RavN is a member of a previously unrecognized group of Legionella pneumophila E3 ubiquitin ligases

The eukaryotic ubiquitylation machinery catalyzes the covalent attachment of the small protein modifier ubiquitin to cellular target proteins in order to alter their fate. Microbial pathogens exploit this post-translational modification process by encoding molecular mimics of E3 ubiquitin ligases, eukaryotic enzymes that catalyze the final step in the ubiquitylation cascade. Here, we show that the Legionella pneumophila effector protein RavN belongs to a growing class of bacterial proteins that mimic host cell E3 ligases to exploit the ubiquitylation pathway. The E3 ligase activity of RavN was located within its N-terminal region and was dependent upon interaction with a defined subset of E2 ubiquitin-conjugating enzymes. The crystal structure of the N-terminal region of RavN revealed a U-box-like motif that was only remotely similar to other U-box domains, indicating that RavN is an E3 ligase relic that has undergone significant evolutionary alteration. Substitution of residues within the predicted E2 binding interface rendered RavN inactive, indicating that, despite significant structural changes, the mode of E2 recognition has remained conserved. Using hidden Markov model-based secondary structure analyses, we identified and experimentally validated four additional L. pneumophila effectors that were not previously recognized to possess E3 ligase activity, including Lpg2452/SdcB, a new paralog of SidC. Our study provides strong evidence that L. pneumophila is dedicating a considerable fraction of its effector arsenal to the manipulation of the host ubiquitylation pathway.Funding: This work was funded by the Intramural Research Program of the National Institutes of Health (to MPM)(Project Number: 1ZIAHD008893-07) and by the Spanish Ministry of Economy and Competitiveness Grant (to AH)(BFU2014-59759-R) and the Severo Ochoa Excellence Accreditation (to AH)(SEV-2016-0644). This study made use of the Diamond Light Source beamline I04 (Oxfordshire, UK) and ALBA synchrotron beamline BL13-XALOC, funded in part by the Horizon 2020 programme of the European Union, iNEXT (H2020 Grant # 653706). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Computational Expansion of High-Resolution-MSn Spectral Libraries

<p>Commonly, in MS-based untargeted metabolomics, some metabolites cannot be confidently identified due to ambiguities in resolving isobars and structurally similar species. To address this, analytical techniques beyond traditional MS2 analysis, such as MSn fragmentation, can be applied to probe metabolites for additional structural information. In MSn fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS1 spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS2 spectra alone, which helps improve the performance of metabolite identification workflows. However, most approaches for metabolite identification require mass-to-charge (<i>m</i>/<i>z</i>) values measured with high resolution, as this enables the determination of accurate mass values. Unfortunately, high-resolution-MSn spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MSn spectra by converting existing low-resolution-MSn spectra using complementary high-resolution-MS2 spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MSn spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.</p><p>The GLOMICAVE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 952908. </p&gt

Protocol for mapping the metabolome and lipidome of medulloblastoma cells using liquid chromatography-mass spectrometry

Summary: Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and lipidomics have recently been used to show that MYC-amplified group 3 medulloblastoma tumors are driven by metabolic reprogramming. Here, we present a protocol to extract metabolites and lipids from human medulloblastoma brain tumor-initiating cells and normal neural stem cells. We describe untargeted LC-MS methods that can be used to achieve extensive coverage of the polar metabolome and lipidome. Finally, we detail strategies for metabolite identification and data analysis.For complete details on the use and execution of this protocol, please refer to Gwynne et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

Discovery of Ubiquitin Deamidases in the Pathogenic Arsenal of Legionella pneumophila

Summary: Legionella pneumophila translocates the largest known arsenal of over 330 pathogenic factors, called “effectors,” into host cells during infection, enabling L. pneumophila to establish a replicative niche inside diverse amebas and human macrophages. Here, we reveal that the L. pneumophila effectors MavC (Lpg2147) and MvcA (Lpg2148) are structural homologs of cycle inhibiting factor (Cif) effectors and that the adjacent gene, lpg2149, produces a protein that directly inhibits their activity. In contrast to canonical Cifs, both MavC and MvcA contain an insertion domain and deamidate the residue Gln40 of ubiquitin but not Gln40 of NEDD8. MavC and MvcA are functionally diverse, with only MavC interacting with the human E2-conjugating enzyme UBE2N (Ubc13). MavC deamidates the UBE2N∼Ub conjugate, disrupting Lys63 ubiquitination and dampening NF-κB signaling. Combined, our data reveal a molecular mechanism of host manipulation by pathogenic bacteria and highlight the complex regulatory mechanisms integral to L. pneumophila’s pathogenic strategy. : Legionella pneumophila, possessing the largest known arsenal of effectors, continues to reveal unique approaches to host cell control. Valleau et al. decrypt the functions of a trio of effectors, discovering a pair of ubiquitin-specific deamidases, their regulation by a neighboring dual-specificity protein inhibitor, and a mechanism of NF-κB suppression. Keywords: pathogen-host interaction, ubiquitination, Legionella, UBE2N/Ubc13, NF-κB signaling, Type IV secretion system, effectors, metaeffector, cycle inhibiting facto

Structural and Functional Survey of Environmental Aminoglycoside Acetyltransferases Reveals Functionality of Resistance Enzymes

Aminoglycoside <i>N</i>-acetyltransferases (AACs) confer resistance against the clinical use of aminoglycoside antibiotics. The origin of AACs can be traced to environmental microbial species representing a vast reservoir for new and emerging resistance enzymes, which are currently undercharacterized. Here, we performed detailed structural characterization and functional analyses of four metagenomic AAC (meta-AACs) enzymes recently identified in a survey of agricultural and grassland soil microbiomes (Forsberg et al. Nature 2014, 509, 612). These enzymes are new members of the Gcn5-Related-<i>N</i>-Acetyltransferase superfamily and confer resistance to the aminoglycosides gentamicin C, sisomicin, and tobramycin. Moreover, the meta-AAC0020 enzyme demonstrated activity comparable with an AAC(3)-I enzyme that serves as a model AAC enzyme identified in a clinical bacterial isolate. The crystal structure of meta-AAC0020 in complex with sisomicin confirmed an unexpected AAC(6′) regiospecificity of this enzyme and revealed a drug binding mechanism distinct from previously characterized AAC(6′) enzymes. Together, our data highlights the presence of highly active antibiotic-modifying enzymes in the environmental microbiome and reveals unexpected diversity in substrate specificity. These observations of additional AAC enzymes must be considered in the search for novel aminoglycosides less prone to resistance

SGA output for analysis sets 21-30.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 21-30

SGA output for analysis sets 1-10.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 1-10

SGA output for analysis sets 31-37.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 31-37