Recognition elements in rRNA for the tylosin resistance methyltransferase RlmA(II)

Ribosome-targeting antibiotics and resistance via ribosomal RNA methylation

The rise of multidrug-resistant bacterial infections is a cause of global concern. There is an urgent need to both revitalize antibacterial agents that are ineffective due to resistance while concurrently developing new antibiotics with novel targets and mechanisms of action. Pathogen associated resistance-conferring ribosomal RNA (rRNA) methyltransferases are a growing threat that, as a group, collectively render a total of seven clinically-relevant ribosome-targeting antibiotic classes ineffective. Increasing frequency of identification and their growing prevalence relative to other resistance mechanisms suggests that these resistance determinants are rapidly spreading among human pathogens and could contribute significantly to the increased likelihood of a post-antibiotic era. Herein, with a view toward stimulating future studies to counter the effects of these rRNA methyltransferases, we summarize their prevalence, the fitness cost(s) to bacteria of their acquisition and expression, and current efforts toward targeting clinically relevant enzymes of this class.

Structural insights into substrate selectivity of ribosomal RNA methyltransferase RlmCD

RlmCD has recently been identified as the S-adenosyl methionine (SAM)-dependent methyltransferase responsible for the formation of m⁵U at U747 and U1939 of 23S ribosomal RNA in Streptococcus pneumoniae. In this research, we determine the high-resolution crystal structures of apo-form RlmCD and its complex with SAH. Using an in-vitro methyltransferase assay, we reveal the crucial residues for its catalytic functions. Furthermore, structural comparison between RlmCD and its structural homologue RumA, which only catalyzes the m⁵U1939 in Escherichia coli, implicates that a unique long linker in the central domain of RlmCD is the key factor in determining its substrate selectivity. Its significance in the enzyme activity of RlmCD is further confirmed by in-vitro methyltransferase assay.

Use of next generation sequence to investigate potential novel macrolide resistance mechanisms in a population of Moraxella catarrhalis isolates

Although previous studies have confirmed that 23S rRNA gene mutation could be responsible for most of macrolide resistance in M. catarrhalis, a recent study suggested otherwise. Next generation sequence based comparative genomics has revolutionized the mining of potential novel drug resistant mechanisms. In this study, two pairs of resistant and susceptible M. catarrhalis isolates with different multilocus sequence types, were investigated for potential differential genes or informative single nucleotide polymorphisms (SNPs). The identified genes and SNPs were evaluated in 188 clinical isolates. From initially 12 selected differential genes and 12 informative SNPs, 10 differential genes (mboIA, mcbC, mcbI, mboIB, MCR_1794, MCR_1795, lgt2B/C, dpnI, mcbB, and mcbA) and 6 SNPs (C619T of rumA, T140C of rplF, G643A of MCR_0020, T270G of MCR_1465, C1348A of copB, and G238A of rrmA) were identified as possibly linked to macrolide resistance in M. catarrhalis. Most of the identified differential genes and SNPs are related to methylation of ribosomal RNA (rRNA) or DNA, especially MCR_0020 and rrmA. Further studies are needed to determine the function and/or evolution process, of the identified genes or SNPs, to establish whether some novel or combined mechanisms are truly involved in M. catarrhalis macrolide resistance mechanism.

RlmCD-mediated U747 methylation promotes efficient G748 methylation by methyltransferase RlmAII in 23S rRNA in Streptococcus pneumoniae; interplay between two rRNA methylations responsible for telithromycin susceptibility

Adenine at position 752 in a loop of helix 35 from positions 745 to 752 in domain II of 23S rRNA is involved in binding to the ribosome of telithromycin (TEL), a member of ketolides. Methylation of guanine at position 748 by the intrinsic methyltransferase RlmA^II enhances binding of telithromycin (TEL) to A752 in Streptococcus pneumoniae. We have found that another intrinsic methylation of the adjacent uridine at position 747 enhances G748 methylation by RlmA^II, rendering TEL susceptibility. U747 and another nucleotide, U1939, were methylated by the dual-specific methyltransferase RlmCD encoded by SP_1029 in S. pneumoniae. Inactivation of RlmCD reduced N1-methylated level of G748 by RlmA^IIin vivo, leading to TEL resistance when the nucleotide A2058, located in domain V of 23S rRNA, was dimethylated by the dimethyltransferase Erm(B). In vitro methylation of rRNA showed that RlmA^II activity was significantly enhanced by RlmCD-mediated pre-methylation of 23S rRNA. These results suggest that RlmCD-mediated U747 methylation promotes efficient G748 methylation by RlmA^II, thereby facilitating TEL binding to the ribosome.

