Comparative genomic assessment of novel broad-spectrum targets for antibacterial drugs

Antimicrobial resistance in Klebsiella pneumoniae: identification of bacterial DNA adenine methyltransferase as a novel drug target from hypothetical proteins using subtractive genomics

Klebsiella pneumoniae is a gram-negative bacterium that is known for causing infection innosocomial settings. As reported by the World Health Organization, carbapenem-resistantEnterobacteriaceae, a category that includes K. pneumoniae, are classified as an urgentthreat, and the greatest concern is that these bacterial pathogens may acquire genetictraits that make them resistant towards antibiotics. The last class of antibiotics, carbapenems, are not able to combat these bacterial pathogens, allowing them to clonally expandantibiotic-resistant strains. Most antibiotics target essential pathways of bacterial cells;however, these targets are no longer susceptible to antibiotics. Hence, in our study, we focused on a hypothetical protein in K. pneumoniae that contains a DNA methylation proteindomain, suggesting a new potential site as a drug target. DNA methylation regulates theattenuation of bacterial virulence. We integrated computational-aided drug design by using a bioinformatics approach to perform subtractive genomics, virtual screening, and fingerprint similarity search. We identified a new potential drug, koenimbine, which could bea novel antibiotic.

tRNA methylation resolves codon usage bias at the limit of cell viability

Codon usage of each genome is closely correlated with the abundance of tRNA isoacceptors. How codon usage bias is resolved by tRNA post-transcriptional modifications is largely unknown. Here we demonstrate that the N¹-methylation of guanosine at position 37 (m¹G37) on the 3′-side of the anticodon, while not directly responsible for reading of codons, is a neutralizer that resolves differential decoding of proline codons. A genome-wide suppressor screen of a non-viable Escherichia coli strain, lacking m¹G37, identifies proS suppressor mutations, indicating a coupling of methylation with tRNA prolyl-aminoacylation that sets the limit of cell viability. Using these suppressors, where prolyl-aminoacylation is decoupled from tRNA methylation, we show that m¹G37 neutralizes differential translation of proline codons by the major isoacceptor. Lack of m¹G37 inactivates this neutralization and exposes the need for a minor isoacceptor for cell viability. This work has medical implications for bacterial species that exclusively use the major isoacceptor for survival.

Masuda et al. show that loss of m¹G37 from the 3′ side of the tRNA anticodon renders a modified wobble nucleotide of the anticodon insufficient to decode a set of rare codons, providing a functional underpinning for the “modification circuit” between position 37 and the wobble position of the tRNA anticodon.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Loss of N1-methylation of G37 in tRNA induces ribosome stalling and reprograms gene expression

N¹-methylation of G37 is required for a subset of tRNAs to maintain the translational reading-frame. While loss of m¹G37 increases ribosomal +1 frameshifting, whether it incurs additional translational defects is unknown. Here, we address this question by applying ribosome profiling to gain a genome-wide view of the effects of m¹G37 deficiency on protein synthesis. Using E coli as a model, we show that m¹G37 deficiency induces ribosome stalling at codons that are normally translated by m¹G37-containing tRNAs. Stalling occurs during decoding of affected codons at the ribosomal A site, indicating a distinct mechanism than that of +1 frameshifting, which occurs after the affected codons leave the A site. Enzyme- and cell-based assays show that m¹G37 deficiency reduces tRNA aminoacylation and in some cases peptide-bond formation. We observe changes of gene expression in m¹G37 deficiency similar to those in the stringent response that is typically induced by deficiency of amino acids. This work demonstrates a previously unrecognized function of m¹G37 that emphasizes its role throughout the entire elongation cycle of protein synthesis, providing new insight into its essentiality for bacterial growth and survival.

Mg2+-Dependent Methyl Transfer by a Knotted Protein: A Molecular Dynamics Simulation and Quantum Mechanics Study

