Heat Capacity of ^3He in Aerogel

The heat capacity of pure ^3He in low density aerogel is measured at 22.5 bar. The superfluid response is simultaneously monitored with a torsional oscillator. A slightly rounded heat capacity peak, 65 mu K in width, is observed at the ^3He-aerogel superfluid transition, T_{ca}. Subtracting the bulk ^3He contribution, the heat capacity shows a Fermi-liquid form above T_{ca}. The heat capacity attributed to superfluid within the aerogel can be fit with a rounded BCS form, and accounts for 0.30 of the non-bulk fluid in the aerogel, indicating a substantial reduction in the superfluid order parameter consistent with earlier superfluid density measurements.Comment: 4 pages, 5 figure

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

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

The Shewanella Federation: Functional Genomic Investigations of Dissimilatory Metal-Reducing Shewanella

Generation and validation of a Shewanella oneidensis MR-1 clone set for protein expression and phage display. An ORF clone set for S. oneidensis was created using the lambda recombinase system. ORFs within entry vectors in this system can be readily transferred into multiple destination vectors, making the clone set a useful resource for research groups studying this microorganism. To establish that the S. oneidensis clone set could be used for protein expression and functional studies, three sets of ORFs were examined for expression of His-tag proteins, expression of His/GST-tag proteins, or for effective display on phage. A total of 21 out of 30 (70%) predicted two-component transcriptional regulators from S. oneidensis were successfully expressed in the His-tag format. The use of the S. oneidensis clone set for functional studies was tested using a phage display system. The method involves the fusion of peptides or proteins to a coat protein of a bacteriophage. This results in display of the fused protein on the exterior of the phage, while the DNA encoding the fusion resides within the virion. The physical linkage between the displayed protein and the DNA encoding it allows screening of vast numbers of proteins for ligand-binding properties. With this technology, a phage clone encoding thioredoxin TrxA was isolated from a sub-library consisting of 80 clones. It is evident that the S. oneidensis clone set can be used for expression of functional S. oneidensis proteins in E. coli using the appropriate destination vectors. Characterization of ArcA. In Escherichia coli, metabolic transitions between aerobic and anaerobic growth states occur when cells enter an oxygen-limited condition. Many of these metabolic transitions are controlled at the transcriptional level by the activities of the global regulatory proteins ArcA (aerobic respiration control) and Fnr (fumarate nitrate regulator). A homolog of ArcA (81% amino acid sequence identity) was identified in S. oneidensis MR-1, and arcA mutants with MR-1 as the parental strain were generated. Phenotype characterization showed the arcA deletion mutant grew slower than the wild-type and was hypersensitive to H2O2 stress. Microarray analysis indicated that S. oneidensis ArcA regulates a large number of different genes from that in E. coli although they do have overlapping regulatory functions on a small set of genes. The S. oneidensis arcA gene was also cloned and expressed in E. coli. The ArcA proteins from the wild-type and a point mutant strains (D54N) were purified and their DNA binding properties were analyzed by electrophoretic motility shift (EMS) and DNase I footprinting assays. The results indicate that phosphorylated ArcA proteins bind to a DNA site similar in sequence to the E. coli ArcA binding site. The common feature of the binding sites is the presence of a conserved 15 base pair motif that contains 2-3 mismatches when compared to the E. coli ArcA-P consensus binding motif. Genome scale computational predictions of binding sites were also performed and 331 putative ArcA regulatory targets were identified. Therefore, the regulation of aerobic/anaerobic respiration may be more complex than it was expected in S. oneidensis. A high-throughput percentage-of-binding strategy to measure binding energies in DNA–protein interactions. Based on results of studies on ArcA of S. oneidensis, we developed a high-throughput approach to measure binding energies in DNA-protein interactions, which enables a more precise prediction for DNA-binding sites in genomes. With this approach, the importance of each position within the ArcA-P binding site was quantitatively established by characterizing the interaction between Shewanella ArcA-P and a series of mutant promoter DNAs, whereby each position in the binding site was systematically mutated to all possible single nucleotide changes. The results of the fine mapping were used to create a position-specific energy matrix (PEM) that was used for a genome-scale prediction of 45 ArcA-P sites in Shewanella. A further examination suggests that this prediction is >81% consistency with in vivo gene regulation according to microarray studies and >92% (13/14) accuracy in comparison with published in vitro gel shift validation binding assays. In addition, this study predicted 27 ArcA-P sites for 15 published E. coli ArcA-P footprinted DNAs, and 24 of them were found exactly within the footprinting protected regions and the other three sites fall into the regions that were not examined by footprinting assays. This is the first report showing that footprinting protected regions can be effectively predicted by starting from a single known transcription factor binding site. Finally, the predicted H. influenzae ArcA-P sites correlate well with in vivo regulation determined by a microarray analysis in that the eight predicted binding sites with the most favorable ∆∆G scores all exhibit ArcA dependent gene regulation

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

Elevated CO2 and Warming Altered Grassland Microbial Communities in Soil Top-Layers.

As two central issues of global climate change, the continuous increase of both atmospheric CO2 concentrations and global temperature has profound effects on various terrestrial ecosystems. Microbial communities play pivotal roles in these ecosystems by responding to environmental changes through regulation of soil biogeochemical processes. However, little is known about the effect of elevated CO2 (eCO2) and global warming on soil microbial communities, especially in semiarid zones. We used a functional gene array (GeoChip 3.0) to measure the functional gene composition, structure, and metabolic potential of soil microbial communities under warming, eCO2, and eCO2 + warming conditions in a semiarid grassland. The results showed that the composition and structure of microbial communities was dramatically altered by multiple climate factors, including elevated CO2 and increased temperature. Key functional genes, those involved in carbon (C) degradation and fixation, methane metabolism, nitrogen (N) fixation, denitrification and N mineralization, were all stimulated under eCO2, while those genes involved in denitrification and ammonification were inhibited under warming alone. The interaction effects of eCO2 and warming on soil functional processes were similar to eCO2 alone, whereas some genes involved in recalcitrant C degradation showed no significant changes. In addition, canonical correspondence analysis and Mantel test results suggested that NO3-N and moisture significantly correlated with variations in microbial functional genes. Overall, this study revealed the possible feedback of soil microbial communities to multiple climate change factors by the suppression of N cycling under warming, and enhancement of C and N cycling processes under either eCO2 alone or in interaction with warming. These findings may enhance our understanding of semiarid grassland ecosystem responses to integrated factors of global climate change