CAPRI targets T29-T42: proving ground for new docking procedures

The critical assessment of protein interactions (CAPRI) experiment provides a unique opportunity for unbiased assessment of docking procedures. The recent CAPRI targets T29-T42 entailed docking of bound, unbound, and modeled structures, presenting a wide range of prediction difficulty. We submitted accurate predictions for targets T40, T41, and T42, a good prediction for T32 and acceptable predictions for T29 and T34. The accuracy of our docking results generally matched the prediction difficulty; hence, docking of modeled proteins produced less accurate results. However, there were interesting exceptions: an accurate prediction was submitted for the dimer of modeled tetratricopeptide repeat (T42) and only an acceptable prediction for the bound/unbound case T29. The ensembles of docking models produced in the scans included an acceptable or better prediction for every target. We show here that our recently developed postscan reevaluation procedure, which tests propensity and solvation measures of the whole interface and the interface core, successfully distinguished these predictions from false docking models. For enzyme-inhibitor targets, we show that the distance of the interface from the enzyme's centroid ranked high native like docking models. Also, for one case we demonstrate that docking of an ensemble of conformers produced by normal modes analysis can improve the accuracy of the prediction.

Selection of near-native poses in CAPRI rounds 13-19

In CAPRI rounds 13-19, we submitted models that are of acceptable or higher quality for 6 of the total of 13 targets. This success builds on our record in previous CAPRI rounds. The docking problem can be divided into two steps. In the first, translational/rotational and conformational space is searched to generate a pool of docked poses; the success of this search step is measured by whether near-native poses are included in the pool. In the second step, the pool is selected for near-native poses. In our previous assessment of CAPRI results, we suggested that the search problem is largely solved; a remaining problem is to select near-native poses. Our work in these new rounds of CAPRI was guided by this assessment. To solve the selection problem, we used an assortment of criteria on the interfaces of candidate poses. In one extreme, represented by T29, with very little known interface information, our criterion for top models was based on interface prediction. Poses in which the predicted interface residues occurred in interfaces were selected. Our model 1 for T29 was of medium quality. In the other extreme, represented by T40, with reliably known interface information, our selection was solely based on such information. Nine of the ten models submitted for T40 were of high (3 models), medium (4 models), and acceptable (2 models) quality. Our strategy of mixing predicted and known interface information appears to be widely applicable for the selection of near-native poses.

Performance of ZDOCK and ZRANK in CAPRI rounds 13-19

We report the performance of the ZDOCK and ZRANK algorithms in CAPRI rounds 13-19 and introduce a novel measure atom contact frequency (ACF). To compute ACF, we identify the residues that most often make contact with the binding partner in the complete set of ZDOCK predictions for each target. We used ACF to predict the interface of the proteins, which, in combination with the biological data available in the literature, is a valuable addition to our docking pipeline. Furthermore, we incorporated a straightforward and efficient clustering algorithm with two purposes: (1) to determine clusters of similar docking poses (corresponding to energy funnels) and (2) to remove redundancies from the final set of predictions. With these new developments, we achieved at least one acceptable prediction for targets 29 and 36, at least one medium-quality prediction for targets 41 and 42, and at least one high-quality prediction for targets 37 and 40; thus, we succeeded for six out of a total of 12 targets.

Exploring TAR-RNA aptamer loop-loop interaction by X-ray crystallography, UV spectroscopy and surface plasmon resonance

In HIV-1, trans-activation of transcription of the viral genome is regulated by an imperfect hairpin, the trans-activating responsive (TAR) RNA element, located at the 5′ untranslated end of all viral transcripts. TAR acts as a binding site for viral and cellular proteins. In an attempt to identify RNA ligands that would interfere with the virus life-cycle by interacting with TAR, an in vitro selection was previously carried out. RNA hairpins that formed kissing-loop dimers with TAR were selected [Ducongé F. and Toulmé JJ (1999) RNA, 5:1605–1614]. We describe here the crystal structure of TAR bound to a high-affinity RNA aptamer. The two hairpins form a kissing complex and interact through six Watson–Crick base pairs. The complex adopts an overall conformation with an inter-helix angle of 28.1°, thus contrasting with previously reported solution and modelling studies. Structural analysis reveals that inter-backbone hydrogen bonds between ribose 2′ hydroxyl and phosphate oxygens at the stem-loop junctions can be formed. Thermal denaturation and surface plasmon resonance experiments with chemically modified 2′-O-methyl incorporated into both hairpins at key positions, clearly demonstrate the involvement of this intermolecular network of hydrogen bonds in complex stability.

Interaction of the tylosin-resistance methyltransferase RlmA II at its rRNA target differs from the orthologue RlmA I

RlmA(II) methylates the N1-position of nucleotide G748 in hairpin 35 of 23 S rRNA. The resultant methyl group extends into the peptide channel of the 50 S ribosomal subunit and confers resistance to tylosin and other mycinosylated macrolide antibiotics. Methylation at G748 occurs in several groups of Gram-positive bacteria, including the tylosin-producer Streptomyces fradiae and the pathogen Streptococcus pneumoniae. Recombinant S. pneumoniae RlmA(II) was purified and shown to retain its activity and specificity in vitro when tested on unmethylated 23 S rRNA substrates. RlmA(II) makes multiple footprint contacts with nucleotides in stem-loops 33, 34 and 35, and does not interact elsewhere in the rRNA. Binding of RlmA(II) to the rRNA is dependent on the cofactor S-adenosylmethionine (or S-adenosylhomocysteine). RlmA(II) interacts with the same rRNA region as the orthologous enzyme RlmA(I) that methylates at nucleotide G745. Differences in nucleotide contacts within hairpin 35 indicate how the two methyltransferases recognize their distinct targets.