Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

The oligonucleotide specificity for microarray hybridization can be predicted by its sequence identity to non-targets, continuous stretch to non-targets, and/or binding free energy to non-targets. Most currently available programs only use one or two of these criteria, which may choose ‘false’ specific oligonucleotides or miss ‘true’ optimal probes in a considerable proportion. We have developed a software tool, called CommOligo using new algorithms and all three criteria for selection of optimal oligonucleotide probes. A series of filters, including sequence identity, free energy, continuous stretch, GC content, self-annealing, distance to the 3′-untranslated region (3′-UTR) and melting temperature (T(m)), are used to check each possible oligonucleotide. A sequence identity is calculated based on gapped global alignments. A traversal algorithm is used to generate alignments for free energy calculation. The optimal T(m) interval is determined based on probe candidates that have passed all other filters. Final probes are picked using a combination of user-configurable piece-wise linear functions and an iterative process. The thresholds for identity, stretch and free energy filters are automatically determined from experimental data by an accessory software tool, CommOligo_PE (CommOligo Parameter Estimator). The program was used to design probes for both whole-genome and highly homologous sequence data. CommOligo and CommOligo_PE are freely available to academic users upon request

Dramatic Increases of Soil Microbial Functional Gene Diversity at the Treeline Ecotone of Changbai Mountain.

The elevational and latitudinal diversity patterns of microbial taxa have attracted great attention in the past decade. Recently, the distribution of functional attributes has been in the spotlight. Here, we report a study profiling soil microbial communities along an elevation gradient (500-2200 m) on Changbai Mountain. Using a comprehensive functional gene microarray (GeoChip 5.0), we found that microbial functional gene richness exhibited a dramatic increase at the treeline ecotone, but the bacterial taxonomic and phylogenetic diversity based on 16S rRNA gene sequencing did not exhibit such a similar trend. However, the β-diversity (compositional dissimilarity among sites) pattern for both bacterial taxa and functional genes was similar, showing significant elevational distance-decay patterns which presented increased dissimilarity with elevation. The bacterial taxonomic diversity/structure was strongly influenced by soil pH, while the functional gene diversity/structure was significantly correlated with soil dissolved organic carbon (DOC). This finding highlights that soil DOC may be a good predictor in determining the elevational distribution of microbial functional genes. The finding of significant shifts in functional gene diversity at the treeline ecotone could also provide valuable information for predicting the responses of microbial functions to climate change

Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2

Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management

Genomic and microarray analysis of aromatics degradation in Geobacter metallireducens and comparison to a Geobacter isolate from a contaminated field site

<p>Abstract</p> <p>Background</p> <p>Groundwater and subsurface environments contaminated with aromatic compounds can be remediated <it>in situ </it>by <it>Geobacter </it>species that couple oxidation of these compounds to reduction of Fe(III)-oxides. <it>Geobacter metallireducens </it>metabolizes many aromatic compounds, but the enzymes involved are not well known.</p> <p>Results</p> <p>The complete <it>G. metallireducens </it>genome contained a 300 kb island predicted to encode enzymes for the degradation of phenol, <it>p</it>-cresol, 4-hydroxybenzaldehyde, 4-hydroxybenzoate, benzyl alcohol, benzaldehyde, and benzoate. Toluene degradation genes were encoded in a separate region. None of these genes was found in closely related species that cannot degrade aromatic compounds. Abundant transposons and phage-like genes in the island suggest mobility, but nucleotide composition and lack of synteny with other species do not suggest a recent transfer. The inferred degradation pathways are similar to those in species that anaerobically oxidize aromatic compounds with nitrate as an electron acceptor. In these pathways the aromatic compounds are converted to benzoyl-CoA and then to 3-hydroxypimelyl-CoA. However, in <it>G. metallireducens </it>there were no genes for the energetically-expensive dearomatizing enzyme. Whole-genome changes in transcript levels were identified in cells oxidizing benzoate. These supported the predicted pathway, identified induced fatty-acid oxidation genes, and identified an apparent shift in the TCA cycle to a putative ATP-yielding succinyl-CoA synthase. Paralogs to several genes in the pathway were also induced, as were several putative molybdo-proteins. Comparison of the aromatics degradation pathway genes to the genome of an isolate from a contaminated field site showed very similar content, and suggested this strain degrades many of the same compounds. This strain also lacked a classical dearomatizing enzyme, but contained two copies of an eight-gene cluster encoding redox proteins that was 30-fold induced during benzoate oxidation.</p> <p>Conclusion</p> <p><it>G. metallireducens </it>appears to convert aromatic compounds to benzoyl-CoA, then to acetyl-CoA via fatty acid oxidation, and then to carbon dioxide via the TCA cycle. The enzyme responsible for dearomatizing the aromatic ring may be novel, and energetic investments at this step may be offset by a change in succinate metabolism. Analysis of a field isolate suggests that the pathways inferred for <it>G. metallireducens </it>may be applicable to modeling <it>in situ </it>bioremediation.</p

Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities

Interconnection of Key Microbial Functional Genes for Enhanced Benzo[a]pyrene Biodegradation in Sediments by Microbial Electrochemistry.

Sediment microbial fuel cells (SMFCs) can stimulate the degradation of polycyclic aromatic hydrocarbons in sediments, but the mechanism of this process is poorly understood at the microbial functional gene level. Here, the use of SMFC resulted in 92% benzo[a]pyrene (BaP) removal over 970 days relative to 54% in the controls. Sediment functions, microbial community structure, and network interactions were dramatically altered by the SMFC employment. Functional gene analysis showed that c-type cytochrome genes for electron transfer, aromatic degradation genes, and extracellular ligninolytic enzymes involved in lignin degradation were significantly enriched in bulk sediments during SMFC operation. Correspondingly, chemical analysis of the system showed that these genetic changes resulted in increases in the levels of easily oxidizable organic carbon and humic acids which may have resulted in increased BaP bioavailability and increased degradation rates. Tracking microbial functional genes and corresponding organic matter responses should aid mechanistic understanding of BaP enhanced biodegradation by microbial electrochemistry and development of sustainable bioremediation strategies

Empirical Evaluation of a New Method for Calculating Signal to Noise Ratio (SNR) for Microarray Data Analysis

Signal-to-noise-ratio (SNR) thresholds for microarray data analysis were experimentally determined with an oligonucleotide array that contained perfect match (PM) and mismatch (MM) probes based upon four genes from Shewanella oneidensis MR-1. A new SNR calculation, called signal to both standard deviations ratio (SSDR) was developed, and evaluated along with other two methods, signal to standard deviation ratio (SSR), and signal to background ratio (SBR). At a low stringency, the thresholds of SSR, SBR, and SSDR were 2.5, 1.60 and 0.80 with oligonucleotide and PCR amplicon as target templates, and 2.0, 1.60 and 0.70 with genomic DNA as target templates. Slightly higher thresholds were obtained at the high stringency condition. The thresholds of SSR and SSDR decreased with an increase in the complexity of targets (e.g., target types), and the presence of background DNA, and a decrease in the composition of targets, while SBR remained unchanged under all situations. The lowest percentage of false positives (FP) and false negatives (FN) was observed with the SSDR calculation method, suggesting that it may be a better SNR calculation for more accurate determination of SNR thresholds. Positive spots identified by SNR thresholds were verified by the Student t-test, and consistent results were observed. This study provides general guidance for users to select appropriate SNR thresholds for different samples under different hybridization conditions

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances