The crystal structure of the TonB-dependent transporter YncD reveals a positively charged substrate-binding site

Substrate Uptake by TonB-Dependent Outer Membrane Transporters

TonB is an essential component of an energy-generating system that powers active transport across the outer membrane (OM) of compounds that are too large or too scarce to diffuse through porins. The TonB-dependent OM transport proteins (TBDTs) consist of β barrels forming pores that are closed by plugs. The binding of TonB to TBDTs elicits plug movement, which opens the pores and enables nutrient translocation from the cell surface into the periplasm. TonB is also involved in the uptake of certain proteins, particularly toxins, through OM proteins that differ structurally from TBDTs. TonB binds to a sequence of five residues, designated as the TonB box, which is conserved in all TBDTs. Energy from the proton motive force (pmf) of the cytoplasmic membrane is transmitted to TonB by two proteins, ExbB and ExbD. These proteins form an energy-transmitting protein complex consisting of five ExbB proteins, forming a pore that encloses the ExbD dimer. This review discusses the structural changes that occur in TBDTs upon interaction with TonB, as well as the interaction of ExbB-ExbD with TonB, which is required to transmit the energy of the pmf and thereby open TBDT pores. TonB facilitates import of a wide range of substrates.

Specificity and mechanism of TonB-dependent ferric catecholate uptake by Fiu

We studied the Escherichia coli outer membrane protein Fiu, a presumed transporter of monomeric ferric catecholates, by introducing Cys residues in its surface loops and modifying them with fluorescein maleimide (FM). Fiu-FM bound iron complexes of the tricatecholate siderophore enterobactin (FeEnt) and glucosylated enterobactin (FeGEnt), their dicatecholate degradation product Fe(DHBS)2 (FeEnt*), the monocatecholates dihydroxybenzoic acid (FeDHBA) and dihydroxybenzoyl serine (FeDHBS), and the siderophore antibiotics cefiderocol (FDC) and MB-1. Unlike high-affinity ligand-gated porins (LGPs), Fiu-FM had only micromolar affinity for iron complexes. Its apparent KD values for FeDHBS, FeDHBA, FeEnt*, FeEnt, FeGEnt, FeFDC, and FeMB-1 were 0.1, 0.7, 0.7, 1.0, 0.3, 0.4, and 4 μM, respectively. Despite its broad binding abilities, the transport repertoires of E. coli Fiu, as well as those of Cir and FepA, were less broad. Fiu only transported FeEnt*. Cir transported FeEnt* and FeDHBS (weakly); FepA transported FeEnt, FeEnt*, and FeDHBA. Both Cir and FepA bound FeGEnt, albeit with lower affinity. Related transporters of Acinetobacter baumannii (PiuA, PirA, BauA) had similarly moderate affinity and broad specificity for di- or monomeric ferric catecholates. Both microbiological and radioisotopic experiments showed Fiu's exclusive transport of FeEnt*, rather than ferric monocatecholate compounds. Molecular docking and molecular dynamics simulations predicted three binding sites for FeEnt*in the external vestibule of Fiu, and a fourth site deeper in its interior. Alanine scanning mutagenesis in the outermost sites (1a, 1b, and 2) decreased FeEnt* binding affinity as much as 20-fold and reduced or eliminated FeEnt* uptake. Finally, the molecular dynamics simulations suggested a pathway of FeEnt* movement through Fiu that may generally describe the process of metal transport by TonB-dependent receptors.

Weathered granites and soils harbour microbes with lanthanide-dependent methylotrophic enzymes

BACKGROUND

Prior to soil formation, phosphate liberated by rock weathering is often sequestered into highly insoluble lanthanide phosphate minerals. Dissolution of these minerals releases phosphate and lanthanides to the biosphere. Currently, the microorganisms involved in phosphate mineral dissolution and the role of lanthanides in microbial metabolism are poorly understood.

RESULTS

Although there have been many studies of soil microbiology, very little research has investigated microbiomes of weathered rock. Here, we sampled weathered granite and associated soil to identify the zones of lanthanide phosphate mineral solubilisation and genomically define the organisms implicated in lanthanide utilisation. We reconstructed 136 genomes from 11 bacterial phyla and found that gene clusters implicated in lanthanide-based metabolism of methanol (primarily xoxF3 and xoxF5) are surprisingly common in microbial communities in moderately weathered granite. Notably, xoxF3 systems were found in Verrucomicrobia for the first time, and in Acidobacteria, Gemmatimonadetes and Alphaproteobacteria. The xoxF-containing gene clusters are shared by diverse Acidobacteria and Gemmatimonadetes, and include conserved hypothetical proteins and transporters not associated with the few well studied xoxF systems. Given that siderophore-like molecules that strongly bind lanthanides may be required to solubilise lanthanide phosphates, it is notable that candidate metallophore biosynthesis systems were most prevalent in bacteria in moderately weathered rock, especially in Acidobacteria with lanthanide-based systems.

CONCLUSIONS

Phosphate mineral dissolution, putative metallophore production and lanthanide utilisation by enzymes involved in methanol oxidation linked to carbonic acid production co-occur in the zone of moderate granite weathering. In combination, these microbial processes likely accelerate the conversion of granitic rock to soil.

