article: The generation of genome-scale data is becoming more routine, yet the subsequent analysis of omics data remains a significant challenge. Here, an approach that integrates multiple omics datasets with bioinformatics tools was developed that produces a detailed annotation of several microbial genomic features. This methodology was used to characterize the genome of Thermotoga maritima—a phylogenetically deep-branching, hyperthermophilic bacterium. Experimental data were generated for whole-genome resequencing, transcription start site (TSS) determination, transcriptome profiling, and proteome profiling. These datasets, analyzed in combination with bioinformatics tools, served as a basis for the improvement of gene annotation, the elucidation of transcription units (TUs), the identification of putative non-coding RNAs (ncRNAs), and the determination of promoters and ribosome binding sites. This revealed many distinctive properties of the T. maritima genome organization relative to other bacteria. This genome has a high number of genes per TU (3. 3), a paucity of putative ncRNAs (12), and few TUs with multiple TSSs (3. 7%). Quantitative analysis of promoters and ribosome binding sites showed increased sequence conservation relative to other bacteria. The 5′UTRs follow an atypical bimodal length distribution comprised of “Short” 5′UTRs (11–17 nt) and “Common” 5′UTRs (26–32 nt). Transcriptional regulation is limited by a lack of intergenic space for the majority of TUs. Lastly, a high fraction of annotated genes are expressed independent of growth state and a linear correlation of mRNA/protein is observed (Pearson r = 0. 63, p<2. 2×10−16 t-test). These distinctive properties are hypothesized to be a reflection of this organism' s hyperthermophilic lifestyle and could yield novel insights into the evolutionary trajectory of microbial life on earth.
  A fundamental step towards obtaining a systems-level understanding of organisms is to obtain an accurate inventory of cellular components and their interconnectivities [1]–[3]. The genome sequence and in silico predictions of gene annotation are the starting points for assembling a network. For prokaryotes, these in silico approaches detect open reading frames and structural RNAs with varying degrees of accuracy [4]. Recently, multi-omic data generation and analysis studies [5]–[11] have revealed an abundance of genomic features that are not detected computationally such as transcription start sites (TSSs), promoters, untranslated regions (UTRs), non-coding RNAs, ribosome binding sites (RBSs) and transcription termination sites [12]. However, the rate at which multi-omic datasets are being generated is substantially outpacing the development of analysis workflows for these inherently dissimilar data types [13]. Here, multi-omic experimental data is generated and analyzed in conjunction with bioinformatics tools to annotate numerous bacterial genomic features that cannot accurately be detected using in silico approaches alone. This methodology was applied to study the genome organization of Thermotoga maritima—a phylogenetically deep-branching, hyperthermophilic bacterium with a compact 1. 86 Mb genome. Originally isolated from geothermally heated marine sediment, T. maritima grows between 60–90°C with an optimal growth temperature of 80°C [14]. This species belongs to the order Thermotogales that have, until recently, been exclusively comprised of thermophilic or hyperthermophilic organisms. Compared to most bacteria, Thermotogales are capable of sustaining growth over a remarkably wide range of temperatures. For instance, Kosmotoga olearia can be cultivated between 20–80 °C [15]. Recently, the existence of mesophilic Thermotogales [16], [17] was confirmed with the description of Mesotoga prima, which grows from 20–50 °C with an optimum at 37 °C [18]. Sequencing of M. prima revealed that it has the largest genome of all the Thermotogales at 2. 97 Mb with ∼15% noncoding DNA [19]. T. maritima, which grows at the upper-limit known for Thermotogales, has one of the smallest genomes in this order and maintains one of the most compact genomes among all sequenced bacterial species (<5% noncoding DNA) [20], [21]. The short intergenic regions in the T. maritima genome (5 bp median) resemble those in the genome of Pelagibacter ubique, a bacterium that has undergone genome streamlining and has the shortest median intergenic space (3 bp) among free-living bacteria [20]. Although it remains unclear whether T. maritima has also undergone streamlining, both organisms encode only a few global regulators (four sigma factors in T. maritima versus two in P. ubique) and carry just a single rRNA operon. In contrast with P. ubique, T. maritima displays more metabolic diversity through its ability to ferment numerous mono- and polysaccharides [14], [22]. Thermotogales have been the focus of many evolutionary studies [23]–[25]. Organisms in hydrothermal vent communities, where many Thermotogales have been isolated, are thought to harbor traits of early microorganisms [26]. Phylogenetic analysis of 16S rRNA sequences place the Thermotogae at the base of the bacterial phylogenetic tree [27], [28]; however, Zhaxybayeva et al. [25] determined through analysis of 16S rRNA and ribosomal protein genes that Thermotogae and Aquificales (a hyperthermophilic order) are sister taxa. The authors also determined that the majority of Thermotogae proteins align best with those found