PHANOTATE: a novel approach to gene identification in phage genomes.

MOTIVATION: Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path. RESULTS: We compare PHANOTATE to other gene callers by annotating a set of 2133 complete phage genomes from GenBank, using PHANOTATE and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with PHANOTATE predicting more genes than the other three. We searched for these extra genes in both GenBank's non-redundant protein database and all of the metagenomes in the sequence read archive, and found that they are present at levels that suggest that these are functional protein-coding genes. AVAILABILITY AND IMPLEMENTATION: https://github.com/deprekate/PHANOTATE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

multiPhATE: bioinformatics pipeline for functional annotation of phage isolates.

SUMMARY: To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE's modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system. AVAILABILITY AND IMPLEMENTATION: multiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combining multiple functional annotation tools increases coverage of metabolic annotation.

BACKGROUND: Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30-50% of genes without functional annotation, severely limiting our knowledge of the "parts lists" that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. RESULTS: Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3-8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli. CONCLUSIONS: Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways.

A comprehensive multi-omics approach uncovers adaptations for growth and survival of Pseudomonas aeruginosa on n-alkanes.

BACKGROUND: Examination of complex biological systems has long been achieved through methodical investigation of the system's individual components. While informative, this strategy often leads to inappropriate conclusions about the system as a whole. With the advent of high-throughput "omic" technologies, however, researchers can now simultaneously analyze an entire system at the level of molecule (DNA, RNA, protein, metabolite) and process (transcription, translation, enzyme catalysis). This strategy reduces the likelihood of improper conclusions, provides a framework for elucidation of genotype-phenotype relationships, and brings finer resolution to comparative genomic experiments. Here, we apply a multi-omic approach to analyze the gene expression profiles of two closely related Pseudomonas aeruginosa strains grown in n-alkanes or glycerol. RESULTS: The environmental P. aeruginosa isolate ATCC 33988 consumed medium-length (C10-C16) n-alkanes more rapidly than the laboratory strain PAO1, despite high genome sequence identity (average nucleotide identity >99%). Our data shows that ATCC 33988 induces a characteristic set of genes at the transcriptional, translational and post-translational levels during growth on alkanes, many of which differ from those expressed by PAO1. Of particular interest was the lack of expression from the rhl operon of the quorum sensing (QS) system, resulting in no measurable rhamnolipid production by ATCC 33988. Further examination showed that ATCC 33988 lacked the entire lasI/lasR arm of the QS response. Instead of promoting expression of QS genes, ATCC 33988 up-regulates a small subset of its genome, including operons responsible for specific alkaline proteases and sphingosine metabolism. CONCLUSION: This work represents the first time results from RNA-seq, microarray, ribosome footprinting, proteomics, and small molecule LC-MS experiments have been integrated to compare gene expression in bacteria. Together, these data provide insights as to why strain ATCC 33988 is better adapted for growth and survival on n-alkanes.

Transcriptomic Analyses Elucidate Adaptive Differences of Closely Related Strains of Pseudomonas aeruginosa in Fuel.

Pseudomonas aeruginosa can utilize hydrocarbons, but different strains have various degrees of adaptation despite their highly conserved genome. P. aeruginosa ATCC 33988 is highly adapted to hydrocarbons, while P. aeruginosa strain PAO1, a human pathogen, is less adapted and degrades jet fuel at a lower rate than does ATCC 33988. We investigated fuel-specific transcriptomic differences between these strains in order to ascertain the underlying mechanisms utilized by the adapted strain to proliferate in fuel. During growth in fuel, the genes related to alkane degradation, heat shock response, membrane proteins, efflux pumps, and several novel genes were upregulated in ATCC 33988. Overexpression of alk genes in PAO1 provided some improvement in growth, but it was not as robust as that of ATCC 33988, suggesting the role of other genes in adaptation. Expression of the function unknown gene PA5359 from ATCC 33988 in PAO1 increased the growth in fuel. Bioinformatic analysis revealed that PA5359 is a predicted lipoprotein with a conserved Yx(FWY)xxD motif, which is shared among bacterial adhesins. Overexpression of the putative resistance-nodulation-division (RND) efflux pump PA3521 to PA3523 increased the growth of the ATCC 33988 strain, suggesting a possible role in fuel tolerance. Interestingly, the PAO1 strain cannot utilize n-C8 and n-C10 The expression of green fluorescent protein (GFP) under the control of alkB promoters confirmed that alk gene promoter polymorphism affects the expression of alk genes. Promoter fusion assays further confirmed that the regulation of alk genes was different in the two strains. Protein sequence analysis showed low amino acid differences for many of the upregulated genes, further supporting transcriptional control as the main mechanism for enhanced adaptation.IMPORTANCE These results support that specific signal transduction, gene regulation, and coordination of multiple biological responses are required to improve the survival, growth, and metabolism of fuel in adapted strains. This study provides new insight into the mechanistic differences between strains and helpful information that may be applied in the improvement of bacterial strains for resistance to biotic and abiotic factors encountered during bioremediation and industrial biotechnological processes.

Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations.

BACKGROUND: Here we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. RESULTS: In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resulting functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. CONCLUSIONS: PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequence-based genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.

Ultra-Low Intensity Post-Pulse Affects Cellular Responses Caused by Nanosecond Pulsed Electric Fields.

High-intensity nanosecond pulse electric fields (nsPEF) can preferentially induce various effects, most notably regulated cell death and tumor elimination. These effects have almost exclusively been shown to be associated with nsPEF waveforms defined by pulse duration, rise time, amplitude (electric field), and pulse number. Other factors, such as low-intensity post-pulse waveform, have been completely overlooked. In this study, we show that post-pulse waveforms can alter the cell responses produced by the primary pulse waveform and can even elicit unique cellular responses, despite the primary pulse waveform being nearly identical. We employed two commonly used pulse generator designs, namely the Blumlein line (BL) and the pulse forming line (PFL), both featuring nearly identical 100 ns pulse durations, to investigate various cellular effects. Although the primary pulse waveforms were nearly identical in electric field and frequency distribution, the post-pulses differed between the two designs. The BL's post-pulse was relatively long-lasting (~50 µs) and had an opposite polarity to the main pulse, whereas the PFL's post-pulse was much shorter (~2 µs) and had the same polarity as the main pulse. Both post-pulse amplitudes were less than 5% of the main pulse, but the different post-pulses caused distinctly different cellular responses. The thresholds for dissipation of the mitochondrial membrane potential, loss of viability, and increase in plasma membrane PI permeability all occurred at lower pulsing numbers for the PFL than the BL, while mitochondrial reactive oxygen species generation occurred at similar pulsing numbers for both pulser designs. The PFL decreased spare respiratory capacity (SRC), whereas the BL increased SRC. Only the PFL caused a biphasic effect on trans-plasma membrane electron transport (tPMET). These studies demonstrate, for the first time, that conditions resulting from low post-pulse intensity charging have a significant impact on cell responses and should be considered when comparing the results from similar pulse waveforms.

StralSV: assessment of sequence variability within similar 3D structures and application to polio RNA-dependent RNA polymerase.

BACKGROUND: Most of the currently used methods for protein function prediction rely on sequence-based comparisons between a query protein and those for which a functional annotation is provided. A serious limitation of sequence similarity-based approaches for identifying residue conservation among proteins is the low confidence in assigning residue-residue correspondences among proteins when the level of sequence identity between the compared proteins is poor. Multiple sequence alignment methods are more satisfactory--still, they cannot provide reliable results at low levels of sequence identity. Our goal in the current work was to develop an algorithm that could help overcome these difficulties by facilitating the identification of structurally (and possibly functionally) relevant residue-residue correspondences between compared protein structures. RESULTS: Here we present StralSV (structure-alignment sequence variability), a new algorithm for detecting closely related structure fragments and quantifying residue frequency from tight local structure alignments. We apply StralSV in a study of the RNA-dependent RNA polymerase of poliovirus, and we demonstrate that the algorithm can be used to determine regions of the protein that are relatively unique, or that share structural similarity with proteins that would be considered distantly related. By quantifying residue frequencies among many residue-residue pairs extracted from local structural alignments, one can infer potential structural or functional importance of specific residues that are determined to be highly conserved or that deviate from a consensus. We further demonstrate that considerable detailed structural and phylogenetic information can be derived from StralSV analyses. CONCLUSIONS: StralSV is a new structure-based algorithm for identifying and aligning structure fragments that have similarity to a reference protein. StralSV analysis can be used to quantify residue-residue correspondences and identify residues that may be of particular structural or functional importance, as well as unusual or unexpected residues at a given sequence position. StralSV is provided as a web service at http://proteinmodel.org/AS2TS/STRALSV/.

Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes.

