinity) was consistent for all the laboratories. When other clusters appeared, this was a result of the particular technique and the protocol used in each case, but more affected by the primers set used. Eukaryotic microorganisms changed more from pond to pond; 4-5% and 8-37% salinity were but the two main groups detected. Archaea showed the lowest number of bands whereas Eukarya showed the highest number of operational taxonomic units (OTUs) in the initial ponds. Artefacts appeared in the DGGE from ponds with extremely low microbial richness. On the other hand, different 16S rDNA fragments with the same restriction or internal transcribed spacer (ITS) length were the main limitations for T-RFLP and RISA analyses, respectively, in ponds with the highest OTUs richness. However, although the particular taxonomic composition could vary among protocols, the general structure of the microbial assemblages was maintained.     

Advances in the use of terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbial communities

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a popular high-throughput fingerprinting technique used to monitor changes in the structure and composition of microbial communities. This approach is widely used because it offers a compromise between the information gained and labor intensity. In this review, we discuss the progress made in T-RFLP analysis of 16S rRNA genes and functional genes over the last 10 years and evaluate the performance of this technique when used in conjunction with different statistical methods. Web-based tools designed to perform virtual polymerase chain reaction and restriction enzyme digests greatly facilitate the choice of primers and restriction enzymes for T-RFLP analysis. Significant improvements have also been made in the statistical analysis of T-RFLP profiles such as the introduction of objective procedures to distinguish between signal and noise, the alignment of T-RFLP peaks between profiles, and the use of multivariate statistical methods to detect changes in the structure and composition of microbial communities due to spatial and temporal variation or treatment effects. The progress made in T-RFLP analysis of 16S rRNA and genes allows researchers to make methodological and statistical choices appropriate for the hypotheses of their studies.     

Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study

The analysis of T-RFLP data has developed considerably over the last decade, but there remains a lack of consensus about which statistical analyses offer the best means for finding trends in these data. In this study, we empirically tested and theoretically compared ten diverse T-RFLP datasets derived from soil microbial communities using the more common ordination methods in the literature: principal component analysis (PCA), nonmetric multidimensional scaling (NMS) with Sørensen, Jaccard and Euclidean distance measures, correspondence analysis (CA), detrended correspondence analysis (DCA) and a technique new to T-RFLP data analysis, the Additive Main Effects and Multiplicative Interaction (AMMI) model. Our objectives were i) to determine the distribution of variation in T-RFLP datasets using analysis of variance (ANOVA), ii) to determine the more robust and informative multivariate ordination methods for analyzing T-RFLP data, and iii) to compare the methods based on theoretical considerations. For the 10 datasets examined in this study, ANOVA revealed that the variation from Environment main effects was always small, variation from T-RFs main effects was large, and variation from T-RF × Environment (T × E) interactions was intermediate. Larger variation due to T × E indicated larger differences in microbial communities between environments/treatments and thus demonstrated the utility of ANOVA to provide an objective assessment of community dissimilarity. The comparison of statistical methods typically yielded similar empirical results. AMMI, T-RF-centered PCA, and DCA were the most robust methods in terms of producing ordinations that consistently reached a consensus with other methods. In datasets with high sample heterogeneity, NMS analyses with Sørensen and Jaccard distance were the most sensitive for recovery of complex gradients. The theoretical comparison showed that some methods hold distinct advantages for T-RFLP analysis, such as estimations of variation captured, realistic or minimal assumptions about the data, reduced weight placed on rare T-RFs, and uniqueness of solutions. Our results lead us to recommend that method selection be guided by T-RFLP dataset complexity and the outlined theoretical criteria. Finally, we recommend using binary or relativized peak height data with soil-based T-RFLP data for ordination-based exploratory microbial analyses.     

Accessing the soil metagenome for studies of microbial diversity

Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome.     