in the order Firmicutes [25]; therefore, the exact phylogenetic position of Thermotogae is still unresolved. Nevertheless, members of this phylum are among the deepest branching bacterial species and, as such, prime candidates for evolutionary studies. Thermophiles such as T. maritima implement numerous strategies at both the protein and nucleic acid levels to support growth at high temperatures. For instance, intrinsic protein stabilization is achieved by utilizing more charged residues at the protein surface, encoding for a dense hydrophobic core, and increasing disulfide bond usage [29], [30]. DNA is typically kept from denaturing by introducing positive supercoils via reverse gyrase activity while phosphodiester bond degradation is prevented by stabilization through interaction with cations (e. g. K+, Mg2+) and polyamines [31], [32]. However, the impact of temperature on genome features essential to gene expression such as promoters and RBSs remains largely unexplored. Bacterial transcription initiation is governed by recognition of promoter sequences by sigma factors, which load the RNA polymerase holoenzyme upstream of the transcription start site (TSS). Translation initiation is predominantly reliant on base pairing between the anti-Shine-Dalgarno sequence found near the 3′-terminus of the 16S rRNA and the Shine-Dalgarno sequence (i. e. the RBS). Therefore, thermophilic macromolecular synthesis machinery must establish and retain contacts with nucleic acids while facing greater thermodynamic challenges. The integrated approach described here enables an experimentally anchored annotation of several bacterial genomic features including protein-coding genes, functional RNAs, non-coding RNAs, transcription units (TUs), promoters, ribosome binding sites (RBSs) and regulatory sites such as transcription factor (TF) binding sites, 5′ and 3′ untranslated regions (UTRs) and intergenic regions. This is achieved through the simultaneous analysis of genomic, transcriptomic and proteomic experimental datasets with complementary bioinformatics approaches. In addition to providing a valuable resource to the research community, this analysis framework facilitates quantitative and comparative analysis of annotated features across microbial species. For the genome of T. maritima, several distinguishing characteristics were identified and their potential causal factors are discussed.
  An integrative workflow was developed to re-annotate the genome of T. maritima. The re-annotated genome is the result of the simultaneous reconciliation of multiple omics data sources (Figure 1, upper left) with bioinformatics approaches (Figure 1, upper right). Omics data generated included: (1) genome resequencing, (2) transcription start site (TSS) identification using a modified 5' RACE (Rapid Amplification of cDNA Ends) protocol, (3) transcriptome profiling using both high-density tiling arrays and strand-specific RNA-seq, and (4) LC-MS/MS shotgun proteomics. Transcriptome data were generated from cultures grown in diverse conditions including log phase growth, late exponential phase, heat shock, and growth inhibition by hydrogen (See Materials and Methods). Proteomic datasets include log phase growth and late exponential phase growth conditions. In combination with various bioinformatics approaches, integration of these omics datasets allowed for the definition of gene and transcription units (TU) boundaries with single base-pair resolution. The updated and expanded annotation served as the basis for genome-wide identification of promoters, ribosome binding sites (RBSs), intrinsic transcriptional terminators and UTRs. The genome-wide identification of promoter and RBS sites was facilitated by the annotated TU start loci and protein start codons (Figure 2A). Promoter and RBS sequences were then quantitatively analyzed using thermodynamic principles. These same quantitative measures were applied to numerous organisms for interspecies comparison. Regulation in T. maritima was studied from the vantage point of an organism with extremely short intergenic regions. In both microbes [55] and higher organisms [56] it was shown that the regulatory complexity of an operon positively correlates with the amount of intergenic space found upstream of that operon. Promoter-containing intergenic regions (PIRs) served as well-defined genomic regions for this analysis (Figure 3A). PIRs contain target sites for transcriptional regulation (e. g. promoters and TF binding sites) as well as translational regulation (e. g. RBSs). Each PIR can be divided into two components in relation to the TSS: the sequence downstream of the TSS (the 5′UTR) and the sequence upstream of the TSS. Transcriptome data indicate that the genome of T. maritima is exceptionally active irrespective of growth condition (Figure 4A) with 91–96% of genes expressed above an FPKM threshold of 8. This fraction of genes transcribed is uncharacteristically high compared to other free-living bacteria (see Table S7). Furthermore, translational evidence supporting the high gene expression activity of T. maritima is found in the proteomic datasets. In each condition tested, peptide evidence was detected for 74% of the annotated proteins. It is also found that mRNA and protein abundances are tightly linked (Pearson r = 0. 63, p<2. 2×10−16 t-test) (Figure 4B). This correlation is stronger and more significant than those reported in comparable studies for other bacteria [58], [59].
