Communities In Environmental Samples Research Articles

Evaluation of different primers of the 18S rRNA gene to profile amoeba communities in environmental samples

Amoeboid protists, an assemblage of organisms belonging to different phylogenetic lineages, have drawn increasing attention due to their crucial ecological roles in various environments and their potential health risks. Currently, 18S rRNA gene sequencing is widely applied for the detection of amoebae. However, it is not clear which is the best primer pair for 18S rRNA gene amplification in amoebae. This study compared the four most commonly used primer pairs for revealing the diversity, composition, core species, and community assembly processes of amoebae in water and sediments. We found that the choice of primers artificially influences the detection of community composition of amoebae. We also found that short-read fragments may lead to mismatches in taxonomy and were not suitable for phylogenetic analyses. In contrast, full-length primers could detect the highest number of amoeba lineages and annotate 80% of reads belonging to amoebae to known species. However, full-length primers did not detect as many amoeba species as V4 primers. Moreover, we showed that beta diversity and community assembly determination were largely unaffected by primer choice, but different primers could influence our interpretations of the ecological process underlying stochasticity and determinism. This study indicates that full-length read sequencing and V4 region Illumina sequencing are suitable for profiling amoeba diversity in the environment.

HiLi-chip: A high-throughput library construction chip for comprehensive profiling of environmental microbial communities

Investigating the contribution and associations of environmental microbes to ecological health and human well-being is in great demand with the goal of One Health proposed. To achieve the goal, there is an urgent need for accurate approaches to obtaining a large amount of high-resolution molecular information from various microbes. In this study, we developed a high-throughput library construction chip (HiLi-Chip) for profiling environmental microbial communities and evaluated its performance. The HiLi-Chip showed high conformity with the conventional Pacbio method in terms of α-diversity, community composition of abundant bacteria (>83%), as well as rare taxa (>84%) and human pathogens detection (>67%), indicating its advantages of accuracy, high-throughput, cost-efficiency, and broad practicability. It is suggested that the optimal strategy of the HiLi-Chip was a 2.4 μL PCR mixture per sample (∼2.4 ng DNA) with a 216-sample × 24-replicate format. We have successfully applied the HiLi-Chip to the Jiulongjiang River and identified 51 potential human bacterial pathogens with a total relative abundance of 0.22%. Additionally, under limited nutrients and similar upstream environments, bacteria tended to impose competitive pressures, resulting in a more connected network at the downstream river confluence (RC). Whereas narrow niche breadth of bacteria and upstream environmental heterogeneity probably promoted niche complementary and environment selection leading to fewer links at RC in the midsection of the river. Core bacteria might represent the entire bacterial community and enhance network stability through synergistic interactions with other core bacteria. Collectively, our results demonstrate that the HiLi-Chip is a robust tool for rapid comprehensive profiling of microbial communities in environmental samples and has significant implications for a profound understanding of environmental microbial interactions.

Use and abuse of potential rates in soil microbiology

Potential rate assays are used in soil microbial ecology to determine the rates of a functional process in environmental samples under a defined set of conditions. While they can be used appropriately to provide mechanistic insights, potential rates are also often used to estimate the abundance of specific taxonomic groups and their in situ activity. These estimates incorrectly assume that all contributing organisms in a community are active at a maximum rate under one set of ‘optimal’ incubation conditions and that potential rates reflect activity in the soil. While investigators now recognise that populations within communities are physiologically diverse, they often ignore the consequent suboptimal activity, or even inactivity, of the majority of community members performing that function. In this short perspective article, we discuss when potential assays can be informative and highlight the underlying conceptual problems under circumstances where potential assays are misused, using potential nitrification rate (PNR) as an example. PNR was originally developed to estimate the size of active ammonia oxidising communities in environmental samples. It is routinely determined in short-term shaken slurry incubations by measuring assumed maximum rates of nitrate or nitrite production under optimal, non-substrate-limiting conditions. As with other functional processes, it is now recognised that a broad diversity of organisms contribute to aerobic ammonia oxidation in terrestrial and other habitats, and this diversity represents a substantial range of physiologies, including variation in substrate affinity, ammonia tolerance, cell specific activity and substrate preference. Despite this, PNR, and other potential rate assays, are often inappropriately used in an attempt to determine an ecologically relevant measurement of activity in soil. As with any potential assay, PNR has inherent biases towards particular functional groups and its use in investigating the ecology of ammonia oxidisers in natural systems should be carefully considered.

