Molecular analysis of microbial communities in endotracheal tube biofilms

Background

Ventilator-associated pneumonia is the most prevalent acquired infection of patients on intensive care units and is associated with considerable morbidity and mortality. Evidence suggests that an improved understanding of the composition of the biofilm communities that form on endotracheal tubes may result in the development of improved preventative strategies for ventilator-associated pneumonia.

Methodology/Principal Findings

The aim of this study was to characterise microbial biofilms on the inner luminal surface of extubated endotracheal tubes from ICU patients using PCR and molecular profiling. Twenty-four endotracheal tubes were obtained from twenty mechanically ventilated patients. Denaturing gradient gel electrophoresis (DGGE) profiling of 16S rRNA gene amplicons was used to assess the diversity of the bacterial population, together with species specific PCR of key marker oral microorganisms and a quantitative assessment of culturable aerobic bacteria. Analysis of culturable aerobic bacteria revealed a range of colonisation from no growth to 2.1×10⁸ colony forming units (cfu)/cm² of endotracheal tube (mean 1.4×10⁷ cfu/cm²). PCR targeting of specific bacterial species detected the oral bacteria Streptococcus mutans (n = 5) and Porphyromonas gingivalis (n = 5). DGGE profiling of the endotracheal biofilms revealed complex banding patterns containing between 3 and 22 (mean 6) bands per tube, thus demonstrating the marked complexity of the constituent biofilms. Significant inter-patient diversity was evident. The number of DGGE bands detected was not related to total viable microbial counts or the duration of intubation.

Conclusions/Significance

Molecular profiling using DGGE demonstrated considerable biofilm compositional complexity and inter-patient diversity and provides a rapid method for the further study of biofilm composition in longitudinal and interventional studies. The presence of oral microorganisms in endotracheal tube biofilms suggests that these may be important in biofilm development and may provide a therapeutic target for the prevention of ventilator-associated pneumonia.     

In situ analysis of native microbial communities in complex samples with high particulate loads

In the present study a procedure combining a cell extraction method and Fluorescence In Situ Hybridization (FISH) for molecular monitoring and quantification of bacteria in soil and aquifer samples is presented. FISH was applied to bacterial cells extracted from the matrix by density gradient centrifugation. This separation method was applied to soil and aquifer samples and produced high cell recovery of 76.5%+/-4.4 and 78.0%+/-3.2, respectively. FISH, performed on the harvested cells, permitted a perfect visualization and quantification of bacteria. This approach is therefore promising for in situ detection of indigenous bacterial communities in complex samples.     

Multilevel selection analysis of a microbial social trait

The study of microbial communities often leads to arguments for the evolution of cooperation due to group benefits. However, multilevel selection models caution against the uncritical assumption that group benefits will lead to the evolution of cooperation. We analyze a microbial social trait to precisely define the conditions favoring cooperation. We combine the multilevel partition of the Price equation with a laboratory model system: swarming in Pseudomonas aeruginosa. We parameterize a population dynamics model using competition experiments where we manipulate expression, and therefore the cost‐to‐benefit ratio of swarming cooperation. Our analysis shows that multilevel selection can favor costly swarming cooperation because it causes population expansion. However, due to high costs and diminishing returns constitutive cooperation can only be favored by natural selection when relatedness is high. Regulated expression of cooperative genes is a more robust strategy because it provides the benefits of swarming expansion without the high cost or the diminishing returns. Our analysis supports the key prediction that strong group selection does not necessarily mean that microbial cooperation will always emerge.     

The use of gene probes in the rapid analysis of natural microbial communities

Summary Hybridization probes produced from DNA sequences have proven to be a powerful tool in the rapid and sensitive analysis of natural microbial communities. By using function-specific probes, such as those identifying genes coding for photosynthesis, the potential a microbial community has for performing a given function may be rapidly determined. Gene probes have also been used in the identification and isolation of a specific catabolic genotype in less than one-fourth the time required for the conventional culture enrichment technique. Species-specific probes constructed from portions of genes coding for ribosomal RNA have been used for the rapid identification and enumeration of bacterial species in environmental samples. The use of reassociation kinetics as a measure of community diversity and complexity is also discussed. The successful application of this technique to community analysis may reduce the time required from 1 year, for conventional analysis, to 2 weeks.     