土壤微生物群落多样性解析法:从培养到非培养

土壤微生物群落多样性是土壤微生物生态学和环境科学的重点研究内容之一.传统的土壤微生物群落多样性解析技术是指纯培养分离法(平板分离和形态分析法以及群落水平生理学指纹法).后来,研究者们建立了多样性评价较为客观的生物标记法(磷脂脂肪酸法和呼吸醌指纹法).随着土壤基因组提取技术和基因片段扩增(PCR)技术的发展,大量的现代分子生物学技术不断地涌现并极大地推动了土壤微生物群落多样性的研究进程.这些技术主要包括:G+C％含量、DNA复性动力学、核酸杂交法(FISH和DNA芯片技术)、土壤宏基因组学以及DNA指纹图谱技术等.综述了这些技术的基本原理、比较了各种技术的优缺点并且介绍了他们在土壤微生物群落多样性研究中的应用,展望了这些技术的发展方向.     

Application of a clustering-based peak alignment algorithm to analyze various DNA fingerprinting data

DNA fingerprinting analysis such as amplified ribosomal DNA restriction analysis (ARDRA), repetitive extragenic palindromic PCR (rep-PCR), ribosomal intergenic spacer analysis (RISA), and denaturing gradient gel electrophoresis (DGGE) are frequently used in various fields of microbiology. The major difficulty in DNA fingerprinting data analysis is the alignment of multiple peak sets. We report here an R program for a clustering-based peak alignment algorithm, and its application to analyze various DNA fingerprinting data, such as ARDRA, rep-PCR, RISA, and DGGE data. The results obtained by our clustering algorithm and by BioNumerics software showed high similarity. Since several R packages have been established to statistically analyze various biological data, the distance matrix obtained by our R program can be used for subsequent statistical analyses, some of which were not previously performed but are useful in DNA fingerprinting studies.     

Maintenance of soil functioning following erosion of microbial diversity

The paradigm that soil microbial communities, being very diverse, have high functional redundancy levels, so that erosion of microbial diversity is less important for ecosystem functioning than erosion of plant or animal diversity, is often taken for granted. However, this has only been demonstrated for decomposition/respiration functions, performed by a large proportion of the total microbial community, but not for specialized microbial groups. Here, we determined the impact of a decrease in soil microbial diversity on soil ecosystem processes using a removal approach, in which less abundant species were removed preferentially. This was achieved by inoculation of sterile soil microcosms with serial dilutions of a suspension obtained from the same non-sterile soil and subsequent incubation, to enable recovery of community size. The sensitivity to diversity erosion was evaluated for three microbial functional groups with known contrasting taxonomic diversities (ammonia oxidizers < denitrifiers < heterotrophs). Diversity erosion within each functional group was characterized using molecular fingerprinting techniques: ribosomal intergenic spacer analysis (RISA) for the eubacterial community, denaturing gradient gel electrophoresis (DGGE) analysis of nirK genes for denitrifiers, and DGGE analysis of 16S rRNA genes for betaproteobacterial ammonia oxidizers. In addition, we simulated the impact of the removal approach by dilution on the number of soil bacterial species remaining in the inoculum using values of abundance distribution of bacterial species reported in the literature. The reduction of the diversity of the functional groups observed from genetic fingerprints did not impair the associated functioning of these groups, i.e. carbon mineralization, denitrification and nitrification. This was remarkable, because the amplitude of diversity erosion generated by the dilution approach was huge (level of bacterial species loss was estimated to be around 99.99% for the highest dilution). Our results demonstrate that the vast diversity of the soil microbiota makes soil ecosystem functioning largely insensitive to biodiversity erosion even for functions performed by specialized groups.     

Linking the Physicochemical Properties with the Abundance and Diversity of Rhizospheric Bacterial Population Inhabiting Paddy Soil Based on a Concerted Multivariate Analysis of PCR-DGGE and RISA

