Detecting network communities: an application to phylogenetic analysis

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis.     

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F₂ mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users. Tova F. Fuller, Anatole Ghazalpour contributed equally to this work.     

Unraveling assembly of stream biofilm communities

Microbial biofilms assemble from cells that attach to a surface, where they develop into matrix-enclosed communities. Mechanistic insights into community assembly are crucial to better understand the functioning of natural biofilms, which drive key ecosystem processes in numerous aquatic habitats. We studied the role of the suspended microbial community as the source of the biofilm community in three streams using terminal-restriction fragment length polymorphism and 454 pyrosequencing of the 16S ribosomal RNA (rRNA) and the 16S rRNA gene (as a measure for the active and the bulk community, respectively). Diversity was consistently lower in the biofilm communities than in the suspended stream water communities. We propose that the higher diversity in the suspended communities is supported by continuous inflow from various sources within the catchment. Community composition clearly differed between biofilms and suspended communities, whereas biofilm communities were similar in all three streams. This suggests that biofilm assembly did not simply reflect differences in the source communities, but that certain microbial groups from the source community proliferate in the biofilm. We compared the biofilm communities with random samples of the respective community suspended in the stream water. This analysis confirmed that stochastic dispersal from the source community was unlikely to shape the observed community composition of the biofilms, in support of species sorting as a major biofilm assembly mechanism. Bulk and active populations generated comparable patterns of community composition in the biofilms and the suspended communities, which suggests similar assembly controls on these populations.     

Asymptotics applied to a neural network

A mathematical model of neural processing is proposed which incorporates a theory for the storage of information. The model consists of a network of neurons that linearly processes incoming neural activity. The network stores the input by modifying the synaptic properties of all of its neurons. The model lends support to a distributive theory of memory using synaptic modification. The dynamics of the processing and storage are represented by a discrete system. Asymptotic analysis is applied to the system to show the learning capabilities of the network under constant input. Results are also given to predict the network's ability to learn periodic input, and input subjected to small random fluctuations.     

Characteristics of a biofilm photobioreactor as applied to photo-hydrogen production

The application of a cell immobilization technique to a biofilm-based photobioreactor was developed to enhance its photo-hydrogen production rate and light conversion efficiency. Rhodopseudomonas palustris CQK 01 was initially attached to the surface of packed glass beads to form a biofilm in this experiment. Then, the biofilm photobioreactor (BPBR) was illuminated by light-emitting diodes with light wavelengths of 470, 590 and 630 nm and hydrogen was evolved with glucose being the sole carbon source. Under the illumination condition of 5000 lux illumination intensity and 590 nm wavelength, the BPBR showed good hydrogen production performance: the hydrogen production rate was 38.9 ml/l/h and light conversion efficiency was 56%, while the hydrogen yield was 0.2 mol H₂/mol glucose. Furthermore, results show that the highest hydrogen production rate and glucose removal rate were obtained when the glucose concentration is 0.12 M, the optimal pH 7 and optimal temperature of influent liquid 25 °C.     

Managing microbial communities in membrane biofilm reactors

Membrane biofilm reactors (MBfRs) deliver gaseous substrates to biofilms that develop on the outside of gas-transfer membranes. When an MBfR delivers electron donors hydrogen (H₂) or methane (CH₄), a wide range of oxidized contaminants can be reduced as electron acceptors, e.g., nitrate, perchlorate, selenate, and trichloroethene. When O₂ is delivered as an electron acceptor, reduced contaminants can be oxidized, e.g., benzene, toluene, and surfactants. The MBfR’s biofilm often harbors a complex microbial community; failure to control the growth of undesirable microorganisms can result in poor performance. Fortunately, the community’s structure and function can be managed using a set of design and operation features as follows: gas pressure, membrane type, and surface loadings. Proper selection of these features ensures that the best microbial community is selected and sustained. Successful design and operation of an MBfR depends on a holistic understanding of the microbial community’s structure and function. This involves integrating performance data with omics results, such as with stoichiometric and kinetic modeling.

     

Meta-network: optimized species-species network analysis for microbial communities

Background

The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need.

Results

Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all samples) as well as association rule mining (mining more complex forms of correlations like indirect correlation and mutual information), this framework outperforms other methods in restoring the microbial communities, based on two cohorts of microbial communities: (a) the loose definition strategy is capable to generate more reasonable relationships among species in the species-species co-occurrence network; (b) important species-species co-occurrence patterns could not be identified by other existing approaches, but could successfully generated by association rule mining.

Conclusions

Results have shown that the species-species co-occurrence network we generated are much more informative than those based on traditional methods. Meta-Network has consistently constructed more meaningful networks with biologically important clusters, hubs, and provides a general approach towards deciphering the species-species co-occurrence networks.