  Genome-scale technologies have provided researchers unprecedented access to large volumes of data detailing the composition of a cell. However, approaches for data analysis and interpretation have lagged behind due to the scope and complexity of these data types. Here, we present a framework for multi-omic data analysis that annotates genomic features involved in transcription, translation and regulation. This methodology integrates genome-scale datasets with bioinformatics predictions to produce 1) an improvement of the gene annotation, 2) an experimentally validated TU architecture and 3) the identification of putative antisense, non-coding transcripts and alternative TSSs. Using these annotated genomic features enabled the genome-wide identification of promoters and RBSs, which are difficult to identify solely using in silico approaches [60], [61]. Furthermore, the relative binding strength of individual promoters and RBSs was quantitatively measured using thermodynamic principles enabling multi-species comparison of these sequence features. The annotated genome organization served as a scaffold for analyzing regulatory features. Transcription factor regulation was examined with respect to promoter containing intergenic regions while the translational impact of the 5′UTR distribution was considered. The multi-omic data generation and analysis demonstrated here is applicable to many microbial species. Applying this methodology to study the genome organization of T. maritima revealed that it has many distinctive properties compared to other organisms. Genome-scale analysis of promoters showed that T. maritima encodes a highly conserved, robust architecture that ensures transcription initiation. Similarly, RBS sequence conservation was shown to be thermodynamically sufficient for translation initiation for almost all T. maritima genes at 80°C compared with only a fraction of E. coli genes. The distinctive properties of the T. maritima genome extend beyond sequence composition and are apparent at the organizational level. The high protein-coding density and minimal intergenic space found in this organism have resulted in a high number of genes per TU, a paucity of putative ncRNAs and few TUs with multiple start sites. Furthermore, transcriptional regulation appears to be limited to a few TUs due to a lack of genomic space in PIRs. Interestingly, the 5′UTR component of the PIR was found to be uncharacteristically bimodal and was comprised of an unusually short grouping of 5′UTRs. Lastly, the constrained genome organization of T. maritima is reflected in the physiological state of the cell. Transcription of the vast majority of genes is detected independent of culture condition and the correlation between protein and mRNA is stronger than previously observed in other bacteria. We hypothesize that the hyperthermophilic lifestyle of T. maritima could potentially explain the distinctive characteristics of this organism' s genome organization. For instance, the increased sequence conservation of promoter elements and RBSs throughout the T. maritima genome may be attributed to the need to maintain gene expression under extreme temperature conditions. Macromolecular interactions (e. g. protein/protein, protein/DNA and RNA/RNA) are intrinsically harder to maintain at higher temperatures. In the case of TF binding sites, it has been shown that each nucleotide deviation from consensus results in a ∼2kbT penalty to the maximum binding free energy for a given TF (where kb is Boltzmann' s constant and T is temperature) [62]. Increasing the temperature amplifies the binding free energy penalty for every non-conserved base pair. Therefore at 80°C, mismatches between the Shine-Dalgarno and anti-Shine-Dalgarno sequence are especially severe. Thus, T. maritima must overcome the intrinsic challenge of recognizing and retaining contact at the initiation site for both transcription and translation. Our data suggests that high sequence conservation of promoter and RBS sequences is one of the mechanisms used by T. maritima to ensure sufficient gene expression. This sequence-level adaptation could be analogous to many others observed in thermophilic organisms such as the amino acid composition of proteins [29], [30] and the GC content of structural RNAs [63]. The minimal intergenic space found in the T. maritima genome is reminiscent of a streamlined genome, which could explain the limited regulatory capacity observed in this organism. Inflexibility of metabolic regulons has been previously alluded to for other Thermotogales [64]. Here it is demonstrated that, for most TUs, a lack of physical space exists for transcriptional regulation by TFs. Furthermore, the Short 