To unravel the existence of dominant bacterial population in the paddy fields of Eastern Uttar Pradesh, India and their relation to the prevailing soil physicochemistry using multivariate statistical analyses, a cumulative culture-independent 16S rRNA based Polymerase chain reaction-Denaturing gradient gel electrophoresis (PCR-DGGE) and a 16S-23S ribosomal intergenic spacer analysis (RISA) have been performed. Detrended correspondence analysis (DCA) and principal component analysis (PCA) biplot analyses were used to assess the relation between soil bacterial population and its physicochemistry. DCA analysis exhibited a strong dependence of bacterial existence on the soil physicochemical variables, such as organic matter, total nitrogen, inorganic nutrients, temperatures, and moisture status. Soil dehydrogenase activity (DHA) was assessed to check the metabolic activity of all soil samples which showed a range of 0.012–0.050 nmol TPF g^?1 min^?1 with significant variation (p < 0.01). Out of 96 bands excised, 45 different phylotypes were obtained using both techniques which elucidated the abundance of Cyanobacteria over other soil bacterial population. Scytonema sp., Leptolyngbya sp. and different uncultured cyanobacterial species were the major genera found. Profiling data obtained through PCR-DGGE and RISA were used in alpha diversity and rarefaction curve analysis suggested site 6 (Chandauli) as the most diversity rich site. Thus extensive dataset of weighted and unweighted variables generated through DGGE and RISA coupled with metabolic functioning of soil and multivariate analyses provided an excellent opportunity to map the soil microbial structure in paddy fields and their regulation with existing soil environment.     

Evaluation of PCR amplification bias by terminal restriction fragment length polymorphism analysis of small-subunit rRNA and mcrA genes by using defined template mixtures of methanogenic pure cultures and soil DNA extracts

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a widely used method for profiling microbial community structure in different habitats by targeting small-subunit (SSU) rRNA and also functional marker genes. It is not known, however, whether relative gene frequencies of individual community members are adequately represented in post-PCR amplicon frequencies as shown by T-RFLP. In this study, precisely defined artificial template mixtures containing genomic DNA of four different methanogens in various ratios were prepared for subsequent T-RFLP analysis. PCR amplicons were generated from defined mixtures targeting not only the SSU rRNA but also the methyl-coenzyme M reductase (mcrA/mrtA) genes of methanogens. Relative amplicon frequencies of microorganisms were quantified by comparing fluorescence intensities of characteristic terminal restriction fragments. SSU ribosomal DNA (rDNA) template ratios in defined template mixtures of the four-membered community were recovered absolutely by PCR-T-RFLP analysis, which demonstrates that the T-RFLP analysis evaluated can give a quantitative view of the template pool. SSU rDNA-targeted T-RFLP analysis of a natural community was found to be highly reproducible, independent of PCR annealing temperature, and unaffected by increasing PCR cycle numbers. Ratios of mcrA-targeted T-RFLP analysis were biased, most likely by PCR selection due to the degeneracy of the primers used. Consequently, for microbial community analyses, each primer system used should be evaluated carefully for possible PCR bias. In fact, such bias can be detected by using T-RFLP analysis as a tool for the precise quantification of the PCR product pool.     

Evaluation of PCR Amplification Bias by Terminal Restriction Fragment Length Polymorphism Analysis of Small-Subunit rRNA and mcrA Genes by Using Defined Template Mixtures of Methanogenic Pure Cultures and Soil DNA Extracts

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a widely used method for profiling microbial community structure in different habitats by targeting small-subunit (SSU) rRNA and also functional marker genes. It is not known, however, whether relative gene frequencies of individual community members are adequately represented in post-PCR amplicon frequencies as shown by T-RFLP. In this study, precisely defined artificial template mixtures containing genomic DNA of four different methanogens in various ratios were prepared for subsequent T-RFLP analysis. PCR amplicons were generated from defined mixtures targeting not only the SSU rRNA but also the methyl-coenzyme M reductase (mcrA/mrtA) genes of methanogens. Relative amplicon frequencies of microorganisms were quantified by comparing fluorescence intensities of characteristic terminal restriction fragments. SSU ribosomal DNA (rDNA) template ratios in defined template mixtures of the four-membered community were recovered absolutely by PCR-T-RFLP analysis, which demonstrates that the T-RFLP analysis evaluated can give a quantitative view of the template pool. SSU rDNA-targeted T-RFLP analysis of a natural community was found to be highly reproducible, independent of PCR annealing temperature, and unaffected by increasing PCR cycle numbers. Ratios of mcrA-targeted T-RFLP analysis were biased, most likely by PCR selection due to the degeneracy of the primers used. Consequently, for microbial community analyses, each primer system used should be evaluated carefully for possible PCR bias. In fact, such bias can be detected by using T-RFLP analysis as a tool for the precise quantification of the PCR product pool.     