     

Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome

Background

Advances in “omics” technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting “big data.” Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome.

Methodology

Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data.

Conclusions

PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined “modules” of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.     

Comparative analysis of biodiversity in the planktonic and biofilm bacterial communities in Lake Baikal

Bacterial communities of the water and the biofilm formed during five years on an artificial substrate in Lake Baikal were studied by the pyrosequencing of 16S rRNA gene fragments; taxonomic diversity of bacterial communities and differences in their structure were revealed. The biofilm community contained mainly representatives of three phyla: Cyanobacteria, Bacteroidetes, and Proteobacteria; the amounts of other groups were within 1%. Bacterial community of the plankton was more heterogeneous; along with the dominant phyla (Bacteroidetes, Actinobacteria, and Proteobacteria) 15% of the members were of the other phyla. The use of pyrosequencing allowed to reveal 35 bacterial phyla in Lake Baikal, some of which were identified for the first time; moreover, minor groups of microorganisms (including only several sequences), which were not earlier determined by other molecular methods were found.     

Effects of selected pharmaceuticals on riverine biofilm communities

Although pharmaceutical and therapeutic products are widely found in the natural environment, there is limited understanding of their ecological effects. Here we used rotating annular bioreactors to assess the impact of 10 microg.L(-1) of the selected pharmaceuticals ibuprofen, carbamazepine, furosemide, and caffeine on riverine biofilms. After 8 weeks of development, community structure was assessed using in situ microscopic analyses, fluor-conjugated lectin binding, standard plate counts, fluorescent in situ hybridization, carbon utilization spectra, and stable carbon isotope analyses. The biofilm communities varied markedly in architecture although only caffeine treated biofilms were significantly thicker. Cyanobacteria were suppressed by all 4 compounds, whereas the nitrogen containing caffeine, furosemide, and carbamazepine increased algal biomass. Ibuprofen and carbamazepine reduced bacterial biomass, while caffeine and furosemide increased it. Exopolymer content and composition of the biofilms was also influenced. Significant positive and negative effects were observed in carbon utilization spectra. In situ hybridization analyses indicated all treatments significantly decreased the gamma-proteobacterial populations and increased beta-proteobacteria. Ibuprofen in particular increased the alpha-proteobacteria, beta-proteobacteria, cytophaga-flavobacteria, and SRB385 probe positive populations. Caffeine and carbamazepine additions resulted in significant increases in the high GC354c and low GC69a probe positive cells. Live-dead analyses of the biofilms indicated that all treatments influenced the ratio of live-to-dead cells with controls having a ratio of 2.4, carbamazepine and ibuprofen being 3.2 and 3.5, respectively, and furosemide and caffeine being 1.9 and 1.7, respectively. Stable isotope analyses of the biofilms indicated delta 13C values shifted to more negative values relative to control biofilms. This shift may be consistent with proportional loss of cyanobacteria and relative increase in algal biomass rather than incorporation of pharmaceutical carbon into microbial biofilm. Thus, at 10 microg.L(-1) levels pharmaceuticals exhibit both nutrient-like and toxic effects on riverine microbial communities.     

Molecular survey of concrete sewer biofilm microbial communities

The microbial composition of concrete biofilms within wastewater collection systems was studied using molecular assays. SSU rDNA clone libraries were generated from 16 concrete surfaces of manholes, a combined sewer overflow, and sections of a corroded sewer pipe. Of the 2457 sequences analyzed, α-, β-, γ-, and δ-Proteobacteria represented 15%, 22%, 11%, and 4% of the clones, respectively. β-Proteobacteria (47%) sequences were more abundant in the pipe crown than any of the other concrete surfaces. While 178 to 493 Operational Taxonomic Units (OTUs) were associated with the different concrete samples, only four sequences were shared among the different clone libraries. Bacteria implicated in concrete corrosion were found in the clone libraries while archaea, fungi, and several bacterial groups were also detected using group-specific assays. The results showed that concrete sewer biofilms are more diverse than previously reported. A more comprehensive molecular database will be needed to better study the dynamics of concrete biofilms.     

Phospholipid analysis as a tool to study complex microbial communities in marine sediments.

To complement information on microbial communities in marine sediments that can be obtained using microbiological methods, we developed an analytical procedure to trace microbial lipids in environmental samples. We focused on analyzing intact phospholipids as these membrane constituents are known to be biomarkers for viable cells. Analysis of intact phospholipids from a fractionated and preconcentrated sediment extract was achieved using liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS). The combined analysis of phospholipid types and their fatty acid substituents allowed a differentiation between various groups of microorganisms living in the sediment. For comparison three strains of marine sulfate-reducing bacteria (SRB) were analysed for their lipid content.     