Mg²⁺ is required for the catalytic activity of TrmD, a bacteria-specific methyltransferase that is made up of a protein topological knot-fold, to synthesize methylated m¹G37-tRNA to support life. However, neither the location of Mg²⁺ in the structure of TrmD nor its role in the catalytic mechanism is known. Using molecular dynamics (MD) simulations, we identify a plausible Mg²⁺ binding pocket within the active site of the enzyme, wherein the ion is coordinated by two aspartates and a glutamate. In this position, Mg²⁺ additionally interacts with the carboxylate of a methyl donor cofactor S-adenosylmethionine (SAM). The computational results are validated by experimental mutation studies, which demonstrate the importance of the Mg²⁺-binding residues for the catalytic activity. The presence of Mg²⁺ in the binding pocket induces SAM to adopt a unique bent shape required for the methyl transfer activity and causes a structural reorganization of the active site. Quantum mechanical calculations show that the methyl transfer is energetically feasible only when Mg²⁺ is bound in the position revealed by the MD simulations, demonstrating that its function is to align the active site residues within the topological knot-fold in a geometry optimal for catalysis. The obtained insights provide the opportunity for developing a strategy of antibacterial drug discovery based on targeting of Mg²⁺-binding to TrmD.

tRNA methylation: An unexpected link to bacterial resistance and persistence to antibiotics and beyond

A major threat to public health is the resistance and persistence of Gram-negative bacteria to multiple drugs during antibiotic treatment. The resistance is due to the ability of these bacteria to block antibiotics from permeating into and accumulating inside the cell, while the persistence is due to the ability of these bacteria to enter into a nonreplicating state that shuts down major metabolic pathways but remains active in drug efflux. Resistance and persistence are permitted by the unique cell envelope structure of Gram-negative bacteria, which consists of both an outer and an inner membrane (OM and IM, respectively) that lay above and below the cell wall. Unexpectedly, recent work reveals that m¹ G37 methylation of tRNA, at the N¹ of guanosine at position 37 on the 3'-side of the tRNA anticodon, controls biosynthesis of both membranes and determines the integrity of cell envelope structure, thus providing a novel link to the development of bacterial resistance and persistence to antibiotics. The impact of m¹ G37-tRNA methylation on Gram-negative bacteria can reach further, by determining the ability of these bacteria to exit from the persistence state when the antibiotic treatment is removed. These conceptual advances raise the possibility that successful targeting of m¹ G37-tRNA methylation can provide new approaches for treating acute and chronic infections caused by Gram-negative bacteria. This article is categorized under: Translation > Translation Regulation RNA Processing > RNA Editing and Modification RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

tRNA Methylation Is a Global Determinant of Bacterial Multi-drug Resistance

Gram-negative bacteria are intrinsically resistant to drugs because of their double-membrane envelope structure that acts as a permeability barrier and as an anchor for efflux pumps. Antibiotics are blocked and expelled from cells and cannot reach high-enough intracellular concentrations to exert a therapeutic effect. Efforts to target one membrane protein at a time have been ineffective. Here, we show that m1G37-tRNA methylation determines the synthesis of a multitude of membrane proteins via its control of translation at proline codons near the start of open reading frames. Decreases in m1G37 levels in Escherichia coli and Salmonella impair membrane structure and sensitize these bacteria to multiple classes of antibiotics, rendering them incapable of developing resistance or persistence. Codon engineering of membrane-associated genes reduces their translational dependence on m1G37 and confers resistance. These findings highlight the potential of tRNA methylation in codon-specific translation to control the development of multi-drug resistance in Gram-negative bacteria.

To Tie or Not to Tie? That Is the Question

In this review, we provide an overview of entangled proteins. Around 6% of protein structures deposited in the PBD are entangled, forming knots, slipknots, lassos and links. We present theoretical methods and tools that enabled discovering and classifying such structures. We discuss the advantages and disadvantages of the non-trivial topology in proteins, based on available data about folding, stability, biological properties and evolutionary conservation. We also formulate intriguing and challenging questions on the border of biophysics, bioinformatics, biology and mathematics, which arise from the discovery of an entanglement in proteins. Finally, we discuss possible applications of entangled proteins in medicine and nanotechnology, such as the chance to design super stable proteins, whose stability could be controlled by chemical potential.

TrmD: A Methyl Transferase for tRNA Methylation With m1G37

TrmD is an S-adenosyl methionine (AdoMet)-dependent methyl transferase that synthesizes the methylated m¹G37 in tRNA. TrmD is specific to and essential for bacterial growth, and it is fundamentally distinct from its eukaryotic and archaeal counterpart Trm5. TrmD is unusual by using a topological protein knot to bind AdoMet. Despite its restricted mobility, the TrmD knot has complex dynamics necessary to transmit the signal of AdoMet binding to promote tRNA binding and methyl transfer. Mutations in the TrmD knot block this intramolecular signaling and decrease the synthesis of m¹G37-tRNA, prompting ribosomes to +1-frameshifts and premature termination of protein synthesis. TrmD is unique among AdoMet-dependent methyl transferases in that it requires Mg²⁺ in the catalytic mechanism. This Mg²⁺ dependence is important for regulating Mg²⁺ transport to Salmonella for survival of the pathogen in the host cell. The strict conservation of TrmD among bacterial species suggests that a better characterization of its enzymology and biology will have a broad impact on our understanding of bacterial pathogenesis.

Transfer RNA methyltransferases with a SpoU-TrmD (SPOUT) fold and their modified nucleosides in tRNA

The existence of SpoU-TrmD (SPOUT) RNA methyltransferase superfamily was first predicted by bioinformatics. SpoU is the previous name of TrmH, which catalyzes the 2’-O-methylation of ribose of G18 in tRNA; TrmD catalyzes the formation of N¹-methylguanosine at position 37 in tRNA. Although SpoU (TrmH) and TrmD were originally considered to be unrelated, the bioinformatics study suggested that they might share a common evolution origin and form a single superfamily. The common feature of SPOUT RNA methyltransferases is the formation of a deep trefoil knot in the catalytic domain. In the past decade, the SPOUT RNA methyltransferase superfamily has grown; furthermore, knowledge concerning the functions of their modified nucleosides in tRNA has also increased. Some enzymes are potential targets in the design of anti-bacterial drugs. In humans, defects in some genes may be related to carcinogenesis. In this review, recent findings on the tRNA methyltransferases with a SPOUT fold and their methylated nucleosides in tRNA, including classification of tRNA methyltransferases with a SPOUT fold; knot structures, domain arrangements, subunit structures and reaction mechanisms; tRNA recognition mechanisms, and functions of modified nucleosides synthesized by this superfamily, are summarized. Lastly, the future perspective for studies on tRNA modification enzymes are considered.

Evolutionary Adaptation of the Essential tRNA Methyltransferase TrmD to the Signaling Molecule 3',5'-cAMP in Bacteria

The nucleotide signaling molecule 3',5'-cyclic adenosine monophosphate (3',5'-cAMP) plays important physiological roles, ranging from carbon catabolite repression in bacteria to mediating the action of hormones in higher eukaryotes, including human. However, it remains unclear whether 3',5'-cAMP is universally present in the Firmicutes group of bacteria. We hypothesized that searching for proteins that bind 3',5'-cAMP might provide new insight into this question. Accordingly, we performed a genome-wide screen and identified the essential Staphylococcus aureus tRNA m1G37 methyltransferase enzyme TrmD, which is conserved in all three domains of life as a tight 3',5'-cAMP-binding protein. TrmD enzymes are known to use S-adenosyl-l-methionine (AdoMet) as substrate; we have shown that 3',5'-cAMP binds competitively with AdoMet to the S. aureus TrmD protein, indicating an overlapping binding site. However, the physiological relevance of this discovery remained unclear, as we were unable to identify a functional adenylate cyclase in S. aureus and only detected 2',3'-cAMP but not 3',5'-cAMP in cellular extracts. Interestingly, TrmD proteins from Escherichia coli and Mycobacterium tuberculosis, organisms known to synthesize 3',5'-cAMP, did not bind this signaling nucleotide. Comparative bioinformatics, mutagenesis, and biochemical analyses revealed that the highly conserved Tyr-86 residue in E. coli TrmD is essential to discriminate between 3',5'-cAMP and the native substrate AdoMet. Combined with a phylogenetic analysis, these results suggest that amino acids in the substrate binding pocket of TrmD underwent an adaptive evolution to accommodate the emergence of adenylate cyclases and thus the signaling molecule 3',5'-cAMP. Altogether this further indicates that S. aureus does not produce 3',5'-cAMP, which would otherwise competitively inhibit an essential enzyme.