One of the main steps in gene-finding in prokaryotes is determining which open reading frames encode for a protein, and which occur by chance alone. There are many different methods to differentiate the two; the most prevalent approach is using shared homology with a database of known genes. This method presents many pitfalls, most notably the catch that you only find genes that you have seen before. The four most popular prokaryotic gene-prediction programs (GeneMark, Glimmer, Prodigal, Phanotate) all use a protein-coding training model to predict protein-coding genes, with the latter three allowing for the training model to be created ab initio from the input genome. Different methods are available for creating the training model, and to increase the accuracy of such tools, we present here GOODORFS, a method for identifying protein-coding genes within a set of all possible open reading frames (ORFS). Our workflow begins with taking the amino acid frequencies of each ORF, calculating an entropy density profile (EDP), using KMeans to cluster the EDPs, and then selecting the cluster with the lowest variation as the coding ORFs. To test the efficacy of our method, we ran GOODORFS on 14,179 annotated phage genomes, and compared our results to the initial training-set creation step of four other similar methods (Glimmer, MED2, PHANOTATE, Prodigal). We found that GOODORFS was the most accurate (0.94) and had the best F1-score (0.85), while Glimmer had the highest precision (0.92) and PHANOTATE had the highest recall (0.96).

MultiPhATE2: code for functional annotation and comparison of phage genomes.

To address a need for improved tools for annotation and comparative genomics of bacteriophage genomes, we developed multiPhATE2. As an extension of multiPhATE, a functional annotation code released previously, multiPhATE2 performs gene finding using multiple algorithms, compares the results of the algorithms, performs functional annotation of coding sequences, and incorporates additional search algorithms and databases to extend the search space of the original code. MultiPhATE2 performs gene matching among sets of closely related bacteriophage genomes, and uses multiprocessing to speed computations. MultiPhATE2 can be re-started at multiple points within the workflow to allow the user to examine intermediate results and adjust the subsequent computations accordingly. In addition, multiPhATE2 accommodates custom gene calls and sequence databases, again adding flexibility. MultiPhATE2 was implemented in Python 3.7 and runs as a command-line code under Linux or MAC operating systems. Full documentation is provided as a README file and a Wiki website.

SpaK/SpaR two-component system characterized by a structure-driven domain-fusion method and in vitro phosphorylation studies.

Here we introduce a quantitative structure-driven computational domain-fusion method, which we used to predict the structures of proteins believed to be involved in regulation of the subtilin pathway in Bacillus subtilis, and used to predict a protein-protein complex formed by interaction between the proteins. Homology modeling of SpaK and SpaR yielded preliminary structural models based on a best template for SpaK comprising a dimer of a histidine kinase, and for SpaR a response regulator protein. Our LGA code was used to identify multi-domain proteins with structure homology to both modeled structures, yielding a set of domain-fusion templates then used to model a hypothetical SpaK/SpaR complex. The models were used to identify putative functional residues and residues at the protein-protein interface, and bioinformatics was used to compare functionally and structurally relevant residues in corresponding positions among proteins with structural homology to the templates. Models of the complex were evaluated in light of known properties of the functional residues within two-component systems involving His-Asp phosphorelays. Based on this analysis, a phosphotransferase complexed with a beryllofluoride was selected as the optimal template for modeling a SpaK/SpaR complex conformation. In vitro phosphorylation studies performed using wild type and site-directed SpaK mutant proteins validated the predictions derived from application of the structure-driven domain-fusion method: SpaK was phosphorylated in the presence of (32)P-ATP and the phosphate moiety was subsequently transferred to SpaR, supporting the hypothesis that SpaK and SpaR function as sensor and response regulator, respectively, in a two-component signal transduction system, and furthermore suggesting that the structure-driven domain-fusion approach correctly predicted a physical interaction between SpaK and SpaR. Our domain-fusion algorithm leverages quantitative structure information and provides a tool for generation of hypotheses regarding protein function, which can then be tested using empirical methods.

Quadrupoles for Remote Electrostimulation Incorporating Bipolar Cancellation.

Introduction: A method that utilizes nanosecond bipolar cancellation (BPC) near a quadrupole electrodes to suppress a biological response but cancels the distal BPC at the quadrupole center, i.e., cancellation of cancellation (CANCAN), may allow for a remote focused stimulation at the quadrupole center. Objectives: The primary object of this study was to outline the requirement of the CANCAN implementation and select an effective quadrupole configuration. Results: We have studied three quadrupole electrode configurations, a rod quadrupole, a plate quadrupole (Plate-Q), and a resistor quadrupole. The pulse shapes of electric fields include monophasic pulses, cancellation pulses, and additive pulses. The Plate-Q appears the best for CANCAN as it shows the highest percentage of cancellation pulses among all pulse shapes, allowing for the best spatial focus. Conclusion: For the region of interest characterized in the Plate-Q configuration, the maximum magnitude of bipolar field is twice as that of the unipolar field, which allows for the CANCAN demonstration that involves membrane electropermeabilization.