User-friendly bioinformatics pipeline gDAT (graphical downstream analysis tool) for analysing rDNA sequences.

High-throughput sequencing (HTS) of multiple organisms in parallel (metabarcoding) has become a routine and cost-effective method for the analysis of microbial communities in environmental samples. However, careful data treatment is required to identify potential errors in HTS data, and the large volume of data generated by HTS requires in-house experience with command line tools for downstream analysis. This paper introduces a pipeline that incorporates the most common command line tools into an easy-to-use graphical interface-gDAT. By using the Python scripting language, the pipeline is compatible with the latest Windows, macOS and Linux operating systems. The pipeline supports analysis of Sanger, 454, IonTorrent, Illumina and PacBio sequences, allows custom modification of quality filtering steps, and implements both open and closed-reference operational taxonomic unit-picking for sequence identification. Predefined parameters are optimized for analysis of small subunit (SSU) rRNA gene amplicons from arbuscular mycorrhizal fungi, but the pipeline is widely applicable to metabarcoding studies targeting a broad range of organisms. The pipeline was additionally tested with data using general eukaryotic primers from the SSU gene region and fungal primers from the internal transcribed spacer (ITS) marker region. We describe the pipeline design and evaluate its performance and speed by conducting analysis of example data sets using different marker regions sequenced on Illumina platforms. The graphical interface, with the option to use the command line if needed, provides an accessible tool for rapid data analysis with repeatability and logging capabilities. Keeping the software open-source maximizes code accessibility, allowing scrutiny and bug fixes by the community.

Lab-on-a-Chip Technology for Environmental Monitoring of Microorganisms

Improved environmental monitoring of microorganisms is currently an important issue, since traditional detection methods are costly and time consuming (often requiring 2–3 days). In contrast, microfluidic chip-based technologies provide promising alternatives. The presence of environmental contaminants, especially bacterial, viral, and parasitic pathogens and their toxins, is creating a major public health problem. The detection of microbial communities in environmental samples is the first step to recognizing these organisms and insuring health and safety against their infection. This review addresses both the existing and innovative technologies for the detection of pathogens present in the environmental samples. First, impacts and challenges related to pathogens in the environmental samples are discussed, and standard techniques for their identification are addressed. Next, the microfluidic technologies are introduced as an alternative strategy with a particular focus on the detection of environmental contaminants. Challenges that still need to be overcome are also discussed. The review concludes with a section on paper-based microfluidic device and a discussion on future prospects.

Comparison of Database Search Methods for the Detection of Legionella pneumophila in Water Samples Using Metagenomic Analysis.

Metagenomic analysis has become a powerful tool to analyze bacterial communities in environmental samples. However, the detection of a specific bacterial species using metagenomic analysis remains difficult due to false positive detections of sequences shared between different bacterial species. In this study, 16S rRNA amplicon and shotgun metagenomic analyses were conducted on samples collected along a stream and ponds in the campus of Hokkaido University. We compared different database search methods for bacterial detection by focusing on Legionella pneumophila. In this study, we used L. pneumophila-specific nested PCR as a gold standard to evaluate the results of the metagenomic analysis. Comparison with the results from L. pneumophila-specific nested PCR indicated that a blastn search of shotgun reads against the NCBI-NT database led to false positive results and had problems with specificity. We also found that a blastn search of shotgun reads against a database of the catalase-peroxidase (katB) gene detected L. pneumophila with the highest area under the receiver operating characteristic curve among the tested search methods; indicating that a blastn search against the katB gene database had better diagnostic ability than searches against other databases. Our results suggest that sequence searches targeting long genes specifically associated with the bacterial species of interest is a prerequisite to detecting the bacterial species in environmental samples using metagenomic analyses.

