Analysis of methanotrophic communities in landfill biofilters using diagnostic microarray

Biofilters operated for the microbial oxidation of landfill methane at two sites in Northern Germany were analysed for the composition of their methanotrophic community by means of diagnostic microarray targeting the pmoA gene of methanotrophs. The gas emitted from site Francop (FR) contained the typical principal components (CH4, CO2, N2) only, while the gas at the second site Müggenburger Strasse (MU) was additionally charged with non-methane volatile organic compounds (NMVOCs). Methane oxidation activity measured at 22 degrees C varied between 7 and 103 microg CH4 (g dw)(-1) h(-1) at site FR and between 0.9 and 21 microg CH4 (g dw)(-1) h(-1) at site MU, depending on the depth considered. The calculated size of the active methanotrophic population varied between 3 x 10(9) and 5 x 10(11) cells (g dw)(-1) for biofilter FR and 4 x 10(8) to 1 x 10(10) cells (g dw)(-1) for biofilter MU. The methanotrophic community in both biofilters as well as the methanotrophs present in the landfill gas at site FR was strongly dominated by type II organisms, presumably as a result of high methane loads, low copper concentration and low nitrogen availability. Within each biofilter, community composition differed markedly with depth, reflecting either the different conditions of diffusive oxygen supply or the properties of the two layers of materials used in the filters or both. The two biofilter communities differed significantly. Type I methanotrophs were detected in biofilter FR but not in biofilter MU. The type II community in biofilter FR was dominated by Methylocystis species, whereas the biofilter at site MU hosted a high abundance of Methylosinus species while showing less overall methanotroph diversity. It is speculated that the differing composition of the type II population at site MU is driven by the presence of NMVOCs in the landfill gas fed to the biofilter, selecting for organisms capable of co-oxidative degradation of these compounds.     

Ammonia-oxidizing bacterial community composition in estuarine and oceanic environments assessed using a functional gene microarray

The relationship between environmental factors and functional gene diversity of ammonia-oxidizing bacteria (AOB) was investigated across a transect from the freshwater portions of the Chesapeake Bay and Choptank River out into the Sargasso Sea. Oligonucleotide probes (70-bp) designed to represent the diversity of ammonia monooxygenase (amoA) genes from Chesapeake Bay clone libraries and cultivated AOB were used to construct a glass slide microarray. Hybridization patterns among the probes in 14 samples along the transect showed clear variations in amoA community composition. Probes representing uncultivated members of the Nitrosospira-like AOB dominated the probe signal, especially in the more marine samples. Of the cultivated species, only Nitrosospira briensis was detected at appreciable levels. Discrimination analysis of hybridization signals detected two guilds. Guild 1 was dominated by the marine Nitrosospira-like probe signal, and Guild 2's largest contribution was from upper bay (freshwater) sediment probes. Principal components analysis showed that Guild 1 was positively correlated with salinity, temperature and chlorophyll a concentration, while Guild 2 was positively correlated with concentrations of oxygen, dissolved organic carbon, and particulate nitrogen and carbon, suggesting that different amoA sequences represent organisms that occupy different ecological niches within the estuarine/marine environment. The trend from most diversity of AOB in the upper estuary towards dominance of a single type in the polyhaline region of the Bay is consistent with the declining importance of AOB with increasing salinity, and with the idea that AO-Archaea are the more important ammonia oxidizers in the ocean.     

Classification of microarray data using gene networks

Background  

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation.     

Mechanisms of DNA-binding specificity and functional gene regulation by transcription factors

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Tumor classification by partial least squares using microarray gene expression data

MOTIVATION: One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p(genes) far exceeding the number of samples N. Standard statistical methodologies in classification and prediction do not work well or even at all when N < p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. RESULTS: We propose a novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions. This procedure involves dimension reduction using Partial Least Squares (PLS) and classification using Logistic Discrimination (LD) and Quadratic Discriminant Analysis (QDA). We compare PLS to the well known dimension reduction method of Principal Components Analysis (PCA). Under many circumstances PLS proves superior; we illustrate a condition when PCA particularly fails to predict well relative to PLS. The proposed methods were applied to five different microarray data sets involving various human tumor samples: (1) normal versus ovarian tumor; (2) Acute Myeloid Leukemia (AML) versus Acute Lymphoblastic Leukemia (ALL); (3) Diffuse Large B-cell Lymphoma (DLBCLL) versus B-cell Chronic Lymphocytic Leukemia (BCLL); (4) normal versus colon tumor; and (5) Non-Small-Cell-Lung-Carcinoma (NSCLC) versus renal samples. Stability of classification results and methods were further assessed by re-randomization studies.     

