Development of functional gene microarrays for microbial community analysis

Functional gene arrays (FGAs) are a special type of microarrays containing probes for key genes involved in microbial functional processes, such as biogeochemical cycling of carbon, nitrogen, sulfur, phosphorus and metals, virulence and antibiotic resistance, biodegradation of environmental contaminants, and stress responses. FGAs have been demonstrated to be a specific, sensitive, and quantitative tool for rapid analysis of microbial communities from different habitats, such as waters, soils, extreme environments, bioreactors, and human microbiomes. In this review, we first summarize currently reported FGAs, and then focus on the FGA development. We will also discuss several key issues of FGA technology as well as challenges and directions in future FGA development.     

Monitoring gene expression in mixed microbial communities by using DNA microarrays

A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes (tfdA and tfdC) from populations of JMP134 as low as 10(5) cells/ml was clearly detected against a background of 10(8) cells/ml. Induction of two others (tfdB and tfdE) was detected from populations of 10(6) cells/ml in the same background; however, the last gene, tfdF, showed no significant induction due to high variability. In another experiment, the induction of resin acid degradative genes was statistically detectable in sludge-fed pulp mill effluent exposed to dehydroabietic acid in batch experiments. We conclude that microarrays will be useful tools for the detection of bacterial gene expression in wastewaters and other complex systems.     

Activity-based protein profiling in bacteria: Applications for identification of therapeutic targets and characterization of microbial communities

Activity-based protein profiling (ABPP) is a robust chemoproteomic technique that uses activity-based probes to globally measure endogenous enzymatic activity in complex proteomes. It has been utilized extensively to characterize human disease states and identify druggable targets in diverse disease conditions. ABPP has also recently found applications in microbiology. This includes using activity-based probes (ABPs) for functional studies of pathogenic bacteria as well as complex communities within a microbiome. This review will focus on recent advances in the use of ABPs to profile enzyme activity in disease models, screen for selective inhibitors of key enzymes, and develop imaging tools to better understand the host–bacterial interface.     

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   

The PathoChip,a functional gene array for assessing pathogenic properties of diverse microbial communities

Pathogens present in the environment pose a serious threat to human, plant and animal health as evidenced by recent outbreaks. As many pathogens can survive and proliferate in the environment, it is important to understand their population dynamics and pathogenic potential in the environment. To assess pathogenic potential in diverse habitats, we developed a functional gene array, the PathoChip, constructed with key virulence genes related to major virulence factors, such as adherence, colonization, motility, invasion, toxin, immune evasion and iron uptake. A total of 3715 best probes were selected from 13 virulence factors, covering 7417 coding sequences from 1397 microbial species (2336 strains). The specificity of the PathoChip was computationally verified, and approximately 98% of the probes provided specificity at or below the species level, proving its excellent capability for the detection of target sequences with high discrimination power. We applied this array to community samples from soil, seawater and human saliva to assess the occurrence of virulence genes in natural environments. Both the abundance and diversity of virulence genes increased in stressed conditions compared with their corresponding controls, indicating a possible increase in abundance of pathogenic bacteria under environmental perturbations such as warming or oil spills. Statistical analyses showed that microbial communities harboring virulence genes were responsive to environmental perturbations, which drove changes in abundance and distribution of virulence genes. The PathoChip provides a useful tool to identify virulence genes in microbial populations, examine the dynamics of virulence genes in response to environmental perturbations and determine the pathogenic potential of microbial communities.     

Overall functional gene diversity of microbial communities in three full-scale activated sludge bioreactors

Understanding microbial community composition is thought to be crucial for improving process functioning and stabilities of full-scale activated sludge reactors in wastewater treatment plants (WWTPs). However, functional gene compositions of microbial communities within them have not been clearly elucidated. To gain a complete picture of microbial community, in this study, GeoChip 4.2 was used to profile the overall functional genes of three full-scale activated sludge bioreactors, the 16S rRNA gene diversities of which had been unveiled by 454-pyrosequencing in our previous investigation. Triplicate activated sludge samples from each system were analyzed, with the detection of 38,507 to 40,654 functional genes. A high similarity of 77.3–81.2 % shared functional genes was noted among the nine samples, verified by the high 16S rRNA gene similarity with shared operational taxonomic units (OTUs) constituting 66.4–70.0 % of the detected sequences in each system. Correlation analyses showed that the abundances of a wide array of functional genes were associated with system performances. For example, the abundances of carbon degradation genes were strongly correlated to chemical oxygen demand (COD) removal efficiencies (r?=?0.8697, P?<?0.01). Lastly, we found that sludge retention time (SRT), influent total nitrogen concentrations (TN inf), and dissolved oxygen (DO) concentrations were key environmental factors shaping the overall functional genes. Together, the results revealed vast functional gene diversity and some links between the functional gene compositions and microbe-mediated processes.     