Culture-independent analysis of hydrocarbonoclastic bacterial communities in environmental samples during oil-bioremediation.

To analyze microbial communities in environmental samples, this study combined Denaturing Gradient Gel Electrophoresis of amplified 16S rRNA‐genes in total genomic DNA extracts from those samples with gene sequencing. The environmental samples studied were oily seawater and soil samples, that had been bioaugmented with natural materials rich in hydrocarbonoclastic bacteria. This molecular approach revealed much more diverse bacterial taxa than the culture‐dependent method we had used in an earlier study for the analysis of the same samples. The study described the dynamics of bacterial communities during bioremediation. The main limitation associated with this molecular approach, namely of not distinguishing hydrocarbonoclastic taxa from others, was overcome by consulting the literature for the hydrocarbonoclastic potential of taxa related to those identified in this study. By doing so, it was concluded that the hydrocarbonoclastic bacterial taxa were much more diverse than those captured by the culture‐dependent approach. The molecular analysis also revealed the frequent occurrence of nifH‐genes in the total genomic DNA extracts of all the studied environmental samples, which reflects a nitrogen‐fixation potential. Nitrogen fertilization is long known to enhance microbial oil‐bioremediation. The study revealed that bioaugmentation using plant rhizospheres or soil with long history of oil‐pollution was more effective in oil‐removal in the desert soil than in seawater microcosms.

Indirect DNA extraction method suitable for acidic soil with high clay content

DNA extraction is an essential procedure when investigating microbial communities in environmental samples by sequencing technologies. High clay soils can be problematic as DNA adsorbs to the clay particles and can thereby be preserved from lysed, non-viable cells for a substantial period of time. In order to accurately estimate the intact and living microbial community in the soil, extracellular DNA from dead, remnant bacterial cells needs to be removed prior to DNA extraction. One possibility is to use a sodium phosphate buffer to release both extracellular DNA and bacterial cells from the clay particles. After removing the extracellular DNA by centrifugation, the remaining viable cells can be harvested and DNA extracted. The described method is a modification of a procedure for separating extracellular DNA and bacterial cells from acidic clay soils.•The modified method increases bacterial cell yields from acidic clay soils, such as acid sulfate soil.•The modified method eliminates some steps from the original method, as only DNA from intact bacterial cells is required.•The indirect DNA extraction method increases the workload compared to standard direct extraction methods, but subsequent downstream analyses will give a more representative picture of the viable microbial community composition in the soil.

Barcoded NS31/AML2 primers for sequencing of arbuscular mycorrhizal communities in environmental samples.

Premise of the study:Arbuscular mycorrhizal fungi (AMF) are globally important root symbioses that enhance plant growth and nutrition and influence ecosystem structure and function. To better characterize levels of AMF diversity relevant to ecosystem function, deeper sequencing depth in environmental samples is needed. In this study, Illumina barcoded primers and a bioinformatics pipeline were developed and applied to study AMF diversity and community structure in environmental samples.Methods:Libraries of small subunit ribosomal RNA fragment amplicons were amplified from environmental DNA using a single-step PCR reaction with barcoded NS31/AML2 primers. Amplicons were sequenced on an Illumina MiSeq sequencer using version 2, 2 × 250-bp paired-end chemistry, and analyzed using QIIME and RDP Classifier.Results:Sequencing captured 196 to 6416 operational taxonomic units (OTUs; depending on clustering parameters) representing nine AMF genera. Regardless of clustering parameters, ∼20 OTUs dominated AMF communities (78–87% reads) with the remaining reads distributed among other OTUs. Analyses also showed significant biogeographic differences in AMF communities and that community composition could be linked to specific edaphic factors.Discussion:Barcoded NS31/AML2 primers and Illumina MiSeq sequencing provide a powerful approach to address AMF diversity and variations in fungal assemblages across host plants, ecosystems, and responses to environmental drivers including global change.

