Soda lakes are saline alkaline lakes characterized by high concentrations of sodium carbonate/bicarbonate which lead to a stable elevated pH (>9), and moderate to extremely high salinity. Despite this combination of extreme conditions, biodiversity in soda lakes is high, and the presence of diverse microbial communities provides a driving force for highly active biogeochemical cycles. The sulfur cycle is one of the most important of these and bacterial sulfur oxidation is dominated by members of the obligately chemolithoautotrophic genus Thioalkalivibrio. Currently, 10 species have been described in this genus, but over one hundred isolates have been obtained from soda lake samples. The genomes of 75 strains were sequenced and annotated previously, and used in this study to provide a comprehensive picture of the diversity and distribution of genes related to dissimilatory sulfur metabolism in Thioalkalivibrio. Initially, all annotated genes in 75 Thioalkalivibrio genomes were placed in ortholog groups and filtered by bi-directional best BLAST analysis. Investigation of the ortholog groups containing genes related to sulfur oxidation showed that flavocytochrome c (fcc), the truncated sox system, and sulfite:quinone oxidoreductase (soe) are present in all strains, whereas dissimilatory sulfite reductase (dsr; which catalyzes the oxidation of elemental sulfur) was found in only six strains. The heterodisulfide reductase system (hdr), which is proposed to oxidize sulfur to sulfite in strains lacking both dsr and soxCD, was detected in 73 genomes. Hierarchical clustering of strains based on sulfur gene repertoire correlated closely with previous phylogenomic analysis. The phylogenetic analysis of several sulfur oxidation genes showed a complex evolutionary history. All in all, this study presents a comprehensive investigation of sulfur metabolism-related genes in cultivated Thioalkalivibrio strains and provides several avenues for future research.

Thiocyanate is a C1 compound containing carbon, nitrogen, and sulfur. It is a (by)product in a number of natural and industrial processes. Because thiocyanate is toxic to many organisms, including humans, its removal from industrial waste streams is an important problem. Although a number of bacteria can use thiocyanate as a nitrogen source, only a few can use it as an electron donor. There are two distinct pathways to use thiocyanate: (i) the "carbonyl sulfide pathway," which has been extensively studied, and (ii) the "cyanate pathway," whose key enzyme, thiocyanate dehydrogenase, was recently purified and studied. Three species of Thioalkalivibrio, a group of haloalkaliphilic sulfur-oxidizing bacteria isolated from soda lakes, have been described as thiocyanate oxidizers: (i) Thioalkalivibrio paradoxus ("cyanate pathway"), (ii) Thioalkalivibrio thiocyanoxidans ("cyanate pathway") and (iii) Thioalkalivibrio thiocyanodenitrificans ("carbonyl sulfide pathway"). In this study we provide a comparative genome analysis of these described thiocyanate oxidizers, with genomes ranging in size from 2.5 to 3.8 million base pairs. While focusing on thiocyanate degradation, we also analyzed the differences in sulfur, carbon, and nitrogen metabolism. We found that the thiocyanate dehydrogenase gene is present in 10 different Thioalkalivibrio strains, in two distinct genomic contexts/genotypes. The first genotype is defined by having genes for flavocytochrome c sulfide dehydrogenase upstream from the thiocyanate dehydrogenase operon (present in two strains including the type strain of Tv. paradoxus), whereas in the second genotype these genes are located downstream, together with two additional genes of unknown function (present in eight strains, including the type strains of Tv. thiocyanoxidans). Additionally, we found differences in the presence/absence of genes for various sulfur oxidation pathways, such as sulfide:quinone oxidoreductase, dissimilatory sulfite reductase, and sulfite dehydrogenase. One strain (Tv. thiocyanodenitrificans) lacks genes encoding a carbon concentrating mechanism and none of the investigated genomes were shown to contain known bicarbonate transporters. This study gives insight into the genomic variation of thiocyanate oxidizing bacteria and may lead to improvements in the application of these organisms in the bioremediation of industrial waste streams.

Lactic acid bacteria (LAB) are utilized widely for the fermentation of foods. In the current post-genomic era, tools have been developed that explore genetic diversity among LAB strains aiming to link these variations to differential phenotypes observed in the strains investigated. However, these genotype-phenotype matching approaches fail to assess the role of conserved genes in the determination of physiological characteristics of cultures by environmental conditions. This manuscript describes a complementary approach in which Lactobacillus plantarum WCFS1 was fermented under a variety of conditions that differ in temperature, pH, as well as NaCl, amino acid, and O(2) levels. Samples derived from these fermentations were analyzed by full-genome transcriptomics, paralleled by the assessment of physiological characteristics, e.g., maximum growth rate, yield, and organic acid profiles. A data-storage and -mining suite designated FermDB was constructed and exploited to identify correlations between fermentation conditions and industrially relevant physiological characteristics of L. plantarum, as well as the associated transcriptome signatures. Finally, integration of the specific fermentation variables with the transcriptomes enabled the reconstruction of the gene-regulatory networks involved. The fermentation-genomics platform presented here is a valuable complementary approach to earlier described genotype-phenotype matching strategies which allows the identification of transcriptome signatures underlying physiological variations imposed by different fermentation conditions.

The identification of translation initiation sites (TISs) constitutes an important aspect of sequence-based genome analysis. An erroneous TIS annotation can impair the identification of regulatory elements and N-terminal signal peptides, and also may flaw the determination of descent, for any particular gene. We have formulated a reference-free method to score the TIS annotation quality. The method is based on a comparison of the observed and expected distribution of all TISs in a particular genome given prior gene-calling. We have assessed the TIS annotations for all available NCBI RefSeq microbial genomes and found that approximately 87% is of appropriate quality, whereas 13% needs substantial improvement. We have analyzed a number of factors that could affect TIS annotation quality such as GC-content, taxonomy, the fraction of genes with a Shine-Dalgarno sequence and the year of publication. The analysis showed that only the first factor has a clear effect. We have then formulated a straightforward Principle Component Analysis-based TIS identification strategy to self-organize and score potential TISs. The strategy is independent of reference data and a priori calculations. A representative set of 277 genomes was subjected to the analysis and we found a clear increase in TIS annotation quality for the genomes with a low quality score. The PCA-based annotation was also compared with annotation with the current tool of reference, Prodigal. The comparison for the model genome of Escherichia coli K12 showed that both methods supplement each other and that prediction agreement can be used as an indicator of a correct TIS annotation. Importantly, the data suggest that the addition of a PCA-based strategy to a Prodigal prediction can be used to 'flag' TIS annotations for re-evaluation and in addition can be used to evaluate a given annotation in case a Prodigal annotation is lacking.

There is a growing interest in the Non-ribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) of microbes, fungi and plants because they can produce bioactive peptides such as antibiotics. The ability to identify the substrate specificity of the enzyme's adenylation (A) and acyl-transferase (AT) domains is essential to rationally deduce or engineer new products. We here report on a Hidden Markov Model (HMM)-based ensemble method to predict the substrate specificity at high quality. We collected a new reference set of experimentally validated sequences. An initial classification based on alignment and Neighbor Joining was performed in line with most of the previously published prediction methods. We then created and tested single substrate specific HMMs and found that their use improved the correct identification significantly for A as well as for AT domains. A major advantage of the use of HMMs is that it abolishes the dependency on multiple sequence alignment and residue selection that is hampering the alignment-based clustering methods. Using our models we obtained a high prediction quality for the substrate specificity of the A domains similar to two recently published tools that make use of HMMs or Support Vector Machines (NRPSsp and NRPS predictor2, respectively). Moreover, replacement of the single substrate specific HMMs by ensembles of models caused a clear increase in prediction quality. We argue that the superiority of the ensemble over the single model is caused by the way substrate specificity evolves for the studied systems. It is likely that this also holds true for other protein domains. The ensemble predictor has been implemented in a simple web-based tool that is available at http://www.cmbi.ru.nl/NRPS-PKS-substrate-predictor/.

