Optimal methylation noise for best chemotactic performance of E. coli

Run-to-Tumble Variability Controls the Surface Residence Times of E. coli Bacteria

Motile bacteria are known to accumulate at surfaces, eventually leading to changes in bacterial motility and biofilm formation. We use a novel two-color, three-dimensional Lagrangian tracking technique to follow simultaneously the body and the flagella of a wild-type Escherichia coli. We observe long surface residence times and surface escape corresponding mostly to immediately antecedent tumbling. A motility model accounting for a large behavioral variability in run-time duration reproduces all experimental findings and gives new insights into surface trapping efficiency.

Effect of receptor cooperativity on methylation dynamics in bacterial chemotaxis with weak and strong gradient

We study methylation dynamics of the chemoreceptors as an Escherichia coli cell moves around in a spatially varying chemoattractant environment. We consider attractant concentration with strong and weak spatial gradient. During the uphill and downhill motion of the cell along the gradient, we measure the temporal variation of average methylation level of the receptor clusters. Our numerical simulations we show that the methylation dynamics depends sensitively on the size of the receptor clusters and also on the strength of the gradient. At short times after the beginning of a run, the methylation dynamics is mainly controlled by short runs which are generally associated with high receptor activity. This results in demethylation at short times. But for intermediate or large times, long runs play an important role and depending on receptor cooperativity or gradient strength, the qualitative variation of methylation can be completely different in this time regime. For weak gradient, both for uphill and downhill runs, after the initial demethylation, we find methylation level increases steadily with time for all cluster sizes. Similar qualitative behavior is observed for strong gradient during uphill runs as well. However, the methylation dynamics for downhill runs in strong gradient show highly nontrivial dependence on the receptor cluster size. We explain this behavior as a result of interplay between the sensing and adaptation modules of the signaling network.

Effect of receptor clustering on chemotactic performance of E. coli: Sensing versus adaptation

We show how the competition between sensing and adaptation can result in a performance peak in Escherichia coli chemotaxis using extensive numerical simulations in a detailed theoretical model. Receptor clustering amplifies the input signal coming from ligand binding which enhances chemotactic efficiency. But large clusters also induce large fluctuations in total activity since the number of clusters goes down. The activity and hence the run-tumble motility now gets controlled by methylation levels which are part of adaptation module rather than ligand binding. This reduces chemotactic efficiency.

A-to-I RNA editing in bacteria increases pathogenicity and tolerance to oxidative stress

Adenosine-to-inosine (A-to-I) RNA editing is an important posttranscriptional event in eukaryotes; however, many features remain largely unexplored in prokaryotes. This study focuses on a serine-to-proline recoding event (S128P) that originated in the mRNA of fliC, which encodes a flagellar filament protein; the editing event was observed in RNA-seq samples exposed to oxidative stress. Using Sanger sequencing, we show that the S128P editing event is induced by H₂O₂. To investigate the in vivo interaction between RNAs and TadA, which is the principal enzyme for A-to-I editing, genome-wide RNA immunoprecipitation–coupled high-throughput sequencing (iRIP-Seq) analysis was performed using HA-tagged TadA from Xanthomonas oryzae pv. oryzicola. We found that TadA can bind to the mRNA of fliC and the binding motif is identical to that previously reported by Bar-Yaacov and colleagues. This editing event increased motility and enhanced tolerance to oxidative stress due to changes in flagellar filament structure, which was modelled in 3D and measured by TEM. The change in filament structure due to the S128P mutant increased biofilm formation, which was measured by the 3D laser scanning confocal microscopy. RNA-seq revealed that a gene cluster that contributes to siderophore biosynthesis and Fe³⁺ uptake was upregulated in S128P compared with WT. Based on intracellular levels of reactive oxygen species and an oxidative stress survival assay, we found that this gene cluster can contribute to the reduction of the Fenton reaction and increases biofilm formation and bacterial virulence. This oxidative stress response was also confirmed in Pseudomonas putida. Overall, our work demonstrates that A-to-I RNA editing plays a role in bacterial pathogenicity and adaptation to oxidative stress.

Adenosine-to-inosine (A-to-I) RNA editing is an important posttranscriptional event in eukaryotes that has only been recently documented in bacteria. In this study, we use multiple ‘omic’ approaches to show that A-to-I RNA editing can occur in fliC, a flagellar filament protein. We show that TadA, which encodes adenosine deaminase, can directly bind to mRNA of target genes through recognition of a GACG motif. This editing event changes a single amino acid residue from serine to proline in FliC, resulting in a structural change in the flagellar filament. This posttranscriptional editing event contributes to virulence and increases tolerance to oxidative stress by enhancing biofilm formation. Our results provide insight into a new mechanism that bacterial pathogens use to adapt to oxidative stress, which can also increase virulence.

Run-and-tumble motion with steplike responses to a stochastic input

We study a simple run-and-tumble random walk whose switching frequencies between run mode and tumble mode depend on a stochastic signal. We consider a particularly sharp, steplike dependence, where the run-to-tumble switching probability jumps from zero to one as the signal crosses a particular value (say y_{1}) from below. Similarly, tumble-to-run switching probability also shows a jump like this as the signal crosses another value (y_{2}<y_{1}) from above. We are interested in characterizing the effect of signaling noise on the long-time behavior of the random walker. We consider two different time-evolutions of the stochastic signal. In one case, the signal dynamics is an independent stochastic process and does not depend on the run-and-tumble motion. In this case we can analytically calculate the mean value and the complete distribution function of the run duration and tumble duration. In the second case, we assume that the signal dynamics is influenced by the spatial location of the random walker. For this system, we numerically measure the steady state position distribution of the random walker. We discuss some similarities and differences between our system and Escherichia coli chemotaxis, which is another well-known run-and-tumble motion encountered in nature.