Impact of genome reduction on bacterial metabolism and its regulation

Comparative genome analysis of Mycoplasma pneumoniae

Background

Mycoplasma pneumoniae is a common pathogen that causes upper and lower respiratory tract infections in people of all ages, responsible for up to 40 % of community-acquired pneumonias. It also causes a wide array of extrapulmonary infections and autoimmune phenomena. Phylogenetic studies of the organism have been generally restricted to specific genes or regions of the genome, because whole genome sequencing has been completed for only 4 strains. To better understand the physiology and pathogenicity of this important human pathogen, we performed comparative genomic analysis of 15 strains of M. pneumoniae that were isolated between the 1940s to 2009 from respiratory specimens and cerebrospinal fluid originating from the USA, China and England.

Results

Illumina MiSeq whole genome sequencing was performed on the 15 strains and all genome sequences were completed. Results from the comparative genomic analysis indicate that although about 1500 SNP and indel variants exist between type1 and type 2 strains, there is an overall high degree of sequence similarity among the strains (>99 % identical to each other). Within the two subtypes, conservation of most genes, including the CARDS toxin gene and arginine deiminase genes, was observed. The major variation occurs in the P1 and ORF6 genes associated with the adhesin complex. Multiple hsdS genes (encodes S subunit of type I restriction enzyme) with variable tandem repeat copy numbers were found in all 15 genomes.

Conclusions

These data indicate that despite conclusions drawn from 16S rRNA sequences suggesting rapid evolution, the M. pneumoniae genome is extraordinarily stable over time and geographic distance across the globe with a striking lack of evidence of horizontal gene transfer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1801-0) contains supplementary material, which is available to authorized users.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology

A model for the cytoplasm of Mycoplasma genitalium is presented that integrates data from a variety of sources into a physically and biochemically consistent model. Based on gene annotations, core genes expected to be present in the cytoplasm were determined and a metabolic reaction network was reconstructed. The set of cytoplasmic genes and metabolites from the predicted reactions were assembled into a comprehensive atomistic model consisting of proteins with predicted structures, RNA, protein/RNA complexes, metabolites, ions, and solvent. The resulting model bridges between atomistic and cellular scales, between physical and biochemical aspects, and between structural and systems views of cellular systems and is meant as a starting point for a variety of simulation studies.

Protein complexes in bacteria

Large-scale analyses of protein complexes have recently become available for Escherichia coli and Mycoplasma pneumoniae, yielding 443 and 116 heteromultimeric soluble protein complexes, respectively. We have coupled the results of these mass spectrometry-characterized protein complexes with the 285 “gold standard” protein complexes identified by EcoCyc. A comparison with databases of gene orthology, conservation, and essentiality identified proteins conserved or lost in complexes of other species. For instance, of 285 “gold standard” protein complexes in E. coli, less than 10% are fully conserved among a set of 7 distantly-related bacterial “model” species. Complex conservation follows one of three models: well-conserved complexes, complexes with a conserved core, and complexes with partial conservation but no conserved core. Expanding the comparison to 894 distinct bacterial genomes illustrates fractional conservation and the limits of co-conservation among components of protein complexes: just 14 out of 285 model protein complexes are perfectly conserved across 95% of the genomes used, yet we predict more than 180 may be partially conserved across at least half of the genomes. No clear relationship between gene essentiality and protein complex conservation is observed, as even poorly conserved complexes contain a significant number of essential proteins. Finally, we identify 183 complexes containing well-conserved components and uncharacterized proteins which will be interesting targets for future experimental studies.

Though more than 20,000 binary protein-protein interactions have been published for a few well-studied bacterial species, the results rarely capture the full extent to which proteins take part in complexes. Here, we use experimentally-observed protein complexes from E. coli or Mycoplasma pneumoniae, as well as gene orthology, to predict protein complexes across many species of bacteria. Surprisingly, the majority of protein complexes is not conserved, demonstrating an unexpected evolutionary flexibility. We also observe broader trends within protein complex conservation, especially in genome-reduced species with minimal sets of protein complexes.

Defining a minimal cell: essentiality of small ORFs and ncRNAs in a genome-reduced bacterium

Identifying all essential genomic components is critical for the assembly of minimal artificial life. In the genome-reduced bacterium Mycoplasma pneumoniae, we found that small ORFs (smORFs; < 100 residues), accounting for 10% of all ORFs, are the most frequently essential genomic components (53%), followed by conventional ORFs (49%). Essentiality of smORFs may be explained by their function as members of protein and/or DNA/RNA complexes. In larger proteins, essentiality applied to individual domains and not entire proteins, a notion we could confirm by expression of truncated domains. The fraction of essential non-coding RNAs (ncRNAs) non-overlapping with essential genes is 5% higher than of non-transcribed regions (0.9%), pointing to the important functions of the former. We found that the minimal essential genome is comprised of 33% (269,410 bp) of the M. pneumoniae genome. Our data highlight an unexpected hidden layer of smORFs with essential functions, as well as non-coding regions, thus changing the focus when aiming to define the minimal essential genome.

The evolutionary origin of form and function

We regard the basic unit of the organism, the cell, as a complex dissipative natural process functioning under the second law of thermodynamics and the principle of least action. Organisms are conglomerates of information bearing cells that optimise the efficiency of energy (nutrient) extraction from its ecosystem. Dissipative processes, such as peptide folding and protein interaction, yield phenotypic information from which form and function emerge from cell to cell interactions within the organism. Organisms, in Darwin's 'proportional numbers', in turn interact to minimise the free energy of their ecosystems. Genetic variation plays no role in this holistic conceptualisation of the life process.