Methyl transfer by substrate signaling from a knotted protein fold

Proteins with knotted configurations, in comparison with unknotted proteins, are restricted in conformational space. Little is known regarding whether knotted proteins have sufficient dynamics to communicate between spatially separated substrate-binding sites. TrmD is a bacterial methyltransferase that uses a knotted protein fold to catalyze methyl transfer from S-adenosyl methionine (AdoMet) to G37-tRNA. The product, m1G37-tRNA, is essential for life and maintains protein-synthesis reading frames. Using an integrated approach of structural, kinetic, and computational analysis, we show that the structurally constrained TrmD knot is required for its catalytic activity. Unexpectedly, the TrmD knot undergoes complex internal movements that respond to AdoMet binding and signaling. Most of the signaling propagates the free energy of AdoMet binding, thereby stabilizing tRNA binding and allowing assembly of the active site. This work demonstrates new principles of knots as organized structures that capture the free energies of substrate binding and facilitate catalysis.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bio-informatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E. coli O157:H7 were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets and DNA polymerase III alpha (dnaE) was the top-ranked among these targets. Moreover, druggability of each of the identified drug targets was evaluated by the DrugBank database. In addition, 3D structure of the dnaE was modeled and explored further for in silico docking with ligands having potential druggability. Finally, we confirmed that the compounds N-coeleneterazine and N-(1,4-dihydro-5H-tetrazol-5-ylidene)-9-oxo-9H-xanthene-2-sulfon-amide were the most suitable ligands of dnaE and hence proposed as the potential inhibitors of this target protein. The results of this study could facilitate the discovery and release of new and effective drugs against E. coli O157:H7 and other deadly human bacterial pathogens.

Gene network analysis reveals the association of important functional partners involved in antibiotic resistance: A report on an important pathogenic bacterium Staphylococcus aureus

Staphylococcus aureus (S. aureus) is an emerging concern in hospital settings as it causes serious human infections. The multidrug resistance (MDR) in S. aureus is a complicated problem that is difficult to overcome due to the presence of numerous antibiotic resistance genes and it exhibit resistance to most of the currently available antibiotics. Presently, the resistance mechanisms of these genes/proteins are not completely understood. Therefore, identifying and understanding the functional relationship between the antibiotic resistant genes and their associated proteins might provide necessary information on resistance mechanisms and thereby help in designing successful drugs to combat the antibiotic resistance. In this study, we propose a model based on protein/gene network to identify genes/proteins associated with drug resistance in S. aureus. We filtered 50 functional partners in NorA, aacA-aphD (aac6ie), aad9ib (ant), aadd (knt), baca (uppP), bl2a_pc (blaZ), ble, ermA, SAV0052 (ermb), ermc, fosB, mecA (mecI), mecR (mecr1), mepA, msrA1, qacA, vraR (str), tet38 and tetM while 40 functional partners are identified in tet and aphA-3 (aph3iiia). The average shortest path length and betweenness centrality of functional partners in the clusters are calculated and they are functionally enriched with the Gene Ontology (GO) terms with a p-value cut-off ≤0.05. Interestingly, the constructed network reveals many associated antibiotic resistant genes and proteins and their role in resistance mechanisms. Thus, our results might provide a better understanding of the molecular mechanisms of action and their mode of drug resistance that will be useful for researchers exploring in the field of antibiotic resistance mechanisms.

Structural basis for methyl-donor-dependent and sequence-specific binding to tRNA substrates by knotted methyltransferase TrmD

The deep trefoil knot architecture is unique to the SpoU and tRNA methyltransferase D (TrmD) (SPOUT) family of methyltransferases (MTases) in all three domains of life. In bacteria, TrmD catalyzes the N(1)-methylguanosine (m(1)G) modification at position 37 in transfer RNAs (tRNAs) with the (36)GG(37) sequence, using S-adenosyl-l-methionine (AdoMet) as the methyl donor. The m(1)G37-modified tRNA functions properly to prevent +1 frameshift errors on the ribosome. Here we report the crystal structure of the TrmD homodimer in complex with a substrate tRNA and an AdoMet analog. Our structural analysis revealed the mechanism by which TrmD binds the substrate tRNA in an AdoMet-dependent manner. The trefoil-knot center, which is structurally conserved among SPOUT MTases, accommodates the adenosine moiety of AdoMet by loosening/retightening of the knot. The TrmD-specific regions surrounding the trefoil knot recognize the methionine moiety of AdoMet, and thereby establish the entire TrmD structure for global interactions with tRNA and sequential and specific accommodations of G37 and G36, resulting in the synthesis of m(1)G37-tRNA.

Quantitative proteomic analysis of host--pathogen interactions: a study of Acinetobacter baumannii responses to host airways

Background

Acinetobacter baumannii is a major health problem. The most common infection caused by A. baumannii is hospital acquired pneumonia, and the associated mortality rate is approximately 50 %. Neither in vivo nor ex vivo expression profiling has been performed at the proteomic or transcriptomic level for pneumonia caused by A. baumannii. In this study, we characterized the proteome of A. baumannii under conditions that simulate those found in the airways, to gain some insight into how A. baumannii adapts to the host and to improve knowledge about the pathogenesis and virulence of this bacterium. A clinical strain of A. baumannii was grown under different conditions: in the presence of bronchoalveolar lavage fluid from infected rats, of RAW 264.7 cells to simulate conditions in the respiratory tract and in control conditions. We used iTRAQ labelling and LC-MALDI-TOF/TOF to investigate how A. baumannii responds on exposure to macrophages/BALF.

