Significance Macrolide antibiotics inhibit translation by binding in the ribosomal nascent peptide exit tunnel. It was believed that macrolides interfere with protein synthesis by obstructing the egress of nascent proteins. In contrast to this view, the results of ribosome profiling analysis suggest that the main mode of macrolide action is context-specific inhibition of peptide bond formation. The ribosome with a macrolide molecule bound in the tunnel is impaired in catalysis of peptide bond formation between specific combinations of the peptidyl donors and aminoacyl acceptors, leading to interruption of translation when such problematic substrates are encountered. These findings underscore the existence of a link between the ribosomal tunnel and the peptidyl transferase center and pave the way for development of superior antibiotics.

Endophytes are microbes and fungi that live inside plant tissues without damaging the host. Herein we examine the dynamic changes in the endophytic bacterial community in potato (Solanum tuberosum) tuber in response to pathogenic infection by Pectobacterium atrosepticum, which causes soft rot in numerous economically important crops. We quantified community changes using both cultivation and next-generation sequencing of the 16S rRNA gene and found that, despite observing significant variability in both the mass of macerated tissue and structure of the endophytic community between individual potato tubers, P. atrosepticum is always taken over by the endophytes during maceration. 16S rDNA sequencing revealed bacteria from the phyla Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, Fusobacteria, Verrucomicrobia, Acidobacteria, TM7, and Deinococcus-Thermus. Prior to infection, Propionibacterium acnes is frequently among the dominant taxa, yet is out competed by relatively few dominant taxa as the infection proceeds. Two days post-infection, the most abundant sequences in macerated potato tissue are Gammaproteobacteria. The most dominant genera are Enterobacter and Pseudomonas. Eight days post-infection, the number of anaerobic pectolytic Clostridia increases, probably due to oxygen depletion. These results demonstrate that the pathogenesis is strictly initiated by the pathogen (sensu stricto) and proceeds with a major contribution from the endophytic community.

The endoribonuclease toxins of the E. coli toxin-antitoxin systems arrest bacterial growth and protein synthesis by targeting cellular mRNAs. As an exception, E. coli MazF was reported to cleave also 16S rRNA at a single site and separate an anti-Shine-Dalgarno sequence-containing RNA fragment from the ribosome. We noticed extensive rRNA fragmentation in response to induction of the toxins MazF and MqsR, which suggested that these toxins can cleave rRNA at multiple sites. We adapted differential RNA-sequencing to map the toxin-cleaved 5'- and 3'-ends. Our results show that the MazF and MqsR cleavage sites are located within structured rRNA regions and, therefore, are not accessible in assembled ribosomes. Most of the rRNA fragments are located in the aberrant ribosomal subunits that accumulate in response to toxin induction and contain unprocessed rRNA precursors. We did not detect MazF- or MqsR-cleaved rRNA in stationary phase bacteria and in assembled ribosomes. Thus, we conclude that MazF and MqsR cleave rRNA precursors before the ribosomes are assembled and potentially facilitate the decay of surplus rRNA transcripts during stress.

Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.

BackgroundWith the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks.ResultsA symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast.ConclusionsWe were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.