The identification of oppA gene homologues as part of the oligopeptide transport system in mycoplasmas

The lack of an annotated oppA gene as part of many oligopeptide permease (opp) operons has questioned the necessity of the oligopeptide-binding domain (OppA) as a part of the Opp transport system in mycoplasmas. This study investigated the occurrence of an oppA gene as part of the oppBCDF operon in 42 mycoplasma genomes. Except for hemoplasma, all mycoplasmas were found to possess one or more copies of the oppBCDF operon and with the help of similarity searches their oppA genes could be identified. Phylogenetic analysis of the combined OppABCDF amino acid sequences allowed them to be grouped into three types. Each type has a unique set of conserved motifs, which are likely to reflect substrate preference and adaption strategies. Our approach allowed the identification of oppA gene homologues for all mycoplasma opp operons and thereby provides a method for re-evaluating the current annotation of oppA genes in mycoplasma genomes.

Widespread expression of conserved small RNAs in small symbiont genomes

Genome architecture of a microbe markedly changes when it transitions from a free-living lifestyle to an obligate symbiotic association within eukaryotic cells. These symbiont genomes experience numerous rearrangements and massive gene loss, which is expected to radically alter gene regulatory networks compared with those of free-living relatives. As such, it remains unclear whether and how these small symbiont genomes regulate gene expression. Here, using a label-free mass-spec quantification approach we found that differential protein regulation occurs in Buchnera, a model symbiont with a reduced genome, when it transitions between two distinct life stages. However, differential mRNA expression could not be detected between Buchnera life stages, despite the presence of a small number of putative transcriptional regulators. Instead a comparative analysis of small RNA expression profiles among five divergent Buchnera lineages, spanning a variety of Buchnera life stages, reveals 140 novel intergenic and antisense small RNAs and 517 untranslated regions that were significantly expressed, some of which have been conserved for ∼65 million years. In addition, the majority of these small RNAs exhibit both sequence covariation and thermodynamic stability, indicators of a potential structural RNA role. Together, these data suggest that gene regulation at the post-transcriptional level may be important in Buchnera. This is the first study to empirically identify Buchnera small RNAs, and we propose that these novel small RNAs may facilitate post-transcriptional regulation through translational inhibition/activation, and/or transcript stability. Ultimately, post-transcriptional regulation may shape metabolic complementation between Buchnera and its aphid host, thus impacting the animal's ecology and evolution.

MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae

MyMpn (http://mympn.crg.eu) is an online resource devoted to studying the human pathogen Mycoplasma pneumoniae, a minimal bacterium causing lower respiratory tract infections. Due to its small size, its ability to grow in vitro, and the amount of data produced over the past decades, M. pneumoniae is an interesting model organisms for the development of systems biology approaches for unicellular organisms. Our database hosts a wealth of omics-scale datasets generated by hundreds of experimental and computational analyses. These include data obtained from gene expression profiling experiments, gene essentiality studies, protein abundance profiling, protein complex analysis, metabolic reactions and network modeling, cell growth experiments, comparative genomics and 3D tomography. In addition, the intuitive web interface provides access to several visualization and analysis tools as well as to different data search options. The availability and—even more relevant—the accessibility of properly structured and organized data are of up-most importance when aiming to understand the biology of an organism on a global scale. Therefore, MyMpn constitutes a unique and valuable new resource for the large systems biology and microbiology community.

Natural selection for operons depends on genome size

In prokaryotes, genome size is associated with metabolic versatility, regulatory complexity, effective population size, and horizontal transfer rates. We therefore analyzed the covariation of genome size and operon conservation to assess the evolutionary models of operon formation and maintenance. In agreement with previous results, intraoperonic pairs of essential and of highly expressed genes are more conserved. Interestingly, intraoperonic pairs of genes are also more conserved when they encode proteins at similar cell concentrations, suggesting a role of cotranscription in diminishing the cost of waste and shortfall in gene expression. Larger genomes have fewer and smaller operons that are also less conserved. Importantly, lower conservation in larger genomes was observed for all classes of operons in terms of gene expression, essentiality, and balanced protein concentration. We reached very similar conclusions in independent analyses of three major bacterial clades (α- and β-Proteobacteria and Firmicutes). Operon conservation is inversely correlated to the abundance of transcription factors in the genome when controlled for genome size. This suggests a negative association between the complexity of genetic networks and operon conservation. These results show that genome size and/or its proxies are key determinants of the intensity of natural selection for operon organization. Our data fit better the evolutionary models based on the advantage of coregulation than those based on genetic linkage or stochastic gene expression. We suggest that larger genomes with highly complex genetic networks and many transcription factors endure weaker selection for operons than smaller genomes with fewer alternative tools for genetic regulation.

An evolutionary analysis of genome expansion and pathogenicity in Escherichia coli

BACKGROUND

There are several studies describing loss of genes through reductive evolution in microbes, but how selective forces are associated with genome expansion due to horizontal gene transfer (HGT) has not received similar attention. The aim of this study was therefore to examine how selective pressures influence genome expansion in 53 fully sequenced and assembled Escherichia coli strains. We also explored potential connections between genome expansion and the attainment of virulence factors. This was performed using estimations of several genomic parameters such as AT content, genomic drift (measured using relative entropy), genome size and estimated HGT size, which were subsequently compared to analogous parameters computed from the core genome consisting of 1729 genes common to the 53 E. coli strains. Moreover, we analyzed how selective pressures (quantified using relative entropy and dN/dS), acting on the E. coli core genome, influenced lineage and phylogroup formation.

RESULTS

Hierarchical clustering of dS and dN estimations from the E. coli core genome resulted in phylogenetic trees with topologies in agreement with known E. coli taxonomy and phylogroups. High values of dS, compared to dN, indicate that the E. coli core genome has been subjected to substantial purifying selection over time; significantly more than the non-core part of the genome (p<0.001). This is further supported by a linear association between strain-wise dS and dN values (β = 26.94 ± 0.44, R2~0.98, p<0.001). The non-core part of the genome was also significantly more AT-rich (p<0.001) than the core genome and E. coli genome size correlated with estimated HGT size (p<0.001). In addition, genome size (p<0.001), AT content (p<0.001) as well as estimated HGT size (p<0.005) were all associated with the presence of virulence factors, suggesting that pathogenicity traits in E. coli are largely attained through HGT. No associations were found between selective pressures operating on the E. coli core genome, as estimated using relative entropy, and genome size (p~0.98).

