Terry L. Marsh

The Ribosomal Database Project: improved alignments and new tools for rRNA analysis

The Ribosomal Database Project (RDP) provides researchers with quality-controlled bacterial and archaeal small subunit rRNA alignments and analysis tools. An improved alignment strategy uses the Infernal secondary structure aware aligner to provide a more consistent higher quality alignment and faster processing of user sequences. Substantial new analysis features include a new Pyrosequencing Pipeline that provides tools to support analysis of ultra high-throughput rRNA sequencing data. This pipeline offers a collection of tools that automate the data processing and simplify the computationally intensive analysis of large sequencing libraries. In addition, a new Taxomatic visualization tool allows rapid visualization of taxonomic inconsistencies and suggests corrections, and a new class Assignment Generator provides instructors with a lesson plan and individualized teaching materials. Details about RDP data and analytical functions can be found at http://rdp.cme.msu.edu/.

The ribosomal database project

The Ribosomal Database Project (RDP) is a curated database that offers ribosome data along with related programs and services. The offerings include phylogenetically ordered alignments of ribosomal RNA (rRNA) sequences, derived phylogenetic trees, rRNA secondary structure diagrams and various software packages for handling, analyzing and displaying alignments and trees. The data are available via ftp and electronic mail. Certain analytic services are also provided by the electronic mail server.

Spatial and resource factors influencing high microbial diversity in soil

ABSTRACT To begin defining the key determinants that drive microbial community structure in soil, we examined 29 soil samples from four geographically distinct locations taken from the surface, vadose zone, and saturated subsurface using a small-subunit rRNA-based cloning approach. While microbial communities in low-carbon, saturated, subsurface soils showed dominance, microbial communities in low-carbon surface soils showed remarkably uniform distributions, and all species were equally abundant. Two diversity indices, the reciprocal of Simpson’s index (1/D) and the log series index, effectively distinguished between the dominant and uniform diversity patterns. For example, the uniform profiles characteristic of the surface communities had diversity index values that were 2 to 3 orders of magnitude greater than those for the high-dominance, saturated, subsurface communities. In a site richer in organic carbon, microbial communities consistently exhibited the uniform distribution pattern regardless of soil water content and depth. The uniform distribution implies that competition does not shape the structure of these microbial communities. Theoretical studies based on mathematical modeling suggested that spatial isolation could limit competition in surface soils, thereby supporting the high diversity and a uniform community structure. Carbon resource heterogeneity may explain the uniform diversity patterns observed in the high-carbon samples even in the saturated zone. Very high levels of chromium contamination (e.g., >20%) in the high-organic-matter soils did not greatly reduce the diversity. Understanding mechanisms that may control community structure, such as spatial isolation, has important implications for preservation of biodiversity, management of microbial communities for bioremediation, biocontrol of root diseases, and improved soil fertility.

Opening the black box of soil microbial diversity

Abstract Soil probably harbours most of our planets undiscovered biodiversity. Recent results from both, culturing and nucleic acid-based approaches indicate that soil microbial diversity is even higher than previously imagined. One reason for the high diversity is that much of the diversity can be found at very small scales. If the same genotypes are not repeated at other locations, the large-scale diversity is greatly multiplied. It remains to be seen to what extent this large genotypic diversity actually affects functional diversity, microbial ecology, or biotechnological significance. Here we present a framework of methods for opening the soil black box that provides different levels of resolution of both microbial community structure and activity. The rationale for and examples of use of three of these methods are presented: guanine plus cytosine content of total soil DNA (G+C), terminal restriction fragment length polymorphism (T-RFLP) of 16S rRNA genes amplified from soil DNA, and amplified ribosomal DNA restriction analysis (ARDRA) of rRNA genes from soil DNA and from isolates. These methods give coarse and moderate scale resolution of the soil community. The G+C method, which is one of the few comprehensive coarse scale methods, is also quantitative and can be used to separate DNA into G+C fractions for a second level of composition or activity analysis. The example of the ARDRA method used here illustrates that the same populations of 2,4-D degraders became dominant in three soils of very different land use history and that several of the 2,4-D degrading isolates from these sites had the same ARDRA pattern found from the soil DNA indicating that the isolates represent the dominant populations in the 2,4-D treated soil.