TonB-Dependent Transport Across the Bacterial Outer Membrane

TonB-dependent transporters (TBDTs) are present in all gram-negative bacteria and mediate energy-dependent uptake of molecules that are too scarce or large to be taken up efficiently by outer membrane (OM) diffusion channels. This process requires energy that is derived from the proton motive force and delivered to TBDTs by the TonB-ExbBD motor complex in the inner membrane. Together with the need to preserve the OM permeability barrier, this has led to an extremely complex and fascinating transport mechanism for which the fundamentals, despite decades of research, are still unclear. In this review, we describe our current understanding of the transport mechanism of TBDTs, their potential role in the delivery of novel antibiotics, and the important contributions made by TBDT-associated (lipo)proteins.

Surveying membrane landscapes: a new look at the bacterial cell surface

Recent studies applying advanced imaging techniques are changing the way we understand bacterial cell surfaces, bringing new knowledge on everything from single-cell heterogeneity in bacterial populations to their drug sensitivity and mechanisms of antimicrobial resistance. In both Gram-positive and Gram-negative bacteria, the outermost surface of the bacterial cell is being imaged at nanoscale; as a result, topographical maps of bacterial cell surfaces can be constructed, revealing distinct zones and specific features that might uniquely identify each cell in a population. Functionally defined assembly precincts for protein insertion into the membrane have been mapped at nanoscale, and equivalent lipid-assembly precincts are suggested from discrete lipopolysaccharide patches. As we review here, particularly for Gram-negative bacteria, the applications of various modalities of nanoscale imaging are reawakening our curiosity about what is conceptually a 3D cell surface landscape: what it looks like, how it is made and how it provides resilience to respond to environmental impacts.

Machine learning for identifying resistance features of Klebsiella pneumoniae using whole-genome sequence single nucleotide polymorphisms

Introduction. Klebsiella pneumoniae, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of K. pneumoniae is increasing year by year, posing a severe threat to public health worldwide. K. pneumoniae has been listed as one of the pathogens causing the global crisis of antimicrobial resistance in nosocomial infections. We need to explore the drug resistance of K. pneumoniae for clinical diagnosis. Single nucleotide polymorphisms (SNPs) are of high density and have rich genetic information in whole-genome sequencing (WGS), which can affect the structure or expression of proteins. SNPs can be used to explore mutation sites associated with bacterial resistance.Hypothesis/Gap Statement. Machine learning methods can detect genetic features associated with the drug resistance of K. pneumoniae from whole-genome SNP data.Aims. This work used Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning methods to detect genetic features related to drug resistance of K. pneumoniae from whole-genome SNP data.Methods. WGS data on resistance of K. pneumoniae strains to four antibiotics (tetracycline, gentamicin, imipenem, amikacin) were downloaded from the European Nucleotide Archive (ENA). Sequence alignments were performed with MUMmer 3 to complete SNP calling using K. pneumoniae HS11286 chromosome as the reference genome. The FFS algorithm was applied to feature selection of the SNP dataset. The training set was constructed based on mutation sites with mutation frequency >0.995. Based on the original SNP training set, 70% of SNPs were randomly selected from each dataset as the test set to verify the accuracy of the training results. Finally, the resistance genes were obtained by the CMD algorithm and Venny.Results. The number of strains resistant to tetracycline, gentamicin, imipenem and amikacin was 931, 1048, 789 and 203, respectively. Machine learning algorithms were applied to the SNP training set and test set, and 28 and 23 resistance genes were predicted, respectively. The 28 resistance genes in the training set included 22 genes in the test set, which verified the accuracy of gene prediction. Among them, some genes (KPHS_35310, KPHS_18220, KPHS_35880, etc.) corresponded to known resistance genes (Eef2, lpxK, MdtC, etc). Logistic regression classifiers were established based on the identified SNPs in the training set. The area under the curves (AUCs) of the four antibiotics was 0.939, 0.950, 0.912 and 0.935, showing a strong ability to predict bacterial resistance.Conclusion. Machine learning methods can effectively be used to predict resistance genes and associated SNPs. The FFS and CMD algorithms have wide applicability. They can be used for the drug-resistance analysis of any microorganism with genomic variation and phenotypic data. This work lays a foundation for resistance research in clinical applications.

Simple Methods for the Preparation of Colloidal Chitin, Cell Free Supernatant and Estimation of Laminarinase

Colloidal chitin (CC) is a common substrate used in research work involving chitin-active enzymes (chitinases). Cell free supernatant (CFS) is prepared from fermented broth. Preparation of CC and CFS usually involve large amounts of liquid, which must be separated from the solids. This necessitates the use of a large volume centrifugation facility, which may not be accessible to everyone. Filtration is a viable alternative to centrifugation, and several filter elements are described in the literature. Each of those elements has its own set of disadvantages like non-availability, high cost, fragility, and non-reusability. Here we describe the use of lab coat clothing material (LCCM) for the preparation of CC and CFS. For filtration purposes, the LCCM was found to be functional, rugged, reusable, and cost-effective. Also described here is a new method for the estimation of laminarinase using a laminarin infused agarose gel plate. An easily available optical fabric brightener (OFB) was used as a stain for the agarose plate. The laminarin infused agarose plate assay is simple, inexpensive, and was found to be impervious to high amounts of ammonium sulfate (AS) in enzyme precipitates.