Statistical model comparison applied to common network motifs

Background  

Network motifs are small modules that show interesting functional and dynamic properties, and are believed to be the building blocks of complex cellular processes. However, the mechanistic details of such modules are often unknown: there is uncertainty about the motif architecture as well as the functional form and parameter values when converted to ordinary differential equations (ODEs). This translates into a number of candidate models being compatible with the system under study. A variety of statistical methods exist for ranking models including maximum likelihood-based and Bayesian methods. Our objective is to show how such methods can be applied in a typical systems biology setting.     

Chaotic neural network applied to two-dimensional motion control

Chaotic dynamics generated in a chaotic neural network model are applied to 2-dimensional (2-D) motion control. The change of position of a moving object in each control time step is determined by a motion function which is calculated from the firing activity of the chaotic neural network. Prototype attractors which correspond to simple motions of the object toward four directions in 2-D space are embedded in the neural network model by designing synaptic connection strengths. Chaotic dynamics introduced by changing system parameters sample intermediate points in the high-dimensional state space between the embedded attractors, resulting in motion in various directions. By means of adaptive switching of the system parameters between a chaotic regime and an attractor regime, the object is able to reach a target in a 2-D maze. In computer experiments, the success rate of this method over many trials not only shows better performance than that of stochastic random pattern generators but also shows that chaotic dynamics can be useful for realizing robust, adaptive and complex control function with simple rules.     

Artificial metaplasticity neural network applied to credit scoring

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.     

Seasonal response of stream biofilm communities to dissolved organic matter and nutrient enrichments

Dissolved organic matter (DOM) and inorganic nutrients may affect microbial communities in streams, but little is known about the impact of these factors on specific taxa within bacterial assemblages in biofilms. In this study, nutrient diffusing artificial substrates were used to examine bacterial responses to DOM (i.e., glucose, leaf leachate, and algal exudates) and inorganic nutrients (nitrate and phosphate singly and in combination). Artificial substrates were deployed for five seasons, from summer 2002 to summer 2003, in a northeastern Ohio stream. Differences were observed in the responses of bacterial taxa examined to various DOM and inorganic nutrient treatments, and the response patterns varied seasonally, indicating that resources that limit the bacterial communities change over time. Overall, the greatest responses were to labile, low-molecular-weight DOM (i.e., glucose) at times when chlorophyll a concentrations were low due to scouring during significant storm events. Different types of DOM and inorganic nutrients induced various responses among bacterial taxa in the biofilms examined, and these responses would not have been apparent if they were examined at the community level or if seasonal changes were not taken into account.     

Integrated metagenomic and metaproteomic analyses of marine biofilm communities

Metagenomic and metaproteomic analyses were utilized to determine the composition and function of complex air–water interface biofilms sampled from the hulls of two US Navy destroyers. Prokaryotic community analyses using PhyloChip-based 16S rDNA profiling revealed two significantly different and taxonomically rich biofilm communities (6,942 taxa) in which the majority of unique taxa were ascribed to members of the Gammaproteobacteria, Alphaproteobacteria and Clostridia. Although metagenomic sequencing indicated that both biofilms were dominated by prokaryotic sequence reads (> 91%) with the majority of the bacterial reads belonging to the Alphaproteobacteria, the Ship-1 metagenome harbored greater organismal and functional diversity and was comparatively enriched for sequences from Cyanobacteria, Bacteroidetes and macroscopic eukaryotes, whereas the Ship-2 metagenome was enriched for sequences from Proteobacteria and microscopic photosynthetic eukaryotes. Qualitative liquid chromatography-tandem mass spectrometry metaproteome analyses identified 678 unique proteins, revealed little overlap in species and protein composition between the ships and contrasted with the metagenomic data in that ~80% of classified and annotated proteins were of eukaryotic origin and dominated by members of the Bacillariophyta, Cnidaria, Chordata and Arthropoda (data deposited to the ProteomeXchange, identifier PXD000961). Within the shared metaproteome, quantitative ¹⁸O and iTRAQ analyses demonstrated a significantly greater abundance of structural proteins from macroscopic eukaryotes on Ship-1 and diatom photosynthesis proteins on Ship-2. Photosynthetic pigment composition and elemental analyses confirmed that both biofilms were dominated by phototrophic processes. These data begin to provide a better understanding of the complex organismal and biomolecular composition of marine biofilms while highlighting caveats in the interpretation of stand-alone environmental ‘-omics’ datasets.