Terminal Restriction Fragment Length Polymorphism Data Analysis for Quantitative Comparison of Microbial Communities

ABSTRACT Terminal restriction fragment length polymorphism (T-RFLP) is a culture-independent method of obtaining a genetic fingerprint of the composition of a microbial community. Comparisons of the utility of different methods of (i) including peaks, (ii) computing the difference (or distance) between profiles, and (iii) performing statistical analysis were made by using replicated profiles of eubacterial communities. These samples included soil collected from three regions of the United States, soil fractions derived from three agronomic field treatments, soil samples taken from within one meter of each other in an alfalfa field, and replicate laboratory bioreactors. Cluster analysis by Wards method and by the unweighted-pair group method using arithmetic averages (UPGMA) were compared. Wards method was more effective at differentiating major groups within sets of profiles; UPGMA had a slightly reduced error rate in clustering of replicate profiles and was more sensitive to outliers. Most replicate profiles were clustered together when relative peak height or Hellinger-transformed peak height was used, in contrast to raw peak height. Redundancy analysis was more effective than cluster analysis at detecting differences between similar samples. Redundancy analysis using Hellinger distance was more sensitive than that using Euclidean distance between relative peak height profiles. Analysis of Jaccard distance between profiles, which considers only the presence or absence of a terminal restriction fragment, was the most sensitive in redundancy analysis, and was equally sensitive in cluster analysis, if all profiles had cumulative peak heights greater than 10,000 fluorescence units. It is concluded that T-RFLP is a sensitive method of differentiating between microbial communities when the optimal statistical method is used for the situation at hand. It is recommended that hypothesis testing be performed by redundancy analysis of Hellinger-transformed data and that exploratory data analysis be performed by cluster analysis using Wards method to find natural groups or by UPGMA to identify potential outliers. Analyses can also be based on Jaccard distance if all profiles have cumulative peak heights greater than 10,000 fluorescence units.

Antagonistic Interactions and Biofilm Forming Capabilities Among Bacterial Strains Isolated from the Egg Surfaces of Lake Sturgeon (Acipenser fulvescens)

Characterization of interactions within a host-associated microbiome can help elucidate the mechanisms of microbial community formation on hosts and can be used to identify potential probiotics that protect hosts from pathogens. Microbes employ various modes of antagonism when interacting with other members of the community. The formation of biofilm by some strains can be a defense against antimicrobial compounds produced by other taxa. We characterized the magnitude of antagonistic interactions and biofilm formation of 25 phylogenetically diverse taxa that are representative of isolates obtained from egg surfaces of the threatened fish species lake sturgeon (Acipenser fulvescens) at two ecologically relevant temperature regimes. Eight isolates exhibited aggression to at least one other isolate. Pseudomonas sp. C22 was found to be the most aggressive strain, while Flavobacterium spp. were found to be one of the least aggressive and the most susceptible genera. Temperature affected the prevalence and intensity of antagonism. The aggressive strains identified also inhibited growth of known fish pathogens. Biofilm formations were observed for nine isolates and were dependent on temperature and growth medium. The most aggressive of the isolates disrupted biofilm formation of two well-characterized isolates but enhanced biofilm formation of a fish pathogen. Our results revealed the complex nature of interactions among members of an egg associated microbial community yet underscored the potential of specific microbial populations as host probiotics.

Relating function and community structure of complex microbial systems using neural networks

Publisher Summary This chapter presents data on the application of artificial neural networks (ANN) to link the function and community structure of the two quadruplicate sets of laboratory-scale methanogenic microbial communities under shock load conditions. It presents ANN as a potential tool for the modeling of microbial communities, when community structure data is obtained through molecular techniques and the application of mechanistic models is not feasible. The techniques for the characterization of microbial community structure are terminal restriction fragment length polymorphism, amplified ribosomal DNA restriction analysis, denaturing gradient gel electrophoresis, microscopy/image analysis, reverse sample genome probing, fatty acid methyl ester analysis, flow cytometry with probes, fluorescent in situ hybridization, and 16S rRNA microarrays and protein spectrum analysis. The ANN analysis was performed with three hidden layers, full connections, and back propagation algorithm using NeuroSolutions. The chapter concludes that artificial neural networks are more suitable modeling tools for molecular data than mechanistic approaches. They do not require the underlying rules and therefore, utilize the data more efficiently.