An artificial intelligence approach fit for tRNA gene studies in the era of big sequence data.

Unsupervised data mining capable of extracting a wide range of knowledge from big data without prior knowledge or particular models is a timely application in the era of big sequence data accumulation in genome research. By handling oligonucleotide compositions as high-dimensional data, we have previously modified the conventional self-organizing map (SOM) for genome informatics and established BLSOM, which can analyze more than ten million sequences simultaneously. Here, we develop BLSOM specialized for tRNA genes (tDNAs) that can cluster (self-organize) more than one million microbial tDNAs according to their cognate amino acid solely depending on tetra- and pentanucleotide compositions. This unsupervised clustering can reveal combinatorial oligonucleotide motifs that are responsible for the amino acid-dependent clustering, as well as other functionally and structurally important consensus motifs, which have been evolutionarily conserved. BLSOM is also useful for identifying tDNAs as phylogenetic markers for special phylotypes. When we constructed BLSOM with 'species-unknown' tDNAs from metagenomic sequences plus 'species-known' microbial tDNAs, a large portion of metagenomic tDNAs self-organized with species-known tDNAs, yielding information on microbial communities in environmental samples. BLSOM can also enhance accuracy in the tDNA database obtained from big sequence data. This unsupervised data mining should become important for studying numerous functionally unclear RNAs obtained from a wide range of organisms.

MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression.

BackgroundMetagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and hence tend to result in large file sizes, typically ranging between 1–10 GB. This leads to challenges in analyzing, transferring and storing metagenomic data. In order to overcome these data processing issues, we introduce MetaCRAM, the first de novo, parallelized software suite specialized for FASTA and FASTQ format metagenomic read processing and lossless compression.ResultsMetaCRAM integrates algorithms for taxonomy identification and assembly, and introduces parallel execution methods; furthermore, it enables genome reference selection and CRAM based compression. MetaCRAM also uses novel reference-based compression methods designed through extensive studies of integer compression techniques and through fitting of empirical distributions of metagenomic read-reference positions. MetaCRAM is a lossless method compatible with standard CRAM formats, and it allows for fast selection of relevant files in the compressed domain via maintenance of taxonomy information. The performance of MetaCRAM as a stand-alone compression platform was evaluated on various metagenomic samples from the NCBI Sequence Read Archive, suggesting 2- to 4-fold compression ratio improvements compared to gzip. On average, the compressed file sizes were 2-13 percent of the original raw metagenomic file sizes.ConclusionsWe described the first architecture for reference-based, lossless compression of metagenomic data. The compression scheme proposed offers significantly improved compression ratios as compared to off-the-shelf methods such as zip programs. Furthermore, it enables running different components in parallel and it provides the user with taxonomic and assembly information generated during execution of the compression pipeline.AvailabilityThe MetaCRAM software is freely available at http://web.engr.illinois.edu/~mkim158/metacram.html. The website also contains a README file and other relevant instructions for running the code. Note that to run the code one needs a minimum of 16 GB of RAM. In addition, virtual box is set up on a 4GB RAM machine for users to run a simple demonstration.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0932-x) contains supplementary material, which is available to authorized users.

Phospholipid fatty acid profiling of microbial communities--a review of interpretations and recent applications.