Variations in T-RFLP profiles with differing chemistries of fluorescent dyes used for labeling the PCR primers

Culture independent molecular methods have emerged as indispensable tools for studying microbial community structure and dynamics in natural habitats, since they allow a closer look at microbial diversity that is not reflected by culturing techniques. Terminal Restriction Fragment Length Polymorphism (T-RFLP) analysis is one of the informative and widely used techniques for such studies. However, the method has a few limitations to predict microbial community structure with significant accuracy. One of the major limitations is variation in real Terminal Restriction Fragment (TRF) length and observed TRF length. In the present study we report the generation of TRF length variations using different fluorescent dyes to label the PCR primers. T-RFLP profiles generated from primers labeled with different dyes varied significantly and led to inconsistent microbial species identification. Occurrence of such variations can have serious consequences on interpretation of the T-RFLP profiles from environmental samples representing complex microbial community. Therefore, in a T-RFLP study, the primers and labeling dye system should be carefully evaluated and optimized for an individual community under investigation. Further, it would be recommended to establish a target gene library in parallel with T-RFLP analysis to facilitate the accurate prediction of microbial community structure.     

Fingerprinting closely related xanthomonas pathovars with random nonamer oligonucleotide microarrays

Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.     

Microbial indices of soil fertility

AIMS: To find the new microbial parameters explaining the soil fertility from the microbial community viewpoint. METHODS AND RESULTS: Fatty acid methyl ester (FAME) analysis and terminal-restriction fragment length polymorphism (T-RFLP) analysis were carried out using 16 differently treated plots from the same field that had been kept under different fertilizer management systems since 1984. It was found that organic fertilizer application had small impact, whereas chemical fertilizer application, especially ammonium-nitrogen fertilizer, had strong impact on microbial community structures. Principal component analysis was conducted based on soil chemical and physical parameters, crop yields, FAMEs and terminal-restriction fragments (T-RFs) to provide 10 FAMEs and 10 T-RFs showing strong relation with soil fertility. CONCLUSION: We defined these 10 FAMEs and 10 T-RFs as 'keystone' biological parameters explaining soil fertility in the soil. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first report on the FAMEs and T-RFs related to soil fertility. Both analyses are rapid, inexpensive and reproducible means. As field assessment needs precise and rapid analysis, FAME and T-RFLP analyses and these new parameters are very useful to analyse soil fertility at biological viewpoint.     

Efficient DNA Fingerprinting Based on the Targeted Sequencing of Active Retrotransposon Insertion Sites Using a Bench-Top High-Throughput Sequencing Platform

In many crop species, DNA fingerprinting is required for the precise identification of cultivars to protect the rights of breeders. Many families of retrotransposons have multiple copies throughout the eukaryotic genome and their integrated copies are inherited genetically. Thus, their insertion polymorphisms among cultivars are useful for DNA fingerprinting. In this study, we conducted a DNA fingerprinting based on the insertion polymorphisms of active retrotransposon families (Rtsp-1 and LIb) in sweet potato. Using 38 cultivars, we identified 2,024 insertion sites in the two families with an Illumina MiSeq sequencing platform. Of these insertion sites, 91.4% appeared to be polymorphic among the cultivars and 376 cultivar-specific insertion sites were identified, which were converted directly into cultivar-specific sequence-characterized amplified region (SCAR) markers. A phylogenetic tree was constructed using these insertion sites, which corresponded well with known pedigree information, thereby indicating their suitability for genetic diversity studies. Thus, the genome-wide comparative analysis of active retrotransposon insertion sites using the bench-top MiSeq sequencing platform is highly effective for DNA fingerprinting without any requirement for whole genome sequence information. This approach may facilitate the development of practical polymerase chain reaction-based cultivar diagnostic system and could also be applied to the determination of genetic relationships.