The lichen symbiosis re-viewed through the genomes of Cladonia grayi and its algal partner Asterochloris glomerata.

BACKGROUND: Lichens, encompassing 20,000 known species, are symbioses between specialized fungi (mycobionts), mostly ascomycetes, and unicellular green algae or cyanobacteria (photobionts). Here we describe the first parallel genomic analysis of the mycobiont Cladonia grayi and of its green algal photobiont Asterochloris glomerata. We focus on genes/predicted proteins of potential symbiotic significance, sought by surveying proteins differentially activated during early stages of mycobiont and photobiont interaction in coculture, expanded or contracted protein families, and proteins with differential rates of evolution. RESULTS: A) In coculture, the fungus upregulated small secreted proteins, membrane transport proteins, signal transduction components, extracellular hydrolases and, notably, a ribitol transporter and an ammonium transporter, and the alga activated DNA metabolism, signal transduction, and expression of flagellar components. B) Expanded fungal protein families include heterokaryon incompatibility proteins, polyketide synthases, and a unique set of G-protein α subunit paralogs. Expanded algal protein families include carbohydrate active enzymes and a specific subclass of cytoplasmic carbonic anhydrases. The alga also appears to have acquired by horizontal gene transfer from prokaryotes novel archaeal ATPases and Desiccation-Related Proteins. Expanded in both symbionts are signal transduction components, ankyrin domain proteins and transcription factors involved in chromatin remodeling and stress responses. The fungal transportome is contracted, as are algal nitrate assimilation genes. C) In the mycobiont, slow-evolving proteins were enriched for components involved in protein translation, translocation and sorting. CONCLUSIONS: The surveyed genes affect stress resistance, signaling, genome reprogramming, nutritional and structural interactions. The alga carries many genes likely transferred horizontally through viruses, yet we found no evidence of inter-symbiont gene transfer. The presence in the photobiont of meiosis-specific genes supports the notion that sexual reproduction occurs in Asterochloris while they are free-living, a phenomenon with implications for the adaptability of lichens and the persistent autonomy of the symbionts. The diversity of the genes affecting the symbiosis suggests that lichens evolved by accretion of many scattered regulatory and structural changes rather than through introduction of a few key innovations. This predicts that paths to lichenization were variable in different phyla, which is consistent with the emerging consensus that ascolichens could have had a few independent origins.

Pseudomonas fluorescens increases mycorrhization and modulates expression of antifungal defense response genes in roots of aspen seedlings.

BACKGROUND: Plants, fungi, and bacteria form complex, mutually-beneficial communities within the soil environment. In return for photosynthetically derived sugars in the form of exudates from plant roots, the microbial symbionts in these rhizosphere communities provide their host plants access to otherwise inaccessible nutrients in soils and help defend the plant against biotic and abiotic stresses. One role that bacteria may play in these communities is that of Mycorrhizal Helper Bacteria (MHB). MHB are bacteria that facilitate the interactions between plant roots and symbiotic mycorrhizal fungi and, while the effects of MHB on the formation of plant-fungal symbiosis and on plant health have been well documented, the specific molecular mechanisms by which MHB drive gene regulation in plant roots leading to these benefits remain largely uncharacterized. RESULTS: Here, we investigate the effects of the bacterium Pseudomonas fluorescens SBW25 (SBW25) on aspen root transcriptome using a tripartite laboratory community comprised of Populus tremuloides (aspen) seedlings and the ectomycorrhizal fungus Laccaria bicolor (Laccaria). We show that SBW25 has MHB activity and promotes mycorrhization of aspen roots by Laccaria. Using transcriptomic analysis of aspen roots under multiple community compositions, we identify clusters of co-regulated genes associated with mycorrhization, the presence of SBW25, and MHB-associated functions, and we generate a combinatorial logic network that links causal relationships in observed patterns of gene expression in aspen seedling roots in a single Boolean circuit diagram. The predicted regulatory circuit is used to infer regulatory mechanisms associated with MHB activity. CONCLUSIONS: In our laboratory conditions, SBW25 increases the ability of Laccaria to form ectomycorrhizal interactions with aspen seedling roots through the suppression of aspen root antifungal defense responses. Analysis of transcriptomic data identifies that potential molecular mechanisms in aspen roots that respond to MHB activity are proteins with homology to pollen recognition sensors. Pollen recognition sensors integrate multiple environmental signals to down-regulate pollenization-associated gene clusters, making proteins with homology to this system an excellent fit for a predicted mechanism that integrates information from the rhizosphere to down-regulate antifungal defense response genes in the root. These results provide a deeper understanding of aspen gene regulation in response to MHB and suggest additional, hypothesis-driven biological experiments to validate putative molecular mechanisms of MHB activity in the aspen-Laccaria ectomycorrhizal symbiosis.

Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information.

Bacteria are not simply passive consumers of nutrients or merely steady-state systems. Rather, bacteria are active participants in their environments, collecting information from their surroundings and processing and using that information to adapt their behavior and optimize survival. The bacterial regulome is the set of physical interactions that link environmental information to the expression of genes by way of networks of sensors, transporters, signal cascades, and transcription factors. As bacteria cannot have one dedicated sensor and regulatory response system for every possible condition that they may encounter, the sensor systems must respond to a variety of overlapping stimuli and collate multiple forms of information to make "decisions" about the most appropriate response to a specific set of environmental conditions. Here, we analyze Pseudomonas fluorescens transcriptional responses to multiple sulfur nutrient sources to generate a predictive, computational model of the sulfur regulome. To model the regulome, we utilize a transmitter-channel-receiver scheme of information transfer and utilize principles from information theory to portray P. fluorescens as an informatics system. This approach enables us to exploit the well-established metrics associated with information theory to model the sulfur regulome. Our computational modeling analysis results in the accurate prediction of gene expression patterns in response to the specific sulfur nutrient environments and provides insights into the molecular mechanisms of Pseudomonas sensory capabilities and gene regulatory networks. In addition, modeling the bacterial regulome using the tools of information theory is a powerful and generalizable approach that will have multiple future applications to other bacterial regulomes. IMPORTANCE Bacteria sense and respond to their environments using a sophisticated array of sensors and regulatory networks to optimize their fitness and survival in a constantly changing environment. Understanding how these regulatory and sensory networks work will provide the capacity to predict bacterial behaviors and, potentially, to manipulate their interactions with an environment or host. Leveraging the information theory provides useful quantitative metrics for modeling the information processing capacity of bacterial regulatory networks. As our model accurately predicted gene expression profiles in a bacterial model system, we posit that the information theory-based approaches will be important to enhance our understanding of a wide variety of bacterial regulomes and our ability to engineer bacterial sensory and regulatory networks.

Pseudomonas fluorescens Transportome Is Linked to Strain-Specific Plant Growth Promotion in Aspen Seedlings under Nutrient Stress.

Diverse communities of bacteria colonize plant roots and the rhizosphere. Many of these rhizobacteria are symbionts and provide plant growth promotion (PGP) services, protecting the plant from biotic and abiotic stresses and increasing plant productivity by providing access to nutrients that would otherwise be unavailable to roots. In return, these symbiotic bacteria receive photosynthetically-derived carbon (C), in the form of sugars and organic acids, from plant root exudates. PGP activities have been characterized for a variety of forest tree species and are important in C cycling and sequestration in terrestrial ecosystems. The molecular mechanisms of these PGP activities, however, are less well-known. In a previous analysis of Pseudomonas genomes, we found that the bacterial transportome, the aggregate activity of a bacteria's transmembrane transporters, was most predictive for the ecological niche of Pseudomonads in the rhizosphere. Here, we used Populus tremuloides Michx. (trembling aspen) seedlings inoculated with one of three Pseudomonas fluorescens strains (Pf0-1, SBW25, and WH6) and one Pseudomonas protegens (Pf-5) as a laboratory model to further investigate the relationships between the predicted transportomic capacity of a bacterial strain and its observed PGP effects in laboratory cultures. Conditions of low nitrogen (N) or low phosphorus (P) availability and the corresponding replete media conditions were investigated. We measured phenotypic and biochemical parameters of P. tremuloides seedlings and correlated P. fluorescens strain-specific transportomic capacities with P. tremuloides seedling phenotype to predict the strain and nutrient environment-specific transporter functions that lead to experimentally observed, strain, and media-specific PGP activities and the capacity to protect plants against nutrient stress. These predicted transportomic functions fall in three groups: (i) transport of compounds that modulate aspen seedling root architecture, (ii) transport of compounds that help to mobilize nutrients for aspen roots, and (iii) transporters that enable bacterial acquisition of C sources from seedling root exudates. These predictions point to specific molecular mechanisms of PGP activities that can be directly tested through future, hypothesis-driven biological experiments.