5′UTR group carries the minimum number of nucleotides needed to recruit the ribosome based on Shine-Dalgarno/anti-Shine-Dalgarno interactions [54]. Further reduction in 5′UTR length would abolish translation. Short 5′UTRs also reduce the capacity to regulate by limiting 5′UTR interactions [65], [66]. Though thermodynamics and physical space are hypothesized to contribute to the characteristic features of the T. maritima genome, the phylogenetic contribution cannot be dismissed. These potential causal factors are difficult to decouple. For RBSs, we were able to determine the impact of phylogeny and optimal growth temperature on RBS binding strength. By analyzing RBSs from 109 bacterial species spanning many phyla and having a diverse range of optimal growth temperatures we were able to demonstrate that both phylogeny and optimal growth temperature were significant determinants of RBSs sequence composition. However, a recent analysis of genome size among species of the order Thermotogales could not resolve the impact of phylogeny from optimal growth temperature [19]. The authors found that a negative correlation between genome size and optimal growth temperature exists within this order but the correlation did not hold when phylogeny was accounted for in the analysis. Interestingly, this study also found that the number of predicted transcriptional regulators and intergenic space is higher in Mesotoga prima, a mesophilic member of the Thermotogales. Thus, the relationship between phylogeny and the genome organization is difficult to elucidate without the generation of more datasets similar to the one presented here. Thermotogae are an ideal phylum for future investigations on the causal impact of factors such as temperature, intergenic space and phylogeny on genome organization. This phylum contains organisms that are found in many diverse environments with a wide range of optimal growth temperatures. Generating multi-omic datasets and analyzing them using an integrated, quantitative workflow for numerous Thermotogae species would enable assessment of various environmental factors in the context of phylogenetic distance. Furthermore, given their phylogenetic depth, characterization of the Thermotogae will also provide insights in the evolutionary trajectory of microbial life on earth.
  T. maritima MSB8 ATCC derived cultures were grown at 80°C under anoxic conditions in a chemically defined, minimal medium [67]. Cultures were maintained in either serum bottles or pH-controlled (6. 5) fermenters with continuous 80% N2,20% CO2 sparging. Maltose and acetate concentrations were measured using an HPLC. HPLC parameters were previously described [68]. The following growth conditions were used for omics analysis: 1) log phase, 2) carbon-limited late exponential phase, 3) heat shock and 4) H2 inhibition. Log phase samples were collected from mid-exponential phase cultures grown in 125 mL serum bottles with 50 mL working volume of media and 10 mM maltose as the sole carbon source. Carbon-limited late exponential phase cultures were grown in pH controlled fermenters with pH control and continuous stripping of evolved hydrogen. Cultures were monitored for OD and maltose concentration and samples were collected upon depletion of maltose. The heat shock condition was achieved by rapidly heating mid-exponential phase cultures grown in serum bottles (similar to the log phase condition) to 90°C and sampled after 10 minutes for transcriptome analysis. This has been shown to result in the heat shock response [69]. H2 inhibition was achieved by allowing the native evolution of hydrogen to accumulate in serum bottles (similar to the log phase condition). Arrested growth was indicated by successive OD readings that showed no change measured every 30 minutes. Growth profiles for these conditions are shown in Figure S4. The recent identification of a 9 kb gap in the T. maritima MSB8 genome [33] prompted genome resequencing. Genomic DNA was isolated using Promega' s Wizard Genomic DNA Purification Kit. Paired-end resequencing libraries were generated following standard Illumina protocols and sequenced on an Illumina GAIIx platform. The updated genome sequence was assembled as follows: (1) Reads were aligned to the 8. 9 kb region identified in the T. maritima MSB8 DSMZ genomovar (AGIJ00000000. 1) [33] and the TIGR genomovar (AE000512. 1) sequence using SHOREmap [70] and MosaikAligner (http: //bioinformatics. bc. edu/marthlab/Mosaik). (2) Unaligned reads were de novo assembled using Velvet [71] to ensure no additional assemblies were present. (3) The sequence was corrected for SNPs and indels detected during read alignment. An updated genome annotation was generated using the RAST pipeline with the default parameters [34]. Predicted gene sequences were mapped to the AE000512. 