Pyrosequencing-based analysis of fecal microbial communities in three purebred pig lines

This study examined the fecal bacterial diversity of 15-weekold pigs from three purebred lines: Duroc, Landrace, and Yorkshire. Taxon-dependent and -independent analyses were performed to evaluate differences in the fecal bacterial communities and to identify bacterial genera that can be used to discriminate breeds, following high-throughput pyrosequencing of 16S rRNA genes. Among the breeds evaluated, Landrace had the most diverse bacterial community composition. Prevotella, Blautia, Oscillibacter, and Clostridium were detected in all samples regardless of breed. On the other hand, Catenibacterium, Blautia, Dialister, and Sphaerochaeta were differentially detected among breeds, as demonstrated by the canonical loading plot. The discriminant analysis of principal components plot also showed clear separation of the three purebred pig lines, with a certain degree of similarity between Landrace and Yorkshire pigs and a distinct separation between Duroc pigs and the other two breeds. Other factors not related to breed, such as season or time of sampling and pen effects, may contribute to shaping the gut microbiota of pigs.     

Enzymic and molecular analysis of microbial communities associated with lotic particulate organic matter

1. Most allochthonous plant detritus moves through stream ecosystems as fine particulate organic matter (FPOM), whose associated microbial communities, unlike those of coarse detritus, have received little scrutiny. 2. Benthic POM was collected from a fourth-order boreal river and two first-order tributaries in northern New York during July 1991, sorted it into eight size fractions ranging from 38 to >4000 μm, and assayed each fraction for ergosterol, DNA, and the activity of nine extracellular enzymes: β-1, 4-glucosidase, endocellulase, cellobiohydrolase, phenol oxidase, peroxidase, β-N-acetylglucosaminidase, acid phosphatase, alkaline phosphatase, and aryl sulphatase. In addition, DNA-DNA hybridization was used to investigate the structural similarity of the microbial communities associated with the FPOM fractions. 3. Various enzymes showed distinct activity patterns in relation to particle size as well as among sites. Half of the possible pairwise correlations among enzyme variables were statistically significant, but no enzyme activities were correlated with biomass indices (DNA and ergosterol concentration). DNA-DNA dot-blot hybridizations suggested extensive structural similarity among the microbial communities associated with FPOM fractions. 4. Cluster analysis was used to compare microbial functional similarity, measured by enzyme assays, and structural similarity, measured by DNA—DNA hybridization. Comparison of cluster coefficients showed a weak correlation between community structural similarity and functional similarity (r= 0.51) with fifteen of eighteen fractions grouped within a narrow range of distance. 5. The results suggest a convergence in microbially mediated FPOM processing despite system-level differences in litter and water quality.     

Two algorithms for image analysis and its applications

An algorithm for sequential edge detection and an algorithm for quantitative estimation of flagella of microorganisms based on the of edge detection are presented. The method of edge detection is chosen among the segmentation methods due to the aim of the image processing - calculating the sizes and shape of different microorganisms. The edge detection algorithm does not depend on the choice of the starting contour point. Comparisons of the edge detection algorithm with other similar algorithms are made.     

Meta-network: optimized species-species network analysis for microbial communities

Background

The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need.

Results

Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all samples) as well as association rule mining (mining more complex forms of correlations like indirect correlation and mutual information), this framework outperforms other methods in restoring the microbial communities, based on two cohorts of microbial communities: (a) the loose definition strategy is capable to generate more reasonable relationships among species in the species-species co-occurrence network; (b) important species-species co-occurrence patterns could not be identified by other existing approaches, but could successfully generated by association rule mining.

Conclusions

Results have shown that the species-species co-occurrence network we generated are much more informative than those based on traditional methods. Meta-Network has consistently constructed more meaningful networks with biologically important clusters, hubs, and provides a general approach towards deciphering the species-species co-occurrence networks.