Metabolic profiling as a means of characterizing plant-associated microbial communities

Abstract Microbial communities from leaf and root habitats associated with sugar beet ( Beta vulgaris ) were characterized according to their capacity to metabolize a range of 95 sole carbon sources available in a commercial assay, GN-BIOLOG MicroPlates. Metabolic profiling was assessed as a method for evaluating perturbation of microbial communities of glasshouse-grown sugar beet inoculated with a genetically modified microorganism. This technique has allowed microbial communities, colonising the immature leaves of treated and untreated plants to be differentiated, although no differences were observed when plants inoculated with genetically modified microorganisms and unmodified inoculated plants were compared. As plants developed and differentiated, the carbon utilization patterns observed allowed communities to be grouped according to the habitat from which they were isolated, irrespective of treatment. These studies demonstrate that the genetically modified microorganism, introduced as a seed dressing colonised developing immature tissue throughout the 231-day study but did not disrupt the natural succession of microbial communities in glasshouse-grown sugar beet plants.     

Reliability for detecting composition and changes of microbial communities by T-RFLP genetic profiling

Terminal restriction fragment length polymorphism (T-RFLP) analysis is commonly used for profiling microbial communities in various environments. However, it may suffer from biases during the analytic process. This study addressed the potential of T-RFLP profiles (1) to reflect real community structures and diversities, as well as (2) to reliably detect changing components of microbial community structures. For this purpose, defined artificial communities of 30 SSU rRNA gene clones, derived from nine bacterial phyla, were used. PCR amplification efficiency was one primary bias with a maximum variability factor of 3.5 among clones. PCR downstream analyses such as enzymatic restriction and capillary electrophoresis introduced a maximum bias factor of 4 to terminal restriction fragment (T-RF) signal intensities, resulting in a total maximum bias factor of 14 in the final T-RFLP profiles. In addition, the quotient between amplification efficiency and T-RF size allowed predicting T-RF abundances in the profiles with high accuracy. Although these biases impaired detection of real community structures, the relative changes in structures and diversities were reliably reflected in the T-RFLP profiles. These data support the suitability of T-RFLP profiling for monitoring effects on microbial communities.     

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.     

GeoChip-based analysis of the functional gene diversity and metabolic potential of soil microbial communities of mangroves

Mangroves are unique and highly productive ecosystems and harbor very special microbial communities. Although the phylogenetic diversity of sediment microbial communities of mangrove habitats has been examined extensively, little is known regarding their functional gene diversity and metabolic potential. In this study, a high-throughput functional gene array (GeoChip 4.0) was used to analyze the functional diversity, composition, structure, and metabolic potential of microbial communities in mangrove habitats from mangrove national nature reserves in China. GeoChip data indicated that these microbial communities were functionally diverse as measured by the number of genes detected, unique genes, and various diversity indices. Almost all key functional gene categories targeted by GeoChip 4.0 were detected in the mangrove microbial communities, including carbon (C) fixation, C degradation, methane generation, nitrogen (N) fixation, nitrification, denitrification, ammonification, N reduction, sulfur (S) metabolism, metal resistance, antibiotic resistance, and organic contaminant degradation. Detrended correspondence analysis (DCA) of all detected genes showed that Spartina alterniflora (HH), an invasive species, did not harbor significantly different microbial communities from Aegiceras corniculatum (THY), a native species, but did differ from other species, Kenaelia candel (QQ), Aricennia marina (BGR), and mangrove-free mud flat (GT). Canonical correspondence analysis (CCA) results indicated the microbial community structure was largely shaped by surrounding environmental variables, such as total nitrogen (TN), total carbon (TC), pH, C/N ratio, and especially salinity. This study presents a comprehensive survey of functional gene diversity of soil microbial communities from different mangrove habitats/species and provides new insights into our understanding of the functional potential of microbial communities in mangrove ecosystems.     

Gas chromatographic metabolic profiling: A sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from (‘exometabolome’) or existing in (‘endometabolome’) a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog® microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 °C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

Gas chromatographic metabolic profiling: a sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from ('exometabolome') or existing in ('endometabolome') a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 degrees C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

Phylogenetic and functional metagenomic profiling for assessing microbial biodiversity in environmental monitoring

Decisions guiding environmental management need to be based on a broad and comprehensive understanding of the biodiversity and functional capability within ecosystems. Microbes are of particular importance since they drive biogeochemical cycles, being both producers and decomposers. Their quick and direct responses to changes in environmental conditions modulate the ecosystem accordingly, thus providing a sensitive readout. Here we have used direct sequencing of total DNA from water samples to compare the microbial communities of two distinct coastal regions exposed to different anthropogenic pressures: the highly polluted Port of Genoa and the protected area of Montecristo Island in the Mediterranean Sea. Analysis of the metagenomes revealed significant differences in both microbial diversity and abundance between the two areas, reflecting their distinct ecological habitats and anthropogenic stress conditions. Our results indicate that the combination of next generation sequencing (NGS) technologies and bioinformatics tools presents a new approach to monitor the diversity and the ecological status of aquatic ecosystems. Integration of metagenomics into environmental monitoring campaigns should enable the impact of the anthropogenic pressure on microbial biodiversity in various ecosystems to be better assessed and also predicted.