1 annotation using a bidirectional Smith-Waterman alignment to identify the corresponding locus tags. Instances where ≥30 bp separated the predicted gene length between annotations were reconciled through manual inspection of gene expression data and bioinformatics predictions. Gene length differences <30 bp could not be reconciled (unless peptide data supported only one annotation). In these cases, the updated sequence annotation was retained. Total RNA was isolated from log phase cultures using the hot SDS/phenol approach as previously described (http: //www. bio. davidson. edu/projects/GCAT/protocols/ecoli/RNApurification. pdf). DNase-treated total RNA samples were recovered using Fisher SurePrep TrueTotal RNA columns. Two biological replicate TSS sequencing libraries were constructed as previously described [7]. Illumina reads were aligned to the updated T. maritima genome using the Mosaik Aligner. The number of sequenced reads and the number of aligned reads can be found in Table S10. Only uniquely mapped 5′ ends with ≥5 reads were retained as potential TSSs. Tiling array and RNA-seq data were generated under log phase growth, carbon-limiting late exponential phase, heat shock and hydrogen inhibited conditions. Total RNA was isolated using the TRIzol (Invitrogen) extraction procedure followed by DNase treatment and purification using either the Qiagen RNeasy Mini Kit (Tiling Arrays) or the SurePrep TrueTotal RNA columns (RNA-seq). Custom tiling arrays were synthesized based on the AE000512. 1 genome sequence by Roche Nimblegen to carry 71,548 probes with a mean interval of 25 bp. Probe information was remapped to the updated genome sequence. Of the original 71,548 probes, only 125 did not map. Labeled cDNA was generated and processed as previously described [7]. The Transcription Detector algorithm [72] determined probes expressed above background at a FDR = 0. 05. Paired-end, strand-specific RNA-seq was performed using the dUTP method [73] with the following modifications. rRNA was removed with Epicentre' s Ribo-Zero rRNA Removal Kit. Subtracted RNA was fragmented for 3 min using Ambion' s RNA Fragmentation Reagents. cDNA was generated using Invitrogen' s SuperScript III First-Strand Synthesis protocol with random hexamer priming. Illumina reads were aligned to the updated T. maritima genome using Bowtie [74] with up to 2 mismatches per read alignment. The number of sequenced reads and the number of aligned reads can be found in Table S10. FPKM values were calculated using Cufflinks [75]. Functional RNA transcripts were excluded from FPKM determination. Proteomics samples and data were generally prepared as previously described [76]. In summary, triplicate samples of both log phase and late exponential phase culture were lysed by French press, and proteins were extracted into global, soluble, and insoluble fractions. The three protein fractions were digested with trypsin (Promega) for 4 h at 37°C and then cleaned-up using C18 or SCX SPE columns (Supelco), as appropriate. Resulting peptide samples were separated in the first dimension by high pH HPLC (Agilent) and then analyzed by LC-MS/MS using C18 resin (Phenomenex) with an expontial gradient on a custom built LC platform coupled to a linear ion trap (LTQ) or a Velos Orbitrap mass spectrometer (Thermo Scientific) operated in data dependent mode. Peptides were identified by SEQUEST (Thermo Scientific) against a six-frame translation of the T. maritima genome with no protease specified in the search. Xcorr values were refined to conform to generally accepted criteria and were applied to result in a false discovery rate of 0. 16% at the peptide level. Non-quantitative peptide-level data can be found in Table S8. Normalized protein abundances can be found in Table S9. Quantitative Peptide-level data was extracted from Lerman et al. [77] and mapped to the CP004077 genome annotation. The following criteria were used to filter proteins for quantitative analysis: 1) the protein has a total spectral count ≥2 across all conditions (minimum of two unique peptides or a single unique peptide with two observations), 2) the protein has ≥1 observed peptide under log phase since our data was correlated against log phase transcriptome data. Redundant peptides (i. e. peptides mapping to multiple protein entries) were excluded from the analysis to minimize potential ambiguity. For quantitative analysis, we normalized the observed spectral counts for each ORF by the number of possible fully tryptic peptides in the ORF. The number of possible fully tryptic peptides for each ORF was determined using