     

Comparative phylogenetic analysis of microbial communities in pristine and hydrocarbon-contaminated Alpine soils

A molecular characterization of pristine and petroleum hydrocarbon-contaminated Alpine soils sampled in Tyrol (Austria) was performed. To identify predominant bacteria, PCR-amplified 16S rRNA gene fragments from five pristine and nine contaminated soils were analysed using denaturing gradient gel electrophoresis (DGGE). Sequencing and phylogenetic analyses demonstrated that the majority of the DGGE bands represented bacteria in the Actinobacteria and Proteobacteria phyla: 18 and 73%, respectively, in pristine soils, compared with 20 and 76%, respectively, in contaminated soils. A different distribution pattern of bacterial classes in the Proteobacteria was observed between pristine and contaminated soils. The relative proportion of microorganisms belonging to the Alphaproteobacteria was larger in pristine (46%) than in contaminated (24%) soils, while Betaproteobacteria and Gammaproteobacteria were detected only in the hydrocarbon-contaminated soils. This result compared favourably with earlier work in which hydrocarbon-degradation genotypes, largely pseudomonads and Acinetobacter, belonging to the Gammaproteobacteria, were enriched following oil hydrocarbon contamination. In contrast, members of the Actinobacteria phylum, represented by Rhodococcus and Mycobacterium, were found in pristine soils where contamination events had not occurred. The results demonstrate a significant shift in the microbial community structure in Alpine soils following contamination. Furthermore, more potentially novel phylotypes were found in the pristine soils than in the contaminated soils.     

Distinct microbial communities associated with buried soils in the Siberian tundra

Cryoturbation, the burial of topsoil material into deeper soil horizons by repeated freeze–thaw events, is an important storage mechanism for soil organic matter (SOM) in permafrost-affected soils. Besides abiotic conditions, microbial community structure and the accessibility of SOM to the decomposer community are hypothesized to control SOM decomposition and thus have a crucial role in SOM accumulation in buried soils. We surveyed the microbial community structure in cryoturbated soils from nine soil profiles in the northeastern Siberian tundra using high-throughput sequencing and quantification of bacterial, archaeal and fungal marker genes. We found that bacterial abundances in buried topsoils were as high as in unburied topsoils. In contrast, fungal abundances decreased with depth and were significantly lower in buried than in unburied topsoils resulting in remarkably low fungal to bacterial ratios in buried topsoils. Fungal community profiling revealed an associated decrease in presumably ectomycorrhizal (ECM) fungi. The abiotic conditions (low to subzero temperatures, anoxia) and the reduced abundance of fungi likely provide a niche for bacterial, facultative anaerobic decomposers of SOM such as members of the Actinobacteria, which were found in significantly higher relative abundances in buried than in unburied topsoils. Our study expands the knowledge on the microbial community structure in soils of Northern latitude permafrost regions, and attributes the delayed decomposition of SOM in buried soils to specific microbial taxa, and particularly to a decrease in abundance and activity of ECM fungi, and to the extent to which bacterial decomposers are able to act as their functional substitutes.     

Toward an understanding of microbial communities through analysis of communication networks

Bacteria receive signals from diverse members of their biotic environment. They sense their own species through the process of quorum sensing, which detects the density of bacterial cells and regulates functions such as bioluminescence, virulence, and competence. Bacteria also respond to the presence of other microorganisms and eukaryotic hosts. Most studies of microbial communication focus on signaling between the microbe and one other organism for empirical simplicity and because few experimental systems offer the opportunity to study communication among various types of organisms. But in the real biological world, microorganisms must carry on multiple molecular conversations simultaneously between diverse organisms, thereby constructing communication networks. We propose that biocontrol of plant disease, the process of suppressing disease through application of a microorganism, offers a model for the study of communication among multiple organisms. Successful biocontrol requires the sending and receiving of signals between the biocontrol agent and the pathogen, plant host, and microbial community surrounding the host. We are using Bacillus cereus, a biocontrol agent, and the organisms it must interact with, to dissect a communication network. This system offers an excellent starting point for study because its members are defined and well studied. An understanding of signaling in the B. cereus biocontrol system may provide a model for network communication among organisms that share a habitat and provide a new angle of analysis for understanding the interconnections that define communities. This revised version was published online in August 2006 with corrections to the Cover Date.     

Population genomics in natural microbial communities

Little is known about the evolutionary processes that structure and maintain microbial diversity because, until recently, it was difficult to explore individual-level patterns of variation at the microbial scale. Now, community-genomic sequence data enable such variation to be assessed across large segments of microbial genomes. Here, we discuss how population-genomic analysis of these data can be used to determine how selection and genetic exchange shape the evolution of new microbial lineages. We show that once independent lineages have been identified, such analyses enable the identification of genome changes that drive niche differentiation and promote the coexistence of closely related lineages within the same environment. We suggest that understanding the evolutionary ecology of natural microbial populations through population-genomic analyses will enhance our understanding of genome evolution across all domains of life.