Massively parallel pathogen identification using high‐density microarrays

Identification of microbial pathogens in clinical specimens is still performed by phenotypic methods that are often slow and cumbersome, despite the availability of more comprehensive genotyping technologies. We present an approach based on whole‐genome amplification and resequencing microarrays for unbiased pathogen detection. This 10 h process identifies a broad spectrum of bacterial and viral species and predicts antibiotic resistance and pathogenicity and virulence profiles. We successfully identify a variety of bacteria and viruses, both in isolation and in complex mixtures, and the high specificity of the microarray distinguishes between different pathogens that cause diseases with overlapping symptoms. The resequencing approach also allows identification of organisms whose sequences are not tiled on the array, greatly expanding the repertoire of identifiable organisms and their variants. We identify organisms by hybridization of their DNA in as little as 1–4 h. Using this method, we identified Monkeypox virus and drug‐resistant Staphylococcus aureus in a skin lesion taken from a child suspected of an orthopoxvirus infection, despite poor transport conditions of the sample, and a vast excess of human DNA. Our results suggest this technology could be applied in a clinical setting to test for numerous pathogens in a rapid, sensitive and unbiased manner.     

Construction of bacterial artificial chromosome library from electrochemical microorganisms

A microbial fuel cell is a device that directly converts metabolic energy into electricity, using electrochemical technology. The analysis of large genome fragments recovered directly from microbial communities represents one promising approach to characterizing uncultivated electrochemical microorganisms. To further assess the utility of this approach, we constructed large-insert (140 kb) bacterial artificial chromosome (BAC) libraries from the genomic DNA of a microbial fuel cell, which had been operated for three weeks using acetate media. We screened the expression of several ferric reductase activities in the Escherichia coli host, in order to determine the extent of heterologous expression of metal-ion-reducing enzymes in the library. Phylogenetic analysis of 16S rRNA gene sequences recovered from the BAC libraries indicates that they contain DNA from a wide diversity of microbial organisms. The constructed bacterial library proved a powerful tool for exploring metal-ion reductase activities, providing information on the electron transport pathway of electrochemical microbial (ECM) organisms.     

Design and testing of 'genome-proxy' microarrays to profile marine microbial communities

Microarrays are useful tools for detecting and quantifying specific functional and phylogenetic genes in natural microbial communities. In order to track uncultivated microbial genotypes and their close relatives in an environmental context, we designed and implemented a 'genome-proxy' microarray that targets microbial genome fragments recovered directly from the environment. Fragments consisted of sequenced clones from large-insert genomic libraries from microbial communities in Monterey Bay, the Hawaii Ocean Time-series station ALOHA, and Antarctic coastal waters. In a prototype array, we designed probe sets to 13 of the sequenced genome fragments and to genomic regions of the cultivated cyanobacterium Prochlorococcus MED4. Each probe set consisted of multiple 70-mers, each targeting an individual open reading frame, and distributed along each approximately 40-160 kbp contiguous genomic region. The targeted organisms or clones, and close relatives, were hybridized to the array both as pure DNA mixtures and as additions of cells to a background of coastal seawater. This prototype array correctly identified the presence or absence of the target organisms and their relatives in laboratory mixes, with negligible cross-hybridization to organisms having 相似文献   

Quantitative metagenomic analyses based on average genome size normalization

Over the past quarter-century, microbiologists have used DNA sequence information to aid in the characterization of microbial communities. During the last decade, this has expanded from single genes to microbial community genomics, or metagenomics, in which the gene content of an environment can provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data by estimating average genome sizes. This normalization can relieve comparative biases introduced by differences in community structure, number of sequencing reads, and sequencing read lengths between different metagenomes. We demonstrate the utility of this approach by comparing metagenomes from two different marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how differences in environment affect their abundances, we characterize three different types of autotrophic organisms: aerobic, photosynthetic carbon fixers (the Cyanobacteria); anaerobic, photosynthetic carbon fixers (the Chlorobi); and anaerobic, nonphotosynthetic carbon fixers (the Desulfobacteraceae). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis.     