Transport capabilities of environmental Pseudomonads for sulfur compounds.

Sulfur is an essential element in plant rhizospheres and microbial activity plays a key role in increasing the biological availability of sulfur in soil environments. To better understand the mechanisms facilitating the exchange of sulfur-containing molecules in soil, we profiled the binding specificities of eight previously uncharacterized ABC transporter solute-binding proteins from plant-associated Pseudomonads. A high-throughput screening procedure indicated eighteen significant organosulfur binding ligands, with at least one high-quality screening hit for each protein target. Calorimetric and spectroscopic methods were used to validate the best ligand assignments and catalog the thermodynamic properties of the protein-ligand interactions. Two novel high-affinity ligand-binding activities were identified and quantified in this set of solute-binding proteins. Bacteria were cultured in minimal media with screening library components supplied as the sole sulfur sources, demonstrating that these organosulfur compounds can be metabolized and confirming the relevance of ligand assignments. These results expand the set of experimentally validated ligands amenable to transport by this ABC transporter family and demonstrate the complex range of protein-ligand interactions that can be accomplished by solute-binding proteins. Characterizing new nutrient import pathways provides insight into Pseudomonad metabolic capabilities which can be used to further interrogate bacterial survival and participation in soil and rhizosphere communities.

Live Cell Discovery of Microbial Vitamin Transport and Enzyme-Cofactor Interactions.

The rapid completion of microbial genomes is inducing a conundrum in functional gene discovery. Novel methods are needed to shorten the gap between characterizing a microbial genome and experimentally validating bioinformatically predicted functions. Of particular importance are transport mechanisms, which shuttle nutrients such as B vitamins and metabolites across cell membranes and are required for the survival of microbes ranging from members of environmental microbial communities to pathogens. Methods to accurately assign function and specificity for a wide range of experimentally unidentified and/or predicted membrane-embedded transport proteins, along with characterization of intracellular enzyme-cofactor associations, are needed to enable a significantly improved understanding of microbial biochemistry and physiology, microbial interactions, and microbial responses to perturbations. Chemical probes derived from B vitamins B1, B2, and B7 have allowed us to experimentally address the aforementioned needs by identifying B vitamin transporters and intracellular enzyme-cofactor associations through live cell labeling of the filamentous anoxygenic photoheterotroph, Chloroflexus aurantiacus J-10-fl, known to employ mechanisms for both B vitamin biosynthesis and environmental salvage. Our probes provide a unique opportunity to directly link cellular activity and protein function back to ecosystem and/or host dynamics by identifying B vitamin transport and cofactor-dependent interactions required for survival.

Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling.

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.

Predicting bacterial community assemblages using an artificial neural network approach.

Microbial communities are found in nearly all environments and play a critical role in defining ecosystem service. Understanding the relationship between these microbial communities and their environment is essential for prediction of community structure, robustness, and response to ecosystem changes. Microbial Assemblage Prediction (MAP) describes microbial community structure as an artificial neural network (ANN) that models the microbial community as functions of environmental parameters and community intra-microbial interactions. MAP models can be used to predict community assemblages over a wide range of possible environmental parameters, extrapolate the results of point observations across spatial scales, and make predictions about how microbial communities may fluctuate as the result of changes in their environment.

Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction.

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root-mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with 15 transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and jasmonic acid. This multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.

Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks.

