Translation error clusters induced by aminoglycoside antibiotics

Aminoglycoside antibiotics target the ribosome and induce mistranslation, yet which translation errors induce bacterial cell death is unclear. The analysis of cellular proteins by quantitative mass spectrometry shows that bactericidal aminoglycosides induce not only single translation errors, but also clusters of errors in full-length proteins in vivo with as many as four amino acid substitutions in a row. The downstream errors in a cluster are up to 10,000-fold more frequent than the first error and independent of the intracellular aminoglycoside concentration. The prevalence, length, and composition of error clusters depends not only on the misreading propensity of a given aminoglycoside, but also on its ability to inhibit ribosome translocation along the mRNA. Error clusters constitute a distinct class of misreading events in vivo that may provide the predominant source of proteotoxic stress at low aminoglycoside concentration, which is particularly important for the autocatalytic uptake of the drugs

An uncharged amine in the transition state of the ribosornal peptidyl transfer reaction.

The ribosome has an active site comprised of RNA that catalyzes peptide bond formation. To understand how RNA promotes this reaction requires a detailed understanding of the chemical transition state. Here, we report the Bronsted coefficient of the a-amino nucleophile with a series of puromycin derivatives. Both 50S subunit- and 70S ribosome-catalyzed reactions displayed linear free-energy relationships with slopes close to zero under conditions where chemistry is rate limiting. These results indicate that, at the transition state, the nucleophile is neutral in the ribosome-catalyzed reaction, in contrast to the substantial positive charge reported for typical uncatalyzed aminolysis reactions. This suggests that the ribosomal transition state involves deprotonation to a degree commensurate with nitrogen-carbon bond formation. Such a transition state is significantly different from that of uncatalyzed aminolysis reactions in solution

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Induction of Bacterial Antigen-Specific Colitis by a Simplified Human Microbiota Consortium in Gnotobiotic Interleukin-10-/- Mice

We evaluated whether a simplified human microbiota consortium (SIHUMI) induces colitis in germfree (GF) 129S6/SvEv (129) and C57BL/6 (B6) interleukin-10-deficient (IL-10−/−) mice, determined mouse strain effects on colitis and the microbiota, examined the effects of inflammation on relative bacterial composition, and identified immunodominant bacterial species in “humanized” IL-10−/− mice. GF wild-type (WT) and IL-10−/− 129 and B6 mice were colonized with 7 human-derived inflammatory bowel disease (IBD)-related intestinal bacteria and maintained under gnotobiotic conditions. Quantification of bacteria in feces, ileal and colonic contents, and tissues was performed using 16S rRNA gene selective quantitative PCR. Colonic segments were scored histologically, and gamma interferon (IFN-γ), IL-12p40, and IL-17 levels were measured in supernatants of unstimulated colonic tissue explants and of mesenteric lymph node (MLN) cells stimulated by lysates of individual or aggregate bacterial strains. Relative bacterial species abundances changed over time and differed between 129 and B6 mice, WT and IL-10−/− mice, luminal and mucosal samples, and ileal and colonic or fecal samples. SIHUMI induced colitis in all IL-10−/− mice, with more aggressive colitis and MLN cell activation in 129 mice. Escherichia coli LF82 and Ruminococcus gnavus lysates induced dominant effector ex vivo MLN TH1 and TH17 responses, although the bacterial mucosal concentrations were low. In summary, this study shows that a simplified human bacterial consortium induces colitis in ex-GF 129 and B6 IL-10−/− mice. Relative concentrations of individual SIHUMI species are determined by host genotype, the presence of inflammation, and anatomical location. A subset of IBD-relevant human enteric bacterial species preferentially stimulates bacterial antigen-specific TH1 and TH17 immune responses in this model, independent of luminal and mucosal bacterial concentrations

A compact statistical model of the song syntax in Bengalese finch

Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in a Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are repeatedly revisited, and allows associations of more than one state to the same syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network hypothesis of how syntax is controlled within the premotor song nucleus HVC, and suggests that adaptation and many-to-one mapping from neural substrates to syllables are important features of the neural control of complex song syntax