CONCLUSIONS

On a larger time frame, genome expansion in E. coli, which is significantly associated with the acquisition of virulence factors, appears to be independent of selective forces operating on the core genome.

Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

MOTIVATION

Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited by inherent inconsistencies and gaps.

RESULTS

Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence, the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks.

AVAILABILITY AND IMPLEMENTATION

The method is available as source code on http://users.minet.uni-jena.de/∼m3kach/ASBIG/ASBIG.zip.

CONTACT

christoph.kaleta@uni-jena.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hydrogen peroxide production from glycerol metabolism is dispensable for virulence of Mycoplasma gallisepticum in the tracheas of chickens

Hydrogen peroxide (H2O2) is a by-product of glycerol metabolism in mycoplasmas and has been shown to cause cytotoxicity for cocultured eukaryotic cells. There appears to be selective pressure for mycoplasmas to retain the genes needed for glycerol metabolism. This has generated interest and speculation as to their function during infection. However, the actual effects of glycerol metabolism and H2O2 production on virulence in vivo have never been assessed in any Mycoplasma species. To this end, we determined that the wild-type (WT) R(low) strain of the avian pathogen Mycoplasma gallisepticum is capable of producing H2O2 when grown in glycerol and is cytotoxic to eukaryotic cells in culture. Transposon mutants with mutations in the genes present in the glycerol transport and utilization pathway, namely, glpO, glpK, and glpF, were identified. All mutants assessed were incapable of producing H2O2 and were not cytotoxic when grown in glycerol. We also determined that vaccine strains ts-11 and 6/85 produce little to no H2O2 when grown in glycerol, while the naturally attenuated F strain does produce H2O2. Chickens were infected with one of two glpO mutants, a glpK mutant, R(low), or growth medium, and tracheal mucosal thickness and lesion scores were assessed. Interestingly, all glp mutants were reproducibly virulent in the respiratory tracts of the chickens. Thus, there appears to be no link between glycerol metabolism/H2O2 production/cytotoxicity and virulence for this Mycoplasma species in its natural host. However, it is possible that glycerol metabolism is required by M. gallisepticum in a niche that we have yet to study.

Reduction of hydrogen peroxide accumulation and toxicity by a catalase from Mycoplasma iowae

Mycoplasma iowae is a well-established avian pathogen that can infect and damage many sites throughout the body. One potential mediator of cellular damage by mycoplasmas is the production of H₂O₂ via a glycerol catabolic pathway whose genes are widespread amongst many mycoplasma species. Previous sequencing of M. iowae serovar I strain 695 revealed the presence of not only genes for H₂O₂ production through glycerol catabolism but also the first documented mycoplasma gene for catalase, which degrades H₂O₂. To test the activity of M. iowae catalase in degrading H₂O₂, we studied catalase activity and H₂O₂ accumulation by both M. iowae serovar K strain DK-CPA, whose genome we sequenced, and strains of the H₂O₂-producing species Mycoplasma gallisepticum engineered to produce M. iowae catalase by transformation with the M. iowae putative catalase gene, katE. H₂O₂-mediated virulence by M. iowae serovar K and catalase-producing M. gallisepticum transformants were also analyzed using a Caenorhabditis elegans toxicity assay, which has never previously been used in conjunction with mycoplasmas. We found that M. iowae katE encodes an active catalase that, when expressed in M. gallisepticum, reduces both the amount of H₂O₂ produced and the amount of damage to C. elegans in the presence of glycerol. Therefore, the correlation between the presence of glycerol catabolism genes and the use of H₂O₂ as a virulence factor by mycoplasmas might not be absolute.

Reconstruction of insulin signal flow from phosphoproteome and metabolome data

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

Microscopy and genomic analysis of Mycoplasma parvum strain Indiana

Mycoplasma parvum [Eperythrozoon parvum] is the second hemotrophic mycoplasma (hemoplasma) described in pigs. Unlike M. suis, its closest phylogenetic relative, M. parvum, is considered a non-pathogenic bacterium in this host species. Natural infection of a domestic, 6-month-old splenectomized pig with M. parvum strain Indiana is described herein. Light and scanning electron microscopy of the bacteria were performed in addition to whole genome sequencing, analysis, and comparison to the genome of M. suis strain Illinois. Neither clinical signs nor anemia were observed during the infection. Microscopy analyses revealed coccoid to rod- shaped organisms varying from 0.2 to 0.5 μm; they were observed individually or in short chains by both light and electron microscopy, however less than 30% of the red blood cells were infected at peak bacteremia. The single circular chromosome of M. parvum was only 564 395 bp, smaller than M. genitalium, previously considered the tiniest member of the Mollicutes. Its general genomic features were similar to others in this class and species circumscription was verified by phylogenomic analysis. A gene-by-gene comparison between M. suis and M. parvum revealed all protein coding sequences (CDS) with assigned functions were shared, including metabolic functions, transporters and putative virulence factors. However, the number of CDS in paralogous gene families was remarkably different with about half as many paralogs in M. parvum. The differences in paralogous genes may be implicated in the different pathogenic potential of these two species, however variable gene expression may also play a role. Both are areas of ongoing investigation.

Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis

We unravel how functional plasticity and redundancy are essential mechanisms underlying the ability to survive of metabolic networks. We perform an exhaustive computational screening of synthetic lethal reaction pairs in Escherichia coli in a minimal medium and we find that synthetic lethal pairs divide in two different groups depending on whether the synthetic lethal interaction works as a backup or as a parallel use mechanism, the first corresponding to essential plasticity and the second to essential redundancy. In E. coli, the analysis of pathways entanglement through essential redundancy supports the view that synthetic lethality affects preferentially a single function or pathway. In contrast, essential plasticity, the dominant class, tends to be inter-pathway but strongly localized and unveils Cell Envelope Biosynthesis as an essential backup for Membrane Lipid Metabolism. When comparing E. coli and Mycoplasma pneumoniae, we find that the metabolic networks of the two organisms exhibit a large difference in the relative importance of plasticity and redundancy which is consistent with the conjecture that plasticity is a sophisticated mechanism that requires a complex organization. Finally, coessential reaction pairs are explored in different environmental conditions to uncover the interplay between the two mechanisms. We find that synthetic lethal interactions and their classification in plasticity and redundancy are basically insensitive to medium composition, and are highly conserved even when the environment is enriched with nonessential compounds or overconstrained to decrease maximum biomass formation.

Plasticity and redundancy are essential features of biological systems, from brain to genome, that underlie the ability of organisms to survive. In metabolic networks, these essential mechanisms are unveiled by the analysis and categorization of synthetic lethal pairs of reactions. We find that plasticity acts as a backup mechanism that reorganizes metabolic fluxes, while redundancy corresponds to a simultaneous use of different flux channels that increases fitness. Both capacities ensure viability and are highly insensitive to environmental conditions, but plasticity seems a more sophisticated mechanism requiring a more complex functional organization. Our results have clear implications for biotechnology and biomedicine, since targeting a plasticity or a redundancy synthetic lethal pair will certainly have different requirements and peculiar specific effects.

Predicting the minimal translation apparatus: lessons from the reductive evolution of mollicutes

Mollicutes is a class of parasitic bacteria that have evolved from a common Firmicutes ancestor mostly by massive genome reduction. With genomes under 1 Mbp in size, most Mollicutes species retain the capacity to replicate and grow autonomously. The major goal of this work was to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in these bacteria. Using the experimentally validated genes from the model bacteria Escherichia coli and Bacillus subtilis as input, genes encoding proteins of the core translation machinery were predicted in 39 distinct Mollicutes species, 33 of which are culturable. The set of 260 input genes encodes proteins involved in ribosome biogenesis, tRNA maturation and aminoacylation, as well as proteins cofactors required for mRNA translation and RNA decay. A core set of 104 of these proteins is found in all species analyzed. Genes encoding proteins involved in post-translational modifications of ribosomal proteins and translation cofactors, post-transcriptional modifications of t+rRNA, in ribosome assembly and RNA degradation are the most frequently lost. As expected, genes coding for aminoacyl-tRNA synthetases, ribosomal proteins and initiation, elongation and termination factors are the most persistent (i.e. conserved in a majority of genomes). Enzymes introducing nucleotides modifications in the anticodon loop of tRNA, in helix 44 of 16S rRNA and in helices 69 and 80 of 23S rRNA, all essential for decoding and facilitating peptidyl transfer, are maintained in all species. Reconstruction of genome evolution in Mollicutes revealed that, beside many gene losses, occasional gains by horizontal gene transfer also occurred. This analysis not only showed that slightly different solutions for preserving a functional, albeit minimal, protein synthetizing machinery have emerged in these successive rounds of reductive evolution but also has broad implications in guiding the reconstruction of a minimal cell by synthetic biology approaches.

Phylogeny and evolution of RNA structure

Darwin's conviction that all living beings on Earth are related and the graph of relatedness is tree-shaped has been essentially confirmed by phylogenetic reconstruction first from morphology and later from data obtained by molecular sequencing. Limitations of the phylogenetic tree concept were recognized as more and more sequence information became available. The other path-breaking idea of Darwin, natural selection of fitter variants in populations, is cast into simple mathematical form and extended to mutation-selection dynamics. In this form the theory is directly applicable to RNA evolution in vitro and to virus evolution. Phylogeny and population dynamics of RNA provide complementary insights into evolution and the interplay between the two concepts will be pursued throughout this chapter. The two strategies for understanding evolution are ultimately related through the central paradigm of structural biology: sequence ⇒ structure ⇒ function. We elaborate on the state of the art in modeling both phylogeny and evolution of RNA driven by reproduction and mutation. Thereby the focus will be laid on models for phylogenetic sequence evolution as well as evolution and design of RNA structures with selected examples and notes on simulation methods. In the perspectives an attempt is made to combine molecular structure, population dynamics, and phylogeny in modeling evolution.

Comparative genomics and phylogenomics of hemotrophic mycoplasmas

Hemotrophic mycoplasmas (hemoplasmas) are a group of animal pathogens of the Mollicutes class. Recently, the genomes of 8 hemoplasmas have been completely sequenced. The aim of this study was to gain a better understanding of their genomic features and relationship to other Mycoplasma species. The genome structure and dynamics of hemoplasmas were analyzed by evaluating gene synteny, adaptive evolution of paralogous gene families (PGF) and horizontal gene transfer (HGT). The Mollicutes class was then phylogenetically analyzed by constructing a distance matrix of the 16S rRNA genes and a phylogenetic tree with 32 conserved, concatenated proteins. Our results suggest that the hemoplasmas have dynamic genomes. The genome size variation (from 547 to 1,545 genes) indicates substantial gene gain/loss throughout evolution. Poorly conserved gene syntenies among hemoplasmas, positional shuffling of paralogous genes between strains, HGT, and codons under positive selection in PGFs were also observed. When compared to other Mollicutes species, the hemoplasmas experienced further metabolic reduction, and the 16S rRNA gene distance matrix of the available mollicutes suggests that these organisms presently constitute the most divergent clade within its class. Our phylogenetic tree of concatenated proteins showed some differences when compared to the 16S rRNA gene tree, but non-mycoplasma organisms, such as Ureaplasma spp. and Mesoplasma spp., continue to branch within Mycoplasma clades. In conclusion, while the hemoplasmas experienced further metabolic shrinkage through gene loss, PGFs with positively selected codons are likely beneficial to these species. Phylogeny of the mollicutes based on 16S rRNA genes or concatenated proteins do not obey the current taxonomy. The metabolism and genetic diversity of the mollicutes, the presence of HGT, and lack of standard for genus circumscription are likely to hinder attempts to classify these organisms based on phylogenetic analyses.