Rising atmospheric levels of carbon dioxide and ozone will impact productivity and carbon sequestration in forest ecosystems. The scale of this process and the potential economic consequences provide an incentive for the development of models to predict the types and rates of ecosystem responses and feedbacks that result from and influence of climate change. In this paper, we use phenotypic and molecular data derived from the Aspen Free Air CO2 Enrichment site (Aspen-FACE) to evaluate modeling approaches for ecosystem responses to changing conditions. At FACE, it was observed that different aspen clones exhibit clone-specific responses to elevated atmospheric levels of carbon dioxide and ozone. To identify the molecular basis for these observations, we used artificial neural networks (ANN) to examine above and below-ground community phenotype responses to elevated carbon dioxide, elevated ozone and gene expression profiles. The aspen community models generated using this approach identified specific genes and subnetworks of genes associated with variable sensitivities for aspen clones. The ANN model also predicts specific co-regulated gene clusters associated with differential sensitivity to elevated carbon dioxide and ozone in aspen species. The results suggest ANN is an effective approach to predict relevant gene expression changes resulting from environmental perturbation and provides useful information for the rational design of future biological experiments.

Using metabolomic and transportomic modeling and machine learning to identify putative novel therapeutic targets for antibiotic resistant Pseudomonad infections.

Hospital acquired infections sicken or kill tens of thousands of patients every year. These infections are difficult to treat due to a growing prevalence of resistance to many antibiotics. Among these hospital acquired infections, bacteria of the genus Pseudomonas are among the most common opportunistic pathogens. Computational methods for predicting potential novel antimicrobial therapies for hospital acquired Pseudomonad infections, as well as other hospital acquired infectious pathogens, are desperately needed. Using data generated from sequenced Pseudomonad genomes and metabolomic and transportomic computational approaches developed in our laboratory, we present a support vector machine learning method for identifying the most predictive molecular mechanisms that distinguish pathogenic from non-pathogenic Pseudomonads. Predictions were highly accurate, yielding F-scores between 0.84 and 0.98 in leave one out cross validations. These mechanisms are high-value targets for the development of new antimicrobial therapies.

Structural and functional characterization of solute binding proteins for aromatic compounds derived from lignin: p-coumaric acid and related aromatic acids.

Lignin comprises 15-25% of plant biomass and represents a major environmental carbon source for utilization by soil microorganisms. Access to this energy resource requires the action of fungal and bacterial enzymes to break down the lignin polymer into a complex assortment of aromatic compounds that can be transported into the cells. To improve our understanding of the utilization of lignin by microorganisms, we characterized the molecular properties of solute binding proteins of ATP-binding cassette transporter proteins that interact with these compounds. A combination of functional screens and structural studies characterized the binding specificity of the solute binding proteins for aromatic compounds derived from lignin such as p-coumarate, 3-phenylpropionic acid and compounds with more complex ring substitutions. A ligand screen based on thermal stabilization identified several binding protein clusters that exhibit preferences based on the size or number of aromatic ring substituents. Multiple X-ray crystal structures of protein-ligand complexes for these clusters identified the molecular basis of the binding specificity for the lignin-derived aromatic compounds. The screens and structural data provide new functional assignments for these solute-binding proteins which can be used to infer their transport specificity. This knowledge of the functional roles and molecular binding specificity of these proteins will support the identification of the specific enzymes and regulatory proteins of peripheral pathways that funnel these compounds to central metabolic pathways and will improve the predictive power of sequence-based functional annotation methods for this family of proteins.

Characterization of transport proteins for aromatic compounds derived from lignin: benzoate derivative binding proteins.

In vitro growth experiments have demonstrated that aromatic compounds derived from lignin can be metabolized and represent a major carbon resource for many soil bacteria. However, the proteins that mediate the movement of these metabolites across the cell membrane have not been thoroughly characterized. To address this deficiency, we used a library representative of lignin degradation products and a thermal stability screen to determine ligand specificity for a set of solute-binding proteins (SBPs) from ATP-binding cassette (ABC) transporters. The ligand mapping process identified a set of proteins from Alphaproteobacteria that recognize various benzoate derivatives. Seven high-resolution crystal structures of these proteins in complex with four different aromatic compounds were obtained. The protein-ligand complexes provide details of molecular recognition that can be used to infer binding specificity. This structure-function characterization provides new insight for the biological roles of these ABC transporters and their SBPs, which had been previously annotated as branched-chain amino-acid-binding proteins. The knowledge derived from the crystal structures provides a foundation for development of sequence-based methods to predict the ligand specificity of other uncharacterized transporters. These results also demonstrate that Alphaproteobacteria possess a diverse set of transport capabilities for lignin-derived compounds. Characterization of this new class of transporters improves genomic annotation projects and provides insight into the metabolic potential of soil bacteria.