Results

179 proteins showed differential expression. In both models, proteins involved in the following processes were over-expressed: (i) pathogenesis and virulence (OmpA, YjjK); (ii) cell wall/membrane/envelope biogenesis (MurC); (iii) energy production and conversion (acetyl-CoA hydrolase); and (iv) translation (50S ribosomal protein L9). Proteins involved in the following were under-expressed: (i) lipid metabolism (short-chain dehydrogenase); (ii) amino acid metabolism and transport (aspartate aminotransferase); (iii) unknown function (DNA-binding protein); and (iv) inorganic ion transport and metabolism (hydroperoxidase).

Conclusions

We observed alterations in cell wall synthesis and identified 2 upregulated virulence-associated proteins with >15 peptides/protein in both ex vivo models (OmpA and YjjK), suggesting that these proteins are fundamental for pathogenesis and virulence in the airways. This study is the first comprehensive overview of the ex vivo proteome of A. baumannii and is an important step towards identification of diagnostic biomarkers, novel drug targets and potential vaccine candidates in the fight against pneumonia caused by A. baumannii.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1608-z) contains supplementary material, which is available to authorized users.

Structural and functional features of enzymes of Mycobacterium tuberculosis peptidoglycan biosynthesis as targets for drug development

Tuberculosis (TB) is the second leading cause of human mortality from infectious diseases worldwide. The WHO reported 1.3 million deaths and 8.6 million new cases of TB in 2012. Mycobacterium tuberculosis (M. tuberculosis), the infectious bacteria that causes TB, is encapsulated by a thick and robust cell wall. The innermost segment of the cell wall is comprised of peptidoglycan, a layer that is required for survival and growth of the pathogen. Enzymes that catalyse biosynthesis of the peptidoglycan are essential and are therefore attractive targets for discovery of novel antibiotics as humans lack similar enzymes making it possible to selectively target bacteria only. In this paper, we have reviewed the structures and functions of enzymes GlmS, GlmM, GlmU, MurA, MurB, MurC, MurD, MurE and MurF from M. tuberculosis that are involved in peptidoglycan biosynthesis. In addition, we report homology modelled 3D structures of those key enzymes from M. tuberculosis of which the structures are still unknown. We demonstrated that natural substrates can be successfully docked into the active sites of the GlmS and GlmU respectively. It is therefore expected that the models and the data provided herein will facilitate translational research to develop new drugs to treat TB.

Wolbachia translation initiation factor-1 is copiously expressed by the adult, microfilariae and infective larvae of Brugia malayi and competitively inhibited by tetracycline

The intracellular alphaproteobacteria, Wolbachia, is considered to be a future antimacrofilarial drug target as it is obligatory for filarial endurance. Characterizing wolbachial proteins is necessary to understand wolbachial mechanisms and also for discovering new drug entities. Translation initiation factor-1 (Tl IF-1) is an indispensable prokaryotic factor concerned with bacterial viability. This factor is prioritized as one of the most potent antibacterial drug target. To investigate its role in filarial biology, recombinant Wol Tl IF-1 was purified on metal ion column. The factor was found folded in its monomeric native conformation, and contained a buried fluorophore. Molecular modeling revealed that the factor belonged to the Oligomer Binding family, and consisted of the highly conserved S1 domain with 81.6% of the amino acids occupying the allowed regions in Ramachandran plot. In addition, Wol Tl IF-1 exhibited selective binding to the 30S ribosomal subunit, which declined progressively with tetracycline addition. Tetracycline perturbs interaction of Thr18 and Asn32 of the factor with ribosomal protein S4. The factor was immune-localized in adult, microfilariae (Mf) and infective larvae (L3) of Brugia malayi by immunoblotting. High expression was also observed in Wolbachia within B. malayi Mf, L3 and female adult parasite along the gravid uteri by the confocal microscopy. Therefore, Wol Tl IF-1 appears to be an essential Wolbachia factor whose inhibition leads to extensive cell apoptosis and premature killing of adult worms, validating the antifilarial potential of the factor.