Profiling of microbial communities in environmental samples often utilizes phospholipid fatty acid (PLFA) analysis. This method has been used for more than 35years and is still popular as a means to characterize microbial communities in a diverse range of environmental matrices. This review examines the various recent applications of PLFA analysis in environmental studies with specific reference to the interpretation of the PLFA results. It is evident that interpretations of PLFA results do not always correlate between different investigations. These discrepancies in interpretation and their subsequent applications to environmental studies are discussed. However, in spite of limitations to the manner in which PLFA data are applied, the approach remains one with great potential for improving our understanding of the relationship between microbial populations and the environment. This review highlights the caveats and provides suggestions towards the practicable application of PLFA data interpretation.

Influence of commonly used primer systems on automated ribosomal intergenic spacer analysis of bacterial communities in environmental samples.

Due to the high diversity of bacteria in many ecosystems, their slow generation times, specific but mostly unknown nutrient requirements and syntrophic interactions, isolation based approaches in microbial ecology mostly fail to describe microbial community structure. Thus, cultivation independent techniques, which rely on directly extracted nucleic acids from the environment, are a well-used alternative. For example, bacterial automated ribosomal intergenic spacer analysis (B-ARISA) is one of the widely used methods for fingerprinting bacterial communities after PCR-based amplification of selected regions of the operon coding for rRNA genes using community DNA. However, B-ARISA alone does not provide any taxonomic information and the results may be severely biased in relation to the primer set selection. Furthermore, amplified DNA stemming from mitochondrial or chloroplast templates might strongly bias the obtained fingerprints. In this study, we determined the applicability of three different B-ARISA primer sets to the study of bacterial communities. The results from in silico analysis harnessing publicly available sequence databases showed that all three primer sets tested are specific to bacteria but only two primers sets assure high bacterial taxa coverage (1406f/23Sr and ITSF/ITSReub). Considering the study of bacteria in a plant interface, the primer set ITSF/ITSReub was found to amplify (in silico) sequences of some important crop species such as Sorghum bicolor and Zea mays. Bacterial genera and plant species potentially amplified by different primer sets are given. These data were confirmed when DNA extracted from soil and plant samples were analyzed. The presented information could be useful when interpreting existing B-ARISA results and planning B-ARISA experiments, especially when plant DNA can be expected.

Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies.

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.

Impact of short-term storage temperature on determination of microbial community composition and abundance in aerated forest soil and anoxic pond sediment samples

Sampling strategy is important for unbiased analysis of the characteristics of microbial communities in the environment. During field work it is not always possible to analyze fresh samples immediately or store them frozen. Therefore, the effect of short-term storage temperature was investigated on the abundance and composition of bacterial, archaeal and denitrifying communities in environmental samples from two different sampling sites. Oxic forest soil and anoxic pond sediment were investigated by measuring microbial abundance (DNA) and transcriptional activity (RNA). Prior to investigating the effect of storage temperature, samples were immediately analyzed, in order to represent the original situation in the habitat. The effect of storage temperature was then determined after 11 days at different low temperatures (room temperature, 4°C, −22°C and −80°C). Community profiling using terminal restriction fragment length polymorphism (T-RFLP) showed no significant differences between the immediately analyzed reference sample and the samples stored at different incubation temperatures, both for DNA and RNA extracts. The abundance of microbial communities was determined using quantitative PCR and it also revealed a stable community size at all temperatures tested. By contrast, incubation at an elevated temperature (37°C) resulted in changed bacterial community composition. In conclusion, short-term storage, even at room temperature, did not affect microbial community composition, abundance and transcriptional activity in aerated forest soil and anoxic pond sediment.