BowStrap v1.0: Assigning statistical significance to expressed genes using short-read transcriptome data.

BACKGROUND: Background: Deep RNA sequencing, the application of Next Generation sequencing technology to generate a comprehensive profile of the message RNA present in a set of biological samples, provides unprecedented resolution into the molecular foundations of biological processes. By aligning short read RNA sequence data to a set of gene models, expression patterns for all of the genes and gene variants in a biological sample can be calculated. However, accurate determination of gene model expression from deep RNA sequencing is hindered by the presence of ambiguously aligning short read sequences. FINDINGS: BowStrap, a program for implementing the sequence alignment tool 'Bowtie' in a bootstrap-style approach, accommodates multiply-aligning short read sequences and reports gene model expression as an averaged aligned reads per Kb of gene model sequence per million aligned deep RNA sequence reads with a confidence interval, suitable for calculating statistical significance of presence/absence of detected gene model expression. BowStrap v1.0 was validated against a simulated metatranscriptome. Results were compared with two alternate 'Bowtie'-based calculations of gene model expression. BowStrap is better at accurately identifying expressed gene models in a dataset and provides a more accurate estimate of gene model expression level than methods that do not incorporate a boot-strap style approach. CONCLUSIONS: BowStrap v1.0 is superior in ability to detect significant gene model expression and calculate accurate determination of gene model expression levels compared to other alignment-based methods of determining patterns of gene expression. BowStrap v1.0 also can utilize multiple processors as has decreased run time compared to the previous version, BowStrap 0.5. We anticipate that BowStrap will be a highly useful addition to the available set of Next Generation RNA sequence analysis tools.

Biophysical and structural characterization of a sequence-diverse set of solute-binding proteins for aromatic compounds.

Rhodopseudomonas palustris metabolizes aromatic compounds derived from lignin degradation products and has the potential for bioremediation of xenobiotic compounds. We recently identified four possible solute-binding proteins in R. palustris that demonstrated binding to aromatic lignin monomers. Characterization of these proteins in the absence and presence of the aromatic ligands will provide unprecedented insights into the specificity and mode of aromatic ligand binding in solute-binding proteins. Here, we report the thermodynamic and structural properties of the proteins with aromatic ligands using isothermal titration calorimetry, small/wide angle x-ray scattering, and theoretical predictions. The proteins exhibit high affinity for the aromatic substrates with dissociation constants in the low micromolar to nanomolar range. The global shapes of the proteins are characterized by flexible ellipsoid-like structures with maximum dimensions in the 80-90-Å range. The data demonstrate that the global shapes remained unaltered in the presence of the aromatic ligands. However, local structural changes were detected in the presence of some ligands, as judged by the observed features in the wide angle x-ray scattering regime at q ~0.20-0.40 Å(-1). The theoretical models confirmed the elongated nature of the proteins and showed that they consist of two domains linked by a hinge. Evaluation of the protein-binding sites showed that the ligands were found in the hinge region and that ligand stabilization was primarily driven by hydrophobic interactions. Taken together, this study shows the capability of identifying solute-binding proteins that interact with lignin degradation products using high throughput genomic and biophysical approaches, which can be extended to other organisms.

Environment sensing and response mediated by ABC transporters.

BACKGROUND: Transporter proteins are one of an organism's primary interfaces with the environment. The expressed set of transporters mediates cellular metabolic capabilities and influences signal transduction pathways and regulatory networks. The functional annotation of most transporters is currently limited to general classification into families. The development of capabilities to map ligands with specific transporters would improve our knowledge of the function of these proteins, improve the annotation of related genomes, and facilitate predictions for their role in cellular responses to environmental changes. RESULTS: To improve the utility of the functional annotation for ABC transporters, we expressed and purified the set of solute binding proteins from Rhodopseudomonas palustris and characterized their ligand-binding specificity. Our approach utilized ligand libraries consisting of environmental and cellular metabolic compounds, and fluorescence thermal shift based high throughput ligand binding screens. This process resulted in the identification of specific binding ligands for approximately 64% of the purified and screened proteins. The collection of binding ligands is representative of common functionalities associated with many bacterial organisms as well as specific capabilities linked to the ecological niche occupied by R. palustris. CONCLUSION: The functional screen identified specific ligands that bound to ABC transporter periplasmic binding subunits from R. palustris. These assignments provide unique insight for the metabolic capabilities of this organism and are consistent with the ecological niche of strain isolation. This functional insight can be used to improve the annotation of related organisms and provides a route to evaluate the evolution of this important and diverse group of transporter proteins.