Metabolomic analysis of three Mollicute species

We present a systematic study of three bacterial species that belong to the class Mollicutes, the smallest and simplest bacteria, Spiroplasma melliferum, Mycoplasma gallisepticum, and Acholeplasma laidlawii. To understand the difference in the basic principles of metabolism regulation and adaptation to environmental conditions in the three species, we analyzed the metabolome of these bacteria. Metabolic pathways were reconstructed using the proteogenomic annotation data provided by our lab. The results of metabolome, proteome and genome profiling suggest a fundamental difference in the adaptation of the three closely related Mollicute species to stress conditions. As the transaldolase is not annotated in Mollicutes, we propose variants of the pentose phosphate pathway catalyzed by annotated enzymes for three species. For metabolite detection we employed high performance liquid chromatography coupled with mass spectrometry. We used liquid chromatography method - hydrophilic interaction chromatography with silica column - as it effectively separates highly polar cellular metabolites prior to their detection by mass spectrometer.

The future of whole-cell modeling

Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.

Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae

Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.

O-linked protein glycosylation in Mycoplasma

Although mycoplasmas have a paucity of glycosyltransferases and nucleotidyltransferases recognizable by bioinformatics, these bacteria are known to produce polysaccharides and glycolipids. We show here that mycoplasmas also produce glycoproteins and hence have glycomes more complex than previously realized. Proteins from several species of Mycoplasma reacted with a glycoprotein stain, and the murine pathogen Mycoplasma arthritidis was chosen for further study. The presence of M. arthritidis glycoproteins was confirmed by high-resolution mass spectrometry. O-linked glycosylation was clearly identified at both serine and threonine residues. No consensus amino acid sequence was evident for the glycosylation sites of the glycoproteins. A single hexose was identified as the O-linked modification, and glucose was inferred by (13) C-labelling to be the hexose at several of the glycosylation sites. This is the first study to conclusively identify sites of protein glycosylation in any of the mollicutes.

Use of a global metabolic network to curate organismal metabolic networks

The difficulty in annotating the vast amounts of biological information poses one of the greatest current challenges in biological research. The number of genomic, proteomic, and metabolomic datasets has increased dramatically over the last two decades, far outstripping the pace of curation efforts. Here, we tackle the challenge of curating metabolic network reconstructions. We predict organismal metabolic networks using sequence homology and a global metabolic network constructed from all available organismal networks. While sequence homology has been a standard to annotate metabolic networks it has been faulted for its lack of predictive power. We show, however, that when homology is used with a global metabolic network one is able to predict organismal metabolic networks that have enhanced network connectivity. Additionally, we compare the annotation behavior of current database curation efforts with our predictions and find that curation efforts are biased towards adding (rather than removing) reactions to organismal networks.

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling

A new genome-scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome-reduce human pathogen.

We established a detailed biomass composition for M. pneumoniae, thus allowing for growth simulations.

Using our metabolic model, we corrected the metabolic network topology and the functional annotation of key metabolic enzymes.

M. pneumoniae, unlike other laboratory-grown bacteria, uses a high fraction of energy (up to 89%) for cellular maintenance and not for growth.

Simulating different growth conditions as well as single and double mutant phenotypes, we analyzed pathway connectivity and the impact of gene deletions on the growth performance of M. pneumoniae, highlighting the limited adaptive capabilities of this minimal model organism.

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.

Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.

Flux balance analysis (FBA) is a powerful approach for genome-scale metabolic modeling. It provides metabolic engineers with a tool for manipulating, predicting, and optimizing metabolism for biotechnological and biomedical purposes. However, we posit that it can also be used as tool for fundamental research in understanding and curating metabolic networks. Specifically, by using a genetic algorithm integrated with FBA, we developed a curation approach to identify missing reactions, incomplete reactions, and erroneous reactions. Additionally, it was possible to take advantage of the ensemble information from the genetic algorithm to identify the most critical reactions for curation. We tested our strategy using Mycoplasma gallisepticum as our model organism. Using the genome annotation as the basis, the preliminary genome-scale metabolic model consisted of 446 metabolites involved in 380 reactions. Carrying out our analysis, we found over 80 incorrect reactions and 16 missing reactions. Based upon the guidance of the algorithm, we were able to curate and resolve all discrepancies. The model predicted an average bacterial growth rate of 0.358±0.12 h⁻¹ compared to the experimentally observed 0.244±0.03 h⁻¹. Thus, our approach facilitated the curation of a genome-scale metabolic network and generated a high quality metabolic model.

Transfer RNA misidentification scrambles sense codon recoding

Sense codon recoding is the basis for genetic code expansion with more than two different noncanonical amino acids. It requires an unused (or rarely used) codon, and an orthogonal tRNA synthetase:tRNA pair with the complementary anticodon. The Mycoplasma capricolum genome contains just six CGG arginine codons, without a dedicated tRNA(Arg). We wanted to reassign this codon to pyrrolysine by providing M. capricolum with pyrrolysyl-tRNA synthetase, a synthetic tRNA with a CCG anticodon (tRNA(Pyl)(CCG)), and the genes for pyrrolysine biosynthesis. Here we show that tRNA(Pyl)(CCG) is efficiently recognized by the endogenous arginyl-tRNA synthetase, presumably at the anticodon. Mass spectrometry revealed that in the presence of tRNA(Pyl)(CCG), CGG codons are translated as arginine. This result is not unexpected as most tRNA synthetases use the anticodon as a recognition element. The data suggest that tRNA misidentification by endogenous aminoacyl-tRNA synthetases needs to be overcome for sense codon recoding.