Weighted analysis of microarray gene expression using maximum-likelihood

MOTIVATION: The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need for improvement in quantitative spot variation surveillance. RESULTS: This paper develops a maximum-likelihood method for estimating gene expression using spot mean, variance and pixel number values available from typical microarray scanners. It employs a hierarchical model of variation between and within microarray spots. The hierarchical maximum-likelihood estimate (MLE) is shown to be a more efficient estimator of the mean than the 'conventional' estimate using solely the spot mean values (i.e. without spot variance data). Furthermore, under the assumptions of our model, the spot mean and spot variance are shown to be sufficient statistics that do not require the use of all pixel data.The hierarchical MLE method is applied to data from both Monte Carlo (MC) simulations and a two-channel dye-swapped spotted microarray experiment. The MC simulations show that the hierarchical MLE method leads to improved detection of differential gene expression particularly when 'outlier' spots are present on the arrays. Compared with the conventional method, the MLE method applied to data from the microarray experiment leads to an increase in the number of differentially expressed genes detected for low cut-off P-values of interest.     

Analysis of gene ontology features in microarray data using the Proteome BioKnowledge Library

Microarray technology has resulted in an explosion of complex, valuable data. Integrating data analysis tools with a comprehensive underlying database would allow efficient identification of common properties among differentially regulated genes. In this study we sought to compare the utility of various databases in microarray analysis. The Proteome BioKnowledge Library (BKL), a manually curated, proteome-wide compilation of the scientific literature, was used to generate a list of Gene Ontology (GO) Biological Process (BP) terms enriched among proteins involved in cardiovascular disease. Analysis of DNA microarray data generated in a study of rat vascular smooth muscle cell responses revealed significant enrichment in a number of GO BPs that were also enriched among cardiovascular disease-related proteins. Using annotation from LocusLink and chip annotation from the Gene Expression Omnibus yielded fewer enriched cardiovascular disease-associated GO BP terms. Data sets of orthologous genes from mouse and human were generated using the BKL Retriever. Analysis of these sets focusing on BKL Disease annotation, revealed a significant association of these genes with cardiovascular disease. These results and the extensive presence of experimental evidence for BKL GO and Disease features, underscore the benefits of using this database for microarray analysis.     

Applications of functional gene microarrays for profiling microbial communities

Functional gene arrays (FGAs) have been considered as a specific, sensitive, quantitative, and high throughput metagenomic tool to detect, monitor and characterize microbial communities. Especially GeoChips, the most comprehensive FGAs have been applied to analyze the functional diversity, composition, structure, and metabolic potential or activity of a variety of microbial communities from different habitats, such as aquatic ecosystems, soils, contaminated sites, extreme environments, and bioreactors. FGAs are able to address fundamental questions related to global change, bioremediation, land use, human health, and ecological theories, and link the microbial community structure to environmental properties and ecosystem functioning. This review focuses on applications of FGA technology for profiling microbial communities, including target preparation, hybridization and data processing, and data analysis. We also discuss challenges and future directions of FGA applications.     

Assessing natural resource use by forest-reliant communities in Madagascar using functional diversity and functional redundancy metrics

Biodiversity plays an integral role in the livelihoods of subsistence-based forest-dwelling communities and as a consequence it is increasingly important to develop quantitative approaches that capture not only changes in taxonomic diversity, but also variation in natural resources and provisioning services. We apply a functional diversity metric originally developed for addressing questions in community ecology to assess utilitarian diversity of 56 forest plots in Madagascar. The use categories for utilitarian plants were determined using expert knowledge and household questionnaires. We used a null model approach to examine the utilitarian (functional) diversity and utilitarian redundancy present within ecological communities. Additionally, variables that might influence fluctuations in utilitarian diversity and redundancy--specifically number of felled trees, number of trails, basal area, canopy height, elevation, distance from village--were analyzed using Generalized Linear Models (GLMs). Eighteen of the 56 plots showed utilitarian diversity values significantly higher than expected. This result indicates that these habitats exhibited a low degree of utilitarian redundancy and were therefore comprised of plants with relatively distinct utilitarian properties. One implication of this finding is that minor losses in species richness may result in reductions in utilitarian diversity and redundancy, which may limit local residents' ability to switch between alternative choices. The GLM analysis showed that the most predictive model included basal area, canopy height and distance from village, which suggests that variation in utilitarian redundancy may be a result of local residents harvesting resources from the protected area. Our approach permits an assessment of the diversity of provisioning services available to local communities, offering unique insights that would not be possible using traditional taxonomic diversity measures. These analyses introduce another tool available to conservation biologists for assessing how future losses in biodiversity will lead to a reduction in natural resources and provisioning services from forests.     