STRALCP--structure alignment-based clustering of proteins.

Protein structural annotation and classification is an important and challenging problem in bioinformatics. Research towards analysis of sequence-structure correspondences is critical for better understanding of a protein's structure, function, and its interaction with other molecules. Clustering of protein domains based on their structural similarities provides valuable information for protein classification schemes. In this article, we attempt to determine whether structure information alone is sufficient to adequately classify protein structures. We present an algorithm that identifies regions of structural similarity within a given set of protein structures, and uses those regions for clustering. In our approach, called STRALCP (STRucture ALignment-based Clustering of Proteins), we generate detailed information about global and local similarities between pairs of protein structures, identify fragments (spans) that are structurally conserved among proteins, and use these spans to group the structures accordingly. We also provide a web server at http://as2ts.llnl.gov/AS2TS/STRALCP/ for selecting protein structures, calculating structurally conserved regions and performing automated clustering.

Selective distant electrostimulation by synchronized bipolar nanosecond pulses.

A unique aspect of electrostimulation (ES) with nanosecond electric pulses (nsEP) is the inhibition of effects when the polarity is reversed. This bipolar cancellation feature makes bipolar nsEP less efficient at biostimulation than unipolar nsEP. We propose to minimize stimulation near pulse-delivering electrodes by applying bipolar nsEP, whereas the superposition of two phase-shifted bipolar nsEP from two independent sources yields a biologically-effective unipolar pulse remotely. This is accomplished by electrical compensation of all nsEP phases except the first one, resulting in the restoration of stimulation efficiency due to cancellation of bipolar cancellation (CANCAN-ES). We experimentally proved the CANCAN-ES paradigm by measuring YO-PRO-1 dye uptake in CHO-K1 cells which were permeabilized by multiphasic nsEP (600 ns per phase) from two generators; these nsEP were synchronized either to overlap into a unipolar pulse remotely from electrodes (CANCAN), or not to overlap (control). Enhancement of YO-PRO-1 entry due to CANCAN was observed in all sets of experiments and reached ~3-fold in the center of the gap between electrodes, exactly where the unipolar pulse was formed, and equaled the degree of bipolar cancellation. CANCAN-ES is promising for non-invasive deep tissue stimulation, either alone or combined with other remote stimulation techniques to improve targeting.

Prospects for Fungal Bioremediation of Acidic Radioactive Waste Sites: Characterization and Genome Sequence of Rhodotorula taiwanensis MD1149.

Highly concentrated radionuclide waste produced during the Cold War era is stored at US Department of Energy (DOE) production sites. This radioactive waste was often highly acidic and mixed with heavy metals, and has been leaking into the environment since the 1950s. Because of the danger and expense of cleanup of such radioactive sites by physicochemical processes, in situ bioremediation methods are being developed for cleanup of contaminated ground and groundwater. To date, the most developed microbial treatment proposed for high-level radioactive sites employs the radiation-resistant bacterium Deinococcus radiodurans. However, the use of Deinococcus spp. and other bacteria is limited by their sensitivity to low pH. We report the characterization of 27 diverse environmental yeasts for their resistance to ionizing radiation (chronic and acute), heavy metals, pH minima, temperature maxima and optima, and their ability to form biofilms. Remarkably, many yeasts are extremely resistant to ionizing radiation and heavy metals. They also excrete carboxylic acids and are exceptionally tolerant to low pH. A special focus is placed on Rhodotorula taiwanensis MD1149, which was the most resistant to acid and gamma radiation. MD1149 is capable of growing under 66 Gy/h at pH 2.3 and in the presence of high concentrations of mercury and chromium compounds, and forming biofilms under high-level chronic radiation and low pH. We present the whole genome sequence and annotation of R. taiwanensis strain MD1149, with a comparison to other Rhodotorula species. This survey elevates yeasts to the frontier of biology's most radiation-resistant representatives, presenting a strong rationale for a role of fungi in bioremediation of acidic radioactive waste sites.

MannDB - a microbial database of automated protein sequence analyses and evidence integration for protein characterization.

BACKGROUND: MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. DESCRIPTION: MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. CONCLUSION: MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at http://manndb.llnl.gov/.

S2M: A Stochastic Simulation Model of Poliovirus Genetic State Transition.