Standardizing methylation method during phospholipid fatty acid analysis to profile soil microbial communities

Phospholipid fatty acid (PLFA) as biomarkers, is widely used to profile microbial communities in environmental samples. However, PLFA extraction and derivatization protocols are not standardized and have widely varied among published studies. Specifically investigators have used either HCl/MeOH or KOH/MeOH or both for the methylation step of PLFA analysis, without justification or research to support either one. It seems likely that each method could have very different outcomes and conclusions for PLFA based studies. Therefore, the objective of this study was to determine the effect of catalyst type for methylation on detecting PLFAs and implications for interpreting microbial profiling in soil. Fatty acid samples extracted from soils obtained from a wetland, an intermittently flooded site, and an adjacent upland site were subjected to HCl/MeOH or KOH/MeOH catalyzed methylation procedures during PLFA analyses. The methylation method using HCl/MeOH resulted in significantly higher concentrations of most PLFAs than the KOH/MeOH method. Another important outcome was that fatty acids with a methyl group (18:1ω,7c 11Me, TBSA 10Me 18:0, 10Me 18:0, 17:0 10Me and 16:0 10Me being an actinomycetes biomarker) could not be detected by HCl/MeOH catalyzed methylation but were found in appreciable concentrations with KOH/MeOH method. From our results, because the HCl/MeOH method did not detect the fatty acids containing methyl groups that could strongly influence the microbial community profile, we recommend that the KOH/MeOH catalyzed transesterification method should become the standard procedure for PLFA profiling of soil microbial communities.

A Transcriptome-Targeting EcoChip for Assessing Functional Mycodiversity.

A functional biodiversity microarray (EcoChip) prototype has been developed to facilitate the analysis of fungal communities in environmental samples with broad functional and phylogenetic coverage and to enable the incorporation of nucleic acid sequence data as they become available from large-scale (next generation) sequencing projects. A dual probe set (DPS) was designed to detect a) functional enzyme transcripts at conserved protein sites and b) phylogenetic barcoding transcripts at ITS regions present in precursor rRNA. Deviating from the concept of GeoChip-type microarrays, the presented EcoChip microarray phylogenetic information was obtained using a dedicated set of barcoding microarray probes, whereas functional gene expression was analyzed by conserved domain-specific probes. By unlinking these two target groups, the shortage of broad sequence information of functional enzyme-coding genes in environmental communities became less important. The novel EcoChip microarray could be successfully applied to identify specific degradation activities in environmental samples at considerably high phylogenetic resolution. Reproducible and unbiased microarray signals could be obtained with chemically labeled total RNA preparations, thus avoiding the use of enzymatic labeling steps. ITS precursor rRNA was detected for the first time in a microarray experiment, which confirms the applicability of the EcoChip concept to selectively quantify the transcriptionally active part of fungal communities at high phylogenetic resolution. In addition, the chosen microarray platform facilitates the conducting of experiments with high sample throughput in almost any molecular biology laboratory.

The New Science of Metagenomics: Fourth Domain of Life

Metagenomics, signifying ‘genomics on a huge scale’ is a powerful approach that enables a massive assessment of the plethora of microorganisms present in the environment. Metagenomics is based on the genomic analysis of microbial DNA that is extracted directly from communities in environmental samples. It is a new approach that has revolutionized our understanding of microbial life present on Earth. The application of metagenomic analysis has accelerated the rapid rate of advancement in the study of uncultured microbes that began with the advent of rRNA analysis. It has now enabled the phylogenetic characterization of many entire communities that were otherwise difficult to analyze. In the coming years, metagenomic approaches are expected to become a powerful tool that will help developing alternate strategies for harnessing the power of microbial communities for more sustainable and environmentally friendly, biologically based energy sources, environmental remediation, agricultural biodefense and forensic sciences. Metagenomics could also unlock the massive uncultured microbial diversity present in the environment to provide new molecules for therapeutic and biotechnological applications. While there seem to be several benefits in the offerings from this approach, the analysis of metadata has raised a new debate about the possibility of a fourth domain of life. The current concept based on the rRNA gene sequences only predicts three domains of life, which are eukarya, eubacteria and arachaea. The metagenomic data generated by Craig Venter from the Sargasso Sea on the round the world yatching trip in 2005 [1] has been recently re-analyzed by Prof. Jonathan Eisen, an evolutionary biologist at the University of California [2]. The J. Craig Venter Institute’s Global Ocean Sampling Expedition revealed an enormous amount of previously unexplored diversity in the world’s oceans. Prof. Eisen primarily used two genes recA and rpoB, both of which are old and conserved throughout the living kingdom. These two genes predict a different lineage also called the fourth domain of life. His team designed and implemented new methods for analyzing metagenomic data and used them to search the Global Ocean Sampling Expedition data set for novel lineages in three gene families commonly used in phylogenetic studies of known and unknown organisms: small subunit rRNA and the recA and rpoB superfamilies. Though the methods available could not accurately identify very deeply branched ss-rRNAs, the analysis revealed the existence of multiple novel branches in the recA and rpoB gene families (these code for proteins that are universal and very widely studied). Many hypothesis have been proposed to explain these unique findings in the metadata. It may be recombinations or duplications leading to representations of distant paralogs within these families. However, the best explanation that has been conceived is the origin of these sequences from uncharacterized viruses. This is widely supported by recent unexpected discoveries coming from viral genomics [3]. However, the four domain concept needs to be authenticated by the scientific community.