Using next generation transcriptome sequencing to predict an ectomycorrhizal metabolome.

BACKGROUND: Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. RESULTS: We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose. CONCLUSIONS: The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.

Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset.

BACKGROUND: The world's oceans are home to a diverse array of microbial life whose metabolic activity helps to drive the earth's biogeochemical cycles. Metagenomic analysis has revolutionized our access to these communities, providing a system-scale perspective of microbial community interactions. However, while metagenome sequencing can provide useful estimates of the relative change in abundance of specific genes and taxa between environments or over time, this does not investigate the relative changes in the production or consumption of different metabolites. RESULTS: We propose a methodology, Predicted Relative Metabolic Turnover (PRMT) that defines and enables exploration of metabolite-space inferred from the metagenome. Our analysis of metagenomic data from a time-series study in the Western English Channel demonstrated considerable correlations between predicted relative metabolic turnover and seasonal changes in abundance of measured environmental parameters as well as with observed seasonal changes in bacterial population structure. CONCLUSIONS: The PRMT method was successfully applied to metagenomic data to explore the Western English Channel microbial metabalome to generate specific, biologically testable hypotheses. Generated hypotheses linked organic phosphate utilization to Gammaproteobactaria, Plantcomycetes, and Betaproteobacteria, chitin degradation to Actinomycetes, and potential small molecule biosynthesis pathways for Lentisphaerae, Chlamydiae, and Crenarchaeota. The PRMT method can be applied as a general tool for the analysis of additional metagenomic or transcriptomic datasets.

Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome.

BACKGROUND: Accurate structural annotation is important for prediction of function and required for in vitro approaches to characterize or validate the gene expression products. Despite significant efforts in the field, determination of the gene structure from genomic data alone is a challenging and inaccurate process. The ease of acquisition of transcriptomic sequence provides a direct route to identify expressed sequences and determine the correct gene structure. METHODOLOGY: We developed methods to utilize RNA-seq data to correct errors in the structural annotation and extend the boundaries of current gene models using assembly approaches. The methods were validated with a transcriptomic data set derived from the fungus Laccaria bicolor, which develops a mycorrhizal symbiotic association with the roots of many tree species. Our analysis focused on the subset of 1501 gene models that are differentially expressed in the free living vs. mycorrhizal transcriptome and are expected to be important elements related to carbon metabolism, membrane permeability and transport, and intracellular signaling. Of the set of 1501 gene models, 1439 (96%) successfully generated modified gene models in which all error flags were successfully resolved and the sequences aligned to the genomic sequence. The remaining 4% (62 gene models) either had deviations from transcriptomic data that could not be spanned or generated sequence that did not align to genomic sequence. The outcome of this process is a set of high confidence gene models that can be reliably used for experimental characterization of protein function. CONCLUSIONS: 69% of expressed mycorrhizal JGI "best" gene models deviated from the transcript sequence derived by this method. The transcriptomic sequence enabled correction of a majority of the structural inconsistencies and resulted in a set of validated models for 96% of the mycorrhizal genes. The method described here can be applied to improve gene structural annotation in other species, provided that there is a sequenced genome and a set of gene models.

Bacterial systems for production of heterologous proteins.

Proteins are the working molecules of all biological systems and participate in a majority of cellular chemical reactions and biological processes. Knowledge of the properties and function of these molecules is central to an understanding of chemical and biological processes. In this context, purified proteins are a starting point for biophysical and biochemical characterization methods that can assist in the elucidation of function. The challenge for production of proteins at the scale and quality required for experimental, therapeutic and commercial applications has led to the development of a diverse set of methods for heterologous protein production. Bacterial expression systems are commonly used for protein production as these systems provide an economical route for protein production and require minimal technical expertise to establish a laboratory protein production system.