Reaching new levels of realism in modeling biological macromolecules in cellular environments

An increasing number of studies are aimed at modeling cellular environments in a comprehensive and realistic fashion. A major challenge in these efforts is how to bridge spatial and temporal scales over many orders of magnitude. Furthermore, there are additional challenges in integrating different aspects ranging from questions about biomolecular stability in crowded environments to the description of reactive processes on cellular scales. In this review, recent studies with models of biomolecules in cellular environments at different levels of detail are discussed in terms of their strengths and weaknesses. In particular, atomistic models, implicit representations of cellular environments, coarse-grained and spheroidal models of biomolecules, as well as the inclusion of reactive processes via reaction-diffusion models are described. Furthermore, strategies for integrating the different models into a comprehensive description of cellular environments are discussed.

Inhibition of Mycoplasma pneumoniae growth by FDA-approved anticancer and antiviral nucleoside and nucleobase analogs

BACKGROUND

Mycoplasma pneumoniae (Mpn) is a human pathogen that causes acute and chronic respiratory diseases and has been linked to many extrapulmonary diseases. Due to the lack of cell wall, Mpn is resistant to antibiotics targeting cell wall synthesis such as penicillin. During the last 10 years macrolide-resistant Mpn strains have been frequently reported in Asian countries and have been spreading to Europe and the United States. Therefore, new antibiotics are needed. In this study, 30 FDA-approved anticancer or antiviral drugs were screened for inhibitory effects on Mpn growth and selected analogs were further characterized by inhibition of target enzymes and metabolism of radiolabeled substrates.

RESULTS

Sixteen drugs showed varying inhibitory effects and seven showed strong inhibition of Mpn growth. The anticancer drug 6-thioguanine had a MIC (minimum inhibitory concentration required to cause 90% of growth inhibition) value of 0.20 μg ml(-1), whereas trifluorothymidine, gemcitabine and dipyridamole had MIC values of approximately 2 μg ml(-1). In wild type Mpn culture the presence of 6-thioguanine and dipyridamole strongly inhibited the uptake and metabolism of hypoxanthine and guanine while gemcitabine inhibited the uptake and metabolism of all nucleobases and thymidine. Trifluorothymidine and 5-fluorodeoxyuridine, however, stimulated the uptake and incorporation of radiolabeled thymidine and this stimulation was due to induction of thymidine kinase activity. Furthermore, Mpn hypoxanthine guanine phosphoribosyl transferase (HPRT) was cloned, expressed, and characterized. The 6-thioguanine, but not other purine analogs, strongly inhibited HPRT, which may in part explain the observed growth inhibition. Trifluorothymidine and 5-fluorodeoxyuridine were shown to be good substrates and inhibitors for thymidine kinase from human and Mycoplasma sources.

CONCLUSION

We have shown that several anticancer and antiviral nucleoside and nucleobase analogs are potent inhibitors of Mpn growth and that the mechanism of inhibition are most likely due to inhibition of enzymes in the nucleotide biosynthesis pathway and nucleoside transporter. Our results suggest that enzymes in Mycoplasma nucleotide biosynthesis are potential targets for future design of antibiotics against Mycoplasma infection.

Increments and duplication events of enzymes and transcription factors influence metabolic and regulatory diversity in prokaryotes

In this work, the content of enzymes and DNA-binding transcription factors (TFs) in 794 non-redundant prokaryotic genomes was evaluated. The identification of enzymes was based on annotations deposited in the KEGG database as well as in databases of functional domains (COG and PFAM) and structural domains (Superfamily). For identifications of the TFs, hidden Markov profiles were constructed based on well-known transcriptional regulatory families. From these analyses, we obtained diverse and interesting results, such as the negative rate of incremental changes in the number of detected enzymes with respect to the genome size. On the contrary, for TFs the rate incremented as the complexity of genome increased. This inverse related performance shapes the diversity of metabolic and regulatory networks and impacts the availability of enzymes and TFs. Furthermore, the intersection of the derivatives between enzymes and TFs was identified at 9,659 genes, after this point, the regulatory complexity grows faster than metabolic complexity. In addition, TFs have a low number of duplications, in contrast to the apparent high number of duplications associated with enzymes. Despite the greater number of duplicated enzymes versus TFs, the increment by which duplicates appear is higher in TFs. A lower proportion of enzymes among archaeal genomes (22%) than in the bacterial ones (27%) was also found. This low proportion might be compensated by the interconnection between the metabolic pathways in Archaea. A similar proportion was also found for the archaeal TFs, for which the formation of regulatory complexes has been proposed. Finally, an enrichment of multifunctional enzymes in Bacteria, as a mechanism of ecological adaptation, was detected.

Rhamnose biosynthesis in mycoplasmas requires precursor glycans larger than monosaccharide

Despite the apparent absence of genes coding for the known pathways for biosynthesis, the monosaccharide rhamnose was detected in the d configuration in Mycoplasma pneumoniae and Mycoplasma pulmonis, and in both the d and l configurations in Mycoplasma arthritidis. Surprisingly, the monosaccharide glucose was not a precursor for rhamnose biosynthesis and was not incorporated at detectable levels in glucose-containing polysaccharides or glycoconjugates. In contrast, carbon atoms from starch, a polymer of glucose, were incorporated into rhamnose in each of the three species examined. When grown in a serum-free medium supplemented with starch, M. arthritidis synthesized higher levels of rhamnose, with a shift in the relative amounts of the d and l configurations. Our findings suggest the presence of a novel pathway for rhamnose synthesis that is widespread in the genus Mycoplasma.