Community profiling and quantification of putative autotrophic thaumarchaeal communities in environmental samples

Growth of thaumarchaea with ammonia as a sole energy source has been demonstrated but their mode(s) of carbon metabolism remain uncertain, with evidence for autotrophy, heterotrophy and mixotrophy. Understanding the role played by autotrophy in thaumarchaeal growth strategies has been hindered by the lack of an adequate marker gene and PCR primers. The aim of this study was to develop PCR-based approaches to determine the prevalence of autotrophy associated with thaumarchaea. Primer pairs were designed specifically targeting hcd genes, encoding putative 4-hydroxybutyryl-CoA dehydratase, a key functional enzyme in thaumarchaeal autotrophy. Phylogenetic analysis of hcd gene sequences amplified from soils and sediments indicated the existence of environment-specific sequence clusters and hcd abundance in soil was 10(5) -10(6) g(-1) soil, as assessed by quantitative PCR. Abundance and diversity of hcd genes were also determined in soil microcosms incubated with and without acetylene, a known inhibitor of nitrification. Nitrification was accompanied by increases in hcd gene abundance and in the relative abundance of two bands in denaturing gradient gel electrophoresis profiles. Nitrification, growth and community changes were inhibited by acetylene and the findings are consistent with active autotrophic ammonia oxidation by archaea in soil.

Multiple copies of 16S rRNA gene affect the restriction patterns and DGGE profile revealed by analysis of genome database

The use of 16S rRNA gene has been a "golden" method to determine the diversity of microbial communities in environmental samples, phylogenetic relationships of prokaryotes and taxonomic position of newly isolated organisms. However due to the presence of multiple heterogeneous 16S rRNA gene copies in many strains, the interpretation of microbial ecology via 16S rRNA sequences is complicated. Purpose of present paper is to demonstrate the extent to which the multiple heterogeneous 16S rRNA gene copies affect RFLP patterns and DGG E profiles by using the genome database. In present genome database, there are 782 bacterial strains in total whose genomes have been completely sequenced and annotated. Among the total strains, 639 strains (82%) possess multiple 16S rRNA gene copies, 415 strains (53%) whose multiple copies are heterogeneous in sequences as revealed by alignment, 236 strains (30%) whose multiple copies show different restrict patterns by CSP61 + Hinfl, MspI + Rsal or HhaI as analyzed in silico. Polymorphisms of the multiple copies in certain strains were further characterized by G + C% and phylogentic distances based on the sequences of V3 region, which are linked to DGGE patters. Polymorphisms of a few strains were shown as examples. Using artificial communities, it is demonstrated that the presence of multiple heterogeneous 16S rRNA gene copies potentially leads to over-estimation of the diversity of a community. It is suggested that care must be taken when interpreting 16S rRNA-based RFLP and DGGE data and profiling an environmental community.