Diet Alters Both the Structure and Taxonomy of the Ovine Gut Microbial Ecosystem

We surveyed the ruminal metagenomes of 16 sheep under two different diets using Illumina pair-end DNA sequencing of raw microbial DNA extracted from rumen samples. The resulting sequence data were bioinformatically mapped to known prokaryotic 16S rDNA sequences to identify the taxa present in the samples and then analysed for the presence of potentially new taxa. Strikingly, the majority of the microbial individuals found did not map to known taxa from 16S sequence databases. We used a novel statistical modelling approach to compare the taxonomic distributions between animals fed a forage-based diet and those fed concentrated grains. With this model, we found significant differences between the two groups both in the dominant taxa present in the rumen and in the overall shape of the taxa abundance curves. In general, forage-fed animals have a more diverse microbial ecosystem, whereas the concentrate-fed animals have ruminal systems more heavily dominated by a few taxa. As expected, organisms from methanogenic groups are more prevalent in forage-fed animals. Finally, all of these differences appear to be grounded in an underlying common input of new microbial individuals into the rumen environment, with common organisms from one feed group being present in the other, but at much lower abundance.     

DAPI staining and DNA content estimation of nuclei in uncultivable microbial eukaryotes (Arcellinida and Ciliates)

Though representing a major component of eukaryotic biodiversity, many microbial eukaryotes remain poorly studied, including the focus of the present work, testate amoebae of the order Arcellinida (Amoebozoa) and non-model lineages of ciliates (Alveolata). In particular, knowledge of genome structures and changes in genome content over the often-complex life cycles of these lineages remains enigmatic. However, the limited available knowledge suggests that microbial eukaryotes have the potential to challenge our textbook views on eukaryotic genomes and genome evolution. In this study, we developed protocols for DAPI (4′,6-diamidino-2-phenylindole) staining of Arcellinida nuclei and adapted protocols for ciliates. In addition, image analysis software was used to estimate the DNA content in the nuclei of Arcellinida and ciliates, and the measurements of target organisms were compared to those  of well-known model organisms. The results demonstrate that the methods we have developed for nuclear staining in these lineages are effective and can be applied to other microbial eukaryotic groups by adjusting certain stages in the protocols.     

metaBIT,an integrative and automated metagenomic pipeline for analysing microbial profiles from high‐throughput sequencing shotgun data

Micro‐organisms account for most of the Earth's biodiversity and yet remain largely unknown. The complexity and diversity of microbial communities present in clinical and environmental samples can now be robustly investigated in record times and prices thanks to recent advances in high‐throughput DNA sequencing (HTS). Here, we develop metaBIT, an open‐source computational pipeline automatizing routine microbial profiling of shotgun HTS data. Customizable by the user at different stringency levels, it performs robust taxonomy‐based assignment and relative abundance calculation of microbial taxa, as well as cross‐sample statistical analyses of microbial diversity distributions. We demonstrate the versatility of metaBIT within a range of published HTS data sets sampled from the environment (soil and seawater) and the human body (skin and gut), but also from archaeological specimens. We present the diversity of outputs provided by the pipeline for the visualization of microbial profiles (barplots, heatmaps) and for their characterization and comparison (diversity indices, hierarchical clustering and principal coordinates analyses). We show that metaBIT allows an automatic, fast and user‐friendly profiling of the microbial DNA present in HTS shotgun data sets. The applications of metaBIT are vast, from monitoring of laboratory errors and contaminations, to the reconstruction of past and present microbiota, and the detection of candidate species, including pathogens.     

Metagenomic Sequencing of an In Vitro-Simulated Microbial Community

Background

Microbial life dominates the earth, but many species are difficult or even impossible to study under laboratory conditions. Sequencing DNA directly from the environment, a technique commonly referred to as metagenomics, is an important tool for cataloging microbial life. This culture-independent approach involves collecting samples that include microbes in them, extracting DNA from the samples, and sequencing the DNA. A sample may contain many different microorganisms, macroorganisms, and even free-floating environmental DNA. A fundamental challenge in metagenomics has been estimating the abundance of organisms in a sample based on the frequency with which the organism''s DNA was observed in reads generated via DNA sequencing.

