Environment sensing and response mediated by ABC transporters

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>To improve the utility of the functional annotation for ABC transporters, we expressed and purified the set of solute binding proteins from <it>Rhodopseudomonas palustris</it> 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 <it>R. palustris</it>.</p> <p>Conclusion</p> <p>The functional screen identified specific ligands that bound to ABC transporter periplasmic binding subunits from <it>R. palustris</it>. 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.</p

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

Systematic Two-Hybrid and Comparative Proteomic Analyses Reveal Novel Yeast Pre-mRNA Splicing Factors Connected to Prp19

Prp19 is the founding member of the NineTeen Complex, or NTC, which is a spliceosomal subcomplex essential for spliceosome activation. To define Prp19 connectivity and dynamic protein interactions within the spliceosome, we systematically queried the Saccharomyces cerevisiae proteome for Prp19 WD40 domain interaction partners by two-hybrid analysis. We report that in addition to S. cerevisiae Cwc2, the splicing factor Prp17 binds directly to the Prp19 WD40 domain in a 1∶1 ratio. Prp17 binds simultaneously with Cwc2 indicating that it is part of the core NTC complex. We also find that the previously uncharacterized protein Urn1 (Dre4 in Schizosaccharomyces pombe) directly interacts with Prp19, and that Dre4 is conditionally required for pre-mRNA splicing in S. pombe. S. pombe Dre4 and S. cerevisiae Urn1 co-purify U2, U5, and U6 snRNAs and multiple splicing factors, and dre4Δ and urn1Δ strains display numerous negative genetic interactions with known splicing mutants. The S. pombe Prp19-containing Dre4 complex co-purifies three previously uncharacterized proteins that participate in pre-mRNA splicing, likely before spliceosome activation. Our multi-faceted approach has revealed new low abundance splicing factors connected to NTC function, provides evidence for distinct Prp19 containing complexes, and underscores the role of the Prp19 WD40 domain as a splicing scaffold

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

Abstract 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.</p

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

Assigned Ecological Niche Classifications of Pseudomonad Species.

<p>Assigned Ecological Niche Classifications of Pseudomonad Species.</p

Clustering Pseudomonad genomes using enzyme function counts.

<p>The “Primer 6” core package and enzyme function profile data were used to generate hierarchical clusters. No obvious pattern by species or by ecological function is apparent using only enzyme function count and hierarchical clustering. Suggesting additional data and/or alternate methods are required to deduce Pseudomonad environmental niche using sequenced and annotated genomes.</p

Venn diagram for significant features identified by SVM for each model feature type and for each ecological niche.

<p>All values in diagram are presented as percent of features out of total number of high-weight features for SVM feature type.</p