A divalent metal ion-dependent N(1)-methyl transfer to G37-tRNA

The catalytic mechanism of the majority of S-adenosyl methionine (AdoMet)-dependent methyl transferases requires no divalent metal ions. Here we report that methyl transfer from AdoMet to N(1) of G37-tRNA, catalyzed by the bacterial TrmD enzyme, is strongly dependent on divalent metal ions and that Mg(2+) is the most physiologically relevant. Kinetic isotope analysis, metal rescue, and spectroscopic measurements indicate that Mg(2+) is not involved in substrate binding, but in promoting methyl transfer. On the basis of the pH-activity profile indicating one proton transfer during the TrmD reaction, we propose a catalytic mechanism in which the role of Mg(2+) is to help to increase the nucleophilicity of N(1) of G37 and stabilize the negative developing charge on O(6) during attack on the methyl sulfonium of AdoMet. This work demonstrates how Mg(2+) contributes to the catalysis of AdoMet-dependent methyl transfer in one of the most crucial posttranscriptional modifications to tRNA.

antibacTR: dynamic antibacterial-drug-target ranking integrating comparative genomics, structural analysis and experimental annotation

BACKGROUND

Development of novel antibacterial drugs is both an urgent healthcare necessity and a partially neglected field. The last decades have seen a substantial decrease in the discovery of novel antibiotics, which combined with the recent thrive of multi-drug-resistant pathogens have generated a scenario of general concern. The procedures involved in the discovery and development of novel antibiotics are economically challenging, time consuming and lack any warranty of success. Furthermore, the return-on-investment for an antibacterial drug is usually marginal when compared to other therapeutics, which in part explains the decrease of private investment.

RESULTS

In this work we present antibacTR, a computational pipeline designed to aid researchers in the selection of potential drug targets, one of the initial steps in antibacterial-drug discovery. The approach was designed and implemented as part of two publicly funded initiatives aimed at discovering novel antibacterial targets, mechanisms and drugs for a priority list of Gram-negative pathogens: Acinetobacter baumannii, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. However, at present this list has been extended to cover a total of 74 fully sequenced Gram-negative pathogens. antibacTR is based on sequence comparisons and queries to multiple databases (e.g. gene essentiality, virulence factors) to rank proteins according to their potential as antibacterial targets. The dynamic ranking of potential drug targets can easily be executed, customized and accessed by the user through a web interface which also integrates computational analyses performed in-house and visualizable on-site. These include three-dimensional modeling of protein structures and prediction of active sites among other functionally relevant ligand-binding sites.

CONCLUSIONS

Given its versatility and ease-of-use at integrating both experimental annotation and computational analyses, antibacTR may effectively assist microbiologists, medicinal-chemists and other researchers working in the field of antibacterial drug-discovery. The public web-interface for antibacTR is available at 'http://bioinf.uab.cat/antibactr'.

Conservation of structure and mechanism by Trm5 enzymes

Enzymes of the Trm5 family catalyze methyl transfer from S-adenosyl methionine (AdoMet) to the N¹ of G37 to synthesize m¹ G37-tRNA as a critical determinant to prevent ribosome frameshift errors. Trm5 is specific to eukaryotes and archaea, and it is unrelated in evolution from the bacterial counterpart TrmD, which is a leading anti-bacterial target. The successful targeting of TrmD requires detailed information on Trm5 to avoid cross-species inhibition. However, most information on Trm5 is derived from studies of the archaeal enzyme Methanococcus jannaschii (MjTrm5), whereas little information is available for eukaryotic enzymes. Here we use human Trm5 (Homo sapiens; HsTrm5) as an example of eukaryotic enzymes and demonstrate that it has retained key features of catalytic properties of the archaeal MjTrm5, including the involvement of a general base to mediate one proton transfer. We also address the protease sensitivity of the human enzyme upon expression in bacteria. Using the tRNA-bound crystal structure of the archaeal enzyme as a model, we have identified a single substitution in the human enzyme that improves resistance to proteolysis. These results establish conservation in both the catalytic mechanism and overall structure of Trm5 between evolutionarily distant eukaryotic and archaeal species and validate the crystal structure of the archaeal enzyme as a useful model for studies of the human enzyme.