Linking plant nutritional status to plant-microbe interactions

Plants have developed a wide-range of adaptations to overcome nutrient limitation, including changes to the quantity and composition of carbon-containing compounds released by roots. Root-associated bacteria are largely influenced by these compounds which can be perceived as signals or substrates. Here, we evaluate the effect of root exudates collected from maize plants grown under nitrogen (N), phosphate (P), iron (Fe) and potassium (K) deficiencies on the transcriptome of the plant growth promoting rhizobacterium (PGPR) Bacillus amyloliquefaciens FZB42. The largest shifts in gene expression patterns were observed in cells exposed to exudates from N-, followed by P-deficient plants. Exudates from N-deprived maize triggered a general stress response in FZB42 in the exponential growth phase, which was evidenced by the suppression of numerous genes involved in protein synthesis. Exudates from P-deficient plants induced bacterial genes involved in chemotaxis and motility whilst exudates released by Fe and K deficient plants did not cause dramatic changes in the bacterial transcriptome during exponential growth phase. Global transcriptional changes in bacteria elicited by nutrient deficient maize exudates were significantly correlated with concentrations of the amino acids aspartate, valine and glutamate in root exudates suggesting that transcriptional profiling of FZB42 associated with metabolomics of N, P, Fe and K-deficient maize root exudates is a powerful approach to better understand plant-microbe interactions under conditions of nutritional stress.

Characterization of free exopolysaccharides secreted by Mycoplasma mycoides subsp. mycoides

Contagious bovine pleuropneumonia is a severe respiratory disease of cattle that is caused by a bacterium of the Mycoplasma genus, namely Mycoplasma mycoides subsp. mycoides (Mmm). In the absence of classical virulence determinants, the pathogenicity of Mmm is thought to rely on intrinsic metabolic functions and specific components of the outer cell surface. One of these latter, the capsular polysaccharide galactan has been notably demonstrated to play a role in Mmm persistence and dissemination. The free exopolysaccharides (EPS), also produced by Mmm and shown to circulate in the blood stream of infected cattle, have received little attention so far. Indeed, their characterization has been hindered by the presence of polysaccharide contaminants in the complex mycoplasma culture medium. In this study, we developed a method to produce large quantities of EPS by transfer of mycoplasma cells from their complex broth to a chemically defined medium and subsequent purification. NMR analyses revealed that the purified, free EPS had an identical β(1−>6)-galactofuranosyl structure to that of capsular galactan. We then analyzed intraclonal Mmm variants that produce opaque/translucent colonies on agar. First, we demonstrated that colony opacity was related to the production of a capsule, as observed by electron microscopy. We then compared the EPS extracts and showed that the non-capsulated, translucent colony variants produced higher amounts of free EPS than the capsulated, opaque colony variants. This phenotypic variation was associated with an antigenic variation of a specific glucose phosphotransferase permease. Finally, we conducted in silico analyses of candidate polysaccharide biosynthetic pathways in order to decipher the potential link between glucose phosphotransferase permease activity and attachment/release of galactan. The co-existence of variants producing alternative forms of galactan (capsular versus free extracellular galactan) and associated with an antigenic switch constitutes a finely tuned mechanism that may be involved in virulence.

Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae

Systems metabolomics, the identification and quantification of cellular metabolites and their integration with genomics and proteomics data, promises valuable functional insights into cellular biology. However, technical constraints, sample complexity issues and the lack of suitable complementary quantitative data sets prevented accomplishing such studies in the past. Here, we present an integrative metabolomics study of the genome-reduced bacterium Mycoplasma pneumoniae. We experimentally analysed its metabolome using a cross-platform approach. We explain intracellular metabolite homeostasis by quantitatively integrating our results with the cellular inventory of proteins, DNA and other macromolecules, as well as with available building blocks from the growth medium. We calculated in vivo catalytic parameters of glycolytic enzymes, making use of measured reaction velocities, as well as enzyme and metabolite pool sizes. A quantitative, inter-species comparison of absolute and relative metabolite abundances indicated that metabolic pathways are regulated as functional units, thereby simplifying adaptive responses. Our analysis demonstrates the potential for new scientific insight by integrating different types of large-scale experimental data from a single biological source.

Designing de novo: interdisciplinary debates in synthetic biology

Synthetic biology is often presented as a promissory field that ambitions to produce novelty by design. The ultimate promise is the production of living systems that will perform new and desired functions in predictable ways. Nevertheless, realizing promises of novelty has not proven to be a straightforward endeavour. This paper provides an overview of, and explores the existing debates on, the possibility of designing living systems de novo as they appear in interdisciplinary talks between engineering and biological views within the field of synthetic biology. To broaden such interdisciplinary debates, we include the views from the social sciences and the humanities and we point to some fundamental sources of disagreement within the field. Different views co-exist, sometimes as controversial tensions, but sometimes also pointing to integration in the form of intermediate positions. As the field is emerging, multiple choices are possible. They will inform alternative trajectories in synthetic biology and will certainly shape its future. What direction is best is to be decided in reflexive and socially robust ways.

Microbial quest for food in vivo: 'nutritional virulence' as an emerging paradigm

Microbial access to host nutrients is a fundamental aspect of infectious diseases. Pathogens face complex dynamic nutritional host microenvironments that change with increasing inflammation and local hypoxia. Since the host can actively limit microbial access to nutrient supply, pathogens have evolved various metabolic adaptations to successfully exploit available host nutrients for proliferation. Recent studies have unraveled an emerging paradigm that we propose to designate as 'nutritional virulence'. This paradigm is based on specific virulence mechanisms that target major host biosynthetic and degradation pathways (proteasomes, autophagy and lysosomes) or nutrient-rich sources, such as glutathione, to enhance host supply of limiting nutrients, such as cysteine. Although Cys is the most limiting cellular amino acid, it is a metabolically favourable source of carbon and energy for various pathogens that are auxotrophic for Cys but utilize idiosyncratic nutritional virulence strategies to generate a gratuitous supply of host Cys. Therefore, proliferation of some intracellular pathogens is restricted by a host nutritional rheostat regulated by certain limiting amino acids, and pathogens have evolved idiosyncratic strategies to short circuit the host nutritional rheostat. Deciphering mechanisms of microbial 'nutritional virulence' and metabolism in vivo will facilitate identification of novel microbialand host targets for treatment and prevention of infectious diseases. Host-pathogen synchronization of amino acid auxotrophy indicates that this nutritional synchronization has been a major driving force in the evolution of many intracellular bacterial pathogens.

