Translating Polyproline: Translation of messenger RNA into protein is carried out by the ribosome, together with a variety of accessory factors, which offer the potential for regulation of this critical step in gene expression (see the Perspective by Buskirk and Green ). Ude et al. (p. 82 , published online 13 December), using bacterial genetics and an in vitro reconstituted translation system, and Doerfel et al. (p. 85 , published online 13 December), using a model assay for peptide bond formation, find that the universally conserved bacterial elongation factor P (EF-P) (which is orthologous to the archaeal and eukaryotic initiation factor 5A) is required for the efficient translation of polyproline-containing polypeptides. Such short polyproline stretches (with runs of two, three, or more proline residues) would otherwise cause ribosomal stalling.

Summary: Quorum sensing (QS) refers to the ability of bacterial populations to read out the local environment for cell density and to collectively activate gene expression. Vibrio harveyi, one of the best characterized model organisms in QS, was used to address the question how single cells behave within a QS‐activated community in a homogeneous environment. Analysis of the QS‐regulated bioluminescence of a wild type strain revealed that even at high cell densities only 69% of the cells of the population produced bioluminescence, 25% remained dark and 6% were dead. Moreover, light intensities greatly varied from cell to cell at high population density. Addition of autoinducer to a bright liquid culture of V. harveyi increased the percentage of luminescent cells up to 98%, suggesting that V. harveyi produces and/or keeps the autoinducers at non‐saturating concentrations. In contrast, all living cells of a constitutive QS‐active mutant (ΔluxO) produced light. We also found that QS affects biofilm formation in V. harveyi. Our data provide first evidence that a heterogeneous population produces more biofilm than a homogeneous one. It is suggested that even a QS‐committed population of V. harveyi takes advantage of heterogeneity, which extends the current view of QS‐regulated uniformity.

A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM¹²) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM¹² interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM¹² interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.

Escherichia coli lives in the mammalian gastrointestinal tract anaerobically at high osmolarity as well as in the soil aerobically at varying osmolarities. Adaptation to these varying environmental conditions is crucial for growth and survival of E. coli . Two-dimensional protein gels were used to visualize global time-dependent changes (10 to 60 min) in the proteome of cells responding to osmotic stress (0.4 M NaCl or 0.7 M sorbitol) under aerobic or anaerobic conditions. The protein profiles revealed an induction of 12 proteins (Dps, HchA, HdhA, InfB, OsmC, OsmY, ProX, KatE, PspA, TalA, TktB, and TreF) under osmotic stress in an aerobic milieu. Eleven additional proteins (OtsB, YceI, YciE, YciF, YgaU, YjbJ, AcnA, MetL, PoxB, Ssb, and YhbO) were induced by osmotic stress imposed by NaCl. Most of the accumulated proteins were cross-protecting proteins (e.g., OsmY, OsmC, Dps, and KatE) which are regulated at the transcriptional level predominantly by RpoS and other regulators (e.g., integration host factor, OxyR, H-NS, LRP, and FIS). Comparative analysis of the proteome of E. coli grown under aerobic or anaerobic conditions under osmotic stress (NaCl) revealed an overlap of the up-regulated proteins of more than 50%. Ten proteins (PoxB, AcnA, TalA, TktB, KatE, PspA, Ssb, TreF, MetL, and YhbO) were detectable only under aerobic, high-osmolality conditions. Time-dependent alterations of the proteome were monitored, allowing classification of the up-regulated proteins into early, middle, and long-term phases of adaptation. Only a few proteins were found to be down-regulated upon osmotic stress.