Analysis of developmental gene conservation in the Actinomycetales using DNA/DNA microarray comparisons

Based on available genome sequences, Actinomycetales show significant gene synteny across a wide range of species and genera. In addition, many genera show varying degrees of complex morphological development. Using the presence of gene synteny as a basis, it is clear that an analysis of gene conservation across the Streptomyces and various other Actinomycetales will provide information on both the importance of genes and gene clusters and the evolution of morphogenesis in these bacteria. Genome sequencing, although becoming cheaper, is still relatively expensive for comparing large numbers of strains. Thus, a heterologous DNA/DNA microarray hybridization dataset based on a Streptomyces coelicolor microarray allows a cheaper and greater depth of analysis of gene conservation. This study, using both bioinformatical and microarray approaches, was able to classify genes previously identified as involved in morphogenesis in Streptomyces into various subgroups in terms of conservation across species and genera. This will allow the targeting of genes for further study based on their importance at the species level and at higher evolutionary levels.     

Comparative genomics of methylated amine utilization by marine Roseobacter clade bacteria and development of functional gene markers (tmm, gmaS)

The marine Roseobacter clade bacteria comprise up to 20% of the microbial community in coastal surface seawater. Marine Roseobacter clade bacteria are known to catalyse some important biogeochemical transformations in marine carbon and sulfur cycles. Using a comparative genomic approach, this study revealed that many marine Roseobacter clade bacteria have the genetic potential to utilize methylated amines (MAs) as alternative nitrogen sources. These MAs represent a significant pool of dissolved organic carbon and nitrogen in the marine environment. The marine Roseobacter clade bacterial genomes also encode full sets of genes providing them with the potential to generate energy from complete oxidation of the methyl groups of MAs. Representative species of the marine Roseobacter clade were tested and their abilities to use MAs are directly linked to the presence in their genomes of genes encoding key enzymes involved in MA metabolism, including trimethylamine monooxygenase (tmm) and gamma-glutamylmethylamide synthetase (gmaS). These two genes were chosen as functional markers for detecting MA-utilizing marine Roseobacter clade bacteria in the environment. PCR primers targeting these two genes were designed and used successfully to retrieve corresponding gene sequences from MA-utilizing isolates of the marine Roseobacter clade, as well as directly from DNA extracted from surface seawater obtained from Station L4 off the coast of Plymouth, UK. Taken together, the results suggest that MAs may serve as important nitrogen and possibly energy sources for marine Roseobacter clade bacteria, which helps to explain their global success in the marine environment.     

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.     

Analysis of methanotroph community composition using a pmoA-based microbial diagnostic microarray

Microbial diagnostic microarrays (MDMs) are highly parallel hybridization platforms containing multiple sets of immobilized oligonucleotide probes used for parallel detection and identification of many different microorganisms in environmental and clinical samples. Each probe is approximately specific to a given group of organisms. Here we describe the protocol used to develop and validate an MDM method for the semiquantification of a range of functional genes--in this case, particulate methane monooxygenase (pmoA)--and we give an example of its application to the study of the community structure of methanotrophs and functionally related bacteria in the environment. The development and validation of an MDM, following this protocol, takes ～6 months. The pmoA MDM described in detail comprises 199 probes and addresses ～50 different species-level clades. An experiment comprising 24 samples can be completed, from DNA extraction to data acquisition, within 3 d (12-13 h bench work).     

On gene ranking using replicated microarray time course data

Summary .  Consider the ranking of genes using data from replicated microarray time course experiments, where there are multiple biological conditions, and the genes of interest are those whose temporal profiles differ across conditions. We derive a multisample multivariate empirical Bayes' statistic for ranking genes in the order of differential expression, from both longitudinal and cross-sectional replicated developmental microarray time course data. Our longitudinal multisample model assumes that time course replicates are independent and identically distributed multivariate normal vectors. On the other hand, we construct a cross-sectional model using a normal regression framework with any appropriate basis for the design matrices. In both cases, we use natural conjugate priors in our empirical Bayes' setting which guarantee closed form solutions for the posterior odds. The simulations and two case studies using published worm and mouse microarray time course datasets indicate that the proposed approaches perform satisfactorily.