Methodology/Principal Findings

We created mixtures of ten microbial species for which genome sequences are known. Each mixture contained an equal number of cells of each species. We then extracted DNA from the mixtures, sequenced the DNA, and measured the frequency with which genomic regions from each organism was observed in the sequenced DNA. We found that the observed frequency of reads mapping to each organism did not reflect the equal numbers of cells that were known to be included in each mixture. The relative organism abundances varied significantly depending on the DNA extraction and sequencing protocol utilized.

Conclusions/Significance

We describe a new data resource for measuring the accuracy of metagenomic binning methods, created by in vitro-simulation of a metagenomic community. Our in vitro simulation can be used to complement previous in silico benchmark studies. In constructing a synthetic community and sequencing its metagenome, we encountered several sources of observation bias that likely affect most metagenomic experiments to date and present challenges for comparative metagenomic studies. DNA preparation methods have a particularly profound effect in our study, implying that samples prepared with different protocols are not suitable for comparative metagenomics.     

DNA Repair and Genome Maintenance in Bacillus subtilis

Summary: From microbes to multicellular eukaryotic organisms, all cells contain pathways responsible for genome maintenance. DNA replication allows for the faithful duplication of the genome, whereas DNA repair pathways preserve DNA integrity in response to damage originating from endogenous and exogenous sources. The basic pathways important for DNA replication and repair are often conserved throughout biology. In bacteria, high-fidelity repair is balanced with low-fidelity repair and mutagenesis. Such a balance is important for maintaining viability while providing an opportunity for the advantageous selection of mutations when faced with a changing environment. Over the last decade, studies of DNA repair pathways in bacteria have demonstrated considerable differences between Gram-positive and Gram-negative organisms. Here we review and discuss the DNA repair, genome maintenance, and DNA damage checkpoint pathways of the Gram-positive bacterium Bacillus subtilis. We present their molecular mechanisms and compare the functions and regulation of several pathways with known information on other organisms. We also discuss DNA repair during different growth phases and the developmental program of sporulation. In summary, we present a review of the function, regulation, and molecular mechanisms of DNA repair and mutagenesis in Gram-positive bacteria, with a strong emphasis on B. subtilis.     

Microarray-Based Analysis of Subnanogram Quantities of Microbial Community DNAs by Using Whole-Community Genome Amplification

Microarray technology provides the opportunity to identify thousands of microbial genes or populations simultaneously, but low microbial biomass often prevents application of this technology to many natural microbial communities. We developed a whole-community genome amplification-assisted microarray detection approach based on multiple displacement amplification. The representativeness of amplification was evaluated using several types of microarrays and quantitative indexes. Representative detection of individual genes or genomes was obtained with 1 to 100 ng DNA from individual or mixed genomes, in equal or unequal abundance, and with 1 to 500 ng community DNAs from groundwater. Lower concentrations of DNA (as low as 10 fg) could be detected, but the lower template concentrations affected the representativeness of amplification. Robust quantitative detection was also observed by significant linear relationships between signal intensities and initial DNA concentrations ranging from (i) 0.04 to 125 ng (r² = 0.65 to 0.99) for DNA from pure cultures as detected by whole-genome open reading frame arrays, (ii) 0.1 to 1,000 ng (r² = 0.91) for genomic DNA using community genome arrays, and (iii) 0.01 to 250 ng (r² = 0.96 to 0.98) for community DNAs from ethanol-amended groundwater using 50-mer functional gene arrays. This method allowed us to investigate the oligotrophic microbial communities in groundwater contaminated with uranium and other metals. The results indicated that microorganisms containing genes involved in contaminant degradation and immobilization are present in these communities, that their spatial distribution is heterogeneous, and that microbial diversity is greatly reduced in the highly contaminated environment.