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

Exploitation of genomics in fungicide research: current status and future perspectives

Every year, fungicide use to control plant disease caused by pathogenic fungi increases. The global fungicide market is now worth more than £5.3 billion, second only to the herbicide market in importance. In the UK, over 5500 tonnes of fungicide were applied to crops in 2010 (The Food and Environment Research Agency, Pesticide Usage Statistics), with 95.5% of the wheat-growing area receiving three fungicide sprays. Although dependence on fungicides to produce food securely, reliably and cheaply may be moderated in the future by further developments in crop biotechnology, modern crop protection will continue to require a diversity of solutions, including effective and safe chemical control. Therefore, investment in exploiting the increasingly available genome sequences of the most devastating fungal and oomycete phytopathogenic species should bring an array of new opportunities for chemical intervention. To date, the impact of whole genome research on the development, introduction and stewardship of fungicides has been limited, but ongoing improvements in computational analysis, molecular biology, chemical genetics, genome sequencing and transcriptomics will facilitate the development and registration of the future suite of crop protection chemicals.

Recognition of guanosine by dissimilar tRNA methyltransferases

Guanosines are important for biological activities through their specific functional groups that are recognized for RNA or protein interactions. One example is recognition of N(1) of G37 in tRNA by S-adenosyl-methionine (AdoMet)-dependent tRNA methyltransferases to synthesize m(1)G37-tRNA, which is essential for translational fidelity in all biological domains. Synthesis of m(1)G37-tRNA is catalyzed by TrmD in bacteria and by Trm5 in eukarya and archaea, using unrelated and dissimilar structural folds. This raises the question of how dissimilar proteins recognize the same guanosine. Here we probe the mechanism of discrimination among functional groups of guanosine by TrmD and Trm5. Guanosine analogs were systematically introduced into tRNA through a combination of chemical and enzymatic synthesis. Single turnover kinetic assays and thermodynamic analysis of the effect of each analog on m(1)G37-tRNA synthesis reveal that TrmD and Trm5 discriminate functional groups differently. While both recognize N(1) and O(6) of G37, TrmD places a much stronger emphasis on these functional groups than Trm5. While the exocyclic 2-amino group of G37 is important for TrmD, it is dispensable for Trm5. In addition, while an adjacent G36 is obligatory for TrmD, it is nonessential for Trm5. These results depict a more rigid requirement of guanosine functional groups for TrmD than for Trm5. However, the sensitivity of both enzymes to analog substitutions, together with an experimental revelation of their low cellular concentrations relative to tRNA substrates, suggests a model in which these enzymes rapidly screen tRNA by direct recognition of G37 in order to monitor the global state of m(1)G37-tRNA.

Role for Escherichia coli YidD in membrane protein insertion

YidC has an essential but poorly defined function in membrane protein insertion and folding in bacteria. The yidC gene is located in a gene cluster that is highly conserved in Gram-negative bacteria, the gene order being rpmH, rnpA, yidD, yidC, and trmE. Here, we show that Escherichia coli yidD, which overlaps with rnpA and is only 2 bp upstream of yidC, is expressed and localizes to the inner membrane, probably through an amphipathic helix. Inactivation of yidD had no discernible effect on cell growth and viability. However, compared to control cells, ΔyidD cells were affected in the insertion and processing of three YidC-dependent inner membrane proteins. Furthermore, in vitro cross-linking showed that YidD is in proximity of a nascent inner membrane protein during its localization in the Sec-YidC translocon, suggesting that YidD might be involved in the insertion process.

Mapping the protein interaction network in methicillin-resistant Staphylococcus aureus

Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13,219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets.

Comparative genomics allowed the identification of drug targets against human fungal pathogens

Background

The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases.

Results

In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24)-sterol C-methyltransferase.

Conclusions

Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug targets. The preferred profile for fungal targets includes proteins conserved among fungi, but absent in the human genome. These characteristics potentially minimize toxic side effects exerted by pharmacological inhibition of the cellular targets. From this first step of post-genomic analysis, we obtained information relevant to future new drug development.

Investigation of antimicrobial model of Hemsleya pengxianensis W.J. Chang and its main active component by metabolomics technique

AIM OF THE STUDY

H.pengxianensi is a native plant in China. Dihydrocucurbitacin F-25-O-acetate is one of the major chemical components in H.pengxianensis. In this investigation, we attempted to quantify the content of dihydrocucurbitacin F-25-O-acetate in H.pengxianensis and find the possible antibacterial mode of dihydrocucurbitacin F-25-O-acetate and H.pengxianensis.

MATERIAL AND METHODS

First, the content of dihydrocucurbitacin F-25-O-acetate was quantified by HPLC. Then, metabolic profiles of Staphylococcus aureus treated by dihydrocucurbitacin F-25-O-acetate, extract of H.pengxianensis and nine antibacterial substances with the known modes of action were acquired by HPLC/DAD/ESI-MS. Principal component analysis was carried out upon all metabolic profiles to classify those drugs according to their mechanisms.