Protein charge and mass contribute to the spatio-temporal dynamics of protein-protein interactions in a minimal proteome

We constructed and simulated a "minimal proteome" model using Langevin dynamics. It contains 206 essential protein types that were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins that tend to have larger sizes can provide a large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that "proper" populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of Escherichia coli may have a larger protein-protein interaction network than that based on the lower organism Mycoplasma pneumoniae.

Biological roles of cAMP: variations on a theme in the different kingdoms of life

Cyclic AMP (cAMP) plays a key regulatory role in most types of cells; however, the pathways controlled by cAMP may present important differences between organisms and between tissues within a specific organism. Changes in cAMP levels are caused by multiple triggers, most affecting adenylyl cyclases, the enzymes that synthesize cAMP. Adenylyl cyclases form a large and diverse family including soluble forms and others with one or more transmembrane domains. Regulatory mechanisms for the soluble adenylyl cyclases involve either interaction with diverse proteins, as happens in Escherichia coli or yeasts, or with calcium or bicarbonate ions, as occurs in mammalian cells. The transmembrane cyclases can be regulated by a variety of proteins, among which the α subunit and the βγ complex from G proteins coupled to membrane receptors are prominent. cAMP levels also are controlled by the activity of phosphodiesterases, enzymes that hydrolyze cAMP. Phosphodiesterases can be regulated by cAMP, cGMP or calcium-calmodulin or by phosphorylation by different protein kinases. Regulation through cAMP depends on its binding to diverse proteins, its proximal targets, this in turn causing changes in a variety of distal targets. Specifically, binding of cAMP to regulatory subunits of cAMP-dependent protein kinases (PKAs) affects the activity of substrates of PKA, binding to exchange proteins directly activated by cAMP (Epac) regulates small GTPases, binding to transcription factors such as the cAMP receptor protein (CRP) or the virulence factor regulator (Vfr) modifies the rate of transcription of certain genes, while cAMP binding to ion channels modulates their activity directly. Further studies on cAMP signalling will have important implications, not only for advancing fundamental knowledge but also for identifying targets for the development of new therapeutic agents.

Comprehensive methylome characterization of Mycoplasma genitalium and Mycoplasma pneumoniae at single-base resolution

In the bacterial world, methylation is most commonly associated with restriction-modification systems that provide a defense mechanism against invading foreign genomes. In addition, it is known that methylation plays functionally important roles, including timing of DNA replication, chromosome partitioning, DNA repair, and regulation of gene expression. However, full DNA methylome analyses are scarce due to a lack of a simple methodology for rapid and sensitive detection of common epigenetic marks (ie N⁶-methyladenine (6 mA) and N⁴-methylcytosine (4 mC)), in these organisms. Here, we use Single-Molecule Real-Time (SMRT) sequencing to determine the methylomes of two related human pathogen species, Mycoplasma genitalium G-37 and Mycoplasma pneumoniae M129, with single-base resolution. Our analysis identified two new methylation motifs not previously described in bacteria: a widespread 6 mA methylation motif common to both bacteria (5′-CTAT-3′), as well as a more complex Type I m6A sequence motif in M. pneumoniae (5′-GAN₇TAY-3′/3′-CTN₇ATR-5′). We identify the methyltransferase responsible for the common motif and suggest the one involved in M. pneumoniae only. Analysis of the distribution of methylation sites across the genome of M. pneumoniae suggests a potential role for methylation in regulating the cell cycle, as well as in regulation of gene expression. To our knowledge, this is one of the first direct methylome profiling studies with single-base resolution from a bacterial organism.

DNA methylation in bacteria plays important roles in cell division, DNA repair, regulation of gene expression, and pathogenesis. Here, we use a novel sequencing technique, Single-Molecule Real-Time (SMRT) sequencing, to determine the methylomes of two related human pathogen species, Mycoplasma genitalium G-37 and Mycoplasma pneumoniae M129. Our analysis identified two novel methylation motifs, one of them present uniquely in M. pneumoniae and the other common to both bacteria. We also identify the methyltransferase responsible for the common methylation motif and suggest the one associated with the M. pneumoniae unique motif. Functional analysis of the data suggests a potential role for methylation in regulating the cell cycle of M. pneumoniae, as well as in regulation of gene expression. To our knowledge, this is one of the first genome-wide approaches to study the biological role of methylation in a bacterial organism.

From essential to persistent genes: a functional approach to constructing synthetic life

A central undertaking in synthetic biology (SB) is the quest for the 'minimal genome'. However, 'minimal sets' of essential genes are strongly context-dependent and, in all prokaryotic genomes sequenced to date, not a single protein-coding gene is entirely conserved. Furthermore, a lack of consensus in the field as to what attributes make a gene truly essential adds another aspect of variation. Thus, a universal minimal genome remains elusive. Here, as an alternative to defining a minimal genome, we propose that the concept of gene persistence can be used to classify genes needed for robust long-term survival. Persistent genes, although not ubiquitous, are conserved in a majority of genomes, tend to be expressed at high levels, and are frequently located on the leading DNA strand. These criteria impose constraints on genome organization, and these are important considerations for engineering cells and for creating cellular life-like forms in SB.

Metabolic adaptation and protein complexes in prokaryotes

Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations.

A whole-cell computational model predicts phenotype from genotype

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.