Thioalkalivibrio is a genus of obligate chemolithoautotrophic haloalkaliphilic sulfur-oxidizing bacteria. Their habitat are soda lakes which are dual extreme environments with a pH range from 9.5 to 11 and salt concentrations up to saturation. More than 100 strains of this genus have been isolated from various soda lakes all over the world, but only ten species have been effectively described yet. Therefore, the assignment of the remaining strains to either existing or novel species is important and will further elucidate their genomic diversity as well as give a better general understanding of this genus. Recently, the genomes of 76 Thioalkalivibrio strains were sequenced. On these, we applied different methods including (i) 16S rRNA gene sequence analysis, (ii) Multilocus Sequence Analysis (MLSA) based on eight housekeeping genes, (iii) Average Nucleotide Identity based on BLAST (ANIb) and MUMmer (ANIm), (iv) Tetranucleotide frequency correlation coefficients (TETRA), (v) digital DNA:DNA hybridization (dDDH) as well as (vi) nucleotide- and amino acid-based Genome BLAST Distance Phylogeny (GBDP) analyses. We detected a high genomic diversity by revealing 15 new "genomic" species and 16 new "genomic" subspecies in addition to the ten already described species. Phylogenetic and phylogenomic analyses showed that the genus is not monophyletic, because four strains were clearly separated from the other Thioalkalivibrio by type strains from other genera. Therefore, it is recommended to classify the latter group as a novel genus. The biogeographic distribution of Thioalkalivibrio suggested that the different "genomic" species can be classified as candidate disjunct or candidate endemic species. This study is a detailed genome-based classification and identification of members within the genus Thioalkalivibrio. However, future phenotypical and chemotaxonomical studies will be needed for a full species description of this genus.

Sigma-54 is a central regulator in many pathogenic bacteria and has been linked to a multitude of cellular processes like nitrogen assimilation and important functional traits such as motility, virulence, and biofilm formation. Until now it has remained obscure whether these phenomena and the control by Sigma-54 share an underlying theme.

The assimilation of nitrogen in bacteria is achieved through only a few metabolic conversions between alpha-ketoglutarate, glutamate and glutamine. The enzymes that catalyze these conversions are glutamine synthetase, glutaminase, glutamate dehydrogenase and glutamine alpha-ketoglutarate aminotransferase. In low-GC Gram-positive bacteria the transcriptional control over the levels of the related enzymes is mediated by four regulators: GlnR, TnrA, GltC and CodY. We have analyzed the genomes of all species belonging to the taxonomic families Bacillaceae, Listeriaceae, Staphylococcaceae, Lactobacillaceae, Leuconostocaceae and Streptococcaceae to determine the diversity in central nitrogen metabolism and reconstructed the regulation by GlnR.

Gene regulatory networks can be reconstructed by combining transcriptome data from many different experiments to elucidate relations between the activity of certain transcription factors and the genes they control. To obtain insight in the regulatory network of Lactobacillus plantarum, microarray transcriptome data from more than 70 different experimental conditions were combined and the expression profiles of the transcriptional units (TUs) were compared. The TUs that displayed correlated expression were used to identify putative cis-regulatory elements by searching the upstream regions of the TUs for conserved motifs. Predicted motifs were extended and refined by searching for motifs in the upstream regions of additional TUs with correlated expression. In this way, cis-acting elements were identified for 41 regulons consisting of at least four TUs (correlation > 0.7). This set of regulons included the known regulons of CtsR and LexA, but also several novel ones encompassing genes with coherent biological functions. Visualization of the regulons and their connections revealed a highly interconnected regulatory network. This network contains several subnetworks that encompass genes of correlated biological function, such as sugar and energy metabolism, nitrogen metabolism and stress response.

Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.