RESULTS AND CONCLUSION

The HPLC result show the content of dihydrocucurbitacin F-25-O-acetate in H.pengxianensis (5.3+/-0.4 mg/g). From the result obtained by principal component analysis, dihydrocucurbitacin F-25-O-acetate was found to be the component playing the main antimicrobial roles on Staphylococcus aureus in H.pengxianensis and the possible antibacterial mode of dihydrocucurbitacin F-25-O-acetate and H.pengxianensis may be to inhibit cell wall synthesis.

A new measure for functional similarity of gene products based on Gene Ontology

Background

Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.

Results

We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; sim_Rel and funSim. One measure (sim_Rel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families.

Conclusion

The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

Theodor Bücher Lecture. Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and the 9th IUBMB conference in Budapest

The newly emerging field of systems biology involves a judicious interplay between high-throughput 'wet' experimentation, computational modelling and technology development, coupled to the world of ideas and theory. This interplay involves iterative cycles, such that systems biology is not at all confined to hypothesis-dependent studies, with intelligent, principled, hypothesis-generating studies being of high importance and consequently very far from aimless fishing expeditions. I seek to illustrate each of these facets. Novel technology development in metabolomics can increase substantially the dynamic range and number of metabolites that one can detect, and these can be exploited as disease markers and in the consequent and principled generation of hypotheses that are consistent with the data and achieve this in a value-free manner. Much of classical biochemistry and signalling pathway analysis has concentrated on the analyses of changes in the concentrations of intermediates, with 'local' equations - such as that of Michaelis and Menten v=(Vmax x S)/(S+K m) - that describe individual steps being based solely on the instantaneous values of these concentrations. Recent work using single cells (that are not subject to the intellectually unsupportable averaging of the variable displayed by heterogeneous cells possessing nonlinear kinetics) has led to the recognition that some protein signalling pathways may encode their signals not (just) as concentrations (AM or amplitude-modulated in a radio analogy) but via changes in the dynamics of those concentrations (the signals are FM or frequency-modulated). This contributes in principle to a straightforward solution of the crosstalk problem, leads to a profound reassessment of how to understand the downstream effects of dynamic changes in the concentrations of elements in these pathways, and stresses the role of signal processing (and not merely the intermediates) in biological signalling. It is this signal processing that lies at the heart of understanding the languages of cells. The resolution of many of the modern and postgenomic problems of biochemistry requires the development of a myriad of new technologies (and maybe a new culture), and thus regular input from the physical sciences, engineering, mathematics and computer science. One solution, that we are adopting in the Manchester Interdisciplinary Biocentre (http://www.mib.ac.uk/) and the Manchester Centre for Integrative Systems Biology (http://www.mcisb.org/), is thus to colocate individuals with the necessary combinations of skills. Novel disciplines that require such an integrative approach continue to emerge. These include fields such as chemical genomics, synthetic biology, distributed computational environments for biological data and modelling, single cell diagnostics/bionanotechnology, and computational linguistics/text mining.

Metabolomics, machine learning and modelling: towards an understanding of the language of cells

In answering the question ‘Systems Biology – will it work?’ (which it self-evidently has already), it is appropriate to highlight advances in philosophy, in new technique development and in novel findings. In terms of philosophy, we see that systems biology involves an iterative interplay between linked activities – for instance, between theory and experiment, between induction and deduction and between measurements of parameters and variables – with more emphasis than has perhaps been common now being focused on the first in each of these pairs. In technique development, we highlight closed loop machine learning and its use in the optimization of scientific instrumentation, and the ability to effect high-quality and quasi-continuous optical images of cells. This leads to many important and novel findings. In the first case, these may involve new biomarkers for disease, whereas in the second case, we have determined that many biological signals may be frequency-rather than amplitude-encoded. This leads to a very different view of how signalling ‘works’ (equations such as that of Michaelis and Menten which use only amplitudes, i.e. concentrations, are inadequate descriptors), lays emphasis on the signal processing network elements that lie ‘downstream’ of what are traditionally considered the signals, and allows one simply to understand how cross-talk may be avoided between pathways which nevertheless use common signalling elements. The language of cells is much richer than we had supposed, and we are now well placed to decode it.

Discrimination of modes of action of antifungal substances by use of metabolic footprinting

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.