the Protein Digestion Simulator (http: //omics. pnl. gov/software/ProteinDigestionSimulator. php). Default settings were used, except the parameter “Max Missed Cleavages” was set to 0 and “Minimum Residue Count” was set to 6. These options require fully tryptic peptides of at least length 6. This program only considers peptides 400–2000 m/z up to a charge state (z) of 3, hence a maximum fragment mass of 6000. The process of determining individual σ70 promoter elements upstream of each unique TU start in T. maritima was an iterative process, involving two software packages: BioProspector [78] and MEME [79]. BioProspector is able to identify gapped motif elements so it was used to initially identify T. maritima motifs. In BioProspector, sequences 75 bp upstream of TU starts were searched for bipartite elements (6 and 9 bp in width) with a 10–25 bp allowable gap and visualized through WebLogo [80]. MEME provides deterministic position-weight matrices appropriate for information content calculations. The −10 and extended −10 boxes were searched [−1 to −18] upstream of the TSS while the −35 box was searched [−20 to −44]. E. coli TUs annotated with σ70 promoters and experimentally validated TSSs in the EcoCyc Database (version 15. 0) [49] were extracted for comparative analysis. A similar approach was applied to identify promoter motifs for alternative sigma factors. T. maritima has three annotated alternative sigma factors: RpoE (Tmari_1606), SigH (Tmari_0531) and FliA (Tmari_0904). For RpoE and SigH, the upstream region of TUs having genes showing high differential expression under a given stress condition (heat shock, hydrogen inhibited and carbon-limited late exponential phase) were searched for motif elements. The upstream regions of flagellar gene encoding TUs were searched for a FliA motif. However, no sequence motif could be detected for any of the three alternate sigma factors. Position weight matrices (PWMs) for each promoter element were converted to individual information weight matrices using the following formula established in the field of molecular information theory [43]: Riw (b, i) = 2− (−log2f (b, i) ), where f (b, i) is taken to be the probability of observing base b at position i. The individual information of a sequence, Iseq, was calculated by summing the relevant entries of Riw. For any particular sequence, only one entry of Riw is relevant among 4 bases for each position i in the sequence. Iseq is measured throughout in bits since the log was base 2 in converting the PWM to Riw. Iseq reflects sequence conservation for a single sequence, but natural promoters are often formed by multiple promoter elements, each with their own sequences and corresponding Iseq values. When multiple elements are present, variable length spacers are frequently found between the elements. We applied an approach previously described by Shultzaberger et al. [48] to properly account for all possible promoter elements and the variation in their spacing. This allowed us to assess total sequence conservation for an entire promoter. For each promoter, the information content for a particular binding mode was calculated based on the formulas: (1) Mode 1: Iseq_whole_promoter = Iseq (−10 element) +Iseq (−35 element) −GS (d); (2) Mode 2: Iseq_whole_promoter = Iseq (extended−10 element); (3) Mode 3: Iseq_whole_promoter = Iseq (extended−10 element) +Iseq (−35 element) −GS (d). GS (d) is ‘gap surprisal’ accounting for variable spacing (of length d) between the −10 and −35 elements. GS (d) penalizes for unexpected spacing given the major groove accessibility of B-form DNA and was defined as in equation (3) in Shultzaberger [48] with no small-sample correction factor as the analysis here is performed at genome scale. In accordance with the Shultzaberger model, the space between the −10 and −35 elements was restricted to 15–20 bp as measured from the 3′ end of the −35 element and the 5′ end of the −10 element. This limit on the spacer distance Iseq_whole_promoter is measured in bits. The anti-RBS sequence 5′-UCACCUCCUU-3′ (3′ end of the 16S rRNA) was selected for this study. The hybrid-2s program in the UNAFold software package [81] was used to compute hybridization energies (ΔG) for all possible 10-mers over the temperature range 20–100°C. This dictionary was mined for three applications: (1) binding energy values for all 10-mer sequences in the updated T. maritima genome were computed to aid in annotation improvement, (2) the median positional ΔG for all CDSs ±100 bp from the start codon, and (3) the local minimum ΔG for all CDSs 30 bp upstream of the start codon. RBS binding energies across 109 organisms were calculated using this dictionary. Optimal growth