Modeling the molecular mechanisms that govern genetic variation can be useful in understanding the dynamics that drive genetic state transition in quasispecies viruses. For example, there is considerable interest in understanding how the relatively benign vaccine strains of poliovirus eventually revert to forms that confer neurovirulence and cause disease (ie, vaccine-derived poliovirus). This report describes a stochastic simulation model, S2M, which can be used to generate hypothetical outcomes based on known mechanisms of genetic diversity. S2M begins with predefined genotypes based on the Sabin-1 and Mahoney wild-type sequences, constructs a set of independent cell-based populations, and performs in-cell replication and cell-to-cell infection cycles while quantifying genetic changes that track the transition from Sabin-1 toward Mahoney. Realism is incorporated into the model by assigning defaults for variables that constrain mechanisms of genetic variability based roughly on metrics reported in the literature, yet these values can be modified at the command line in order to generate hypothetical outcomes driven by these parameters. To demonstrate the utility of S2M, simulations were performed to examine the effects of the rates of replication error and recombination and the presence or absence of defective interfering particles, upon reaching the end states of Mahoney resemblance (semblance of a vaccine-derived state), neurovirulence, genome fitness, and cloud diversity. Simulations provide insight into how modeled biological features may drive hypothetical outcomes, independently or in combination, in ways that are not always intuitively obvious.

Transcriptomic analysis of the highly efficient oil-degrading bacterium Acinetobacter venetianus RAG-1 reveals genes important in dodecane uptake and utilization.

The hydrocarbonoclastic bacterium Acinetobacter venetianus RAG-1 has attracted substantial attention due to its powerful oil-degrading capabilities and its potential to play an important ecological role in the cleanup of alkanes. In this study, we compare the transcriptome of the strain RAG-1 grown in dodecane, the corresponding alkanol (dodecanol), and sodium acetate for the characterization of genes involved in dodecane uptake and utilization. Comparison of the transcriptional responses of RAG-1 grown on dodecane led to the identification of 1074 genes that were differentially expressed relative to sodium acetate. Of these, 622 genes were upregulated when grown in dodecane. The highly upregulated genes were involved in alkane catabolism, along with stress response. Our data suggest AlkMb to be primarily involved in dodecane oxidation. Transcriptional response of RAG-1 grown on dodecane relative to dodecanol also led to the identification of permease, outer membrane protein and thin fimbriae coding genes potentially involved in dodecane uptake. This study provides the first model for key genes involved in alkane uptake and metabolism in A. venetianus RAG-1.

CombAlign: a code for generating a one-to-many sequence alignment from a set of pairwise structure-based sequence alignments.

BACKGROUND: In order to better define regions of similarity among related protein structures, it is useful to identify the residue-residue correspondences among proteins. Few codes exist for constructing a one-to-many multiple sequence alignment derived from a set of structure or sequence alignments, and a need was evident for creating such a tool for combining pairwise structure alignments that would allow for insertion of gaps in the reference structure. RESULTS: This report describes a new Python code, CombAlign, which takes as input a set of pairwise sequence alignments (which may be structure based) and generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA). The use and utility of CombAlign was demonstrated by generating gapped MSSAs using sets of pairwise structure-based sequence alignments between structure models of the matrix protein (VP40) and pre-small/secreted glycoprotein (sGP) of Reston Ebolavirus and the corresponding proteins of several other filoviruses. The gapped MSSAs revealed structure-based residue-residue correspondences, which enabled identification of structurally similar versus differing regions in the Reston proteins compared to each of the other corresponding proteins. CONCLUSIONS: CombAlign is a new Python code that generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA) given a set of pairwise sequence alignments (which may be structure based). CombAlign has utility in assisting the user in distinguishing structurally conserved versus divergent regions on a reference protein structure relative to other closely related proteins. CombAlign was developed in Python 2.6, and the source code is available for download from the GitHub code repository.

A strain of Yersinia pestis with a mutator phenotype from the Republic of Georgia.

We describe here a strain of Yersinia pestis, G1670A, which exhibits a baseline mutation rate elevated 250-fold over wild-type Y. pestis. The responsible mutation, a C to T substitution in the mutS gene, results in the transition of a highly conserved leucine at position 689 to arginine (mutS(L689R)). When the MutSL 689R protein of G1670A was expressed in a ΔmutS derivative of Y. pestis strain EV76, mutation rates observed were equivalent to those observed in G1670A, consistent with a causal association between the mutS mutation and the mutator phenotype. The observation of a mutator allele in Yersinia pestis has potential implications for the study of evolution of this and other especially dangerous pathogens.