temperatures for all non-Thermotogae bacteria were collected from Takemoto et al. [82] and the protein coding gene annotation for each bacterium was extracted from NCBI. CDS data for all Thermotogae with a complete genome sequence were extracted from NCBI with the exception of T. maritima for which the annotation generated in this study was used. For each organism, the median RBS ΔG was calculated from the set of minimum RBS ΔG' s found for each CDS 30 bp upstream of the annotated start codon. Three distance matrices were constructed for analysis of the 109 bacterial species for which optimum growth temperatures were found. The matrices included are as follows: (1) the absolute difference of median RBS strength values, (2) the absolute difference of optimal growth temperatures and (3) the distance matrix generated by aligning full-length 16S rRNA gene sequences using ClustalW2 (slow mode) followed by the phylogenetic tree generation script (http: //www. ebi. ac. uk/Tools/phylogeny/) with default settings. Next, the Mantel test, which tests the correlation between two distance matrices, was applied to compute the significance of various correlations. The ‘vegan’ package of R was used with its default settings. Intrinsic terminators were predicted using the TransTermHP program [83]. To avoid bias introduced by annotation, no genome annotation was used in prediction of Rho-independent terminators. Only terminator structures predicted with a “100%” confidence score were included in the curation of TUs. Small RNAs were predicted with Infernal [39] using cmsearch with default settings against the Rfam 10. 0 Database [84] of small RNA families. sRNAs with an E-value<0. 01 were manually curated to verify expression. These sRNAs were checked against the sRNA predictions from Rfam and fRNA-DB (http: //www. ncrna. org) based on the AE000512. 1 genome sequence. TU assembly was accomplished through an iterative procedure beginning with tiling array expression data. Tiling array data was processed with two Bioconductor packages for transcript segmentation based on change point analysis: tilingArray (http: //www. bioconductor. org/packages/2. 2/bioc/html/tilingArray. html) and DNAcopy (http: //www. bioconductor. org/packages/2. 3/bioc/html/DNAcopy. html). Manual comparison of the output from both packages with array data was used to refine the automated set of transcriptional segments. Additional datasets and bioinformatics predictions were added and manually curated to fully characterize the TU assembly. TSS and RNA-seq data provided single-base pair resolution of segment boundaries. Intrinsic terminator predictions were also used for 3′ boundary definition. ncRNAs were identified using the transcript segments. Transcribed regions not associated with a TU and with length exceeding 68 nt (the combined length of the paired end reads with no insert separating them) were quantified using Cufflinks to generate FPKM values across all RNA-seq conditions. Regions with at least two conditions showing FPKM values >8 were retained as putative ncRNAs. TF binding sites were extracted from RegPrecise [57] and coordinates were mapped to the updated genome. Table S6 has the TF binding sites used in Figure 3C. The T. maritima MSB8 ATCC (genomovar) genome and annotation are found under Genbank Accession CP004077. RNA-seq, TSS, and tiling array datasets are available in the Gene Expression Omnibus under Accession GSE37483. Proteogenomic data are made available through PNNL (http: //omics. pnl. gov) and in Table S8.
summary: Genomic studies have greatly benefited from the advent of high-throughput technologies and bioinformatics tools. Here, a methodology integrating genome-scale data and bioinformatics tools is developed to characterize the genome organization of the hyperthermophilic, phylogenetically deep-branching bacterium Thermotoga maritima. This approach elucidates several features of the genome organization and enables comparative analysis of these features across diverse taxa. Our results suggest that the genome of T. maritima is reflective of its hyperthermophilic lifestyle. Ultimately, constraints imposed on the genome have negative impacts on regulatory complexity and phenotypic diversity. Investigating the genome organization of Thermotogae species will help resolve various causal factors contributing to the genome organization such as phylogeny and environment. Applying a similar analysis of the genome organization to numerous taxa will likely provide insights into microbial evolution.
keywords: genomics
  molecular cell biology
  microbiology
  biology
  evolutionary biology
  proteomics
title: The Genome Organization of Thermotoga maritima Reflects Its Lifestyle